Facts, data, and analytics about biomedical matters.
erictopol.substack.com
Facts, data, and analytics about biomedical matters.
erictopol.substack.com
Copyright: © Eric Topol
A book that reads like a novel; it’s humorous, it’s a love story. Dr. Christopher Labos, an imaginative cardiologist and epidemiologist at McGill University, takes us through multiple longstanding misconceptions about different foods and drinks, and along the way provides outstanding educational value.
Video snippet from our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.
Transcript with external links and links to the audio recording
Eric Topol (00:07):
Hello, it's Eric Topol with Ground Truths, and with me today is a cardiologist, Chris Labos from Montreal, who has written an extraordinary book. I just read it on my Kindle, “Does Coffee Cause Cancer? And 8 More Myths about the Food We Eat. Chris teaches at McGill University. He is a prolific writer at the Montreal Gazette and Canadian broadcast system, CBC, CJAD radio, CTV News. And he also has a podcast on the Body of Evidence and he probably has other stuff, but welcome Chris.
Christopher Labos (00:49):
Hello. Hello. Hello. Thank you for having me. It is a great honor to be on your podcast. I am in awe of the work that you've been doing, I mean, for all your career, but especially during Covid. So it's a big thrill for me to be on the podcast.
Eric Topol (01:03):
Well, for me, I have to say I learned about a person who is not only remarkably imaginative but also humorous. And so, have you ever done standup comedy?
Christopher Labos (01:16):
I have not. Although I was asked to chair the research awards that we did here at McGill one year because I've been doing local media stuff and they said, can you come and be like the MC? And I said, sure. And I said, do you want me to be funny? And they were like, well, if you can. And I went up there and people were laughing and laughing and laughing and then people, like some of my former attendings had come up to me and they're like, Chris, I don't remember you being this funny as a resident. And I was like, well, I guess you come into your own when you start your own career. But I think people were very, it's tough MCing a research awards because you're essentially, it's kind of like a high school graduation where you don't read the names in alphabetical order, right? It's like one name after the other. And I went up there and I tried to throw in a little bit of humor and people seem to like it. So I think that was the first, that was when I started to realize, oh, if you inject a little bit of levity into what you're doing, it tends to resonate a little bit more with people.
Eric Topol (02:13):
Well, no question about that. And what I love about this book is that it wasn't anything like I thought it was going to be.
Eric Topol (02:21):
Amazing. It was a surprise. So basically you took these nine myths, which we'll talk to, hopefully we'll get to several of them, but you didn't just get into that myth. You get into teaching medical statistics, how to read papers, all the myths. I mean, you are the master debunker with entertainment, with funny stuff. It's really great. So this is great, before we get into some of these myths and for you to amplify, but this is a gift of communication, science communication that is you get people to learn about things like p-hacking and you throw in love stories and all kinds of stuff. I mean, I don't know how you can dream this stuff up. I really don't.
Christopher Labos (03:10):
I sort of look back at the inception of this. This book did have sort of a few iterations. And I think the first time I was thinking about it, I mean I wrote it during Covid and so I was really thinking about this type of stuff. It's like how do we educate the public to become better consumers of scientific information? Because there was a lot of nonsense during Covid. So teaching them about confounding, which I think through a lot of people when we started talking about low vitamin D levels and Covid and outcomes and all that. And so, I started like, how do I write this type of book? And I thought, yeah, this should probably be a serious science book. And the first version of it was a very serious science book. And then the idea came and try to make it a conversation. And I think I sort of wrote it.
(04:02):
There's a book that may not be that popular in the US but it was kind of popular here in Canada. It was called The Wealthy Barber. And it was all about personal finance. And the idea of the book was these people would go into a barbershop and the barber would talk to them about how to save money and how to invest in all that. And it was fairly popular and people liked that back and forth. And I said, oh, maybe I could do something like that. And then I wrote the first chapter of the doctor who goes in to talk to the barista and I showed it to a friend of mine. I said, what do you think? Do you think this would work? And her response to me by email was two lines. It was pretty good period. But I kept expecting him to ask her out at the end. And the minute she said that I thought, oh my God, this is a love story. And so, I reshaped everything to make this a love story. And I don't think the publishers were expecting that either because they were like, the first comment from the editor was, most science books don't have a narrative arc to them in character, but this one does. So there you go.
Eric Topol (05:00):
This is a unique book. I hope that people who listen or read the transcript will realize that this is a gift. It's a model of communication and it just is teaching things almost like you don't realize it. You're just learning all this stuff. So let's get into some of these because they're just masterful. I guess I should start ask you, you have nine of them. You could have picked 20 more, but which one is your favorite? Or do you have one?
Christopher Labos (05:31):
I think the one, it's hard to say. I think the first one in the book is the vitamin C one. And I think it's the most interesting one to explain to people, not just because vitamin C to fight the common cold is so pervasive as a product and a thing that people believe. But it also, I think has the greatest opportunity to teach people about what is one of the most important ones, which is subgroup analysis and p-hacking. And it's so easy to bring that back into a comedic level with some of the graphs that I put in there. I think a close second would probably be the coffee one where I was talking about selection bias, because those examples of online dating and then all the jokes that came from it. And it's hard to say how much of it was the subject and how much of it was the character.
(06:21):
Because I'd always heard stories of authors when they say like, oh, the characters will tell me what to say. And I always thought that sounds like bollocks. How could that be possible? You're the author, you write what's on the page. But then the minute I started actually writing it and started envisaging these characters, all of a sudden the characters took on a life of their own and they were dictating how the story ended up. So the coffee one I think is also good too. And I guess it became the title of the book. So I guess that's a good indication that was popular. But when you can really spin it out and make it obvious to people using common examples, I think those are interesting ones. So the vitamin C and the coffee ones, I think were probably the most interesting.
Eric Topol (07:02):
Let's take those first because you've mentioned them and then hopefully we'll get into some others. Now in the vitamin C, you're going on a plane and you hook up with this guy, Jim, on the plane. I know none of this stuff really happened, and you're explaining to him the famous ISIS-2 trial about the Gemini and Libra subgroup. So for those of people who are listening, can you review that? Because that of course is just one of so many things you get into.
Christopher Labos (07:33):
I know it's almost amazing how short a memory we have in medicine, right? And again, this is sort of surprising me. I sort of knew the study and then I went back, and I looked at it and I thought ISIS-2 was in 1988. That's not that long ago. The fact that we didn't give aspirin. So for people who don't know, I mean, we did not give aspirin to people with cardiac disease for a very long time. And it was really from 1988 afterwards. So relatively recently, I mean I realized it's been a couple of decades, but still. So ISIS-2 was really the first trial to show that if you give aspirin to somebodywhen they're having a heart attack, you see a benefit. But what was fascinating in the study was this one subgroup analysis of people in whom it did not work.
(08:19):
And when I give public lectures, I often use this example because it's such a beautiful teaching case, and I go ask people, what do you think it was? And people are like, oh, hemophiliacs, smokers, people who drink alcohol. And then you find out, no, the subgroup in whom aspirin does not work is Geminis and Libras. And everybody sort of laughs and they think it's funny. And it's a beautiful example because a lot of people think it's like, oh, it was a joke or it was sort of silly science. But no, it was actually done purposefully. And the authors put that in there because they wanted to make the point that subgroup analysis are potentially misleading. And I sort of am a little bit in awe of, I mean the power or the intelligence to actually make it a point with the editors like, no, we're going to put this in here essentially as a teaching tool.
(09:09):
And it's amazing to me that we're still using it as a teaching tool decades after the fact. But it was just to show that when you have these tables where you have umpteen subgroup analysis, just by random chance, you will get some spurious results. And though our brain understands that Zodiac signs have nothing to do with the effectiveness of aspirin, you do the same subgroup analysis and diabetics and non-diabetics, and everybody was like, oh yeah, that's plausible. And yeah, it might be, but the computer doesn't know the difference, right. To the computer these are all ones and zeros. So if you don't go into it with a healthy skepticism about the limitations of subgroup analysis, you will eventually get fooled. And the problem with vitamin C research is I think a lot of very smart people have gotten fooled on this because they're like, well, overall the data is negative, but if we slice it up, we can find something that's positive. So maybe there's something here. And the number of people who have fallen in that trap over the years is unfortunately quite high.
Eric Topol (10:10):
No, and it's still happening and it is a famous subgroup story, but I just want to remind everybody that this was in the chapter on vitamin C and it's going into aspirin and subgroups. So each one of these chapters is not confined to the myth. They go into all sorts of other teaching examples in a humorous and fun way through conversations. Here it was with Jim on the plane. Now another one you mentioned, I forgot about this one. In the British Medical Journal, there was a paper, the Miracle of DICE Therapy.
Christopher Labos (10:45):
Miracle of DICE Therapy. Yeah, that's another brilliant one, because again, you couldn't do a study like this today, but basically for people who aren't aware of the paper, I mean, I think it was published in the Christmas issue. So again, just to show you how sometimes even in medical science, the humor is really, really effective. So what researchers did was they went to this neurology conference and they got all the people to participate in this live study, and they gave them dice and said, you're going to roll these dice. And they had white, red, and green dice and said, the exercise is for all of you to roll this dice and then analyze the data and tell us which color dice is off which one has been weighted. Because if you roll a one, two, three, four, or five, the patient has survived their stroke. If they roll a six, the patient died of their stroke.
(11:33):
So you go, you roll these dice dozens of times, generate your data. I mean, what we would do today with a random number generator but they were rolling dice. And they said, you figure out which of these dice is skewed. And so, the people at the conference went, they rolled their dice, they crunched their data, and they said, the red dice are skewed. There's a difference between the red dice and the white and green dice. And then the researchers revealed aha jokes on you. All the dice were the same. And the funniest part about that is that a lot of the people in the room didn't believe them. They refused to believe them that the dice were weighted because, and one of my favorite quotes was when student A refused to believe that his days were really loaded, he rolled one six and then a second and then a third, and he said, the room felt eerily quiet as he rolled a fourth six.
(12:25):
He had never rolled four sixes in a row in his life. And if you're there, I mean, yeah, you're going to be like, how do you doubt the power of your own eyes? You roll four sixes in a row, you think to yourself, gee, this must be the loaded dice. But that thing would happen. You put enough people in a room rolling enough dice, you will eventually get four sixes in a row in the same way that if you put enough monkeys in front of enough typewriters, eventually you're going to get all the works of William Shakespeare. So it's shocking how much our own human biases make us immune to the realization that random things are going to happen. And there was another, I think there was a quote in that paper too, where doctors are very willing to admit that chance affects whether they win a raffle, but they are surprisingly unwilling to admit that chance can affect the results of their medical research. And we don't appreciate it, even though, I mean, the reality is it happens all the time and we don't take the necessary steps to fix it sometimes and to address it, and we keep making the same mistakes over and over again.
Eric Topol (13:32):
Yeah, no, that's a great paper to illustrate. Again, a lot of important teaching points. Now as we get into the coffee, does it cause cancer? It brings up another theme in the book that I noticed. What you do is you pick up on papers or broadcasts that were decades ago that have become inculcated in our minds and our thoughts. And in this case, it was a famous New England Journal paper in 1981 raising the question about does coffee, if you drink too much coffee is that a risk factor for pancreatic cancer? So maybe you could take us through that, and somehow that gets into the NBA, it gets into H. pylori for ulcer. I mean, but maybe you could help get us through this coffee and cancer story.
Christopher Labos (14:23):
Yeah, I mean, well, and it's still happening isn't it, right? In 2018 in California, coffee was declared a carcinogen after that court case. I mean, it was ultimately overturned. So I sort of explained that saga in the chapter as well. And of course, we're going through it now with the decaf coffee, right? There are people trying to petition the FDA to get methylene chloride removed from decaf coffee, even though, I mean, I'm fairly dubious that that's a real significant risk factor in the grand scheme of things. And I was a little bit sort of worried when we were trying to pick a title for the books. I was like, are people going to think this is absurd? Are people going to think this is a pseudoscience book? And I was a little bit worried because people are not going to, they're going to think, oh, this is silly.
(15:03):
Obviously, coffee doesn't cause cancer, and yet we still talk about it. And so, I mean, the 1981 paper just to sort of go way, way back, and this was not a nothing paper. This was in the New England Journal of Medicine with some of heavyweights in the field of epidemiology. And I don’t want to discount what these people did. They have more illustrious careers than I will ever have in the field of epidemiology. But this one paper, they made a mistake. What they did was they went around to the local area hospitals, recruited all the patients with pancreatic cancer, recruited controls from the same hospital, and then gave them questionnaires about what they ate, what they drank, how much they smoked, fairly standard stuff. And so, when they were analyzing the data, they saw some associations with tobacco and alcohol, but they saw this really strong association with cancer where the patients who drank a lot of coffee had a near tripling of their risk of pancreatic cancer.
(16:02):
And so, this made headlines, I mean, this was in all the major US newspapers of the time, interviews people were like, well, maybe we should stop drinking coffee. And they pointed to the Amish and other groups that don't drink coffee and have very low rates of cancer. And what was critical in the critical mistake that they made, which is now taught in intro epidemiology classes we know about it, is that if you pick hospital patients as your control, you have a problem. And it's become so common that actually has a name now it's called Berkson's bias. But the problem with picking hospitalized controls is they are not the same as the general population. And in 1981, why were you going to be admitted to a gastrointestinal ward in a major US hospital? It was probably because you have peptic ulcer disease and you tell this to people now, and of course they have no living memory of this.
(16:53):
They've forgotten that we used to do partial gastrectomies to treat peptic ulcer disease, which is a shocking thing to say out loud. And then it gives you also the opportunity to teach people about H. pylori and everything that happened. And then the discovery and the famous case of the researcher drinking a broth of H. pylori to make himself sick and his wife having to drag him to the hospital throwing up every morning. And really how it changed the field of medicine because now we treat peptic ulcer disease with you eradicate H. pylori with two weeks of antibiotics, and we give people a proton pump inhibitor. But back in the day, the people who were in hospital had peptic ulcer disease and other gastrointestinal complaints because of those gastrointestinal issues. They didn't drink a lot of coffee because it would upset their stomach, because coffee can upset people's stomach a little bit.
(17:48):
And so, it wasn't that the pancreatic cancer patients drank more coffee, it's that the control group drank less, and that's why you saw that discrepancy. Whereas if you did the same study in the general population, which was subsequently done, you see no influence of coffee consumption. And so, it’s a prime example of how selection bias can happen. And it’s a seminal paper because it has become a teaching case, and it’s become, for the most part, so well understood that most people are not going to make the same mistake again. And so, the point of highlighting these things is not to make fun of people, which is an unfortunate trend I've started to see online of people being very, very critical and dismissive of the publish research. Like, no, listen, this is how medicine is supposed to work. It's an evolution. We learn from our mistakes and we move on and we have to keep talking about these stories so that people don't make the mistake because choosing the right control group is important.
(18:44):
And so, that's sort of the message of that chapter because each chapter, you're right, it's about a food, but it's also about an epidemiological concept, be it p-hacking or selection bias or information bias or confounding or reverse causation. So I often joke that if you read this book each chapter, you will become very, very smart at dinner parties. You'll be able to figure out terms that no one's heard of before. They're like, Bob, I know you've heard that red wine is good for you, but are you familiar with the concept of reverse causation? And people are going to be very, very impressed with you and keep inviting you to dinner parties the rest of your life afterwards. So there you go. That's another reason to read the book.
Eric Topol (19:20):
Yeah, really. Well, I do want to get into the red wine story too, because it exemplifies this time instead of that New England Journal, this was a 60 Minutes segment in 1991, and then a paper, I guess I went along with that about how red wine is great to reduce heart disease. It still, here it is, what, 30 some years later, 34 years later. And people still believe this. They still think that red wine is preventing heart disease or reducing it. So can you set the record straight on that one?
Christopher Labos (20:06):
Yeah, listen, if you want to drink red wine, you can. I mean, I have nothing against red wine. I mean, I'm drunk right now. No, I'm not.
Eric Topol (20:15):
By the way, that chapter you were drinking wine with your friend, maybe imaginary friend Alex or Alexi. Anyway, yeah. So it was great to hear you are drinking red wine and you're talking to each other about all the cockamamie stuff about it.
Christopher Labos (20:30):
I mean, yeah, the thing, if you're going to do a story, if you're going to do a book chapter about red wine, I think one of the important things is to have two friends drinking at a conference. I mean, let's be honest, that's what usually happens. And so, throughout the evening, they're sitting there polishing off the wine, and then they go on almost a drunken pub crawl. Not quite, it's not quite that bad, but it was almost fun to sort of introduce that element to it of the story. But the red wine thing is fascinating. I get this a lot. I mean, I'm still practicing. I'm still seeing patients and patients come up. I've had, this is not rare, I have had patients literally come to me in clinic and say things like, doctor, my blood pressure is good. I'm checking it at home. I got my blood tests.
(21:12):
My cholesterol is good. I'm eating healthy, I'm exercising. But I find it really hard to drink two glasses of red wine every day. I just don't like red wine all that much. It’s like, wow. No, please sir. Please, for the love of God, stop. It's still there. And what's fascinating is that if you ever go back and watch the 60 Minutes clip by today's standards, it's very weird. You go back and again, it was a product of its time. They were very, very focused on cheese and fat, which of course now we have a much more nuanced understanding about with regard to cholesterol. I mean, a lot of it's genetically mediated and all that, but you go back, it was partially about the red wine being good for you, but it was also there was this really strange subplot, if you will, where they were saying that milk was bad for you and that we should stop getting kids in the US to drink milk. And they thought that a lot of the cardiovascular risk in the US was attributed to the fact that children drink milk routinely, which again, weird by modern standards. Again, I was aware of the 60 Minutes story, but I'd never seen it and I hadn't seen it at the time. And going back to watch it, you're like, wow, that's odd. That's odd.
(22:26):
Again, this idea that, oh, we should be having kids drink wine at a young age. And it was like, really? Do we really want to start having our kids drink alcohol? I'm not so sure about that. It was weird stuff there. But again, it was all part of this French Paradox, which again was a product of its time in the eighties and nineties, this desire to really understand why was heart disease increasing so much in North America and our real failure to really get a handle on it. And with 30 years of hindsight, I think we're in a much better position now to understand why it was the residual effect of all that smoking. It was the residual effect of our more sedentary lifestyle that was starting to happen post World War II. And I think we've largely got a handle on most of those risk factors now.
(23:13):
But the red wine thing persists because I think people like drinking wine and there are not, what's the word I'm looking for, there is not a significant number of people who still believe this. And we had a change in guidelines up here in Canada where the amount of healthy drinking was really reduced down from 2 drinks a day to 1-2 drinks per week, and it caused a bit of a fury. And there was a local cardiologist here who was going on news and saying is like, I don't believe this, red wine is good for you. And I was a little bit taken a breath like, you're a senior cardiologist at a university hospital. You should not be saying stuff like this. And so, they actually had us on to have a debate, and I think they were expecting us to go at each other.
Eric Topol (23:59):
Oh, wow.
Christopher Labos (24:00):
And I was a little bit diplomatic because I've gotten used to this. I know how to bob and weave and avoid the punches. And then at the end, I think it was either me or the reporter asked him, he's like, so what do you tell your patients? And he was like, well, no, I do tell them to drink less because of the AFib risk and the blood pressure and the blood sugar. So I was like, well, you see, you're telling your patients to drink less alcohol for any number of reasons. And irrespective of the U-shaped associations, which is the main statistical argument of the chapter, there's a lot of other reasons to be wary of alcohol. I mean, I think we've proven pretty conclusively the AFib risk. There was that Australian study where if you get people to abstain, you decrease their AFib burden.
(24:42):
So a lot of sugar in alcohol, I mean the blood pressure and diabetes, there's a lot of reasons to not drink this particular sugary beverage and not to mention sort of the cancer associations too that we've seen as well. So it was an interesting thing to argue with him. But the point of the chapter was really to explain why do we see this U-shaped association? And I'll spoil the chapter for people. The statistical concept is called reverse causation. And that happens because it's not that abstaining from alcohol makes you sick. It's that people who are sick end up abstaining from alcohol. So if you have high blood pressure, diabetes, heart disease, AFib, cancer, you've probably been told don't drink alcohol. And so, if you do just a single cross-sectional study where you ask people, how much do you drink? And they say zero, you're probably identifying a high-risk population because most studies, most, not all, but many studies do not make the distinction between former drinkers and never drinkers. And there's a big difference between somebody who used to drink and then quit and somebody who never drank throughout their whole lives.
Eric Topol (25:47):
Yeah, no, it's great. And I think I just want to come back on that. I think Norway and several other countries are now putting on their alcohol products. This may cause cancer, and the American Cancer Society has put a warning on this. So the cancer story is still out there, but you also make among hundreds of important good points in the book about how these food diaries are notoriously inaccurate. And you already touched on that with the survey thing, but it's hard to get, we don't have randomized trials of people drink a lot or don't drink. You can't drink with adherence to that. So it's out there, and of course, people like to drink their wine, but there's a risk that I think has been consistent through many of these studies that is a bit worrisome. I don't know what you would, if you'd say it's conclusive or you'd say it's kind of unsettled.
Christopher Labos (26:49):
I mean, I think it's as settled as it's going to get because I don't see somebody doing a randomized controlled trial on this. And this is the problem. And there has been this trend recently for people to say, well, if there's no randomized controlled trials, I'm not going to believe it. You're like, okay, look, a fair point. And when you're talking about interventions and therapies, then yes, we should absolutely do randomized controlled trials. And I've made that point vociferously when it comes to vitamin D and a lot of the other stuff. The problem is it's going to be very, very hard to do a randomized controlled trial with alcohol. I mean, that was tried. It fell apart and it fell apart for many reasons, not the least of which was the fact that the alcohol industry seemed to be influencing what outcomes people were going to look at.
(27:34):
So that was problematic. I sort of mentioned it right at the tail end of the chapter as well. So if you're not going to have an NIH funded trial to look at in a randomized fashion, does alcohol effect atherosclerosis or cancer outcomes? You're not going to get it. No private industry is going to do it. You're not going to be able to get it done. So given that we have to live in the real world, and I'm always a firm argument in us basing ourselves in reality and living in the real world, we have to make the best decision we can with the evidence that we have available. And I would say, look, I'm pretty sure alcohol is not good for you. I think it is actually detrimental to your cardiovascular health overall. And I think we can say pretty definitively that any potential benefit that people think exists in terms of myocardial infarction, I think that's all a statistical artifact.
(28:26):
I think if you were to analyze it properly, it would all sort of vanish. And I think it largely does. And there's been some really interesting genetic studies using instrumental variables. So what the Mendelian randomization studies that really do suggest that there really is a linear relationship and that the more you drink, the worse it is. And there's no plateau, there's no floor, there's no J shaped curve. It really does appear to be linear. And I've been, I think, fairly convinced because I think the Mendelian randomization studies are as good as we're going to get on this issue.
Eric Topol (29:01):
No, I think it's an important point. And I think there again, the book will hold on so many of these things, but we keep learning all the time. And for example, going back to coffee, there's many studies now that suggest it will reduce type 2 diabetes, it will improve survival, cardiovascular, the mechanism is unknown. Do you think there's, so not only does coffee not cause cancer, but it actually may make you healthier. Any thoughts about that?
Christopher Labos (29:35):
Well, I can state, again, I'm ruining the book. I can state, I think fairly unequivocally coffee does not cause cancer. I think that is pretty clear. Even protective is harder, I think it's possible that a lot of the benefit that's been seen, because it is very observational, could just be the result of residual confounding. I think that is still possible. And again, we have to learn to live with uncertainty in medical research. And when we talk about Bayesian statistics, which is a subject I love, but probably outside of the topic for today, you have to be able to create a framework for what we're certain about and what we're uncertain about. So if you look at the spectrum of risk, clearly the risk ratio for coffee is not above 1. Is it below 1 or is it really straddling the null value? And I'm a little bit uncertain. I think if there is a benefit, it's probably small. I think a lot of it is residual confounding. The one point that would make though, if we're going to talk about coffee being beneficial, we have to talk about coffee. Not a lot of the stuff they are serving at coffee shops now, which are probably more akin to milkshakes than actual coffee.
Eric Topol (30:52):
Yeah, that’s a really good point. Plus, the other thing is the spike of caffeine at much higher levels than you might have with a standard coffee that is typical, these Grande or super Grande, whatever they are. Now another, since we talked about things that people enjoy like coffee and wine, we have to touch on chocolate. The chapter was fun on chocolate, is it a health food and also about the Nobel Laureates. Can you enlighten us on that one?
Christopher Labos (31:26):
This is another, I mean, again, people are going to think that I hate the New England Journal of Medicine. I don't just, that they provided such great teaching material over the years. And to be fair, the study that we're going to talk about the Nobel Laureate chocolate study, I mean if you read it, it really feels like it was meant to be satire and it probably should have belonged in the BMJ Christmas issue. When you read it and you read the disclosure statement where the author is like, disclosure the author admits to loving chocolate, and you're like, okay, that's a weird thing to write in a serious article. So it was probably meant to be a satire. And when you read some of the interviews that Messerli had given afterwards, it does seem that he was trying to just make a point. But it seems to have taken off a life of its own.
(32:10):
What the study was, and it's again, first time I've ever seen a single author on a New England paper, which probably should have been a warning sign for people because generally New England papers don't have single authors on them. But basically, what he did was he was at a conference as the way the story goes, and he was thinking up this idea. So he went on the internet, went onto Wikipedia, and was basically looking up how many Nobel prizes have been won by various countries, looked up the average chocolate consumption on a variety of other websites and basically plotted out a regression line and showed this really linear association between average chocolate consumption per country and number of Nobel prizes per country with the suggested rules that if you eat chocolate, you'll win a Nobel Prize. Except, and notwithstanding all the jokes that came up later, there was another Nobel laureate, and I'm blanking on his name right now, there is in the book. When he was interviewed, he said something like, I believe this is true. Now, milk chocolate might be fine if you want a Nobel Prize in chemistry or medicine, but if you want a Nobel Prize in physics, it really does have to be dark chocolate.
Christopher Labos (33:20):
He said this to the Associated Press, the Associated Press took the quote and put it on the Newswire, and it got reprinted over and over again. And I think he had to publicly apologize to all the people at his university, which to me seemed ridiculous. He was obviously joking, and people took this study very, very seriously. The explanation for why this study is not true, there's actually a word for this, and it's called the ecological bias. And you have to remember something if you're going to look at chocolate and Nobel prizes and look at it in terms of country as the level of exposure, as the unit of exposure. Countries don't eat chocolate and countries don't win Nobel prizes. People eat chocolate and people eat Nobel prizes. And you can't show that the people eating the chocolate in Switzerland are the ones who are winning the Nobel Prizes.
(34:10):
Right. That's the point you can't show, and this is a humorous example, but we've made this type of mistake before when people were talking about saturated fats causing breast cancer. You can look at countries and show that countries that eat a lot of saturated fat have more breast cancer. But that's also because western countries with other basic differences are the countries where you eat a lot of saturated fats and where women develop higher rates of breast cancer. But that doesn't mean that the women who eat the saturated fats are the ones who get breast cancer. And so, the chocolate one is funny because again, it’s exactly what you said. People like eating chocolate, so they want a reason to believe that it is good for you even when it isn’t. And so, they will latch on to the cardiovascular benefits, which have frankly been disproved in the COSMOS study. They will latch on to the neurological, neurocognitive benefits, which have themselves been disproved. And what's fascinating about the whole story is that you would say, oh, we need a large randomized trial. Well, we had that, it was called the COSMOS study. It got published. I mean, maybe it happened during Covid, people didn't notice, but it got published. It was negative. That should have been the end of the story, and it's not, people still believe it.
Eric Topol (35:23):
Well, there's a lot of confirmation bias there, isn't there? Again, the thread through all the chapters is biases, all the different biases that come in play. And this one, knowing Franz Messerli, he's Swiss, so of course he'd want to, yeah, and he eats a lot of chocolate, by the way. And he also comes into play in the chapter you have on salt. It's really interesting. You have chapters on breakfast. Is it really the most important meal? Were there other chapters that you thought about putting in the book that you didn't wind up there, or if you were to write a second edition that you would add?
Christopher Labos (36:01):
I wanted to do a chapter on fish oils. Actually, there's a tweet that you did that I use in my teaching material, which is two days apart, fish oils are good for you, fish oils are bad for you. Because again, that's one of those things where it's just the cycle of all these studies showing no benefit, and yet there's one study that shows a thing and it just keeps coming back. And so yeah, fish oils would've definitely been one. If there is a sequel to this book, and I'm hoping to make a sequel to it.
Eric Topol (36:30):
You should, you should definitely.
Christopher Labos (36:32):
So fish oils is definitely going to be in there because there were originally going to be ten stories. There's only nine in the book. And because it got to the point where the publisher was like, this book is getting a little long, maybe we've got to wrap it up. Maybe it's time to land the plane. And I was like, okay, fair, fair. So we'll cut it at nine. So we had to drop the fish oil one, but that'll be in the sequel if there is a sequel, I want to do, I have a list. It's just off camera actually. I have a little notepad where I've been jotting down ideas. So like fish oils, artificial sweeteners, I'll throw MSG in there, which is a wild story for anybody who's ever dug into the history of MSG. It is a wild and borderline nonsensical story of why we believe that MSG might be bad for us.
(37:14):
Although, I mean, that was, again, very much a product of the eighties and the nineties. So yeah, there's a lot of stuff out there, but fish oil is definitely one that I want to tackle just because it's so relevant. And I still have patients coming in that are going to pharmacy and buying over the counter fish oil supplements. I have to tell them, it's like, look, the evidence on this is pretty clear. It doesn't help. If anything, maybe it slightly increases risk your AFib risk. There's some stuff there. So yeah, again, you could be easily tempted into thinking this is sort of frivolous and funny, but it actually has an implication for people's daily lives because the people out there walking around the street, they believe these things go stop a hundred random people.
Eric Topol (37:59):
Yeah, no, everything in this book is approaching things that are the dogma still, or at least uncertainty, and you get it straight. I mean, you’re an epidemiologist as well as a cardiologist in your training, but you don't use that in a way that is trying to teach people. You're doing it really subtly. And then the other thing just to bring up is that obviously you're debunking all this stuff, and we live in a time where we got all this misinformation and blurred truths. I mean, that's one of the reasons why I pick Ground Truths for this podcast. But it's diminished or certainly challenged the role of physicians and scientists because things are not reliable. They're not constant. They're changing. You touched on that earlier, but can you address, I mean, one of the things besides communicating in a way that makes it easily understandable and fun, which you do so well, it's also addressing trust. How do we promote trust?
Christopher Labos (39:10):
I think you have to, yeah, that's a really challenging question because I think the old model is not going to work anymore. The model of issuing a guideline statement to be like, this is the truth, people will just ignore it because we have issued new guidelines on alcohol consumption. It didn't change behavior. If you want to get people to drink less, you have to address the underlying reason why they do it, and it's this persistent myth. So I think one of the reasons why pseudoscience succeeds as much as it does is because so much of their communication is about storytelling. You can go at people with these large randomized control trials, and yet they will still latch onto an anecdote, right? Because, oh, my friend Bobby had a bad side effect with the Covid vaccine. That's why I'm not getting vaccinated. And so, storytelling is a really, really powerful tool.
(40:05):
And I think the reason why I thought this type of book format could work is it's a story. Because even if you don't remember the details, I was at a lecture last night and I was speaking to a dermatology friend of mine, which sounds like it's an episode from the book, but it's not. But I was speaking to a dermatology friend of mine, and he had read it. He says, Chris, I read it. I really liked it. He goes, I don't remember a lot of the examples you put up. He is a busy guy. He’s got young kids. He read the book, and I was giving a lecture based on this book and exploring all of these concepts. And he was like, I remember when you started talking about the aspirin. I couldn't remember what the example was, but I remembered your point that it's all about subgroups.
(40:47):
And that's the thing is that even if people don't remember the details, even if people don't remember the New England paper about coughing pancreatic cancer, even if they don't remember the COSMOS study about chocolate, even if they don't remember the Nobel chocolate association, they will remember the take home message, which is that you have to be careful. If somebody is torturing the data, they understand why publication bias is a real problem. So that's the point, is that if you tell a story, it sticks in people's minds. So it's almost very Socratic in a way. If you ever read Plato, he's not writing a philosophical treatise in the same way that other philosophers do. It's a conversation between Socrates and other people, and it's a very one-sided conversation because Socrates is telling everybody why they're wrong. So I tried to sort of nuance that and improve upon that framework, but you take away the general gist of it, and that's what we need to give to people.
(41:48):
We need to tell them, we need to give them the tool so that they can say it's like, oh, well wait a second. You're telling me that broccoli is going to prevent pancreatic cancer? Was this a food questionnaire thing? And you're giving people that little bit of background knowledge that they can ask intelligent questions. And I think that's what we have to do going forward, because we have to introduce that little bit of skepticism into their thought process so that they can question what they see on the internet. Because the reality is a lot of what they see on the internet is going to be wrong because it's clickbait, it's headlines, it's all the issues that we have with our modern communication strategies.
Eric Topol (42:31):
Yeah. Well, I think storytelling and what you just described is so darn important. And so, just to wrap up this book, Does Coffee Cause Cancer?: And 8 More Myths about the Food We Eat is much more than what that title says. I hope you're going to do a sequel. You ought to have a Netflix special.
Christopher Labos (42:54):
Please tell somebody that, I don’t how to get a Netflix special, but use your clout and make it happen, and I'll invite you over for dinner.
Eric Topol (43:01):
Sounds good. We'll have red wine together, and drink a lot of decaffeinated coffee. No, this has been fun. You've definitely had an impact. And I hope everybody takes a chance to get through this book because it's like a novel. A novel, which is somehow you've floated in all this really important stuff in medicine, both content, how to interpret data, how to interpret papers, statistics, somehow invisibly in a novel. You've got it all in there. So congratulations on that. It's a new genre medical book like I've never seen before. And so, we'll be following all your next works, and I'm sure your podcast Body of Evidence must be something along these lines as well. So I'll have to take a look and listen to that too.
Christopher Labos (43:56):
Thank you so much. That is very, very, you have no idea how much it means to me to hear you say something like that, that has warmed the cockles of my heart.
Eric Topol (44:07):
Alright, well Chris, thank you.
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The most enthralling conversation I’ve ever had with anyone on cancer.
It’s with Charlie Swanton who is a senior group leader at the Francis Crick Institute, the Royal Society Napier Professor in Cancer and medical oncologist at University College London, co-director of Cancer Research UK.
Video snippet from our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.
Transcript with audio links and many external links
Eric Topol (00:07):
Well, hello, this is Eric Topol with Ground Truths, and I am really fortunate today to connect us with Charlie Swanton, who is if not the most prolific researcher in the space of oncology and medicine, and he's right up there. Charlie is a physician scientist who is an oncologist at Francis Crick and he heads up the lung cancer area there. So Charlie, welcome.
Charles Swanton (00:40):
Thank you, Eric. Nice to meet you.
Learning from a Failure
Eric Topol (00:43):
Well, it really is a treat because I've been reading your papers and they're diverse. They're not just on cancer. Could be connecting things like air pollution, it could be Covid, it could be AI, all sorts of things. And it's really quite extraordinary. So I thought I'd start out with a really interesting short paper you wrote towards the end of last year to give a sense about you. It was called Turning a failing PhD around. And that's good because it's kind of historical anchoring. Before we get into some of your latest contributions, maybe can you tell us about that story about what you went through with your PhD?
Charles Swanton (01:26):
Yeah, well thank you, Eric. I got into research quite early. I did what you in the US would call the MD PhD program. So in my twenties I started a PhD in a molecular biology lab at what was then called the Imperial Cancer Research Fund, which was the sort of the mecca for DNA tumor viruses, if you like. It was really the place to go if you wanted to study how DNA tumor viruses worked, and many of the components of the cell cycle were discovered there in the 80s and 90s. Of course, Paul Nurse was the director of the institute at the time who discovered cdc2, the archetypal regulator of the cell cycle that led to his Nobel Prize. So it was a very exciting place to work, but my PhD wasn't going terribly well. And sort of 18, 19 months into my PhD, I was summoned for my midterm reports and it was not materializing rapidly enough.
(02:25):
And I sat down with my graduate student supervisors who were very kind, very generous, but basically said, Charlie, this isn't going well, is it? You've got two choices. You can either go back to medical school or change PhD projects. What do you want to do? And I said, well, I can't go back to medical school because I’m now two years behind. So instead I think what I'll do is I'll change PhD projects. And they asked me what I'd like to do. And back then we didn't know how p21, the CDK inhibitor bound to cyclin D, and I said, that's what I want to understand how these proteins interact biochemically. And they said, how are you going to do that? And I said, I'm not too sure, but maybe we'll try yeast two-hybrid screen and a mutagenesis screen. And that didn't work either. And in the end, something remarkable happened.
(03:14):
My PhD boss, Nic Jones, who's a great guy, still is, retired though now, but a phenomenal scientist. He put me in touch with a colleague who actually works next door to me now at the Francis Crick Institute called Neil McDonald, a structural biologist. And they had just solved, well, the community had just solved the structure. Pavletich just solved the structure of cyclin A CDK2. And so, Neil could show me this beautiful image of the crystal structure in 3D of cyclin A, and we could mirror cyclin D onto it and find the surface residue. So I spent the whole of my summer holiday mutating every surface exposed acid on cyclin D to an alanine until I found one that failed to interact with p21, but could still bind the CDK. And that little breakthrough, very little breakthrough led to this discovery that I had where the viral cyclins encoded by Kaposi sarcoma herpes virus, very similar to cyclin D, except in this one region that I had found interactive with a CDK inhibitor protein p21.
(04:17):
And so, I asked my boss, what do you think about the possibility this cyclin could have evolved from cyclin D but now mutated its surface residues in a specific area so that it can't be inhibited by any of the control proteins in the mammalian cell cycle? He said, it's a great idea, Charlie, give it a shot. And it worked. And then six months later, we got a Nature paper. And that for me was like, I cannot tell you how exciting, not the Nature paper so much as the discovery that you were the first person in the world to ever see this beautiful aspect of evolutionary biology at play and how this cyclin had adapted to just drive the cell cycle without being inhibited. For me, just, I mean, it was like a dream come true, and I never experienced anything like it before, and I guess it's sizes the equivalent to me of a class A drug. You get such a buzz out of it and over the years you sort of long for that to happen again. And occasionally it does, and it's just a wonderful profession.
Eric Topol (05:20):
Well, I thought that it was such a great story because here you were about to fail. I mean literally fail, and you really were able to turn it around and it should give hope to everybody working in science out there that they could just be right around the corner from a significant discovery.
Charles Swanton (05:36):
I think what doesn't break you makes you stronger. You just got to plow on if you love it enough, you'll find a way forward eventually, I hope.
Tracing the Evolution of Cancer (TRACERx)
Eric Topol (05:44):
Yeah, no question about that. Now, some of your recent contributions, I mean, it's just amazing to me. I just try to keep up with the literature just keeping up with you.
Charles Swanton (05:58):
Eric, it's sweet of you. The first thing to say is it's not just me. This is a big community of lung cancer researchers we have thanks to Cancer Research UK funded around TRACERx and the lung cancer center. Every one of my papers has three corresponding authors, multiple co-first authors that all contribute in this multidisciplinary team to the sort of series of small incremental discoveries. And it's absolutely not just me. I've got an amazing team of scientists who I work with and learn from, so it's sweet to give me the credit.
Eric Topol (06:30):
I think what you're saying is really important. It is a team, but I think what I see through it all is that you're an inspiration to the team. You pull people together from all over the world on these projects and it's pretty extraordinary, so that's what I would say.
Charles Swanton (06:49):
The lung community, Eric, the lung cancer community is just unbelievably conducive to collaboration and advancing understanding of the disease together. It's just such a privilege to be working in this field. I know that sounds terribly corny, but it is true. I don't think I recall a single email to anybody where I've asked if we can collaborate where they've said, no, everybody wants to help. Everybody wants to work together on this challenge. It's just such an amazing field to be working in.
Eric Topol (07:19):
Yeah. Well I was going to ask you about that. And of course you could have restricted your efforts or focused on different cancers. What made you land in lung cancer? Not that that's only part of what you're working on, but that being the main thing, what drew you to that area?
Charles Swanton (07:39):
So I think the answer to your question is back in 2008 when I was looking for a niche, back then it was lung cancer was just on the brink of becoming an exciting place to work, but back then nobody wanted to work in that field. So there was a chair position in thoracic oncology and precision medicine open at University College London Hospital that had been open, as I understand it for two years. And I don't think anybody had applied. So I applied and because I was the only one, I got it and the rest is history.
(08:16):
And of course that was right at the time when the IPASS draft from Tony Mok was published and was just a bit after when the poster child of EGFR TKIs and EGFR mutant lung cancer had finally proven that if you segregate that population of patients with EGFR activating mutation, they do incredibly well on an EGFR inhibitor. And that was sort of the solid tumor poster child along with Herceptin of precision medicine, I think. And you saw the data at ASCO this week of Lorlatinib in re-arranged lung cancer. Patients are living way beyond five years now, and people are actually talking about this disease being more like CML. I mean, it's extraordinary the progress that's been made in the last two decades in my short career.
Eric Topol (09:02):
Actually, I do want to have you put that in perspective because it's really important what you just mentioned. I was going to ask you about this ASCO study with the AKT subgroup. So the cancer landscape of the lung has changed so much from what used to be a disease of cigarette smoking to now one of, I guess adenocarcinoma, non-small cell carcinoma, not related to cigarettes. We're going to talk about air pollution in a minute. This group that had, as you say, 60 month, five year plus survival versus what the standard therapy was a year plus is so extraordinary. But is that just a small subgroup within small cell lung cancer?
Charles Swanton (09:48):
Yes, it is, unfortunately. It’s just a small subgroup. In our practice, probably less than 1% of all presentations often in never smokers, often in female, never smokers. So it is still in the UK at least a minority subset of adenocarcinomas, but it's still, as you rightly say, a minority of patients that we can make a big difference to with a drug that's pretty well tolerated, crosses the blood-brain barrier and prevents central nervous system relapse and progression. It really is an extraordinary breakthrough, I think. But that said, we're also seeing advances in smoking associated lung cancer with a high mutational burden with checkpoint inhibitor therapy, particularly in the neoadjuvant setting now prior to surgery. That's really, really impressive indeed. And adjuvant checkpoint inhibitor therapies as well as in the metastatic setting are absolutely improving survival times and outcomes now in a way that we couldn't have dreamt of 15 years ago. We've got much more than just platinum-based chemo is basically the bottom line now.
Revving Up Immunotherapy
Eric Topol (10:56):
Right, right. Well that actually gets a natural question about immunotherapy also is one of the moving parts actually just amazing to me how that's really, it's almost like we're just scratching the surface of immunotherapy now with checkpoint inhibitors because the more we get the immune system revved up, the more we're seeing results, whether it's with vaccines or CAR-T, I mean it seems like we're just at the early stages of getting the immune system where it needs to be to tackle the cancer. What's your thought about that?
Charles Swanton (11:32):
I think you're absolutely right. We are, we're at the beginning of a very long journey thanks to Jim Allison and Honjo. We've got CTLA4 and PD-1/PDL-1 axis to target that's made a dramatic difference across multiple solid tumor types including melanoma and lung cancer. But undoubtedly, there are other targets we've seen LAG-3 and melanoma and then we're seeing new ways, as you rightly put it to mobilize the immune system to target cancers. And that can be done through vaccine based approaches where you stimulate the immune system against the patient's specific mutations in their cancer or adoptive T-cell therapies where you take the T-cells out of the tumor, you prime them against the mutations found in the tumor, you expand them and then give them back to the patient. And colleagues in the US, Steve Rosenberg and John Haanen in the Netherlands have done a remarkable job there in the context of melanoma, we're not a million miles away from European approvals and academic initiated manufacturing of T-cells for patients in national health systems like in the Netherlands.
(12:50):
John Haanen's work is remarkable in that regard. And then there are really spectacular ways of altering T-cells to be able to either migrate to the tumor or to target specific tumor antigens. You mentioned CAR-T cell therapies in the context of acute leukemia, really extraordinary developments there. And myeloma and diffuse large B-cell lymphoma as well as even in solid tumors are showing efficacy. And I really am very excited about the future of what we call biological therapies, be it vaccines, an antibody drug conjugates and T-cell therapies. I think cancer is a constantly adapting evolutionary force to be reckoned with what better system to combat it than our evolving immune system. It strikes me as being a future solution to many of these refractory cancers we still find difficult to treat.
Eric Topol (13:48):
Yeah, your point is an interesting parallel how the SARS-CoV-2 virus is constantly mutating and becoming more evasive as is the tumor in a person and the fact that we can try to amp up the immune system with these various means that you just were reviewing. You mentioned the other category that's very hot right now, which is the antibody drug conjugates. Could you explain a bit about how they work and why you think this is an important part of the future for cancer?
Antibody-Drug Conjugates
Charles Swanton (14:26):
That's a great question. So one of the challenges with chemotherapy, as you know, is the normal tissue toxicity. So for instance, neutropenia, hair loss, bowel dysfunction, diarrhea, epithelial damage, essentially as you know, cytotoxics affect rapidly dividing tissues, so bone marrow, epithelial tissues. And because until relatively recently we had no way of targeting chemotherapy patients experienced side effects associated with them. So over the last decade or so, pioneers in this field have brought together this idea of biological therapies linked with chemotherapy through a biological linker. And so one poster chart of that would be the drug T-DXd, which is essentially Herceptin linked to a chemotherapy drug. And this is just the most extraordinary drug that obviously binds the HER2 receptor, but brings the chemotherapy and proximity of the tumor. The idea being the more drug you can get into the tumor and the less you're releasing into normal tissue, the more on tumor cytotoxicity you'll have and the less off tumor on target normal tissue side effects you'll have. And to a large extent, that's being shown to be the case. That doesn't mean they're completely toxicity free, they're not. And one of the side effects associated with these drugs is pneumonitis.
(16:03):
But that said, the efficacy is simply extraordinary. And for example, we're having to rewrite the rule books if you like, I think. I mean I'm not a breast cancer physician, I used to be a long time ago, but back in the past in the early 2000s, there was HER2 positive breast cancer and that's it. Now they're talking about HER2 low, HER2 ultra-low, all of which seem to in their own way be sensitive to T-DXd, albeit to a lower extent than HER2 positive disease. But the point is that there doesn't seem to be HER2 completely zero tumor group in breast cancer. And even the HER2-0 seem to benefit from T-DXd to an extent. And the question is why? And I think what people are thinking now is it's a combination of very low cell service expression of HER2 that's undetectable by conventional methods like immunohistochemistry, but also something exquisitely specific about the way in which HER2 is mobilized on the membrane and taken back into the cell. That seems to be specific to the breast cancer cell but not normal tissue. So in other words, the antibody drug conjugate binds the tumor cell, it's thought the whole receptor's internalized into the endosome, and that's where the toxicity then happens. And it's something to do with the endosomal trafficking with the low level expression and internalization of the receptor. That may well be the reason why these HER2 low tumors are so sensitive to this beautiful technology.
Eric Topol (17:38):
Now I mean it is an amazing technology in all these years where we just were basically indiscriminately trying to kill cells and hoping that the cancer would succumb. And now you're finding whether you want to call it a carry or vector or Trojan horse, whatever you want to call it, but do you see that analogy of the HER2 receptor that's going to be seen across the board in other cancers?
Charles Swanton (18:02):
That's the big question, Eric. I think, and have we just lucked out with T-DXd, will we find other T-DXd like ADCs targeting other proteins? I mean there are a lot of ADCs being developed against a lot of different cell surface proteins, and I think the jury's still out. I'm confident we will, but we have to bear in mind that biology is a fickle friend and there may be something here related to the internalization of the receptor in breast cancer that makes this disease so exquisitely sensitive. So I think we just don't know yet. I'm reasonably confident that we will find other targets that are as profoundly sensitive as HER2 positive breast cancer, but time will tell.
Cancer, A Systemic Disease
Eric Topol (18:49):
Right. Now along these lines, well the recent paper that you had in Cell, called embracing cancer complexity, which we've talking about a bit, in fact it's kind of those two words go together awfully well, but hallmarks of systemic disease, this was a masterful review, as you say with the team that you led. But can you tell us about what's your main perspective about this systemic disease? I mean obviously there's been the cancer is like cardiovascular and cancers like this or that, but here you really brought it together with systemic illness. What can you say about that?
Charles Swanton (19:42):
Well, thanks for the question first of all, Eric. So a lot of this comes from some of my medical experience of treating cancer and thinking to myself over the years, molecular biology has had a major footprint on advances in treating the disease undoubtedly. But there are still aspects of medicine where molecular biology has had very little impact, and often that is in areas of suffering in patients with advanced disease and cancer related to things like cancer cachexia, thrombophilia. What is the reason why patients die blood clots? What is the reason patients die of cancer at all? Even a simple question like that, we don't always know the answer to, on death certificates, we write metastatic disease as a cause of cancer death, but we have patients who die with often limited disease burden and no obvious proximal cause of death sometimes. And that's very perplexing, and we need to understand that process better.
(20:41):
And we need to understand aspects like cancer pain, for example, circadian rhythms affect biological sensitivity of cancer cells to drugs and what have you. Thinking about cancer rather than just sort of a single group of chaotically proliferating cells to a vision of cancer interacting both locally within a microenvironment but more distantly across organs and how organs communicate with the cancer through neuronal networks, for example, I think is going to be the next big challenge by setting the field over the next decade or two. And I think then thinking about more broadly what I mean by embracing complexity, I think some of that relates to the limitations of the model systems we use, trying to understand inter-organ crosstalk, some of the things you cover in your beautiful Twitter reviews. (←Ground Truths link)
I remember recently you highlighted four publications that looked at central nervous system, immune cell crosstalk or central nervous system microbiome crosstalk. It's this sort of long range interaction between organs, between the central nervous system and the immune system and the cancer that I'm hugely interested in because I really think there are vital clues there that will unlock new targets that will enable us to control cancers more effectively if we just understood these complex networks better and had more sophisticated animal model systems to be able to interpret these interactions.
Eric Topol (22:11):
No, it's so important what you're bringing out, the mysteries that still we have to deal with cancer, why patients have all these issues or dying without really knowing what's happened no less, as you say, these new connects that are being discovered at a remarkable pace, as you mentioned, that ground truths. And also, for example, when I spoke with Michelle Monje, she's amazing on the cancer, where hijacking the brain cells and just pretty extraordinary things. Now that gets me to another line of work of yours. I mean there are many, but the issue of evolution of the tumor, and if you could put that in context, a hot area that's helping us elucidate these mechanisms is known as spatial omics or spatial biology. This whole idea of being able to get the spatial temporal progression through single cell sequencing and single cell nuclei, all the single cell omics. So if you could kind of take us through what have we learned with this technique and spatial omics that now has changed or illuminated our understanding of how cancer evolves?
Charles Swanton (23:37):
Yeah, great question. Well, I mean I think it helps us sort of rewind a bit and think about evolution in general. Genetic selection brought about by diverse environments and environmental pressures that force evolution, genetic evolution, and speciation down certain evolutionary roots. And I think one can think about cancers in a similar way. They start from a single cell and we can trace the evolutionary paths of cancers by single cell analysis as well as bulk sequencing of spatially separated tumor regions to be able to reconstruct their subclones. And that's taught us to some extent, what are the early events in tumor evolution? What are the biological mechanisms driving branched evolution? How does genome instability begin in tumors? And we found through TRACERx work, whole genome doubling is a major route through to driving chromosome instability along with mutagenic enzymes like APOBEC that drive both mutations and chromosomal instability.
(24:44):
And then that leads to a sort of adaptive radiation in a sense, not dissimilar to I guess the Cambrian explosion of evolutionary opportunity upon which natural selection can act. And that's when you start to see the hallmarks of immune evasion like loss of HLA, the immune recognition molecules that bind the neoantigens or even loss of the neoantigens altogether or mutation of beta 2 microglobulin that allow the tumor cells to now evolve below the radar, so to speak. But you allude to the sort of spatial technologies that allow us to start to interpret the microenvironments as well. And that then tells us what the evolutionary pressures are upon the tumor. And we're learning from those spatial technologies that these environments are incredibly diverse, actually interestingly seem to be converging on one important aspect I'd like to talk to you a little bit more about, which is the myeloid axis, which is these neutrophils, macrophages, et cetera, that seem to be associated with poor outcome and that will perhaps talk about pollution in a minute.
(25:51):
But I think they're creating a sort of chronic inflammatory response that allows these early nascent tumor cells to start to initiate into frankly tumor invasive cells and start to grow. And so, what we're seeing from these spatial technologies in lung cancer is that T-cells, predatory T-cells, force tumors to lose their HLA molecules and what have you to evade the immune system. But for reasons we don't understand, high neutrophil infiltration seems to be associated with poor outcome, poor metastasis free survival. And actually, those same neutrophils we've recently found actually even tracked to the metastasis sites of metastasis. So it's almost like this sort of symbiosis between the myeloid cells and the tumor cells in their biology and growth and progression of the tumor cells.
Eric Topol (26:46):
Yeah, I mean this white cell story, this seems to be getting legs and is relatively new, was this cracked because of the ability to do this type of work to in the past everything was, oh, it's cancer's heterogeneous and now we're getting pinpoint definition of what's going on.
Charles Swanton (27:04):
I think it's certainly contributed, but it's like everything in science, Eric, when you look back, there's evidence in the literature for pretty much everything we've ever discovered. You just need to put the pieces together. And I mean one example would be the neutrophil lymphocyte ratio in the blood as a hallmark of outcome in cancers and to checkpoint inhibitor blockade, maybe this begins to explain it, high neutrophils, immune suppressive environment, high neutrophils, high macrophages, high immune suppression, less benefit from checkpoint inhibitor therapy, whereas you want lymphocyte. So I think there are biomedical medical insights that help inform the biology we do in the lab that have been known for decades or more. And certainly the myeloid M2 axis in macrophages and what have you was known about way before these spatial technologies really came to fruition, I think.
The Impact of Air Pollution
Eric Topol (28:01):
Yeah. Well you touched on this about air pollution and that's another dimension of the work that you and your team have done. As you well know, there was a recent global burden of disease paper in the Lancet, which has now said that air pollution with particulate matter 2.5 less is the leading cause of the burden of disease in the world now.
Charles Swanton (28:32):
What did you think of that, Eric?
Eric Topol (28:34):
I mean, I was blown away. Totally blown away. And this is an era you've really worked on. So can you put it in perspective?
Charles Swanton (28:42):
Yeah. So we got into this because patients of mine, and many of my colleagues would ask the same question, I've never smoked doctor, I'm healthy. I'm in my mid 50s though they're often female and I've got lung cancer. Why is that doctor? I've had a good diet, I exercise, et cetera. And we didn't really have a very good answer for that, and I don't want to pretend for a minute we solved the whole problem. I think hopefully we've contributed to a little bit of understanding of why this may happen. But that aside, we knew that there were risk factors associated with lung cancer that included air pollution, radon exposure, of course, germline genetics, we mustn't forget very important germline variation. And I think there is evidence that all of them are associated with lung cancer risk in different ways. But we wanted to look at air pollution, particularly because there was an awful lot of evidence, several meta-analysis of over half a million individuals showing very convincingly with highly significant results that increasing PM 2.5 micron particulate levels were associated with increased risk of lung cancer.
(29:59):
To put that into perspective, where you are on the west coast of the US, it's relatively unpolluted. You would be talking about maybe five micrograms per meter cubed of PM2.5 in a place like San Diego or Western California, assuming there aren't any forest fires of course. And we estimate that that would translate to about, we think it's about one extra case of never smoking lung cancer per hundred thousand of the population per year per one microgram per meter cube rise in the pollution levels. So if you go to Beijing for example, on a bad day, the air pollution levels could be upwards of a hundred micrograms per meter cubed because there are so many coal fired power stations in China partly. And there I think the risk is considerably higher. And that's certainly what we've seen in the meta-analyses in our limited and relatively crude epidemiological analyses to be the case.
(30:59):
So I think the association was pretty certain, we were very confident from people's prior publications this was important. But of course, association is not causation. So we took a number of animal models and showed that you could promote lung cancer formation in four different oncogene driven lung cancer models. And then the question is how, does air pollution stimulate mutations, which is what I initially thought it would do or something else. It turns out we don't see a significant increase in exogenous like C to A carcinogenic mutations. So that made us put our thinking caps on. And I said to you earlier, often all these discoveries have been made before. Well, Berenblum in 1947, first postulated that actually tumors are initiated through a two-step process, which we now know involves a sort of pre initiated cell with a mutation in that in itself is not sufficient to cause cancer.
(31:58):
But on top of that you need an inflammatory stimulus. So the question was then, well, okay, is inflammation working here? And we found that there was an interleukin-1 beta axis. And what happens is that the macrophages come into the lung on pollution exposure, engulf phagocytose the air pollutants, and we think what's happening is the air pollutants are puncturing membranes in the lung. That's what we think is happening. And interleukin-1 beta preformed IL-1 beta is being released into the extracellular matrix and then stimulating pre-initiated cells stem cells like the AT2 cells with an activating EGFR mutation to form a tumor. But the EGFR mutation alone is not sufficient to form tumors. It's only when you have the interleukin-1 beta and the activated mutation that a tumor can start.
(32:49):
And we found that if we sequence normal lung tissue in a healthy adult 60-year-old adult, we will find about half of biopsies will have an activating KRAS mutation in normal tissue, and about 15% will have an activating mutation in EGFR in histologically normal tissue with nerve and of cancer. In fact, my friend and colleague who's a co-author on the paper, James DeGregori, who you should speak to in Colorado, fascinating evolutionary cancer biologists estimates that in a healthy 60-year-old, there are a hundred billion cells in your body that harbor an oncogenic mutation. So that tells you that at the cellular level, cancer is an incredibly rare event and almost never happens. I mean, our lifetime risk of cancer is perhaps one in two. You covered that beautiful pancreas paper recently where they estimated that there may be 80 to 100 KRAS mutations in a normal adult pancreas, and yet our lifetime risk of pancreas cancer is one in 70. So this tells you that oncogenic mutations are rarely sufficient to drive cancer, so something else must be happening. And in the context of air pollution associated lung cancer, we think that's inflammation driven by these white cells, these myeloid cells, the macrophages.
Cancer Biomarkers
Eric Topol (34:06):
No, it makes a lot of sense. And this, you mentioned the pancreas paper and also what's going in the lung, and it seems like we have this burden of all you need is a tipping point and air pollution seems to qualify, and you seem to be really in the process of icing the mechanism. And like I would've thought it was just mutagenic and it's not so simple, right? But that gets me to this is such an important aspect of cancer, the fact that we harbor these kind of preconditions. And would you think that cancer takes decades to actually manifest most cancers, or do we really have an opportunity here to be able to track whether it's through blood or other biomarkers? Another area you've worked on a lot whereby let's say you could define people at risk for polygenic risk scores or various cancers or genome sequencing for predisposition genes, whatever, and you could monitor in the future over the course of those high-risk people, whether they were starting to manifest microscopic malignancy. Do you have any thoughts about how long it takes for the average person to actually manifest a typical cancer?
Charles Swanton (35:28):
That's a cracking question, and the answer is we've got some clues in various cancers. Peter Campbell would be a good person to speak to. He estimates that some of the earliest steps in renal cancer can occur in adolescence. We've had patients who gave up smoking 30 or so years ago where we can still see the clonal smoking mutations in the trunk of the tumor's evolutionary tree. So the initial footprints of the cancer are made 30 years before the cancer presents. That driver mutation itself may also be a KRAS mutation in a smoking cigarette context, G12C mutation. And those mutations can precede the diagnosis of the disease by decades. So the earliest steps in cancer evolution can occur, we think can precede diagnoses by a long time. So to your point, your question which is, is there an opportunity to intervene? I'm hugely optimistic about this actually, this idea of molecular cancer prevention.
An Anti-Inflammatory Drug Reduces Fatal Cancer and Lung Cancer
(36:41):
How can we use data coming out of various studies in the pancreas, mesothelioma, lung, et cetera to understand the inflammatory responses? I don't think we can do very much about the mutations. The mutations unfortunately are a natural consequence of aging. You and I just sitting here talking for an hour will have accumulated multiple mutations in our bodies over that period, I'm afraid and there's no escaping it. And right now there's not much we can do to eradicate those mutant clones. So if we take that as almost an intractable problem, measuring them is hard enough, eradicating them is even harder. And then we go back to Berenblum in 1947 who said, you need an inflammatory stimulus. Well, could we do something about the inflammation and dampen down the inflammation? And of course, this is why we got so excited about IL-1 beta because of the CANTOS trial, which you may remember in 2017 from Ridker and colleagues showed that anti IL-1 beta used as a mechanism of preventing cardiovascular events was associated with a really impressive dose dependent reduction in new lung cancer primaries.
(37:49):
Really a beautiful example of cancer prevention in action. And that data weren't just a coincidence. The FDA mandated Novartis to collect the solid tumor data and the P-values are 0.001. I mean it's very highly significant dose dependent reduction in lung cancer incidents associated with anti IL-1 beta. So I think that’s really the first clue in my mind that something can be done about this problem. And actually they had five years of follow-up, Eric. So that’s something about that intervening period where you can treat and then over time see a reduction in new lung cancers forming. So I definitely think there’s a window of opportunity here.
Eric Topol (38:31):
Well, what you’re bringing up is fascinating here because this trial, which was a cardiology trial to try to reduce heart attacks, finds a reduction in cancer, and it’s been lost. It’s been buried. I mean, no one’s using this therapy to prevent cancer between ratcheting up the immune system or decreasing inflammation. We have opportunities that we’re not even attempting. Are there any trials that are trying to do this sort of thing?
Charles Swanton (39:02):
So this is the fundamental problem. Nobody wants to invest in prevention because essentially you are dealing with well individuals. It’s like the vaccine challenge all over again. And the problem is you never know who you are benefiting. There’s no economic model for it. So pharma just won’t touch prevention with a barge pole right now. And that’s the problem. There’s no economic model for it. And yet the community, all my academic colleagues are crying out saying, this has got to be possible. This has got to be possible. So CRUK are putting together a group of like-minded individuals to see if we can do something here and we're gradually making progress, but it is tough.
Eric Topol (39:43):
And it's interesting that you bring that up because for GRAIL, one of the multicenter cancer early detection companies, they raised billions of dollars. And in fact, their largest trial is ongoing in the UK, but they haven't really focused on high-risk people. They just took anybody over age 50 or that sort of thing. But that's the only foray to try to reboot how we or make an early microscopic diagnosis of cancer and track people differently. And there's an opportunity there. You've written quite a bit on you and colleagues of the blood markers being able to find a cancer where well before, in fact, I was going to ask you about that is, do you think there's people that are not just having all these mutations every minute, every hour, but that are starting to have the early seeds of cancer, but because their immune system then subsequently kicks in that they basically kind of quash it for that period of time?
Charles Swanton (40:47):
Yeah, I do think that, I mean, the very fact that we see these sort of footprints in the tumor genome of immune evasion tells you that the immune system's having a very profound predatory effect on evolving tumors. So I do think it's very likely that there are tumors occurring that are suppressed by the immune system. There is a clear signature, a signal of negative selection in tumors where clones have been purified during their evolution by the immune system. So I think there's pretty strong evidence for that now. Obviously, it's very difficult to prove something existed when it doesn't now exist, but there absolutely is evidence for that. I think it raises the interesting question of immune system recognizes mutations and our bodies are replete with mutations as we were just discussing. Why is it that we're not just a sort of epithelial lining of autoimmunity with T-cells and immune cells everywhere? And I think what the clever thing about the immune system is it's evolved to target antigens only when they get above a certain burden. Otherwise, I think our epithelial lining, our skin, our guts, all of our tissues will be just full of T-cells eating away our normal clones.
(42:09):
These have to get to a certain size for antigen to be presented at a certain level for the immune system to recognize it. And it's only then that you get the immune predation occurring.
Forever Chemicals and Microplastics
Eric Topol (42:20):
Yeah, well, I mean this is opportunities galore here. I also wanted to extend the air pollution story a bit. Obviously, we talked about particulate matter and there's ozone and nitric NO2, and there's all sorts of other air pollutants, but then there's also in the air and water these forever chemicals PFAS for abbreviation, and they seem to be incriminated like air pollution. Can you comment about that?
Charles Swanton (42:55):
Well, I can comment only insofar as to say I'm worried about the situation. Indeed, I'm worried about microplastics actually, and you actually cover that story as well in the New England Journal, the association of microplastics with plaque rupture and atheroma. And indeed, just as in parenthesis, I wanted to just quickly say we currently think the same mechanisms that are driving lung cancer are probably responsible for atheroma and possibly even neurodegenerative disease. And essentially it all comes down to the macrophages and the microglia becoming clogged up with these pollutants or environmental particulars and releasing chronic inflammatory mediators that ultimately lead to disease. And IL-1 beta being one of those in atheroma and probably IL-6 and TNF in neurodegenerative disease and what have you. But I think this issue that you rightly bring up of what is in our environment and how does it cause pathology is really something that epidemiologists have spent a lot of time focusing on.
(43:56):
But actually in terms of trying to move from association to causation, we've been, I would argue a little bit slow biologically in trying to understand these issues. And I think that is a concern. I mean, to give you an example, Allan Balmain, who works at UCSF quite close to you, published a paper in 2020 showing that 17 out of 20 environmental carcinogens IARC carcinogens class one carcinogens cause tumors in rodent models without driving mutations. So if you take that to a logical conclusion, in my mind, what worries me is that many of the sort of carcinogen assays are based on driving mutagenesis genome instability. But if many carcinogen aren't driving DNA mutagenesis but are still driving cancer, how are they doing it? And do we actually have the right assays to interpret safety of new chemical matter that's being introduced into our environment, these long-lived particles that we're breathing in plastics, pollutants, you name it, until we have the right biological assays, deeming something to be safe I think is tricky.
Eric Topol (45:11):
Absolutely. And I share your concerns on the nanoplastic microplastic story, as you well know, not only have they been seen in arteries that are inflamed and in blood clots and in various tissues, have they been seen so far or even looked for within tumor tissue?
Charles Swanton (45:33):
Good question. I'm not sure they have. I need to check. What I can tell you is we've been doing some experiments in the lab with fluorescent microplastics, 2.5 micron microplastics given inhaled microplastics. We find them in every mouse organ a week after. And these pollutants even get through into the brain through the olfactory bulb we think.
Charles Swanton (45:57):
Permeate every tissue, Eric.
Eric Topol (45:59):
Yeah, no, this is scary because here we are, we have these potentially ingenious ways to prevent cancer in the future, but we're chasing our tails by not doing anything to deal with our environment.
Charles Swanton (46:11):
I think that's right. I totally agree. Yeah.
Eric Topol (46:15):
So I mean, I can talk to you for the rest of the day, but I do want to end up with a topic that we have mutual interest in, which is AI. And also along with that, when you mentioned about aging, I'd like to get your views on these two, how do you see AI fitting into the future of cancer? And then the more general topic is, can we actually at some point modulate the biologic aging process with or without help with from AI? So those are two very dense questions, but maybe you can take us through them.
Charles Swanton (46:57):
How long have we got?
Eric Topol (46:59):
Just however long you have.
A.I. and Cancer
Charles Swanton (47:02):
AI and cancer. Well, AI and medicine actually in general, whether it's biomedical research or medical care, has just infinite potential. And I'm very, very excited about it. I think what excites me about AI is it's almost the infinite possibilities to work across scale. Some of the challenges we raised in the Cell review that you mentioned, tackling, embracing complexity are perfectly suited for an AI problem. Nonlinear data working, for instance in our fields with CT imaging, MRI imaging, clinical outcome data, blood parameters, genomics, transcriptomes and proteomes and trying to relate this all into something that's understandable that relates to risk of disease or potential identification of a new drug target, for example. There are numerous publications that you and others have covered that allude to the incredible possibilities there that are leading to, for instance, the new identification of drug targets. I mean, Eli Van Allen's published some beautiful work here and in the context of prostate cancer with MDM4 and FGF receptor molecules being intimately related to disease biology.
(48:18):
But then it's not just that, not just drug target identification, it's also going all the way through to the clinic through drug discovery. It's how you get these small molecules to interact with oncogenic proteins and to inhibit them. And there are some really spectacular developments going on in, for instance, time resolved cryo-electron microscopy, where in combination with modeling and quantum computing and what have you, you can start to find pockets emerging in mutant proteins, but not the wild type ones that are druggable. And then you can use sort of synthetic AI driven libraries to find small molecules that will be predicted to bind these transiently emerging pockets. So it's almost like AI is primed to help at every stage in scientific investigation from the bench all the way through to the bedside. And there are examples all the way through there in the literature that you and others have covered in the last few years. So I could not be more excited about that.
Eric Topol (49:29):
I couldn't agree with you more. And I think when we get to multimodal AI at the individual level across all their risks for conditions in their future, I hope someday will fulfill that fantasy of primary prevention. And that is getting me to this point that I touched on because I do think they interact to some degree AI and then will we ever be able to have an impact on aging? Most people conflate this because what we've been talking about throughout the hour has been age-related diseases, that is cancer, for example, and cardiovascular and neurodegenerative, which is different than changing aging per se, body wide aging. Do you think we'll ever changed body wide aging?
Charles Swanton (50:18):
Wow, what a question. Well, if you'd asked me 10 years ago, 15 years ago, do you think we'll ever cure melanoma in my lifetime, I'd have said definitely not. And now look where we are. Half of patients with melanoma, advanced melanoma, even with brain metastasis curd with combination checkpoint therapy. So I never say never in biology anymore. It always comes back to bite you and prove you wrong. So I think it's perfectly possible.
Charles Swanton (50:49):
We have ways to slow down the aging process. I guess the question is what will be the consequences of that?
Eric Topol (50:55):
That's what I was going to ask you, because all these things like epigenetic reprogramming and senolytic drugs, and they seem to at least pose some risk for cancer.
Charles Swanton (51:09):
That's the problem. This is an evolutionary phenomenon. It's a sort of biological response to the onslaught of these malignant cells that are potentially occurring every day in our normal tissue. And so, by tackling one problem, do we create another? And I think that's going to be the big challenge over the next 50 years.
Eric Topol (51:31):
Yeah, and I think your point about the multi-decade challenge, because if you can promote healthy aging without any risk of cancer, that would be great. But if the tradeoff is close, it's not going to be very favorable. That seems to be the main liability of modulation aging through many of the, there's many shots on goal here, of course, as you well know. But they do seem to pose that risk in general.
Charles Swanton (51:58):
I think that's right. I think the other thing is, I still find, I don’t know if you agree with me, but it is an immense conundrum. What is the underlying molecular basis for somatic aging, for aging of normal tissues? And it may be multifactorial, it may not be just one answer to that question. And different tissues may age in different ways. I don't know. It's a fascinating area of biology, but I think it really needs to be studied more because as you say, it underpins all of these diseases we've been talking about today, cardiovascular, neurodegeneration, cancer, you name it. We absolutely have to understand this. And actually, the more I work in cancer, the more I feel like actually what I'm working on is aging.
(52:48):
And this is something that James DeGregori and I have discussed a lot. There's an observation that in medicine around patients with alpha-1 antitrypsin deficiency who are at higher risk of lung cancer, but they're also at high risk of COPD, and we know the associations of chronic obstructive pulmonary disease with lung cancer risk. And one of the theories that James had, and I think this is a beautiful idea, actually, is as our tissues age, and COPD is a reflection of aging, to some extent gone wrong. And as our tissues age, they become less good at controlling the expansion of these premalignant clones, harboring, harboring oncogenic mutations in normal tissue. And as those premalignant clones expand, the substrate for evolution also expands. So there's more likely to be a second and third hit genetically. So it may be by disrupting the extracellular matrices through inflammation that triggers COPD through alpha-1 antitrypsin deficiency or smoking, et cetera, you are less effectively controlling these emergent clones that just expand with age, which I think is a fascinating idea actually.
Eric Topol (54:01):
It really is. Well, I want to tell you, Charlie, this has been the most fascinating, exhilarating discussion I've ever had on cancer. I mean, really, I am indebted to you because not just all the work you've done, but your ability to really express it, articulate it in a way that hopefully everyone can understand who's listening or reading the transcript. So we'll keep following what you're doing because you're doing a lot of stuff. I can't thank you enough for joining me today, and you've given me lots of things to think about. I hope the people that are listening or reading feel the same way. I mean, this has been so mind bending in many respects. We're indebted to you.
Charles Swanton (54:49):
Well, we all love reading your Twitter feeds. Keep them coming. It helps us keep a broader view of medicine and biological research, not just cancer, which is why I love it so much.
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In this podcast, Thomas Czech, Distinguished Professor at the University of Colorado, Boulder, with a lineage of remarkable contributions on RNA, ribozyme, and telomeres, discuss why RNA is so incredibly versatile.
Video snippet from our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.
Transcript with links to the audio and external links
Eric Topol (00:07):
Well, hello, this is Eric Topol from Ground Truths, and it's really a delight for me to welcome Tom Cech who just wrote a book, the Catalyst, and who is a Nobel laureate for his work in RNA. And is at the University of Colorado Boulder as an extraordinary chemist and welcome Tom.
Tom Cech (00:32):
Eric, I'm really pleased to be here.
The RNA Guy
Eric Topol (00:35):
Well, I just thoroughly enjoyed your book, and I wanted to start out, if I could, with a quote, which gets us right off the story here, and let me just get to it here. You say, “the DNA guy would need to become an RNA guy. Though I didn’t realize it at the time, jumping ship would turn out to be the most momentous decision in my life.” Can you elaborate a bit on that?
Tom Cech (01:09):
As a graduate student at Berkeley, I was studying DNA and chromosomes. I thought that DNA was king and really somewhat belittled the people in the lab next door who were working on RNA, I thought it was real sort of second fiddle material. Of course, when RNA is acting just as a message, which is an important function, a critical function in all life on earth, but still, it's a function that's subservient to DNA. It's just copying the message that's already written in the playbook of DNA. But little did I know that the wonders of RNA were going to excite me and really the whole world in unimaginable ways.
Eric Topol (02:00):
Well, they sure have, and you've lit up the world well before you had your Nobel Prize in 1989 was Sid Altman with ribozyme. And I think one of the things that struck me, which are so compelling in the book as I think people might know, it's divided in two sections. The first is much more on the biology, and the second is much more on the applications and how it's changing the world. We'll get into it particularly in medicine, but the interesting differentiation from DNA, which is the one trick pony, as you said, all it does is store stuff. And then the incredible versatility of RNA as you discovered as a catalyst, that challenging dogma, that proteins are supposed to be the only enzymes. And here you found RNA was one, but also so much more with respect to genome editing and what we're going to get into here. So I thought what we might get into is the fact that you kind of went into the scum of the pond with this organism, which by the way, you make a great case for the importance of basic science towards the end of the book. But can you tell us about how you, and then of course, many others got into the Tetrahymena thermophila, which I don't know that much about that organism.
Tom Cech (03:34):
Yeah, it's related to Tetrahymena is related to paramecium, which is probably more commonly known because it's an even larger single celled animal. And therefore, in an inexpensive grade school microscope, kids can look through and see these ciliated protozoa swimming around on a glass slide. But I first learned about them when I was a postdoc at MIT and I would drive down to Joe Gall's lab at Yale University where Liz Blackburn was a postdoc at the time, and they were all studying Tetrahymena. It has the remarkable feature that it has 10,000 identical copies of a particular gene and for a higher organism, one that has its DNA in the nucleus and does its protein synthesis in the cytoplasm. Typically, each gene's present in two copies, one from mom, one from dad. And if you're a biochemist, which I am having lots of stuff is a real advantage. So 10,000 copies of a particular gene pumping out RNA copies all the time was a huge experimental advantage. And that's what I started working on when I started my own lab at Boulder.
Eric Topol (04:59):
Well, and that's where, I guess the title of the book, the Catalyst ultimately, that grew into your discovery, right?
Tom Cech (05:08):
Well, at one level, yes, but I also think that the catalyst in a more general conversational sense means just facilitating life in this case. So RNA does much more than just serve as a biocatalyst or a message, and we'll get into that with genome editing and with telomerase as well.
The Big Bang and 11 Nobel Prizes on RNA since 2000
Eric Topol (05:32):
Yes, and I should note that as you did early in the book, that there's been an 11 Nobel prize awardees since 2000 for RNA work. And in fact, we just had Venki who I know you know very well as our last podcast. And prior to that, Kati Karikó, Jennifer Doudna who worked in your lab, and the long list of people working RNA in the younger crowd like David Liu and Fyodor Urnov and just so many others, we need to have an RNA series because it's just exploding. And that one makes me take you back for a moment to 2007. And when I was reading the book, it came back to me about the Economist cover. You may recall almost exactly 17 years ago. It was called the Biology’s Big Bang – Unravelling the secrets of RNA. And in that, there was a notable quote from that article. Let me just get to that. And it says, “it is probably no exaggeration to say that biology is now undergoing its neutron moment.”
(06:52):
This is 17 years ago. “For more than half a century the fundamental story of living things has been a tale of the interplay between genes, in the form of DNA, and proteins, which is genes encode and which do the donkey work of keeping living organisms living. The past couple of years, 17 years ago, however, has seen the rise and rise of a third type of molecule, called RNA.” Okay, so that was 2007. It's pretty extraordinary. And now of course we're talking about the century of biology. So can you kind of put these last 17 years in perspective and where we're headed?
Tom Cech (07:34):
Well, Eric, of course, this didn't all happen in one moment. It wasn't just one big bang. And the scientific community has been really entranced with the wonders of RNA since the 1960s when everyone was trying to figure out how messenger RNA stored the genetic code. But the general public has been really kept in the dark about this, I think. And as scientists, were partially to blame for not reaching out and sharing what we have found with them in a way that's more understandable. The DNA, the general public's very comfortable with, it's the stuff of our heredity. We know about genetic diseases, about tracing our ancestry, about solving crimes with DNA evidence. We even say things like it's in my DNA to mean that it's really fundamental to us. But I think that RNA has been sort of kept in the closet, and now with the mRNA vaccines against Covid-19, at least everyone's heard of RNA. And I think that that sort of allowed me to put my foot in the door and say, hey, if you were curious about the mRNA vaccines, I have some more stories for you that you might be really interested in.
RNA vs RNA
Eric Topol (09:02):
Yeah, well, we'll get to that. Maybe we should get to that now because it is so striking the RNA versus RNA chapter in your book, and basically the story of how this RNA virus SARS-CoV-2 led to a pandemic and it was fought largely through the first at scale mRNA nanoparticle vaccine package. Now, that takes us back to some seminal work of being able to find, giving an mRNA to a person without inciting massive amount of inflammation and the substitution of pseudouridine or uridine in order to do that. Does that really get rid of all the inflammation? Because obviously, as you know, there's been some negativism about mRNA vaccines for that and also for the potential of not having as much immune cell long term activation. Maybe you could speak to that.
Tom Cech (10:03):
Sure. So the discovery by Kati Karikó and Drew Weissman of the pseudouridine substitution certainly went a long way towards damping down the immune response, the inflammatory response that one naturally gets with an RNA injection. And the reason for that is that our bodies are tuned to be on the lookout for foreign RNA because so many viruses don't even mess with DNA at all. They just have a genome made of RNA. And so, RNA replicating itself is a danger sign. It means that our immune system should be on the lookout for this. And so, in the case of the vaccination, it's really very useful to dampen this down. A lot of people thought that this might make the mRNA vaccines strange or foreign or sort of a drug rather than a natural substance. But in fact, modified nucleotides, nucleotides being the building blocks of RNA, so these modified building blocks such as pseudoU, are in fact found in natural RNAs more in some than in others. And there are about 200 modified versions of the RNA building blocks found in cells. So it's really not an unusual modification or something that's all that foreign, but it was very useful for the vaccines. Now your other question Eric had to do with the, what was your other question, Eric?
Eric Topol (11:51):
No, when you use mRNA, which is such an extraordinary way to get the spike protein in a controlled way, exposed without the virus to people, and it saved millions of lives throughout the pandemic. But the other question is compared to other vaccine constructs, there's a question of does it give us long term protective immunity, particularly with T cells, both CD8 cytotoxic, maybe also CD4, as I know immunology is not your main area of interest, but that's been a rub that's been put out there, that it isn't just a weaning of immunity from the virus, but also perhaps that the vaccines themselves are not as good for that purpose. Any thoughts on that?
Tom Cech (12:43):
Well, so my main thought on that is that this is a property of the virus more than of the vaccine. And respiratory viruses are notoriously hard to get long-term immunity. I mean, look at the flu virus. We have to have annual flu shots. If this were like measles, which is a very different kind of virus, one flu shot would protect you against at least that strain of flu for the rest of your life. So I think the bad rap here is not the vaccine's fault nearly as much as it's the nature of respiratory viruses.
RNA And Aging
Eric Topol (13:27):
No, that's extremely helpful. Now, let me switch to an area that's really fascinating, and you've worked quite a bit on the telomerase story because this is, as you know, being pursued quite a bit, has thought, not just because telomeres might indicate something about biologic aging, but maybe they could help us get to an anti-aging remedy or whatever you want to call it. I'm not sure if you call it a treatment, but tell us about this important enzyme, the role of the RNA building telomeres. And maybe you could also connect that with what a lot of people might not be familiar with, at least from years ago when they learned about it, the Hayflick limit.
Tom Cech (14:22):
Yes. Well, Liz Blackburn and Carol Greider got the Nobel Prize for the discovery of telomerase along with Jack Szostak who did important initial work on that system. And what it does is, is it uses an RNA as a template to extend the ends of human chromosomes, and this allows the cell to keep dividing without end. It gives the cell immortality. Now, when I say immortality, people get very excited, but I'm talking about immortality at the cellular level, not for the whole organism. And in the absence of a mechanism to build out the ends of our chromosomes, the telomeres being the end of the chromosome are incompletely replicated with each cell division. And so, they shrink over time, and when they get critically short, they signal the cell to stop dividing. This is what is called the Hayflick limit, first discovered by Leonard Hayflick in Philadelphia.
(15:43):
And he, through his careful observations on cells, growing human cells growing in Petri dishes, saw that they could divide about 50 times and then they wouldn't die. They would just enter a state called senescence. They would change shape, they would change their metabolism, but they would importantly quit dividing. And so, we now see this as a useful feature of human biology that this protects us from getting cancer because one of the hallmarks of cancer is immortality of the tumor cells. And so, if you're wishing for your telomeres to be long and your cells to keep dividing, you have to a little bit be careful what you wish for because this is one foot in the door for cancer formation.
Eric Topol (16:45):
Yeah, I mean, the point is that it seems like the body and the cell is smart to put these cells into the senescent state so they can't divide anymore. And one of the points you made in the book that I think is worth noting is that 90% of cancers have the telomerase, how do you say it?
Tom Cech (17:07):
Telomerase.
Eric Topol (17:08):
Yeah, reactivate.
Tom Cech (17:09):
Right.
Eric Topol (17:10):
That's not a good sign.
Tom Cech (17:12):
Right. And there are efforts to try to target telomerase enzyme for therapeutic purposes, although again, it's tricky because we do have stem cells in our bodies, which are the exception to the Hayflick limit rule. They do still have telomerase, they still have to keep dividing, maybe not as rapidly as a cancer cell, but they still keep dividing. And this is critical for the replenishment of certain worn out tissues in our such as skin cells, such as many of our blood cells, which may live only 30 days before they poop out. That's a scientific term for needing to be replenished, right?
Eric Topol (18:07):
Yeah. Well, that gets me to the everybody's, now I got the buzz about anti-aging, and whether it's senolytics to get rid of these senescent cells or whether it's to rejuvenate the stem cells that are exhausted or work on telomeres, all of these seem to connect with a potential or higher risk of cancer. I wonder what your thoughts are as we go forward using these various biologic constructs to be able to influence the whole organism, the whole human body aging process.
Tom Cech (18:47):
Yes. My view, and others may disagree is that aging is not an affliction. It's not a disease. It's not something that we should try to cure, but what we should work on is having a healthy life into our senior years. And perhaps you and I are two examples of people who are at that stage of our life. And what we would really like is to achieve, is to be able to be active and useful to society and to our families for a long period of time. So using the information about telomerase, for example, to help our stem cells stay healthy until we are, until we're ready to cash it in. And for that matter on the other side of the coin, to try to inhibit the telomerase in cancer because cancer, as we all know, is a disease of aging, right? There are young people who get cancer, but if you look at the statistics, it's really heavily weighted towards people who've been around a long time because mutations accumulate and other damage to cells that would normally protect against cancer accumulates. And so, we have to target both the degradation of our stem cells, but also the occurrence of cancer, particularly in the more senior population. And knowing more about RNA is really helpful in that regard.
RNA Drugs
Eric Topol (20:29):
Yeah. Well, one of the things that comes across throughout the book is versatility of RNA. In fact, you only I think, mentioned somewhere around 12 or 14 of these different RNAs that have a million different shapes, and there's so many other names of different types of RNAs. It's really quite extraordinary. But one of the big classes of RNAs has really hit it. In fact, this week there are two new interfering RNAs that are having extraordinary effects reported in the New England Journal on all the lipids, abnormal triglycerides and LDL cholesterol, APOC3. And can you talk to us about this interfering the small interfering RNAs and how they become, you've mentioned in the book over 400 RNAs are in the clinic now.
Tom Cech (21:21):
Yeah, so the 400 of course is beyond just the siRNAs, but these, again, a wonderful story about how fundamental science done just to understand how nature works without any particular expectation of a medical spinoff, often can have the most phenomenal and transformative effects on medicine. And this is one of those examples. It came from a roundworm, which is about the size of an eyelash, which a scientist named Sydney Brenner in England had suggested would be a great experimental organism because the entire animal has only about a thousand cells, and it's transparent so we can look at, see where the cells are, we can watch the worm develop. And what Andy Fire and Craig Mello found in this experimental worm was that double-stranded RNA, you think about DNA is being double-stranded and RNA as being single stranded. But in this case, it was an unusual case where the RNA was forming a double helix, and these little pieces of double helical RNA could turn off the expression of genes in the worm.
(22:54):
And that seemed remarkable and powerful. But as often happens in biology, at least for those of us who believe in evolution, what goes for the worm goes for the human as well. So a number of scientists quickly found that the same process was going on in the human body as a natural way of regulating the expression of our genes, which means how much of a particular gene product is actually going to be made in a particular cell. But not only was it a natural process, but you could introduce chemically synthesized double helical RNAs. There are only 23 base pairs, 23 units of RNA long, so they're pretty easy to chemically synthesize. And that once these are introduced into a human, the machinery that's already there grabs hold of them and can be used to turn off the expression of a disease causing RNA or the gene makes a messenger RNA, and then this double-stranded RNA can suppress its action. So this has become the main company that is known for doing this is Alnylam in Boston, Cambridge. And they have made quite a few successful products based on this technology.
Eric Topol (24:33):
Oh, absolutely. Not just for amyloidosis, but as I mentioned these, they even have a drug that's being tested now, as you know that you could take once or twice a year to manage your blood pressure. Wouldn't that be something instead of a pill every day? And then of course, all these others that are not just from Alnylam, but other companies I wasn't even familiar with for managing lipids, which is taking us well beyond statins and these, so-called PCSK9 monoclonal antibodies, so it's really blossoming. Now, the other group of RNA drugs are antisense drugs, and it seemed like they took forever to warm up, and then finally they hit. And can you distinguish the antisense versus the siRNA therapeutics?
Tom Cech (25:21):
Yes, in a real general sense, there's some similarity as well as some differences, but the antisense, what are called oligonucleotides, whoa, that's a big word, but oligo just means a few, right? And nucleotides is just the building blocks of nucleic acid. So you have a string of a few of these. And again, it's the power of RNA that it is so good at specifically base pairing only with matching sequences. So if you want to match with a G in a target messenger RNA, you put a C in the antisense because G pairs with C, if you want to put an A, if want to match with an A, you put a U in the antisense because A and U form a base pair U is the RNA equivalent of T and DNA, but they have the same coding capacity. So any school kid can write out on a notepad or on their laptop what the sequence would have to be of an antisense RNA to specifically pair with a particular mRNA.
(26:43):
And this has been, there's a company in your neck of the woods in the San Diego area. It started out with the name Isis that turned out to be the wrong Egyptian God to name your company after, so they're now known as Ionis. Hopefully that name will be around for a while. But they've been very successful in modifying these antisense RNAs or nucleic acids so that they are stable in the body long enough so that they can pair with and thereby inhibit the expression of particular target RNAs. So it has both similarities and differences from the siRNAs, but the common denominator is RNA is great stuff.
RNA and Genome Editing
Eric Topol (27:39):
Well, you have taken that to in catalyst, the catalyst, you've proven that without a doubt and you and so many other extraordinary scientists over the years, cumulatively. Now, another way to interfere with genes is editing. And of course, you have a whole chapter devoted to not just well CRISPR, but the whole genome editing field. And by the way, I should note that I forgot because I had read the Codebreaker and we recently spoke Jennifer Doudna and I, that she was in your lab as a postdoc and you made some wonderful comments about her. I don't know if you want to reflect about having Jennifer, did you know that she was going to do some great things in her career?
Tom Cech (28:24):
Oh, there was no question about it, Eric. She had been a star graduate student at Harvard, had published a series of breathtaking papers in magazines such as Science and Nature already as a graduate student. She won a Markey fellowship to come to Colorado. She chose a very ambitious project trying to determine the molecular structures of folded RNA molecules. We only had one example at the time, and that was the transfer RNA, which is involved in protein synthesis. And here she was trying these catalytic RNAs, which we had discovered, which were much larger than tRNA and was making great progress, which she finished off as an assistant professor at Yale. So what the general public may not know was that in scientific, in the scientific realm, she was already highly appreciated and much awarded before she even heard anything about CRISPR.
Eric Topol (29:38):
Right. No, it was a great line you have describing her, “she had an uncanny talent for designing just the right experiment to test any hypothesis, and she possessed more energy and drive than any scientist I'd ever met.” That's pretty powerful. Now getting into CRISPR, the one thing, it's amazing in just a decade to see basically the discovery of this natural system to then be approved by FDA for sickle cell disease and beta thalassemia. However, the way it exists today, it's very primitive. It's not actually fixing the gene that's responsible, it's doing a workaround plan. It's got double strand breaks in the DNA. And obviously there's better ways of editing, which are going to obviously involve RNA epigenetic editing, if you will as well. What is your sense about the future of genome editing?
Tom Cech (30:36):
Yeah, absolutely, Eric. It is primitive right now. These initial therapies are way too expensive as well to make them broadly applicable to the entire, even in a relatively wealthy country like the United States, we need to drive the cost down. We need to get them to work, we need to get the process of introducing them into the CRISPR machinery into the human body to be less tedious and less time consuming. But you've got to start somewhere. And considering that the Charpentier and Doudna Nobel Prize winning discovery was in 2012, which is only a dozen years ago, this is remarkable progress. More typically, it takes 30 years from a basic science discovery to get a medical product with about a 1% chance of it ever happening. And so, this is clearly a robust RNA driven machine. And so, I think the future is bright. We can talk about that some more, but I don't want to leave RNA out of this conversation, Eric. So what's cool about CRISPR is its incredible specificity. Think of the human genome as a million pages of text file on your computer, a million page PDF, and now CRISPR can find one sentence out of that million pages that matches, and that's because it's using RNA, again, the power of RNA to form AU and GC base pairs to locate just one site in our whole DNA, sit down there and direct this Cas9 enzyme to cut the DNA at that site and start the repair process that actually does the gene editing.
Eric Topol (32:41):
Yeah, it's pretty remarkable. And the fact that it can be so precise and it's going to get even more precise over time in terms of the repair efforts that are needed to get it back to an ideal state. Now, the other thing I wanted to get into with you a bit is on the ribosome, because that applies to antibiotics and as you call it, the mothership. And I love this metaphor that you had about the ribosome, and in the book, “the ribosome is your turntable, the mRNA is the vinyl LP record, and the protein is the music you hear when you lower the needle.” Tell us more about the ribosome and the role of antibiotics.
Tom Cech (33:35):
So do you think today's young people will understand that metaphor?
Eric Topol (33:40):
Oh, they probably will. They're making a comeback. These records are making a comeback.
Tom Cech (33:44):
Okay. Yes, so this is a good analogy in that the ribosome is so versatile it's able to play any music that you feed at the right messenger RNA to make the music being the protein. So you can have in the human body, we have tens of thousands of different messenger RNAs. Each one threads through the same ribosome and spills out the production of whatever protein matches that mRNA. And so that's pretty remarkable. And what Harry Noller at UC Santa Cruz and later the crystallographers Venki Ramakrishnan, Tom Steitz, Ada Yonath proved really through their studies was that this is an RNA machine. It was hard to figure that out because the ribosome has three RNAs and it has dozens of proteins as well. So for a long time people thought it must be one of those proteins that was the heart and soul of the record player, so to speak.
RNA and Antibiotics
(34:57):
And it turned out that it was the RNA. And so, when therefore these scientists, including Venki who you just talked to, looked at where these antibiotics docked on the ribosome, they found that they were blocking the key functional parts of the RNA. So it was really, the antibiotics knew what they were doing long before we knew what they were doing. They were talking to and obstructing the action of the ribosomal RNA. Why is this a good thing for us? Because bacterial ribosomes are just enough different from human ribosomes that there are drugs that will dock to the bacterial ribosomal RNA, throw a monkey wrench into the machine, prevent it from working, but the human ribosomes go on pretty much unfazed.
Eric Topol (36:00):
Yeah, no, the backbone of our antibiotics relies on this. So I think people need to understand about the two subunits, the large and the small and this mothership, and you illuminate that so really well in the book. That also brings me to phage bacteria phage, and we haven't seen that really enter the clinic in a significant way, but there seems to be a great opportunity. What's your view about that?
Tom Cech (36:30):
This is an idea that goes way back because since bacteria have their own viruses which do not infect human cells, why not repurpose those into little therapeutic entities that could kill, for example, what would we want to kill? Well, maybe tuberculosis has been very resistant to drugs, right? There are drug resistant strains of TB, yes, of TB, tuberculosis, and especially in immunocompromised individuals, this bug runs rampant. And so, I don't know the status of that. It's been challenging, and this is the way that biomedicine works, is that for every 10 good ideas, and I would say phage therapy for bacterial disease is a good idea. For every 10 such ideas, one of them ends up being practical. And the other nine, maybe somebody else will come along and find a way to make it work, but it hasn't been a big breakthrough yet.
RNA, Aptamers and Proteins
Eric Topol (37:54):
Yeah, no, it's really interesting. And we'll see. It may still be in store. What about aptamers? Tell us a little bit more about those, because they have been getting used a lot in sorting out the important plasma proteins as therapies. What are aptamers and what do you see as the future in that regard?
Tom Cech (38:17):
Right. Well, in fact, aptamers are a big deal in Boulder because Larry Gold in town was one of the discoverers has a company making aptamers to recognize proteins. Jack Szostak now at University of Chicago has played a big role. And also at your own institution, Jerry Joyce, your president is a big aptamer guy. And you can evolution, normally we think about it as happening out in the environment, but it turns out you can also make it work in the laboratory. You can make it work much faster in the laboratory because you can set up test tube experiments where molecules are being challenged to perform a particular task, like for example, binding to a protein to inactivate it. And if you make a large community of RNA molecules randomly, 99.999% of them aren't going to know how to do this. What are the odds? Very low.
(39:30):
But just by luck, there will be an occasional molecule of RNA that folds up into a shape that actually fits into the proteins active sighting throws a monkey wrench into the works. Okay, so now that's one in a billion. How are you going to find that guy? Well, this is where the polymerase chain reaction, the same one we use for the COVID-19 tests for infection comes into play. Because if you can now isolate this needle in a haystack and use PCR to amplify it and make a whole handful of it, now you've got a whole handful of molecules which are much better at binding this protein than the starting molecule. And now you can go through this cycle several times to enrich for these, maybe mutagen it a little bit more to give it a little more diversity. We all know diversity is good, so you put a little more diversity into the population and now you find some guy that's really good at recognizing some disease causing protein. So this is the, so-called aptamer story, and they have been used therapeutically with some success, but diagnostically certainly they are extremely useful. And it's another area where we've had success and the future could hold even more success.
Eric Topol (41:06):
I think what you're bringing up is so important because the ability to screen that tens of thousands of plasma proteins in a person and coming up with as Tony Wyss-Coray did with the organ clocks, and this is using the SomaLogic technology, and so much is going on now to get us not just the polygenic risk scores, but also these proteomic scores to compliment that at our orthogonal, if you will, to understand risk of people for diseases so we can prevent them, which is fulfilling a dream we've never actually achieved so far.
Tom Cech (41:44):
Eric, just for full disclosure, I'm on the scientific advisory board of SomaLogic in Boulder. I should disclose that.
Eric Topol (41:50):
Well, that was smart. They needed to have you, so thank you for mentioning that. Now, before I wrap up, well, another area that is a favorite of mine is citizen science. And you mentioned in the book a project because the million shapes of RNA and how it can fold with all hairpin terms turns and double stranded and whatever you name it, that there was this project eteRNA that was using citizen scientists to characterize and understand folding of RNA. Can you tell us about that?
RNA Folding and Citizen Science
Tom Cech (42:27):
So my friend Rhiju Das, who's a professor at Stanford University, sort of adopted what had been done with protein folding by one of his former mentors, David Baker in Seattle, and had repurposed this for RNA folding. So the idea is to come up with a goal, a target for the community. Can you design an RNA that will fold up to look like a four pointed cross or a five pointed star? And it turned out that, so they made it into a contest and they had tens of thousands of people playing these games and coming up with some remarkable solutions. But then they got a little bit more practical, said, okay, that was fun, but can we have the community design something like a mRNA for the SARS-CoV-2 spike protein to make maybe a more stable vaccine? And quite remarkably, the community of many of whom are just gamers who really don't know much about what RNA does, were able to find some solutions. They weren't enormous breakthroughs, but they got a several fold, several hundred percent increase in stability of the RNA by making it fold more tightly. So I just find it to be a fascinating approach to science. Somebody of my generation would never think of this, but I think for today's generation, it's great when citizens can become involved in research at that level.
Eric Topol (44:19):
Oh, I think it's extraordinary. And of course, there are other projects folded and others that have exemplified this ability for people with no background in science to contribute in a meaningful way, and they really enjoy, it's like solving a puzzle. The last point is kind of the beginning, the origin of life, and you make a pretty strong case, Tom, that it was RNA. You don't say it definitively, but maybe you can say it here.
RNA and the Origin of Life
Tom Cech (44:50):
Well, Eric, the origin of life happening almost 4 billion years ago on our primitive planet is sort of a historical question. I mean, if you really want to know what happened then, well, we don't have any video surveillance of those moments. So scientists hate to ever say never, but it's hard to sort of believe how we would ever know for sure. So what Leslie Orgel at the Salk Institute next to you taught me when I was a starting assistant professor is even though we'll never know for sure, if we can recapitulate in the laboratory plausible events that could have happened, and if they make sense chemically and biologically, then that's pretty satisfying, even if we can never be absolutely sure. That's what a number of scientists have done in this field is to show that RNA is sort of a, that all the chemistry sort of points to RNA as being something that could have been made under prebiotic conditions and could have folded up into a way that could solve the greatest of all chicken and egg problems, which came first, the informational molecule to pass down to the next generation or the active molecule that could copy that information.
(46:32):
So now that we know that RNA has both of those abilities, maybe at the beginning there was just this RNA world RNA copying itself, and then proteins came along later, and then DNA probably much more recently as a useful but a little bit boring of genetic information, right?
Eric Topol (46:59):
Yeah. Well, that goes back to that cover of the Economist 17 years ago, the Big Bang, and you got me convinced that this is a pretty strong story and candidate. Now what a fun chance to discuss all this with you in an extraordinary book, Tom. Did I miss anything that you want to bring up?
Tom Cech (47:21):
Eric, I just wanted to say that I not only appreciate our conversation, but I also appreciate all you are doing to bring science to the non-scientist public. I think people like me who have taught a lot of freshmen in chemistry, general chemistry, sort of think that that’s the level that we need to aim at. But I think that those kids have had science in high school year after year. We need to aim at the parents of those college freshmen who are intelligent, who are intellectually curious, but have not had science courses in a long time. And so, I'm really joining with you in trying to avoid jargon as much as possible. Use simple language, use analogies and metaphors, and try to share the excitement of what we're doing in the laboratory with the populace.
Eric Topol (48:25):
Well, you sure did that it was palpable. And I thought about it when I read the book about how lucky it would be to be a freshman at the University of Boulder and be having you as the professor. My goodness. Well, thank you so much. This has been so much fun, Tom, and I hope everybody's going to get out there and read the Catalyst to get all the things that we didn't even get a chance to dive into. But this has been great and look forward to future interactions with you.
Tom Cech (48:53):
Take care, Eric.
*********************
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Professor Venki Ramakrishnan, a Nobel laureate for his work on unraveling the structure of function of the ribosome, has written a new book WHY WE DIE which is outstanding. Among many posts and recognitions for his extraordinary work in molecular biology, Venki has been President of the Royal Society, knighted in 2012, and was made a Member of the Order of Merit in 2022. He is a group leader at the MRC Laboratory of Molecular Biology research institute in Cambridge, UK.
A brief video snippet of our conversation below. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are available on Apple and Spotify.
Transcript with links to audio and external links
Eric Topol (00:06):
Hello, this is Eric Topol with Ground Truths, and I have a really special guest today, Professor Venki Ramakrishnan from Cambridge who heads up the MRC Laboratory of Molecular Biology, and I think as you know a Nobel laureate for his seminal work on ribosomes. So thank you, welcome.
Venki Ramakrishnan (00:29):
Thank you. I just want to say that I'm not the head of the lab. I'm simply a staff member here.
Eric Topol (00:38):
Right. No, I don't want to give you more authority than you have, so that was certainly not implied. But today we're here to talk about this amazing book, Why We Die, which is a very provocative title and it mainly gets into the biology of aging, which Venki is especially well suited to be giving us a guided tour and his interpretations and views. And I read this book with fascination, Venki. I have three pages of typed notes from your book.
The Compression of Morbidity
Eric Topol (01:13):
And we could talk obviously for hours, but this is fascinating delving into this hot area, as you know, very hot area of aging. So I thought I'd start off more towards the end of the book where you kind of get philosophical into the ethics. And there this famous concept by James Fries of compression of morbidity that's been circulating for well over two decades. That's really the big question about all this aging effort. So maybe you could give us, do you think there is evidence for compression of morbidity so that you can just extend healthy aging and then you just fall off the cliff?
Venki Ramakrishnan (02:00):
I think that's the goal of most of the sort of what I call the saner end of the aging research community is to improve our health span. That is the number of years we have healthy lives, not so much to extend lifespan, which is how long we live. And the idea is that you take those years that we now spend in poor health or decrepitude and compress them down to just very short time, so you're healthy almost your entire life, and then suddenly go into a rapid decline and die. Now Fries who actually coined that term compression or morbidity compares this to the One-Hoss Shay after poem by Oliver Wendell Holmes from the 19th century, which is about this horse carriage that was designed so perfectly that all its parts wore out equally. And so, a farmer was riding along in this carriage one minute, and the next minute he found himself on the ground surrounded by a heap of dust, which was the entire carriage that had disintegrated.
Venki Ramakrishnan (03:09):
So the question I would ask is, if you are healthy and everything about you is healthy, why would you suddenly go into decline? And it's a fair question. And every advance we've made that has kept us healthier in one respect or another. For example, tackling diabetes or tackling heart disease has also extended our lifespan. So people are not living a bigger fraction of their lives healthily now, even though we're living longer. So the result is we're spending the same or even more number of years with one or more health problems in our old age. And you can see that in the explosion of nursing homes and care homes in almost all western countries. And as you know, they were big factors in Covid deaths. So I'm not sure it can be accomplished. I think that if we push forward with health, we're also going to extend our lifespan.
Venki Ramakrishnan (04:17):
Now the argument against that comes from studies of these, so-called super centenarians and semi super centenarians. These are people who live to be over 105 or 110. And Tom Perls who runs the New England study of centenarians has published findings which show that these supercentenarians live extraordinarily healthy lives for most of their life and undergo rapid decline and then die. So that's almost exactly what we would want. So they have somehow accomplished compression of morbidity. Now, I would say there are two problems with that. One is, I don't know about the data sample size. The number of people who live over 110 is very, very small. The other is they may be benefiting from their own unique genetics. So they may have a particular combination of genetics against a broad genetic background that's unique to each person. So I'm not sure it's a generally translatable thing, and it also may have to do with their particular life history and lifestyle. So I don't know how much of what we learned from these centenarians is going to be applicable to the population as a whole. And otherwise, I don't even know how this would be accomplished. Although some people feel there's a natural limit to our biology, which restricts our lifespan to about 115 or 120 years. Nobody has lived more than 122. And so, as we improve our health, we may come up against that natural limit. And so, you might get a compression of morbidity. I'm skeptical. I think it's an unsolved problem.
Eric Topol (06:14):
I think I'm with you about this, but there's a lot of conflation of the two concepts. One is to suppress age related diseases, and the other is to actually somehow modulate control the biologic aging process. And we lump it all together as you're getting at, which is one of the things I loved about your book is you really give a balanced view. You present the contrarians and the different perspectives, the perspective about people having age limits potentially much greater than 120, even though as you say, we haven't seen anyone live past 122 since 1997, so it's quite a long time. So this, I think, conflation of what we do today as far as things that will reduce heart disease or diabetes, that’s age related diseases, that's very different than controlling the biologic aging process. Now getting into that, one of the things that's particularly alluring right now, my friend here in San Diego, Juan Carlos Belmonte, who went over from Salk, which surprised me to the Altos Labs, as you pointed on in the book.
Venki Ramakrishnan (07:38):
I'm not surprised. I mean, you have a huge salary and all the resources you want to carry out the same kind of research. I wouldn't blame any of these guys.
Rejuvenating Animals With Yamanaka Factors
Eric Topol (07:50):
No, I understand. I understand. It's kind of like the LIV Golf tournament versus the PGA. It's pretty wild. At any rate, he's a good friend of mine, and I visited with him recently, and as you mentioned, he has over a hundred people working on this partial epigenetic reprogramming. And just so reviewing this for the uninitiated is giving the four Yamanaka transcription factors here to the whole animal or the mouse and rejuvenating old mice, essentially at least those with progeria. And then others have, as you point out in the book, done this with just old mice. So one of the things that strikes me about this, and in talking with him recently is it's going to be pretty hard to give these Yamanaka factors to a person, an intravenous infusion. So what are your thoughts about this rejuvenation of a whole person? What do you think?
Venki Ramakrishnan (08:52):
If I hadn't seen some of these papers would've been even more skeptical. But the data from, well, Belmonte's work was done initially on progeria mice. These are mice that age prematurely. And then people thought, well, they may not represent natural aging, and what you're doing is simply helping with some abnormal form of aging. But he and other groups have now done it with normal mice and observed similar effects. Now, I would say reprogramming is one way. It's a very exciting and powerful way to almost try to reverse aging because you're trying to take cells back developmentally. You're taking possibly fully differentiated cells back to stem cells and then helping regenerate tissue, which one of the problems as we age is we start losing stem cells. So we have stem cell depletion, so we can no longer replace our tissues as we do when we're younger. And I think anyone who knows who's had a scrape or been hurt in a fall or something knows this because if I fall and scrape my elbow and get a big bruise and my grandson falls, we repair our tissues at very, very different rates. It takes me days or weeks to recover, and my grandson's fine in two or three days. You can hardly see he had a scrape at all. So I think that's the thing that these guys want to do.
Venki Ramakrishnan (10:48):
And the problem is Yamanaka factors are cancer. Two of them are oncogenic factors, right? If you give Yamanaka factors to cells, you can take them all the way back to what are called pluripotent cells, which are the cells that are capable of forming any tissue in the body. So for example, a fertilized egg or an early embryo cells from the early embryo are pluripotent. They could form anything in the body. Now, if you do that to cells with Yamanaka factors, they often form teratomas, which are these unusual forms of cancer tumors. And so, I think there's a real risk. And so, what these guys say is, well, we'll give these factors transiently, so we'll only take the cells back a little ways and not all the way back to pluripotency. And that way if you start with skin cells, you'll get the progenitor stem cells for skin cells. And the problem with that is when you do it with a population, you're getting a distribution. Some of them will go back just a little, some of them may go back much more. And I don't know how to control all this. So I think it's very exciting research. And of course, if I were one of these guys, I would certainly want to carry on doing that research. But I don't think it's anywhere near ready for primetime in terms of giving it to human beings as a sort of anti-aging therapeutic.
Aging and Cancer Shared Hallmarks
Eric Topol (12:31):
Yeah. Well, I couldn't agree more on that because this is a company that's raised billions of dollars to go into clinical trials. And the question that comes up here, which is a theme in the book and a theme with the aging process to try to artificially, if you will affect it, is this risk of cancer. And as we know, the hallmarks of aging overlap considerably with the hallmarks of cancer. And this is just one example, as you mentioned, where these transcription factors could result in generating cancer. But as you also point out in the book at many places, methylation changes, DNA, repair, and telomeres.
Venki Ramakrishnan (13:21):
And telomeres.
Venki Ramakrishnan (13:24):
All of those are related to cancer as well. And this was first pointed out to me by Titia de Lange, who's a world expert on telomeres at Rockefeller, and she was pointing out to me the intimate connection between cancer and aging and many mechanisms that have evolved to prevent cancer early in life tend to cause aging later in life, including a lot of DNA damage response, which sends cells into senescence and therefore causes aging. Buildup of senescence cells is a problem later in life with aging, but it has a role which is to prevent cancer early in life. And so, I think it's going to be the same problem with stem cell therapy. I think very targeted stem cell therapy, which is involved in replacing certain tissues, the kind of regenerative medicine that stem cells have been trying to address for a very long time, and only now we're beginning to see some of the successes of that. So it's been very slow, even when the goal and target is very specific and well-defined, and there you are using that stem cell to treat a pretty bad disease or some really serious problem. I think with aging, the idea that somebody might take this so they can live an extra 10 years, it's a much higher bar in terms of safety and long-term safety and efficacy. So I don't think that this is going to happen anytime soon, but it’s not to say it'll never happen. There is some serious biology underlying it.
Eric Topol (15:13):
Right. Well, you just touched on this, but of course the other, there's several big areas that are being explored, and one of them is trying to deal with these senescent cells and trying to get rid of them from the body because they can secrete evil humors, if you will. And the problem with that, it seems that these senescent cells are sort of protective. They stop dividing, they're not going to become cancerous, although perhaps they could contribute to that in some way. So like you say, with telomeres and so many things that are trying to be manipulated here, there's this downside risk and it seems like this is what we're going to have to confront this. We have seen Venki with the CAR-T, the T-cell engineering, there's this small risk of engendering cancer while you're trying to deal with the immune system.
Senolytics
Venki Ramakrishnan (16:07):
Yeah, I think with senescent cells, the early in life senescent cells have an important role in biology. They're essentially signaling to the immune system that there's a site that's subject to viral infection or wounds or things like that. So it's a signal to send other kinds of cells there to come and repair the damage. Now, of course, that evolved to help us early in life. And also many senescent cells were a response to DNA damage. And that's again, a way for the body that if your DNA is damaged, you don't want that cell to be able to divide indefinitely because it could become cancerous. And so, you send it into senescence and get it out of harm's way. So early life, we were able to get rid of these senescent cells, we were able to come to the site and then clean up the damage and eventually destroy the senescent cells themselves.
Venki Ramakrishnan (17:08):
But as we get older, the response mechanisms also deteriorate with age. Our immune system deteriorates with age, all the natural signaling mechanisms deteriorate with age. And so, we get this buildup of senescent cells. And there people have asked, well, these senescent cells don't just sit there, they secrete inflammatory compounds, which originally was a feature, not a bug, but then it becomes a problem later in life. And so, people have found that if you target senescent cells in older animals, those animals improve their symptoms of aging improved dramatically or significantly anyway. And so, this has led to this whole field called senolytics, which is being able to specifically target senescent cells. Now there the problem is how would you design compounds that are highly specific for senescent cells and don't damage your other cells and don't have other long-term side effects? So again, I think it's a promising area, but a lot of work needs to be done to establish long-term safety and efficacy.
Eric Topol (18:23):
Right. No, in fact, just today in Nature, there's a feature on killing the zombie cells, and it discusses just what you're pointing out, which is it's not so easy to tag these specifically and target them, even though as you know, there's some early trials and things like diabetic macular edema. And we'll see how that plays out. Now, one of the things that comes up is the young blood story. So in the young blood, whether it's this parabiosis or however you want to get at it, and I guess it even applies to the young microbiome of a gut, but there's this consistent report that there's something special going on there. And of course the reciprocal relationship of giving the old blood to the young mice, whatever, but no one can find the factor, whether it's platelet factor 4, GDF11, or what are your thoughts about this young blood story?
Venki Ramakrishnan (19:25):
I think there's no question that the experiments work because they were reproduced and they were reproduced over quite a long period, and which is that when you connect an old mouse or rat with a young equivalent, then the old mouse or old rat benefits from the young blood from the younger animal. And conversely, the younger animal suffers from the blood from the older animal. And then people were wondering whether this is simply that the young animal has better detoxification and things like that, or whether it's actually the blood. And they gave it just as transfusion without connecting the animals and showed that it really was the blood. And so, this of course then leads to the question, well, what is it about young blood that’s beneficial and what is it about old blood that is bad? But the problem is blood has hundreds of factors. And so, they have to look at which factors are significantly different, and they might be in such small quantities that you might not be able to detect those differences very easily.
Venki Ramakrishnan (20:40):
And then once you've detected differences, then you have to establish their mechanism of action. And first of all, you have to establish that the factor really is beneficial. Then you have to figure out how it works and what its potential side effects could be. And so again, this is a promising area where there's a lot of research, but it has not prevented people from jumping the gun. So in the United States, and I should say a lot of them in your state, California somehow seems to attract all these immortality types. Well, anyway, a lot of companies set up to take blood from young donors, extract the plasma and then give it to rich old recipients for a fee for a healthy fee. And I think the FDA actually shut down one of them on the grounds that they were not following approved procedure. And then they tried to start up under a different name. And then eventually, I don't know what happened, but at one point the CEO said something I thought was very amusing. He said, well, the problem with clinical trials is that they take too long. I'm afraid that's characteristic of some portion of this sort of anti-aging therapeutics community. There's a very mainstream rigorous side to it, but there's also at the other end of the spectrum, kind of the wild west where people just sell whatever they can. And I think this exploits people's fear of getting old and being disabled or things like that and then dying. And I think the fear seems to be stronger in California where people like their lives and don't want to age.
Eric Topol (22:49):
You may be right about that. I like your term in the book immortality merchants, and of course we'll get into a bit, I hope the chapter on the crackpots and prophets that you called it was great. But that quote, by the way, which was precious from, I think it was Ambrosia, the name of the company and the CEO, but there's another quote in the book I want to ask you about. Most scientists working on aging agree that dietary restriction can extend both healthy life and overall life in mice and also lead to reductions in cancer, diabetes, and overall mortality in humans. Is that true? Most scientists think that you can really change these age-related diseases.
Caloric Restriction and Related Pathways
Venki Ramakrishnan (23:38):
I think if you had to pick one area in which there's broad agreement, it is caloric restriction. But I wouldn't say the consensus is complete. And the reason I say that is that most of the comparisons are between animals that can eat as much as they want, called ad libitum diet and mice that are calorically restricted or same with other animals even yeast. You either compared with an extremely rich medium or in a calorically restricted medium. And this is not a great comparison. And people, there's one discrepancy, and that was in monkeys where an NIH study didn't find huge differences, whereas a Wisconsin study found rather dramatic differences between the control group and the calorically restricted group. And so, what was the difference? Well, the difference was that the NIH study, the controlled group didn't have a calorically restricted diet, but still had a pretty reasonable diet.
Venki Ramakrishnan (24:50):
It wasn't given a unhealthy rich diet of all you can eat. And then they tried to somehow reconcile their findings in a later study. But it leads to the question of whether what you can conclude is that a rich all you can eat diet, in other words, gorging on an all you can eat buffet is definitely bad for you. So that's why you could draw that conclusion rather than saying it's actually the caloric restriction. So I think people need to do a little more careful study. There was also a study on mice which took different strains of mice and showed that in some strains, caloric restriction actually shortened lifespan didn't increase lifespan. Now, much of the aging community says, ah, that's just one study. But nobody's actually shown whether there was anything wrong with that study or even tried to reproduce it. So I think that study still stands.
Venki Ramakrishnan (25:51):
So I think it's not completely clear, but the fact is that there's some calorie dependence that's widely been observed across species. So between the control group and the experimental group, whatever you may, however, you may define it as there's been some effective calories intake. And the other interesting thing is that one of the pathways affected by caloric restriction is the so-called TOR pathway and one of the inhibitors of the TOR pathways is rapamycin. And rapamycin in studies has also shown some of these beneficial effects against the symptoms of aging and in lifespan. Although rapamycin has the same issue as with many other remedies, it's an immunosuppressive drug and that means it makes you more prone to infection and wound healing and many other things. I believe one of them was there's a question of whether it affects your libido, but nevertheless, that has not prevented rapamycin clinics from opening up, did I say in California? So I do think that there's often serious science, which leads to sort of promising avenues. But then there are of course people who jump the gun and want to go ahead anyway because they figure by the time trials are done, they'll be dead and they'd rather try act now.
Eric Topol (27:36):
Right. And you make a good, I mean the rapamycin and mTOR pathway, you really developed that quite a bit in the book. It's really quite complex. I mean, this is a pleotropic intervention, whether it's a rapalogs or rapamycin, it's just not so simple at all.
Venki Ramakrishnan (27:53):
Right. It's not at all simple because the TOR pathway has so many consequences. It affects so many different processes in the cell from including my own field of protein synthesis. It's one of the things it does is shut down global protein synthesis, and that's one of the effects of inhibiting TOR. So, and it turns up autophagy, which is this recycling of defective proteins and entirely defective entire organelles. So I think the TOR pathway is like a hub in a very large network. And so, when you start playing with that, you're going to have multiple consequences.
Eric Topol (28:37):
Yeah, no. And another thing that you develop so well is about this garbage disposal waste disposal system, which is remarkably elaborate in the cell, whether it's the proteasome for the proteins and the autophagosome for the autophagy with the lysosomes and the mitochondria mitophagy. Do you want to comment about that? Because this is something I think a lot of people don't appreciate, that waste management in the cell is just, it's a big deal.
Venki Ramakrishnan (29:10):
So we always think of producing things in the cell as being important, making proteins and so on. But the fact is destroying proteins is equally important because sometimes you need proteins for a short time, then they've done their job and you need to get rid of them, or proteins become dysfunctional, they stop working, or even worse, they start clumping together and causing diseases for example you could think of Alzheimer's as a disease, which involves protein tangles. Of course, the relationship between the tangles and the disease is still being worked out, but it's a characteristic of Alzheimer's that you have these protein tangles and the cell has evolved very elaborate mechanisms to constantly turn over defective proteins. Well, for example, it senses when proteins are unfolded and essentially the chain has unraveled and is now sticking to all sorts of things and causing problems. So I think in all of these cases, the cells evolved very elaborate mechanisms to recycle defective products, to have proper turnover of proteins. And in fact, recycling of entire organelles like mitochondria, when they become defective, the whole mitochondria can be recycled. So these systems also break down with aging. And so, as we age, we have more of a tendency to accumulate unfolded proteins or to accumulate defective mitochondria. And it's one of the more serious problems with aging.
Eric Topol (30:59):
Yeah, there's quite a few of them. Unfortunately, quite a few problems. Each of them are being addressed. So there's many different shots on goal here. And as you also aptly point out, they're interconnected. So many of these things are not just standalone strategies. I do want to get your sense about another popular thing, especially here out in California, are the clocks, epigenetic clocks in particular. And these people are paying a few hundred dollars and getting their biologic age, which what is that? And they're also thinking that I can change my future by getting clocks. Some of these companies offer every few months to get a new clock. This is actually remarkable, and I wonder what your thoughts are about it.
Venki Ramakrishnan (31:48):
Well, again, this is an example of some serious biology and then people jumping the gun to use it. So the serious biology comes from the fact that we age at different rates individuals. So anyone who's been to a high school reunion knows this. You'll have classmates who are unrecognizable because they’ve aged so much and others who've hardly changed since you knew them in high school. So of course at my age, that's getting rarer and rarer. But anyway, but you know what I mean. So the thing is that, is there a way that we can ask on an individual level how much has that individual aged? And there are markers that people have identified, some of them are markers on our DNA, which you mentioned in California. Horvath is a very famous scientist who has a clock named after him actually, which has to do with methylation of our DNA and the patterns of methylation affect the pattern of gene expression.
Venki Ramakrishnan (33:01):
And that pattern changes as we age. And they've shown that those patterns are a better predictor of many of the factors of aging. For example, mortality or symptoms of aging. They're a better predictor of that than chronological age. And then of course there are blood markers, for example, levels of various blood enzymes or blood factors, and there are dozens of these factors. So there are many different tests of many different kinds of markers which look at aging. Now the problem is these all work on a population level and they also work on an individual level for time comparison. That is to say, if you want to ask is some intervention working? You could ask, how fast are these markers changing in this person without the intervention and how fast are they changing with the intervention? So for these kind of carefully controlled experiments, they work, but another case is, for example, glycosylation of proteins, especially proteins of your immune system.
Venki Ramakrishnan (34:15):
It turns out that adding sugar groups to your immune system changes with age and causes an immune system to misfire. And that's a symptom of aging. It's called inflammaging. So people have used different markers. Now the problem is these markers are not always consistent with each other because you may be perfectly fine in many respects, but by some particular marker you may be considered old just because they're comparing you to a population average. But how would you say one person said, look, we all lose height as we age, but that doesn't mean if you take a short person, you can consider them old. So it's a difference between an individual versus a population, and it's a difference between what happens to an individual by following that individual over time versus just taking an individual and comparing it to some population average. So that's one problem.
Organ Clocks
Venki Ramakrishnan (35:28):
The other problem is that our aging is not homogeneous. So there's a recent paper from I believe Tony Wyss-Coray group, which talks about the age of different organs in the same person. And it turns out that our organs, and this is not just one paper, there are other papers as well. Our organs don't necessarily age at the same rate. So giving a single person, giving a person a single number saying, this is your biological age, it's not clear what that means. And I would say, alright, even if you do it, what are you going to do about it? What can you do about it knowing your biological, the so-called number of a biological age. So I’m not a big fan. I’m a big fan of using these markers as a tool in research to understand what interventions work because otherwise it would take too long. You’d have to wait 20 years to see some large scale symptoms. And certainly, if you want to look at mortality, you’d have to wait possibly even longer. But if you were to be able to follow track these interventions and see that these markers slowed down with intervention, then you could say, well, your interventions having an effect on something related to aging. So I would say these are very useful research tools, but they’re not meant to be used at $500 a pop in your age.
Venki Ramakrishnan (37:02):
But of course that hasn't stopped lots of companies from doing it.
Eric Topol (37:07):
No, it's just amazing actually. And by the way, we interviewed Tony Wyss-Coray about the organ clock, the paper. I thought it really was quite a great contribution, again, on a research level.
Venki Ramakrishnan (37:19):
He's a very serious scientist. He actually spoke here at the LMB as well. He gave a very nice talk here.
Is Aging A Disease?
Eric Topol (37:26):
He's the real deal. And I think that's going to help us to have that organ specific type of tracking is another edge here to understand the effects. Well, before we wrap up, I want to ask you a question that you asked in the book. Is aging a disease?
Venki Ramakrishnan (37:49):
That's again, a controversial subject. So the WHO, and I believe the FDA decided that aging was not a disease on the grounds that it's inevitable and ubiquitous. It happens to everybody and it's inevitable. So how could something that happens to everybody and inevitable be considered a disease? A disease is an abnormal situation. This is a normal situation, but the anti-aging researchers and especially the anti-aging therapeutics people don't like that because if it's not a disease, how can they run a clinical trial? So they want aging to be considered a disease. And their argument is that if you look at almost every condition of old age, every disease of old age like cancer, diabetes, heart disease, dementia, the biggest risk factor in all of these diseases is age. That's the strongest risk factor. And so, they say, well, actually, you could think of these diseases as secondary diseases, the primary disease being age, and then that results in these other diseases.
Venki Ramakrishnan (39:07):
I am a little skeptical of that idea. I tend to agree with the WHO and the FDA, but I can see both sides of the argument. And as you know, I've laid them both out. My view is that it should be possible to do trials that help with aging regardless of whether you consider aging a disease or not. But that will require the community to agree on what set of markers to use to characterize success. And that's people, for example, Tony Wyss-Coray has his proteome, blood proteome markers, Horvath has his DNA methylation clock. There are a whole bunch of these. And then there are people with glycation or glycosylation of various proteins as markers. These people need to all come together. Maybe we need to organize a nice conference for them in some place like Southern California or Hawaii or somewhere, put them together in a locked room for a week so that they can thrash out a common set of markers and at least agree on what experiments they need to do to even come to that agreement and then use that to evaluate anti-aging therapies. I think that would be a way forward.
Eric Topol (40:35):
Yeah, I think you're bringing up a really valuable point because at the moment, they're kind of competing with one another, whether it's the glycosylated proteins or the transcriptomics or the epigenetics. And we don't know whether these are additive or what they're really measuring.
Venki Ramakrishnan (40:53):
Some of them may be highly correlated, and that's okay, but I think they need to know that. And they also need to come up with some criteria of how do we define age in an individual. It's not one number, just like we have many things that characterize our health. Cholesterol is one, blood pressures another, various other lipids. They're all blood enzymes, liver enzymes. All these things are factors in defining our so-called biological health. So I don't think there's some single number that's going to say this is your age. Just like there isn't one single thing that says you're healthy, you're not healthy.
DNA Repair
Eric Topol (41:38):
Right, that’s well put. Last topic on aging is on about DNA repair, which is an area that you know very well. And one of the quotes in your book, I think is important for people to take in. “Nevertheless, they will make an error once every million or so letters in a genome with a few billion letters. That means several thousand mistakes occur each time a cell divides. So the DNA repair enzyme, as you point out the sentinels of our genome, the better we repair, the better we age.” Can we fix the DNA repair problem?
Venki Ramakrishnan (42:20):
I think maybe, again, I'm not sure what the consequences would be and how much it would take. There's one curious fact, and that is that there was a paradox called Peto’s paradox after the scientist who discovered it, which is why don't big animals get cancer much more frequently than say a mouse? In fact, a mouse gets cancer far more readily than an elephant does, and in reality, the elephant should actually get cancer more because it has many orders of magnitude more cells, and all it takes is for one cell to become cancerous for the animal to get cancer and die. So the chances that one cell would become cancer would be larger if there are many, many more cells. And it turns out that elephants have many copies of DNA repair proteins or DNA damage response proteins, not so much DNA repair, but the response to DNA damage and in particular, a protein called p53. And so, this leads to the question that if you had very good DNA repair or very good DNA damage response, would you then live longer or solve this problem? I'm not entirely sure because it may have other consequences because for example, you don't want to send cells into senescence too easily. So I think these things are all carefully balanced, evolutionarily, depending on what's optimized to optimize fitness for each species.
Venki Ramakrishnan (44:13):
For a mouse, the equation's different than for a large animal because a mouse can get eaten by predators and so on. So there, it doesn't pay for evolution to spend too much select for too much spending of resources in maintenance and repair, for larger animals the equation is different. So I just don't know enough about what the consequences would be.
Eric Topol (44:40):
No, it's really interesting to speculate because as you point out in the book, the elephant has 20 copies of p53, and we have two as humans. And the question is that protection from cancer is very intriguing, especially with the concerns that we've been talking about.
Venki Ramakrishnan (44:57):
And it was also true, I believe they did some analysis of genomics of these whales that live very long, and they found sorts of genes that are probably involved in DNA repair or DNA damage response.
Eric Topol (45:14):
Well, this is a masterful book. Congratulations, Venki. I thoroughly enjoyed it. It's very stimulating. I know a lot of the people that will listen or read the transcript will be grabbed by it.
Crackpots and Prophets
Venki Ramakrishnan (45:28):
I think what I've tried to do is give the general reader a real understanding of the biology of aging so that even a complete non-scientist can get an understanding of the processes, which in turn empowers them to take action to do the sort of things that will actually really help. And also it'll guard them against excessive hype, of which there's a lot in this business. And so, I think that was the goal, and to try and present a balanced view of the field. I'm merely trying to be a realist. I'm not being a pessimist about it, but I also think this excessively optimistic hype is actually bad for the field and bad for science and bad for the public as well.
Eric Topol (46:16):
Well, and you actually were very kind in the chapter you have on crackpots and prophets. You could have been even tougher on some of these guys. You were very relatively diplomatic and gentle, I thought, I don't know if you were holding back.
Venki Ramakrishnan (46:28):
I had two lawyers looked at it, so.
Eric Topol (46:33):
I believe it. And now one thing, apart from what we've been talking about because of your extraordinary contribution on the structural delineation of the ribosome back in the early 2000s and 2009 Nobel Prize. Now, the world of AI now with AlphaFold 3 and all these other large language models, would that have changed your efforts? Would that have accelerated things or is it not really?
Venki Ramakrishnan (47:09):
Well, it would've helped, but you would still need the experimental data to solve something like the ribosome, a large complex like the ribosome. And the other thing that would really change well has changed our world is the advent of cryo-electron microscopy of which Scripps is one of the leading places for it. And that has really changed it so that now nobody would bother to crystallize a ribosome and try to get an X-ray structure out of it. You would just throw it into an EM grid, collect your data and be off to the races. So new ribosome structures are being solved all the time at a fraction, a tiny fraction of the time it took to solve the first one.
Eric Topol (48:02):
Wow, that's fascinating. This has been a real joy for Venki to discuss your book and your work, and thanks so much for what you're doing to enlighten us and keep the balance. And it may not be as popular as the immortality merchants, but it's really important stuff.
Venki Ramakrishnan (48:19):
Yeah, no, I hope actually, I found that many of the public want to read about the biology of aging. They're curious. Humans have been curious ever since we knew about mortality, about why some species live so short lives and other species live such a long time and why we actually have to age and die. So there’s natural curiosity and then it also empowers the public once they understand the basis of aging, to take action, to live healthy lives and do that. It's an empowering book rather than a recipe book.
Venki Ramakrishnan (49:01):
I think a lot of the public actually does appreciate that. And of course, scientists will like the sort of more balanced and tone.
Eric Topol (49:13):
Well, you do it so well. All throughout you have metaphors to help people really understand and the concepts, and I really applaud you for doing this. In fact, a couple of people who we both know, Max and John Brockman, apparently were influential for you to get to do it. So I think it's great that you took it on and all the power to you. So thank you, and I hope that we'll get a chance to visit further as we go forward.
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A Poll on Anti-Aging
After finishing her training in neurology at Mayo Clinic, Dr. Svetlana Blitshteyn started a Dysautonomia Clinic in 2009. Little did she know what was in store many years later when Covid hit!
Ground Truths podcasts are on Apple and Spotify. The video interviews are on YouTube
Transcript with audio and external links
Eric Topol (00:07):
Well, hello, it's Eric Topol from Ground Truths, and I have with me a really great authority on dysautonomia and POTS. We will get into what that is for those who aren't following this closely. And it's Svetlana Blitshteyn who is a faculty member at University of Buffalo and a neurologist who long before there was such a thing as Covid was already onto one of the most important pathways of the body, the autonomic nervous system and how it can go off track. So welcome, Svetlana.
Svetlana Blitshteyn (00:40):
Thank you so much, Eric for having me. And I want to say it's a great honor for me to be here and just to be on the list with your other guests. It's remarkable and I'm very grateful and congratulations on being on the TIME100 Health list for influential people in 2024. And I am grateful for everything that you've done. As I mentioned earlier, I'm a big fan of your work before the pandemic and of course with Covid I followed your podcast and posts because you became the best science communicator and I'm very happy to see you being a strong advocate and thank you for everything you've done.
Eric Topol (01:27):
Well, that's so kind to you. And I think talking about getting things going before the pandemic, back in 2011, you published a book with Jodi Epstein Rhum called POTS - Together We Stand: Riding the Waves of Dysautonomia. And you probably didn't have an idea that there would be an epidemic of that more than a decade later, I guess, right?
Svetlana Blitshteyn (01:54):
Yeah, absolutely. Of course, SARS-CoV-2 is a new virus and we can technically say that Long Covid and post Covid complications could be viewed as a new entity. But practically speaking, we know that post-infectious syndromes have been happening for many decades. And so, the most common trigger for POTS happened to be infection, whether it was influenza or mononucleosis or Lyme or enterovirus. We knew this was happening. So I think it didn't take long for me and my colleagues to realize that we're going to be seeing a lot of patients with autonomic dysfunction after Covid.
On the Front Line
Eric Topol (02:40):
Well, one of the things that's important for having you on is you're in the front lines taking care of lots of patients with Long Covid and this postural orthostatic tachycardia syndrome (POTS). And I wonder if you could tell us what it's care for these patients because so many of them are incapacitated. As a cardiologist, I see of course some because of the cardiovascular aspects, but you are dealing with this on a day-to-day basis.
Svetlana Blitshteyn (03:14):
Yeah, absolutely. As early as April 2020 when everything was closed, I got a call from a young doctor in New York City saying that he had Covid and he couldn't recover, he couldn't return to the hospital. And his colleagues and cardiology attendants also had the same symptoms and the symptoms were palpitations, orthostatic intolerance, tachycardia, fatigue. Now, how he knew to contact me is that his sister was my patient with POTS before Covid pandemic. So he kind of figured this looked like my sister, let me check this out. And it didn't take long for me to have a lot of patience from the early wave. And then fairly soon, I think within months I was thinking, we have to write this up because this is important. And to some of us it was not news, but I was sure that to many physicians and public health officials, this would be something new.
Svetlana Blitshteyn (04:18):
So because I'm a busy clinician and don't have a lot of time for publications, I had to recruit a graduate student from McMasters and together we had this paper out, which was the first and largest case series on post Covid POTS and other autonomic disorders. And interestingly, even though it came out I think in 2021, by the time it was published, it became the most citable paper for me. And so I think from then on organizations and societies became interested in the work that I do because prior to that, I must say in the kind of a niche specialty was I don't think it was very popular or of interest to me.
How Did You Get Interested in Dysautonomia?
Eric Topol (05:06):
Yeah, so that's why I wanted to just take a step back with you Svetlana, because you had the foresight to be the founder and director of the Dysautonomia Clinic when a lot of people weren't in touch with this as an important entity. What prompted you as a neurologist to really zoom in on dysautonomia when you started this clinic?
Svetlana Blitshteyn (05:28):
Sure. So the reasons are how I ended up in this field is kind of a convoluted road and the reasons are many, but one, I will say that I trained at Mayo Clinic where we received very good training on autonomic disorders and EMG and coming back to returning back to Buffalo, I began working at the large multiple sclerosis clinic because Western New York has a high incidence MS. And so, what they quickly realized in that clinic is that there was a subset of women who did not qualify for the diagnostic criteria of multiple sclerosis, yet they had a lot of the same symptoms and they were certainly very disabled. Now I recognize that these women had autonomic disorders of all sorts and small fiber neuropathy, and I think this population sort of grew and eventually I realized there is no one not only in Buffalo but the entire Western New York who is doing this work.
Svetlana Blitshteyn (06:34):
So I kind of fell into that. But another reason is actually more personal that I haven’t talked about. So years ago I was traveling to Toronto, Canada for a neurology meeting to present my big study on meningioma and hormone replacement therapy using Mayo Clinic database. And so, in that year, the study received top 10 noteworthy studies of the year award from the Society of Neuro-Oncology, and it was profiled in Reuters Health. Now, on the way back from the conference, I had the flu, and when they returned I could no longer walk the same hallways of the hospital where I walked previously. And no matter how hard I try to push my body, we all do this in medicine, we push through, I just couldn’t do it. No amount of wishing or positive thinking. And so, I think that’s how I came to know personally the post-infectious syndromes. And I think it almost became a duality of experiencing this and also practicing it.
Eric Topol (07:52):
No, that’s really striking and it wasn’t so common to hear about this post flu, but certainly it changed in 2020. So how does a person with POTS typically present to you?
Clinical Presentation
Svetlana Blitshteyn (08:08):
So these are very important questions because what I want to stress is though POTS is one of the most common autonomic disorders. Even if you don’t have POTS by the diagnostic criteria, you may still have autonomic dysfunction and significant autonomic symptoms. How do they present? Well, they present like most Long Covid patients, the most common symptoms are orthostatic intolerance, fatigue, exercise intolerance, post exertional malaise, dizziness, tachycardia, brain fog. And these are common themes across the board in Long Covid patients, but also in pre-Covid post-acute infection syndrome patients. And you have to recognize because I think what I tell my colleagues is that oftentimes patients are not going to present to you saying, I have orthostatic intolerance. Many times they will say, I’m very tired. I can no longer go to the gym or when I go to the store, I have to be out of there in 15 minutes because the orthostatic intolerance symptoms come up.
Svetlana Blitshteyn (09:22):
So sometimes the patients themselves don’t recognize that and it’s up to us physicians to ask the right questions to get the information down. History is very important, knowing the pattern. And then of course, as I always say in all of my papers and lectures, you have to do a 10-minute stand test by measuring supine and standing blood pressure and heart rate on every Long Covid patients. And that’s how you spot those that have excessive postural tachycardia or their blood pressure dropping or so forth. So we have the tools. We don’t need fancy autonomic labs. We don’t even need a tilt table test. The diagnostic criteria for POTS is that you need to have either a 10-minute stand test or a tilt table test to get the diagnosis for POTS, orthostatic hypotension or even neurocardiogenic syncope. Now I think it's important to stress that even if a patient doesn't qualify, and let's say many patients with Long Covid will not elevate their heart rate by at least 30 beats per minute, it could be 20, it could be 25. These criteria are of course essential when we do research studies. But I think practically speaking, in patient care where everything is gray and nothing is black or white, especially in autonomic disorders, you really have to make a diagnosis saying, this sounds like autonomic dysfunction. Let me treat the patient for this problem.
Eric Topol (11:07):
Well, you brought up something that’s really important because doctors don’t have much time and they’re inpatient. They don’t wait 10 minutes to do a test to check your blood pressure. They send the patients for a tilt table, which nobody likes to have that test done, and it’s unnecessary added appointment and expense and whatnot. So that’s a good tip right there that you can get the same information just by checking the blood pressure and heart rate on standing for an extended period of time, which 10 minutes is a long time in the clinic of course. Now, what is the mechanism, what do you think is going on with the SARS-CoV-2 virus and its predilection to affect the autonomic nervous system? As you know, so many studies have questioned whether you even actually infect neurons or alternatively, which is more likely this an inflammation of the neural tissue. But what do you think is going on here?
Underpinnings
Svetlana Blitshteyn (12:10):
Right, so I think it’s important to say we don’t have exact pathophysiology of what exactly is going on. I think we can only extrapolate that what’s going on in Long Covid is possibly what’s going on in any post infectious onset dysautonomia. And so there are many hypothesis and there are many suggestions, and we share this disorder with cardiologist and immunologist and rheumatologist. The way I view this is what I described in my paper from a few years ago is that this is likely a central nervous system disorder with multisystemic involvement and it involves the cardiovascular system, immunologic, metabolic, possibly prothrombotic. The pathophysiology of all POTS closely parallels to pathophysiology of Long Covid. Now we don’t know if it’s the same thing and certainly I see that there may be more complications in Long Covid patients in the realm of cardiovascular manifestations in the realm of blood clots and things like that.
Svetlana Blitshteyn (13:21):
So we can’t say it’s the same, but it very closely resembles and I think at the core is going to be inflammation, autoimmunity and immunologic dysfunction. Now there are also other things that are very important and that would be mitochondrial dysfunction, that would be hypercoagulable state, it would be endothelial dysfunction. And I think the silver lining of Long Covid and having so many people invested in research and so many funds is that by uncovering what Long Covid is, we’re now going to be uncovering what POTS and other autonomic disorders are. And I think we also need to mention a couple of other things. One is small fiber neuropathy, small fiber neuropathy and POTS are very much comorbid conditions. And similarly, small fiber neuropathy frequently occurs in patients with Long Covid, so that’s a substrate with the damaged small nerve fibers that they're everywhere in our bodies and also innervate the organs as well.
Svetlana Blitshteyn (14:34):
The second big thing is that needs to be mentioned is hyperactive mast cells. So mast cells, small nerve fibers and capillaries are very much located in proximity. And what I have usually is a slide from an old paper in oral biology that gives you a specimen where you see a capillary vessel, a stain small nerve fiber, and in between them there is a mass cell with tryptase in it stained in black. And so there is a close communication between small nerve fibers between endothelial wall and between mast cells, and that’s what we commonly see as a triad. We see this as a triad in Long Covid patients. We see that as a triad in patients with joint hypermobility syndrome and hypermobile EDS, and you also see this in many of the autoimmune disorders where people develop new allergies and new sensitivities concurrent or preceding the onset of autoimmune disease.
Small Fiber Neuropathy
Eric Topol (15:49):
Yeah, no, it’s fascinating. And I know you’ve worked with this in Ehlers-Danlos syndrome (EDS) as you mentioned, the hypermobility, but just to go back on this, when you want to entertain the involvement of small fiber neuropathy, is that diagnosable? I mean it’s obvious that you can get the tachycardia, the change in position blood pressure, but do you have to do other tests to say there is indeed a small fiber neuropathy or is that a clinical diagnosis?
Svetlana Blitshteyn (16:20):
Absolutely. We have the testing and the testing is skin biopsy. That is simply a punch biopsy that you can do in your clinic and it takes about 15 minutes. You have the free kit that the company of, there are many companies, I don’t want to name specific ones, but there are several companies that do this kind of work. You send the biopsy back to them, they look under the microscope, they stain it. You can also stain it with amyloid stain to rule out amyloidosis, which we do in neurology, and I think that’s quite accessible to many clinicians everywhere. Now we also have another test called QSART (quantitative sudomotor axon reflex test), and that’s a test part of autonomic lab. Mayo Clinic has it, Cleveland Clinic has it, other big labs have it, and it’s hard to get there because the wait time is big.
Svetlana Blitshteyn (17:15):
Patients need to travel. Insurance doesn’t always authorize, so access is a big problem, but more accessible is the skin biopsy. And so, by doing skin biopsy and then correlating with neurologic exam findings, which oftentimes involved reduce pain and temperature sensation in the feet, sometimes in the hands you can conclude that the patient has small fiber neuropathy and that's a very tangible and objective diagnosis. There again, with everything related to diagnostics, some neuropathy is very patchy and the patchy neuropathy is the one that may not be in your feet where you do the skin biopsy. It may be in the torso, it may be in the face, and we don't have biopsy there. So you can totally miss it. The results can come back as normal, but you can have patchy type of small fiber neuropathy and there are also diagnostic tests that might be not sensitive to pick up issues. So I think in everything Long Covid, it highlights the fact that many tests that we use in medicine are outdated perhaps and not targeted towards these patients with Long Covid. Therefore we say, well, we did the workup, everything looks good. MRI looks good, cardiac echo looks great, and yet the patient is very sick with all kinds of Long Covid complications.
Pure Post-Viral POTS?
Eric Topol (18:55):
Right. Now, before we get into the treatments, I want to just segment this a bit. Can you get pure POTS that is no Long Covid just POTS, or as you implied that usually there's some coalescence of symptoms with the usual Long Covid symptoms and POTS added to that?
Svetlana Blitshteyn (19:21):
So the studies have shown for us that about 40% of patients with POTS have post-infectious onset, which means more than a half doesn’t. And so of course you can have POTS from other causes and the most common is puberty, hormonal change, the most common age of onset is about 13, 14 years old and 80% of women of childbearing age and other triggers or pregnancy, hormonal change again, surgery, trauma like concussion, post-concussion, autonomic dysfunction is quite common.
Eric Topol (20:05):
So these are pure POTS without the other symptoms. Is that what you're saying in these examples?
Svetlana Blitshteyn (20:12):
Well, it's a very good question. It depends what you mean by pure POTS, and I have seen especially cardiologists cling to this notion that there is pure POTS and then there is POTS plus. Now I think majority of people don't have pure POTS and by pure POTS I think you mean those who have postural tachycardia and nothing else. And so most patients, I think 80% have a number of symptoms. So in my clinic I almost never see someone who is otherwise well and all they have is postural tachycardia and then they're having a great time. Some patients do exist like that, they tend to be athletic, they can still function in their life, but majority of patients come to us with symptoms like dizziness, like fatigue, like exercise intolerance, decline in functioning. So I think there is this notion that while there is pure POTS, let me just fix the postural tachycardia and the patient will be great and we all want that. Certainly sometimes I get lucky and when I give the patient a beta blocker or ivabradine or a calcium channel blocker, sometimes we use it, certainly they get better, but most patients don't have that because the disability that drives POTS isn't actually postural tachycardia, it's all that other stuff and a lot of it's neurologic, which is why I put this as a central nervous system disorder.
Treatments
Eric Topol (21:58):
Yeah, that's so important. Now you mentioned the treatments. These are drug treatments, largely beta blockers, and can you tell us what's the success rate with the various treatments that you use in your clinic?
Svetlana Blitshteyn (22:13):
So the first thing we'll have to mention is that there are no FDA approved therapies for POTS, just like there are no FDA approved therapies for Long Covid. And so, everything we use is off label. Now, oftentimes people think that because it wasn't evidence-based and there are no big trials. We do have trials, we do have trials for beta blockers and we know they work. We have trials for Midodrine and we know that's working. We also have fludrocortisone, which is a medication that improves sodium and water resorption. So we know that there are certain things we've used for decades that have been working, and I think that's what I was trying to convey in this paper of post Covid autonomic dysfunction assessment and treatment is that when you see these patients, and you can be of any specialty, you can be in primary care, you can be a physiatrist, a cardiologist, there are things to do, there are medications to use.
Svetlana Blitshteyn (23:20):
Oftentimes colleagues would say, well, you diagnose them and then what do you treat them with? And then I can refer them to table six in that paper and say, look at this list. You have a lot of options to try. We have the first line treatment options, which are your beta blockers and Midodrine and Florinef and Mestinon. And then we have the second line therapies you can choose from the stimulants are there Provigil, Nuvigil, Wellbutrin, Droxidopa is FDA approved for neurogenic orthostatic hypotension. Now we don't use it commonly, but it can still be tried in people whose blood pressures are falling on your exam. So we have a number of medications to choose from in addition to non-pharmacologic therapies.
Eric Topol (24:14):
Right now, I'm going to get to the non-pharmacologic in a moment, but the beta blocker, which is kind of the first one to give, it's a little bit paradoxical. It makes people tired, and these people already are, don't have much energy. Is the success rate of beta blocker good enough that that should be the first thing to try?
Svetlana Blitshteyn (24:35):
Absolutely. The first line medication treatment options are beta blockers. Why? Okay, why are they working? They're not only working to reduce heart rate, but they may also decrease sympathetic overactivity, which is the driving mechanism of autonomic dysfunction. And when you reduce that overactivity, even your energy level can improve. Now, the key here is to use a low dose. A lot of the time I see this mistake being done where the doctor is just prescribing 25 milligrams of metoprolol twice a day. Well, this is too high. And so, the key is to use very low doses and to use them and then increase them as needed. We have a bunch of beta blockers to choose from. We have the non-selective propranolol that you can use when someone maybe has a migraine headache or significant anxiety, they penetrate the brain, and we have non-selected beta blockers like atenolol, metoprolol and others that you can use at half a tablet. Sometimes I start my patients at quarter of tablet and then go from there. So low doses will block tachycardia, decrease sympathetic overactivity, and in many cases will allow the patient to remain upright for longer periods of time.
Eric Topol (26:09):
That's really helpful. Now, one of the other things, I believe it's approved in Canada, not in the US, is a vagal neuromodulation device. And I wonder, it seems like it would be nice to avoid drugs if there was a device that worked really well. Is there anything that is in the hopper for that?
Svetlana Blitshteyn (26:32):
Yeah, absolutely. Non-invasive vagus nerve stimulator is in clinical trials for POTS and other autonomic disorders, but we have it FDA for treatment of migraine and cluster headaches, so it's already approved here and it can also be helpful for chronic pain and gastroparesis. So there are studies on mice that show that with the application of noninvasive vagus nerve stimulator, there is reduction of pro-inflammatory cytokines. So here is this very important connection that comes from Kevin Tracey's work that showed inflammatory reflex, and that's a reflex between the vagus nerve and the immune system. So when we talk about sympathetic overactivity, we need to also think about that. That's a mechanism for pro-inflammatory state and possibly prothrombotic state. So anything that decreases sympathetic overactivity and enhancing parasympathetic tone is going to be good for you.
Eric Topol (27:51):
Now, let's go over to, I mean, I'm going to get into this body brain axis in a moment because there's another part of the story here that's becoming more interesting, fascinating, in fact every day. But before I do that, you mentioned the small fiber neuropathy. Is there a specific treatment for that or is that just something that is just an added dimension of the problem without a specific treatment available?
Svetlana Blitshteyn (28:21):
Yeah, we certainly have treatment for small fiber neuropathy. We have symptomatic treatment for neuropathic pain, and these medications are gabapentin, pregabalin, amitriptyline and low dose naltrexone that have been gaining popularity. We used that before the pandemic. We used low dose naltrexone for people with chronic pain related to joint hypermobility. And so, we have symptomatic, we also have patches and creams and all kinds of topical applications for people with neuropathic pain. Then we also have, we try to go for the root cause, right? So the number one cause of small fiber neuropathy in the United States is diabetes. And certainly, you need to control hyperglycemia and in some patients you only need a pre-diabetic state, not even full diabetes to already have peripheral neuropathy. So you want to control blood glucose level first and foremost. Now then we have a big category of autoimmune and immune mediated causes, and that's where it gets very interesting because practical experience from many institutions and many neurologists worldwide have shown that when you give a subset of patients with autoimmune small fiber neuropathy, immunotherapy like IVIG, a lot of patients feel significantly better. And so, I think paralleling our field in dysautonomia and POTS, we are looking forward to immunotherapy being more mainstream rather than exception from the rule because access and insurance coverage is a huge barrier for clinicians and patients, but that may be a very effective treatment options for treatment refractory patients whose symptoms do not improve with symptomatic treatment.
Eric Topol (30:38):
Now, with all these treatments that are on the potential menu to try, and of course sometimes it really is a trial and error to get one that hopefully works for Covid, Long Covid, what is the natural history? Does this persist over years, or can it be completely resolved?
Svetlana Blitshteyn (31:00):
That’s a great question. Everyday Long Covid patients ask me, and I think what we are seeing is that there is a good subset of patients for whom Long Covid is going to be temporary and they will improve and even recover close to normal. Now remember that original case series of patients that I reported in early 2021 based on my 2020 experience in that 20 patient case series, very few recovered, three patients recovered back to normal. Most patients had lingering ongoing chronic symptoms. So of course mine is a kind of a referral bias where I get to see the sickest patients and it looks to be like it’s a problem of chronic illness variety. But I also think there is going to be a subset of patients and then we have to study them. We need to study who got better and who didn’t. And people improve significantly and some even recover close to normal. But I think certain symptoms like maybe fatigue and heat intolerance could persist because those are very heavily rooted in autonomic dysfunction.
Vaccination and POTS
Eric Topol (32:26):
Yeah, well, that’s something that’s sobering and why we need trials and to go after this in much more intensity and priority. Now the other issue here is while with Covid, this is almost always the virus infection, there have been reports of the vaccine inducing POTS and Long Covid, and so what does that tell us?
Svetlana Blitshteyn (32:54):
Well, that’s a big, big topic. Years ago, I was the first one to report a patient with POTS that was developed after HPV vaccine Gardasil. Now, at that time I was a young neurologist. Then the patient came to me saying she was an athlete saying two weeks after Gardasil vaccine, she developed these very disabling symptoms. And I thought it was very interesting and unique and I thought, well, I’ll just publish it. I never knew that this would be the start of a whole different discussion and debate on HPV vaccines. There were multiple reports from numerous countries, Denmark, Mexico, Japan. Japan actually suspended their mass HPV vaccination program. So somehow it became a big deal. Now many people, including my colleagues didn’t agree that POTS can begin POTS, small fiber neuropathy, other adverse neurologic events can begin after vaccination in general. And so, this was a topic that was widely debated and the European medical agencies came back saying, we don't have enough evidence.
Svetlana Blitshteyn (34:20):
Of course, we all want to have a good cancer vaccine. And it was amazing to watch this Covid vaccine issue unfolding where more than one study now have shown that indeed you can develop POTS after Covid vaccines and that the rate of POTS after Covid vaccines is actually slightly higher than before vaccination. So I think it was kind of interesting to see this unfold where I was now invited by Nature Journal to write an editorial on this very topic. So I think it's important to mention that sometimes POTS can begin after vaccination and however, I've always advised my patients to be vaccinated even now. Even now, I have patients who are unvaccinated and I say, I'm worried about you getting a second Covid or third without these vaccines, so please get vaccinated. Vaccines are very important public health measure, but we also have to acknowledge that sometimes people develop POTS, small fiber neuropathy and other complications after Covid vaccines.
Prominence of the Vagus Nerve
Eric Topol (35:44):
Yeah, I think this is important to emphasize here because of all vaccinations can lead to neurologic sequelae. I mean look at Guillain-Barre, which is even more worrisome and that brings in the autoimmune component I think. And of course, the Covid vaccines and boosters have a liability in a small, very small percentage of people to do this. And that can't be discounted because it's a small risk and it's always this kind of risk benefit story when you're getting vaccinated that you are again spotlighting. Now gets us to the biggest thing of all besides the practical pearls you've been coming up with to help everyone in patients and clinicians. In recent weeks, there's been explosion of these intra body circuits. There was a paper from Columbia last week that taught us about the body-brain circuits between the vagus nerve and the caudal Nucleus of the Solitary Tract (cNST) of the brain and how this is basically a master switch for the immune system. And so, the vagus nerve there and then you have this gut to brain story, which is the whole gut microbiome is talking to the brain through the vagus nerve. I mean, everything comes down to the vagus nerve. So you've been working all your career and now everything's coming into this vagus nerve kind of final common pathway that's connecting all sorts of parts of the body that we didn't truly understand before. So could you comment about this because it's pretty striking.
Svetlana Blitshteyn (37:34):
Absolutely. I think this pandemic is highlighting the pitfalls of everything we didn't know but should have in the past. And I think this is one of them. How important is the autonomic nervous system and how important is the vagus nerve that is the longest nerve in the body and carries the parasympathetic outflow. And I think this is a very important point that we have to move forward. We cannot stop at the autonomic knowledge that we've gained thus far. Autonomic neurology and autonomic medicine has always been the field with fellowship, and we have American Autonomic Society as well. But I think now is a great time to move forward and study how the autonomic nervous system communicates with the immunologic system. And again, Kevin Tracey's work was groundbreaking in the sense that he connected the dots and realized that if you stimulate the vagus nerve and the parasympathetic outflow, then you can reduce pro-inflammatory cytokines and that he has shown that you can also improve or significantly such disorders like rheumatoid arthritis and other autoimmune inflammatory conditions.
Svetlana Blitshteyn (39:03):
Now we have the invasive vagus nerve stimulation procedures, and quite honestly, we don't want that to be the mainstream because you don't want to have a neurosurgery as you go to treatment. Of course, you want the non-invasive vagus nerve stimulation being the mainstream therapy. But I think a lot of research needs to happen and it's going to be a very much a multidisciplinary field where we'll have immunology, translational sciences, we'll have neurosurgeons like Kevin Tracey, we'll have rheumatologists, neurologists, cardiologists. We'll have a multidisciplinary collaborative group to further understand what's going on in these autoimmune inflammatory disorders, including those of post-infectious origin.
Eric Topol (40:02):
I certainly agree with all of your points there. I mean, I'm really struck now because the immune system is front and center with so much of what we're seeing with of course Long Covid, but also things like Alzheimer's and Parkinson's and across the board with metabolic diseases. And here we have this connection with your sweet spot of the autonomic nervous system, and we have these pathways that had not been delineated before. I didn't know too much about the cNST of the brain to be such an important connect point for this. And I wonder, so here's another example. Concurrently the glucagon-like peptide 1 (GLP-1) drugs have this pronounced effect on reducing inflammation in the body before the weight loss and in the brain through the gut-brain axis, as we recently discussed with Dan Drucker, have you ever tried a GLP-1 drug or noticed that GLP-1 drugs help people with Long Covid or the POTS problem?
Svetlana Blitshteyn (41:12):
So I have heard anecdotally people with Long Covid using these drugs for other reasons, saying I feel much better. In fact, I recently had a woman who said, I have never been more productive than I am now on this medication. And she used the word productive, which is important because non-productive implies so many things. It's the brain fog, it's the physical fatigue, it's the mental fatigue. So I think we are, first of all, I want to say, I always said that the brain is not separate from the body. And neurologic manifestations of systemic disease is a very big untapped area. And I think it's not going to be surprising for me to see that these drugs can improve many brain parameters and possibly even neuroinflammation. We don't know, but we certainly need to study this.
Eric Topol (42:15):
Yeah, it's interesting because statins had been tried for multiple sclerosis, I think maybe not with very clear cut benefit effects, but here you have a new class of drugs which eventually are going to be in pills and not just one receptor but triple receptor, much more potent than what we're seeing in the clinic today. And you wonder if we're onto an anti-inflammatory for the brain and body that could help in this. I mean, we have a crisis here with Long Covid in POTS without a remedy, without adequate resources that are being dedicated to the clinical trials that are so vital to execute and find treatments. And that's just one candidate of many. I mean, obviously there's so many possible ones on the list. So if you could design studies now based on your extraordinary rich experience with Long Covid and POTS, what would you go after right now? What do you think is the thing that's, would it be to evaluate more of these noninvasive, non-pharmacologic treatments like the vagal nerve stimulation, or are there particular drugs that you find intriguing?
Svetlana Blitshteyn (43:33):
Well, a few years ago we published a case series of patients with severe POTS and nothing helped them, but they improved significantly and some even made close to recovery improvement and were able to return to their careers because they were treated with immunotherapy. So the paper is a subcutaneous immunoglobulin and plasmapheresis and the improvement was remarkable. I say there was one physician there who could not start her residency. She got sick in medical school and could not start her residency due to severe POTS and no amount of beta blockers, Midodrine or Florinef helped her get out the house and out of bed. And therefore, sheer luck, she was able to get subcutaneous immunoglobulin and she improved significantly, finished her residency and is now a practicing physician. So I think when we have these cases, it's important to bring them to scientific community. And I think I'm very excited that hopefully soon we're going to have trials of immunotherapy and immunomodulating treatment options for patients with Long Covid and hopefully POTS in general, I believe in novel, but also repurposed, repurposed treatment.
Svetlana Blitshteyn (45:01):
IVIG has been used for decades, so it's not a new medication. And contrary to popular belief, it's actually quite safe. It is expensive, it's a blood product, but we are very familiar with it in medicine and neurology. So I think we have to look forward to everything. And as I tell my patients, I'm always aggressive with medications when they come to me and their doctor said something like, well, let's see, it's going to go away on its own or keep doing your salt and fluids intake or wear compression sucks. Well, they're already doing it. It's not helping. And now it's a good time to try everything we have. And I would like to have more. I would like to have immunotherapy available. I would like to have immunosuppressants even tried potentially, and maybe we'll be able to try medication for possible viral persistence. Let's see how that works out. We have other inflammatory modalities out there that can potentially give us the tools. You see, I think being that it's a multifactorial disorder, that I don't think it's going to be one thing for everyone. We need to have a toolbox where we're going to choose what's best for your specific case because when we talk about Long Covid, we have to remember there are many different phenotypes under that umbrella.
A Serious Matter
Eric Topol (46:40):
Now, before we wrap up, I mean I guess I wanted to emphasize how there are clinicians out there who discount Long Covid in POTS. They think it's something that is a figment of imagination. Now, on the other hand, you and I especially, you know that people are totally disabled. Certain days they can't even get out of bed, they can't get back to their work, their life. And this can go on and on as we've been discussing. So can you set it straight about, I mean, you are seeing these people every day. What do you have to say to our fellow colleague physicians who tend to minimize and say, this is extremely rare, if it even exists, and that these people have some type of psychiatric problem. And it's really, it's distressing of course, but could you speak to that?
Svetlana Blitshteyn (47:39):
Absolutely. So as I always say, Long Covid is not a psychiatric or psychological disorder, and it's also not a functional neurologic disorder. Now, having said that, as I just mentioned, brain is not separate from the body. And neurologic manifestations of systemic disease are numerous. We just had a paper out on neurologic manifestations of mast cell activation syndrome. So certainly some patients will develop psychiatric manifestations and some patients will develop major depression, anxiety, OCD or functional neurologic disorder. But those are complications of systemic disease, meaning that you cannot diagnose a patient with anxiety and send them off to a psychologist or a psychiatrist without diagnosing POTS and treating it. And in many cases, when you approach an underlying systemic disorder with the right medications, like dysautonomia for example, all of the symptoms including psychological and psychiatric, tend to improve as well. And certainly, there is going to be a small subset of Long Covid patients whose primary problem is psychiatric.
Svetlana Blitshteyn (49:01):
And I think that's totally fine. That is not to say that all Long Covid is psychiatric. Some will have significant psychiatric manifestations. I mean, there are cases of post Covid psychosis and autoimmune encephalitis and all kinds of psychiatric problems that people may develop, but I think we can't really stratify well, this is physiologic and this word functional that I'm not a fan of. This is physiologic as we see it on MRI. But here, because we don't see anything on MRI, it means you are fine and can just exercise your way out of it. So I think with this Long Covid, hopefully we'll get answers as to the pathophysiology, but also most importantly, hopefully we'll get these therapies that millions of people before Covid pandemic were looking for.
Eric Topol (50:02):
Well, I just want to thank you because you were onto this well over 10, 15 years before there was such a thing as Covid, you've dedicated your career to this. These are some of the most challenging patients to try to help and has to be vexing, that you can't get their symptoms resolved no less the underlying problem. And we're indebted to you, Svetlana, because you've really been ahead of the curve here. You were writing a patient book before there were such things as patient activists in Long Covid, as we've seen, which have been so many of the heroes of this whole problem. But thank you for all the work you do. We'll continue to follow. We learned from you about POTS and Long Covid from your work and really appreciate everything you've done. Thank you.
Svetlana Blitshteyn (50:58):
Thank you so much, Eric, for having me. As I said, it's a great honor for me to be here. Remarkable, amazing. And thank you for all this work that you're doing and being an advocate for our field because we always need great champions to help us move forward in these complicated disorders.
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“We haven't invested this much money into an infrastructure like this really until you go back to the pyramids”—Kate Crawford
Transcript with links to audio and external links. Ground Truths podcasts are on Apple and Spotify. The video interviews are on YouTube
Eric Topol (00:06):
Well, hello, this is Eric Topol with Ground Truths, and I'm really delighted today to welcome Kate Crawford, who we're very lucky to have as an Australian here in the United States. And she's multidimensional, as I've learned, not just a scholar of AI, all the dimensions of AI, but also an artist, a musician. We're going to get into all this today, so welcome Kate.
Kate Crawford (00:31):
Thank you so much, Eric. It's a pleasure to be here.
Eric Topol (00:34):
Well, I knew of your work coming out of the University of Southern California (USC) as a professor there and at Microsoft Research, and I'm only now learning about all these other things that you've been up to including being recognized in TIME 2023 as one of 100 most influential people in AI and it's really fascinating to see all the things that you've been doing. But I guess I'd start off with one of your recent publications in Nature. It was a world view, and it was about generative AI is guzzling water and energy. And in that you wrote about how these large AI systems, which are getting larger seemingly every day are needing as much energy as entire nations and the water consumption is rampant. So maybe we can just start off with that. You wrote a really compelling piece expressing concerns, and obviously this is not just the beginning of all the different aspects you've been tackling with AI.
Exponential Growth, Exponential Concerns
Kate Crawford (01:39):
Well, we're in a really interesting moment. What I've done as a researcher in this space for a very long time now is really introduce a material analysis of artificial intelligence. So we are often told that AI is a very immaterial technology. It's algorithms in the cloud, it's objective mathematics, but in actual fact, it comes with an enormous material infrastructure. And this is something that I took five years to research for my last book, Atlas of AI. It meant going to the mines where lithium and cobalt are being extracted. It meant going into the Amazon fulfillment warehouses to see how humans collaborate with robotic and AI systems. And it also meant looking at the large-scale labs where training data is being gathered and then labeled by crowd workers. And for me, this really changed my thinking. It meant that going from being a professor for 15 years focusing on AI from a very traditional perspective where we write papers, we're sitting in our offices behind desks, that I really had to go and do these journeys, these field trips, to understand that full extractive infrastructure that is needed to run AI at a planetary scale.
(02:58):
So I've been keeping a very close eye on what would change with generative AI and what we've seen particularly in the last two years has been an extraordinary expansion of the three core elements that I really write about in Atlas, so the extraction of data of non-renewable resources, and of course hidden labor. So what we've seen, particularly on the resources side, is a gigantic spike both in terms of energy and water and that's often the story that we don't hear. We're not aware that when we're told about the fact that there gigantic hundred billion computers that are now being developed for the next stage of generative AI that has an enormous energy and water footprint. So I've been researching that along with many others who are now increasingly concerned about how we might think about AI more holistically.
Eric Topol (03:52):
Well, let's go back to your book, which is an extraordinary book, the AI Atlas and how you dissected not just the well power of politics and planetary costs, but that has won awards and it was a few years back, and I wonder so much has changed since then. I mean ChatGPT in late 2022 caught everybody off guard who wasn't into this knowing that this has been incubating for a number of years, and as you said, these base models are just extraordinary in every parameter you can think about, particularly the computing resource and consumption. So your concerns were of course registered then, have they gone to exponential growth now?
Kate Crawford (04:45):
I love the way you put that. I think you're right. I think my concerns have grown exponentially with the models. But I was like everybody else, even though I've been doing this for a long time and I had something of a heads up in terms of where we were moving with transformer models, I was also quite taken aback at the extraordinary uptake of ChatGPT back in November 2022 in fact, gosh, it still feels like yesterday it's been such an extraordinary timescale. But looking at that shift to a hundred million users in two months and then the sort of rapid competition that was emerging from the major tech companies that I think really took me by surprise, the degree to which everybody was jumping on the bandwagon, applying some form of large language model to everything and anything suddenly the hammer was being applied to every single nail.
(05:42):
And in all of that sound and fury and excitement, I think there will be some really useful applications of these tools. But I also think there's a risk that we apply it in spaces where it's really not well suited that we are not looking at the societal and political risks that come along with these approaches, particularly next token prediction as a way of generating knowledge. And then finally this bigger set of questions around what is it really costing the planet to build these infrastructures that are really gargantuan? I mean, as a species, we haven't invested this much money into an infrastructure like this really until you go back to the pyramids, you really got to go very far back to say that type of just gargantuan spending in terms of capital, in terms of labor, in terms of all of the things are required to really build these kinds of systems. So for me, that's the moment that we're in right now and perhaps here together in 2024, we can take a breath from that extraordinary 18 month period and hopefully be a little more reflective on what we're building and why and where will it be best used.
Propagation of Biases
Eric Topol (06:57):
Yeah. Well, there's so many aspects of this that I'd like to get into with you. I mean, one of course, you're as a keen observer and activist in this whole space, you've made I think a very clear point about how our culture is mirrored in our AI that is our biases, and people are of course very quick to blame AI per se, but it seems like it's a bigger problem than just that. Maybe you could comment about, obviously biases are a profound concern about propagation of them, and where do you see where the problem is and how it can be attacked?
Kate Crawford (07:43):
Well, it is an enormous problem, and it has been for many years. I was first really interested in this question in the era that was known as the big data era. So we can think about the mid-2000s, and I really started studying large scale uses of data in scientific applications, but also in what you call social scientific settings using things like social media to detect and predict opinion, movement, the way that people were assessing key issues. And time and time again, I saw the same problem, which is that we have this tendency to assume that with scale comes greater accuracy without looking at the skews from the data sources. Where is that data coming from? What are the potential skews there? Is there a population that's overrepresented compared to others? And so, I began very early on looking at those questions. And then when we had very large-scale data sets start to emerge, like ImageNet, which was really perhaps the most influential dataset behind computer vision that was released in 2009, it was used widely, it was freely available.
(09:00):
That version was available for over a decade and no one had really looked inside it. And so, working with Trevor Paglen and others, we analyzed how people were being represented in this data set. And it was really quite extraordinary because initially people are labeled with terms that might seem relatively unsurprising, like this is a picture of a nurse, or this is a picture of a doctor, or this is a picture of a CEO. But then you look to see who is the archetypical CEO, and it's all pictures of white men, or if it's a basketball player, it's all pictures of black men. And then the labeling became more and more extreme, and there are terms like, this is an alcoholic, this is a corrupt politician, this is a kleptomaniac, this is a bad person. And then a whole series of labels that are simply not repeatable on your podcast.
(09:54):
So in finding this, we were absolutely horrified. And again, to know that so many AI models had trained on this as a way of doing visual recognition was so concerning because of course, very few people had even traced who was using this model. So trying to do the reverse engineering of where these really problematic assumptions were being built in hardcoded into how AI models see and interpret the world, that was a giant unknown and remains to this day quite problematic. We did a recent study that just came out a couple of months ago looking at one of the biggest data sets behind generative AI systems that are doing text to image generation. It's called LAION-5B, which stands for 5 billion. It has 5 billion images and text captions drawn from the internet. And you might think, as you said, this will just mirror societal biases, but it's actually far more weird than you might imagine.
(10:55):
It's not a representative sample even of the internet because particularly for these data sets that are now trying to use the ALT tags that are used around images, who uses ALT tags the most on the internet? Well, it's e-commerce sites and it's often stock image sites. So what you'll see and what we discovered in our study was that the vast majority of images and labels are coming from sites like Shopify and Pinterest, these kind of shopping aspirational collection sites. And that is a very specific way of seeing the world, so it's by no means even a perfect mirror. It's a skewed mirror in multiple ways. And that's something that we need to think of particularly when we turn to more targeted models that might be working in say healthcare or in education or even in criminal justice, where we see all sorts of problems emerge.
Exploiting Humans for RLHF
Eric Topol (11:51):
Well, that's really interesting. I wonder to extend that a bit about the human labor side of this. Base models are tweaked, fine-tuned, and one of the ways to do that, of course is getting people to weigh in. And this has been written about quite a bit about how the people that are doing this can be exploited, getting wages that are ridiculously weak. And I wonder if you could comment about that because in the ethics of AI, this seems to be one of the many things that a lot of people don't realize about reinforcement learning.
Kate Crawford (12:39):
Oh, I completely agree. It's quite an extraordinary story. And of course now we have a new category of crowd labor that's called reinforcement learning with human feedback or RLHF. And what was discovered by multiple investigations was that these laborers are in many cases paid less than $2 an hour in very exploitative conditions, looking at results that in many cases are really quite horrifying. They could be accounts of murder, suicide, trauma, this can be visual material, it can be text-based material. And again, the workers in these working for these companies, and again, it's often contract labor, it's not directly within a tech company, it's contracted out. It's very hidden, it's very hard to research and find. But these laborers have been experiencing trauma and are really now in many cases bringing lawsuits, but also trying to unionize and say, these are not acceptable conditions for people to be working under.
(13:44):
So in the case of OpenAI, it was found that it was Kenyan workers who were doing this work for just poverty wages, but it's really across the board. It's so common now that humans are doing the hard work behind the scenes to make these systems appear autonomous. And that's the real trap that we're being told that this is the artificial intelligence. But in actual fact, what Jeff Bezos calls Mechanical Turk is that it's artificial, artificial intelligence otherwise known as human beings. So that is a very significant layer in terms of how these systems work that is often unacknowledged. And clearly these workers in many cases are muzzled from speaking, they're not allowed to talk about what they do, they can't even tell their families. They're certainly prevented from collective action, which is why we've seen this push towards unionization. And finally, of course, they're not sharing in any of the profits that are being generated by these extraordinary new systems that are making a very small number of people, very wealthy indeed.
Eric Topol (14:51):
And do you know if that's improving or is it still just as bad as it has been reported? It's really deeply concerning to see human exploitation, and we all know well about sweatshops and all that, but here's another version, and it's really quite distressing.
Kate Crawford (15:09):
It really is. And in fact, there have been several people now working to create really almost like fair work guidelines. So Oxford has the sort of fair work initiative looking specifically at crowd work. They also have a rating system where they rate all of the major technology companies for how well they're treating their crowd laborers. And I have to say the numbers aren't looking good in the last 12 months, so I would love to see much more improvement there. We are also starting to see legislation be tabled specifically on this topic. In fact, Germany was one of the most recent to start to explore how they would create a strong legislative backing to make sure that there's fair labor conditions. Also, Chile was actually one of the first to legislate in this space, but you can imagine it's very difficult to do because it's a system that is operating under the radar through sort of multiple contracted chains. And even some of the people within tech companies will tell me it's really hard to know if they're working with a company that's doing this in the right way and paying people well. But frankly, I'd like to see far greater scrutiny otherwise, as you say, we're building on this system, which looks like AI sweatshops.
Eric Topol (16:24):
Yeah, no, I think people just have this illusion that these machines are doing everything by themselves, and that couldn't be further from the truth, especially when you're trying to take it to the next level. And there's only so much human content you can scrape from the internet, and obviously it needs additional input to take it to that more refined performance. Now, besides your writing and being much of a conscience for AI, you're also a builder. I mean, I first got to know some of your efforts through when you started the AI Now Institute. Maybe you can tell us a bit about that. Now you're onto the Knowing Machines Project and I don't know how many other projects you're working on, so maybe you can tell us about what it's like not just to be a keen observer, but also one to actually get initiatives going.
Kate Crawford (17:22):
Well, I think it's incredibly important that we start to build interdisciplinary coalitions of researchers, but sometimes even beyond the academic field, which is where I really initially trained in this space, and really thinking about how do we involve journalists, how do we involve filmmakers, how do we involve people who will look at these issues in really different ways and tell these stories more widely? Because clearly this really powerful shift that we're making as a society towards using AI in all sorts of domains is also a public issue. It's a democratic issue and it's an issue where we should all be able to really see into how these systems are working and have a say in how they'll be impacting our lives. So one of the things that I've done is really create research groups that are interdisciplinary, starting at Microsoft Research as one of the co-founders of FATE, a group that stands for fairness, accountability, transparency and ethics, and then the AI Now Institute, which was originally at NYU, and now with Knowing Machines, which is an international group, which I've been really delighted to build, rather than just purely focusing on those in the US because of course these systems are inherently transnational, they will be affecting global populations.
(18:42):
So we really need to think about how do you bring people from very different perspectives with different training to ask this question around how are these systems being built, who is benefiting and who might be harmed, and how can we address those issues now in order to actually prevent some of those harms and prevent the greatest risks that I see that are possible with this enormous turn to artificial intelligence everywhere?
Eric Topol (19:07):
Yeah, and it's interesting how you over the years are a key advisor, whether it's the White House, the UN or the European Parliament. And I'm curious about your experience because I didn't know much about the Paris ENS. Can you tell us about you were Visiting Chair, this is AI and Justice at the École Normale Supérieure (ENS), I don’t know if I pronounce that right. My French is horrible, but this sounds like something really interesting.
Kate Crawford (19:42):
Well, it was really fascinating because this was the first time that ENS, which is really one of the top research institutions in Europe, had turned to this focus of how do we contend with artificial intelligence, not just as a technical question, but as a sort of a profound question of justice of society of ethics. And so, I was invited to be the first visiting chair, but tragically this corresponded with the start of the pandemic in 2020. And so, it ended up being a two-year virtual professorship, which is really a tragedy when you’re thinking about spending time in Paris to be spending it on Zoom. It’s not quite the same thing, but I had the great fortune of using that time to assemble a group of scholars around the world who were looking at these questions from very different disciplines. Some were historians of science, others were sociologists, some were philosophers, some were machine learners.
(20:39):
And really essentially assembled this group to think through some of the leading challenges in terms the potential social impacts and current social impacts of these systems. And so, we just recently published that through the academies of Science and Engineering, and it’s been almost like a template for thinking about here are core domains that need more research. And interestingly, we’re at that moment, I think now where we can say we have to look in a much more granular fashion beyond the hype cycles, beyond the sense of potential, the enormous potential upside that we’re always hearing about to look at, okay, how do these systems actually work now? What kinds of questions can we bring into the research space so that we’re really connecting the ideas that come traditionally from the social sciences and the humanistic disciplines into the world of machine learning and AI design. That’s where I see the enormous upside that we can no longer stay in these very rigorously patrolled silos and to really use that interdisciplinary awareness to build systems differently and hopefully more sustainably as well.
Is Working At Microsoft A Conflict?
Eric Topol (21:55):
Yeah, no, that’s what I especially like about your work is that you’re not a doomsday person or force. You’re always just trying to make it better, but now that's what gets me to this really interesting question because you are a senior principal researcher at Microsoft and Microsoft might not like some of these things that you're advocating, how does that potential conflict work out?
Kate Crawford (22:23):
It's interesting. I mean, people often ask me, am I a technology optimist or a technology pessimist? And I always say I'm a technology realist, and we're looking at these systems being used. I think we are not benefited by discourses of AI doomerism nor by AI boosterism. We have to assess the real politic and the political economies into which these systems flow. So obviously part of the way that I've got to know what I know about how systems are designed and how they work at scale is through being at Microsoft Research where I'm working alongside extraordinary colleagues and all of whom come from, in many cases, professorial backgrounds who are deep experts in their fields. And we have this opportunity to work together and to look at these questions very early on in the kinds of production cycles and enormous shifts in the way that we use technology.
(23:20):
But it is interesting of course that at the moment Microsoft is absolutely at the leading edge of this change, and I've always thought that it's incredibly important for researchers and academics who are in industrial spaces to be able to speak freely, to be able to share what they see and to use that as a way that the industry can, well hopefully keep itself honest, but also share between what it knows and what everybody else knows because there's a giant risk in having those spaces be heavily demarcated and having researchers really be muzzled. I think that's where we see real problems emerge. Of course, one of the great concerns a couple of years ago was when Timnit Gebru and others were fired from Google for speaking openly about the concerns they had about the first-generation large language models. And my hope is that there's been a lesson through that really unfortunate set of decisions made at Google that we need people speaking from the inside about these questions in order to actually make these systems better, as you say, over the medium and long term.
Eric Topol (24:26):
Yeah, no, that brings me to thought of Peter Lee, who I'm sure because he wrote a book about GPT-4 and healthcare and was very candid about its potential, real benefits and the liabilities, and he's a very humble kind of guy. He's not one that has any bravado that I know of, so it speaks well to at least another colleague of yours there at Microsoft and their ability to see all the different sides here, not just what we'll talk about in a minute the arms race both across companies and countries. But before I get to that, there's this other part of you and I wonder if there's really two or three of you that is as a composer of music and art, I looked at your Anatomy of an AI System, I guess, which is on exhibit at the Museum of Modern Art (MoMA) in New York, and that in itself is amazing, but how do you get into all these other parts, are these hobbies or is this part of a main part of your creative work or where does it fit in?
Kate Crawford (25:40):
Eric, didn't I mention the cloning program that I participated in early and that there are many Kate’s and it's fantastic we all work together. Yeah, that explains it. Look, it's interesting. Way back as a teenager, I was fascinated with technology. Of course, it was the early stages of the web at that moment, and I could see clearly that this was, the internet was going to completely change everything from my generation in terms of what we would do in terms of the way that we would experience the world. And as I was also at that time an electronic musician in bands, I was like, this was a really fantastic combination of bringing together creative practice with a set of much larger concerns and interests around at a systems level, how technology and society are co-constituted, how they evolve together and shape each other. And that’s really been the map of how I’ve always worked across my life.
(26:48):
And it’s interesting, I've always collaborated with artists and Vladan Joler who I worked with on anatomy of an AI system. We actually met at a conference on voice enabled AI systems, and it was really looking at the ethics of could it be possible to build an open source, publicly accessible version of say Alexa rather than purely a private model owned by a corporation, and could that be done in a more public open source way? And we asked a different question, we looked at each other and we're like, oh, I haven't met you yet, but I can see that there are some problems here. One of them is it's not just about the data and it's not just about the technical pipelines, it's about where the components come from. It's about the mining structures that needed to make all of these systems. It's about the entire end of life what happens when we throw these devices out from generally between three to four years of use and how they go into these giant e-waste tips.
(27:51):
And we basically started looking at this as an enormous sort of life and death of a single AI system, which for us started out by drawing these things on large pieces of butcher's paper, which just expanded and expanded until we had this enormous systems level analysis of what it takes just to ask Alexa what the weather is today. And in doing that, it taught me a couple of things. One that people really want to understand all of the things that go into making an AI system work. This piece has had a very long life. It's been in over a hundred museums around the world. It's traveled further than I have, but it's also very much about that broader political economy that AI systems aren't neutral, they don't just exist to serve us. They are often sort of fed into corporate structures that are using them to generate profits, and that means that they're used in very particular ways and that there are these externalities in terms of how they produced that linger in our environments that have really quite detrimental impacts on systems of labor and how people are recompensed and a whole range of relationships to how data is seen and used as though it's a natural resource that doesn't actually come from people's lives, that doesn't come with risks attached to it.
(29:13):
So that project was really quite profound for me. So we've continued to do these kinds of, I would call them research art projects, and we just released a new one called Calculating Empires, which looks at a 500 year history of technology and power looking specifically at how empires over time have used new technologies to centralize their power and expand and grow, which of course is part of what we're seeing at the moment in the empires of AI.
Eric Topol (29:43):
And what about the music side?
Kate Crawford (29:45):
Well, I have to say I've been a little bit slack on the music side. Things have been busy in AI Eric, I have to say it's kept me away from the music studio, but I always intend to get back there. Fortunately, I have a kid who's very musical and he's always luring me away from my desk and my research saying, let’s write some music. And so, he'll keep me honest.
Geopolitics and the Arms Races
Eric Topol (30:06):
Well, I think it's striking just because you have this blend of the humanities and you're so deep into trying to understand and improve our approaches in technology. And it seems like a very unusual, I don't know, too many techies that have these different dimensions, so that's impressive. Now let's get back to the arms race. You just were talking about tracing history over hundreds of years and empires, but right now we have a little problem. We have the big tech titans that are going after each other on a daily basis, and of course you know the group very well. And then you have China and the US that are vying to be the dominant force and problems with China accessing NVIDIA chips and Taiwan sitting there in a potentially very dangerous position, not just for Taiwan, but also for the US. And I wonder if you could just give us your sense about the tensions here. They're US based as well of course, because that's some of the major forces in companies, but then they're also globally. So we have a lot of stuff in the background that people don't like to think about, but it's actually happening right now.
Kate Crawford (31:35):
I think it's one of the most important things that we can focus on, in fact. I mean and again, this is why I think a materialist analysis of artificial intelligence is so important because not only does it force you to look at the raw components, where does the energy come from? Where does the water come from? But it means you're looking at where the chipsets come from. And you can see that in many cases there are these infrastructural choke points where we are highly dependent on specific components that sit within geopolitical flashpoints. And Taiwan is really the exemplar of this sort of choke point at the moment. And again, several companies are trying to address this by spinning up new factories to build these components, but this takes a lot of time and an enormous amount of resources yet again. So what we're seeing is I think a very difficult moment in the geopolitics of artificial intelligence.
(32:31):
What we've had certainly for the last decade has been almost a geopolitical duopoly. We've had the US and China not only having enormous power and influence in this space, but also goading each other into producing the most extreme forms of both data extractive and surveillance technologies. And unfortunately, this is just as true in the United States that I commonly hear this in rooms in DC where you'll hear advisors say, well, having any type of guardrails or ethical considerations for our AI systems is a problem if it means that China's going to do it anyway. And that creates this race to the bottom dynamic of do as much of whatever you can do regardless of the ethical and in some cases legal problems that will create. And I think that's been the dynamic that we've seen for some time. And of course the last 18 months to two years, we've seen that really extraordinary AI war happening internally in the United States where again, this race dynamic I think does create unfortunately this tendency to just go as fast as possible without thinking about potential downsides.
(33:53):
And I think we're seeing the legacy of that right now. And of course, a lot of the conversations from people designing these systems are now starting to say, look, being first is great, but we don’t want to be in a situation as we saw recently with Google’s Gemini where you have to pull an entire model off the shelves and you have to say, this is not ready. We actually have to remove it and start again. So this is the result I think of that high pressure, high speed dynamic that we’ve been seeing both inside the US but between the US and China. And of course, what that does to the rest of the world is create this kind of client states where we've got the EU trying to say, alright, well we'll export a regulatory model if we're not going to be treated as an equivalent player here. And then of course, so many other countries who are just seen as spaces to extract low paid labor or the mineralogical layer. So that is the big problem that I see is that that dynamic has only intensified in recent years.
A.I. and Medicine
Eric Topol (34:54):
Yeah, I know it's really another level of concern and it seems like it could be pretty volatile if for example, if the US China relations takes another dive and the tensions there go to levels that haven't been seen so far. I guess the other thing, there's so much that is I think controversial, unsettled in this space and so much excitement. I mean, just yesterday for example, was the first AI randomized trial to show that you could save lives. When I wrote that up, it was about the four other studies that showed how it wasn't working. Different studies of course, but there's so much excitement at the same time, there's deep concerns. You've been a master at articulating these deep concerns. What have we missed in our discussion today, I mean we've covered a lot of ground, but what do you see are other things that should be mentioned?
Kate Crawford (36:04):
Well, one of the things that I've loved in terms of following your work, Eric, is that you very carefully walk that line between allowing the excitement when we see really wonderful studies come out that say, look, there's great potential here, but also articulating concerns where you see them. So I think I'd love to hear, I mean take this opportunity to ask you a question and say what's exciting you about the way that this particularly new generation AI is being used in the medical context and what are the biggest concerns you have there?
Eric Topol (36:35):
Yeah, and it's interesting because the biggest advance so far in research and medicine was the study yesterday using deep learning without any transformer large language model effort. And that's where that multiplicative of opportunity or potential is still very iffy, it's wobbly. I mean, it needs much more refinement than where we are right now. It's exciting because it is multimodal and it brings in the ability to bring all the layers of a human being to understand our uniqueness and then do much better in terms of, I got a piece coming out soon in Science about medical forecasting and how we could really get to prevention of conditions that people are at high risk. I mean like for example today the US preventive task force said that all women age 40 should have mammograms, 40.
Kate Crawford (37:30):
I saw that.
Eric Topol (37:30):
Yeah, and this is just crazy Looney Tunes because here we have the potential to know pretty precisely who are those 12%, only 12% of women who would ever get breast cancer in their lifetime, and why should we put the other 88% through all this no less the fact that there are some women even younger than age 40 that have significantly high risk that are not picked up. But I do think eventually when we get these large language models to actualize their potential, we'll do really great forecasting and we'll be able to not just prevent or forestall cancer, Alzheimer’s and so many things. It's quite exciting, but it's the earliest, we're not even at first base yet, but I think I can see our way to get there eventually. And it's interesting because the discussion I had previously with Geoffrey Hinton, and I wonder if you think this as well, that he sees the health medical space as the only really safe space. He thinks most everything else has got more concerns about the downsides is the sweet spot as he called it. But I know that's not particularly an area that you are into, but I wonder if you share that the excitement about your health could be improved in the future with AI.
Kate Crawford (38:52):
Well, I think it's a space of enormous potential, but again, enormous risk for the same reasons that we discussed earlier, which is we have to look at the training data and where it's coming from. Do we have truly representative sources of data? And this of course has been a consistent problem certainly for the last hundred years and longer. When we look at who are the medical patients whose data is being collected, are we seeing skews? And that has created all sorts of problems, particularly in the last 50 years in terms of misdiagnosing women, people of color, missing and not taking seriously the health complaints of people who are already seen as marginalized populations, thus then further skewing the data that is then used to train AI models. So this is something that we have to take very seriously, and I had the great fortune of being invited by Francis Collins to work with the NIH on their AI advisory board.
(39:50):
They produced a board to look just at these questions around how can this moment in AI be harnessed in such a way that we can think about the data layer, think about the quality of data and how we train models. And it was a really fascinating sort of year long discussion because in the room we had people who were just technologists who just wanted as much data as possible and just give us all that data and then we'll do something, but we'll figure it out later. Then there were people who had been part of the Human Genome Project and had worked with Francis on questions around the legal and ethical and social questions, which he had really centered in that project very early on. And they said, no, we have to learn these lessons. We have to learn that data comes from somewhere. It's not divorced of context, and we have to think about who's being represented there and also who's not being represented there because that will then be intensified in any model that we train on that data.
Humans and Automation Bias
(40:48):
And then also thinking about what would happen in terms of if those models are only held by a few companies who can profit from them and not more publicly and widely shared. These were the sorts of conversations that I think at the absolute forefront in terms of how we're going to navigate this moment. But if we get that right, if we center those questions, then I think we have far greater potential here than we might imagine. But I'm also really cognizant of the fact that even if you have a perfect AI model, you are always going to have imperfect people applying it. And I'm sure you saw that same study that came out in JAMA back in December last year, which was looking at how AI bias, even slightly biased models can worsen human medical diagnosis. I don’t know if you saw this study, but I thought it was really extraordinary.
(41:38):
It was sort of 450 doctors and physician's assistants and they were really being shown a handful of cases of patients with acute respiratory failure and they really needed come up with some sort of diagnosis and they were getting suggestions from an AI model. One model was trained very carefully with highly accurate data, and the other was a fairly shoddy, shall we say, AI model with quite biased data. And what was interesting is that the clinicians when they were working with very well-trained AI model, we're actually producing a better diagnosis across the board in terms of the cases they were looking at. I think their accuracy went up by almost 4.5 percentage points, but when they were working with the less accurate model, their capacity actually dropped well below their usual diagnostic baseline, something like almost 12 percentage points below their usual diagnostic quality. And so, this really makes me think of the kind of core problem that's been really studied for 40 years by social scientists, which is called automation bias, which is when even an expert, a technical system which is giving a recommendation, our tendency is to believe it and to discard our own knowledge, our own predictions, our own sense.
(42:58):
And it's been tested with fighter pilots, it's been tested with doctors, it's been tested with judges, and it's the same phenomenon across the board. So one of the things that we're going to need to do collectively, but particularly in the space of medicine and healthcare, is retaining that skepticism, retaining that ability to ask questions of where did this recommendation come from with this AI system and should I trust it? What was it trained on? Where did the data come from? What might those gaps be? Because we're going to need that skepticism if we're going to get through particularly this, as you say, this sort of early stage one period where in many cases these models just haven't had a lot of testing yet and people are going to tend to believe them out of the box.
The Large Language Model Copyright Issue
Eric Topol (43:45):
No, it's so true. And one of the key points is that almost every study that's been published in large language models in medicine are contrived. They're using patient actors or they're using case studies, but they're not in the real world. And that's where you have to really learn, as you know, that's a much more complex and messy world than the in silico world of course. Now, before wrapping up, one of the things that's controversial we didn't yet hit is the fact that in order for these base models to get trained, they basically ingest all human content. So they've ingested everything you've ever written, your books, your articles, my books, my articles, and you have the likes of the New York Times suing OpenAI, and soon it's going to run out of human content and just use synthetic content, I guess. But what's your sense about this? Do you feel that that's trespassing or is this another example of exploiting content and people, or is this really what has to be done in order to really make all this work?
Kate Crawford (44:59):
Well, isn't it a fascinating moment to see this mass grabbing of data, everything that is possibly extractable. I actually just recently published an article in Grey Room with the legal scholar, Jason Schultz, looking at how this is producing a crisis in copyright law because in many ways, copyright law just cannot contend with generative AI in particular because all of the ways in which copyright law and intellectual property more broadly has been understood, has been premised around human ideas of providing an incentive and thus a limited time monopoly based on really inspiring people to create more things. Well, this doesn't apply to algorithms, they don't respond to incentives in this way. The fact that, again, it's a longstanding tradition in copyright that we do not give copyright to non-human authors. So you might remember that there was a very famous monkey selfie case where a monkey had actually stepped on a camera and it had triggered a photograph of the monkey, and could this actually be a copyright image that could be given to the monkey?
(46:12):
Absolutely not, is what the court's decided. And the same has now happened, of course, for all generative AI systems. So right now, everything that you produce be that in GPT or in Midjourney or in Stable Diffusion, you name it, that does not have copyright protections. So we're in the biggest experiment of production after copyright in world history, and I don't think it's going to last very long. To be clear, I think we're going to start to see some real shifts, I think really in the next 6 to 12 months. But it has been this moment of seeing this gigantic gap in what our legal structures can do that they just haven't been able to contend with this moment. The same thing is true, I think, of ingestion, of this capturing of human content without consent. Clearly, many artists, many writers, many publishing houses like the New York Times are very concerned about this, but the difficulty that they're presented with is this idea of fair use, that you can collect large amounts of data if you are doing something with that, which is sufficiently transformative.
(47:17):
I'm really interested in the question of whether or not this does constitute sufficiently transformative uses. Certainly if you looked at the way that large language models a year ago, you could really prompt them into sharing their training data, spitting out entire New York Times articles or entire book chapters. That is no longer the case. All of the major companies building these systems have really safeguarded against that now but nonetheless, you have this question of should we be moving towards a system that is based on licensing, where we're really asking people if we can use their data and paying them a license fee? You can see how that could absolutely work and would address a lot of these concerns, but ultimately it will rely on this question of fair use. And I think with the current legal structures that we have in the current case law, that is unlikely to be seen as something that's actionable.
(48:10):
But I expect what we'll look at is what really happened in the early 20th century around the player piano, which was that I'm sure you remember this extraordinary technology of the player piano. That was one of the first systems that automated the playing of music and you'd have a piano that had a wax cylinder that almost like code had imprinted on a song or a piece of music, and it could be played in the public square or in a bar or in a saloon without having to pay a single artist and artists were terrified. They were furious, they were public hearings, there were sort of congressional hearings and even a Supreme Court case that decided that this was not a copyright infringement. This was a sufficiently transformative use of a piece of music that it could stand. And in the end, it was actually Congress that acted.
(49:01):
And we from that got the 1908 Copyright Act and from that we got this idea of royalties. And that has become the basis of the music industry itself for a very long time. And now we're facing another moment where I think we have a legislative challenge. How would you actually create a different paradigm for AI that would recognize a new licensing system that would reward artists, writers, musicians, all of the people whose work has been ingested into training data for AI so that they are recognized and in some ways, recompensed by this massive at scale extraction?
Eric Topol (49:48):
Wow, this has been an exhilarating conversation, Kate. I've learned so much from you over the years, but especially even just our chance to talk today. You articulate these problems so well, and I know you're working on solutions to almost everything, and you're so young, you could probably make a difference in the decades ahead. This is great, so I want to thank you not just for the chance to visit today, but all the work that you've been doing, you and your colleagues to make AI better, make it fulfill the great promise that it has. It is so extraordinary, and hopefully it'll deliver on some of the things that we have big unmet needs, so thanks to you. This has really been fun.
Kate Crawford (50:35):
This has been wonderful. And likewise, Eric, your work has just been a fantastic influence and I've been delighted to get to know you over the years and let's see what happens. It's going to be a wild ride from now to who knows when.
Eric Topol (50:48):
No question, but you'll keep us straight, I know that. Thank you so much.
Kate Crawford (50:52):
Thanks so much, Eric.
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If there’s one person you’d want to talk to about immunology, the immune system and Covid, holes in our knowledge base about the complex immune system, and where the field is headed, it would be Professor Iwasaki. And add to that the topic of Women in Science. Here’s our wide-ranging conversation.
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Transcript with many external link and links to the audio, recorded 30 April 2024
Eric Topol (00:06):
Hello, it's Eric Topol and I'm really thrilled to have my friend Akiko Iwasaki from Yale, and before I start talking with Akiko, I just want to mention there aren't too many silver linings of the pandemic, but one for me was getting to know Professor Iwasaki. She is my go-to immunologist. I've learned so much from her over the last four years and she's amazing. She just, as you may know, she was just recently named one of the most influential people in the world by TIME100. [and also recognized this week in TIME 100 Health]. And besides that, she's been elected to the National Academy of Medicine, National Academy of Sciences. She's the president of the American Association of Immunologists and she's a Howard Hughes principal investigator. So Akiko, it's wonderful to have you to join into an extended discussion of things that we have of mutual interest.
Akiko Iwasaki (01:04):
Thank you so much, Eric, for having me. I equally appreciate all of what you do, and I follow your blog and tweets and everything. So thank you Eric.
Eric Topol (01:14):
Well, you are a phenom. I mean just, that's all I can say because I think it was so appropriate that TIME recognize your contributions, not just over the pandemic, but of course throughout your career, a brilliant career in immunology. I thought we'd start out with our topic of great interest on Long Covid. You've done seminal work here and this is an evolving topic obviously. I wonder what your latest thoughts are on the pathogenesis and where things are headed.
Long Covid
Akiko Iwasaki (01:55):
Yeah, so as I have been saying throughout the pandemic, I think that Long Covid is not one disease. It's a collection of multiple diseases and that are sort of ending up in similar sets of symptoms. Obviously, there are over 200 symptoms and not everyone has the same set of symptoms, but what we are going for is trying to understand the disease drivers, so persistent viral infection is one of them. There are overwhelming evidence for that theory now, all the way from autopsy and biopsy studies to looking at peripheral blood RNA signatures as well as circulating spike protein and nucleocapsid proteins that are detected in people with Long Covid. Now whether that persistent virus or remnants of virus is driving the disease itself is unclear still. And that's why trials like the one that we are engaging with Harlan Krumholz on Paxlovid should tell us what percentage of the people are suffering from that type of driver and whether antivirals like Paxlovid might be able to mitigate those. If I may, I'd like to talk about three other hypotheses.
Eric Topol (03:15):
Yeah, I'd love for you to do that.
Akiko Iwasaki (03:18):
Okay, great. So the second hypothesis that we've been working on is autoimmune disease. And so, this is clearly happening in a subset of people, again, it's a heterogeneous disease, but we can actually not only look at reactogenicity of antibodies from people with Long Covid where we can transfer IgG from patients with Long Covid into an animal, a healthy animal, and really measure outcomes of a pathogenesis. So that's a functional evidence that antibodies in some people with Long Covid is really actually causing some of the damages that are occurring in vivo. And the third hypothesis is the reactivation of herpes viruses. So many of us adults have multiple latent herpes virus family members that are just dormant and are not really causing any pathologies. But in people with Long Covid, we're seeing elevated reactivation of viruses like Epstein-Barr virus (EBV) or Varicella-zoster virus (VZV) and that may again be just a signature of Long Covid, but it may also be driving some of the symptoms that people are suffering from.
(04:32):
So that's again, we see the signature over and over, not just our group, but multiple other groups, Michael Peluso's group, Jim Heath, and many others. So that's also an emerging evidence from multiple groups showing that. And finally, we think that inflammation that occurs during the acute phase can sort of chronically change some tissue tone. For instance, in the brain with Michelle Monje’s team, we developed a sort of localized mild Covid model of infection and showed that changes in microglia can be seen seven weeks post infection even though the virus is completely gone. So that means that inflammation that's established as a result of this initial infection can have prolonged sequence and sequela within the person and that may also be driving disease. And Eric, the reason we need to understand these diseases separately is because not only for diagnostic purposes, but for therapeutic purposes because to target a persistent virus is very different approach from targeting autoantibodies, for example.
Eric Topol (05:49):
Well, that's great. There's a lot to unpack there as you laid out four distinct paths that could result in the clinical syndrome and sequelae. I think you know I had the chance to have a really fun conversation with Michelle about their joint work that you've done, and she reminded me how she made a cold call to you to start as a collaboration, which I thought was fantastic. Look what that yielded. But yeah, this is fascinating because as I think you're getting at is that it may not be the same pathogenesis in any given individual so that all these, and even others might be operative. I guess maybe I first delve into the antibody story as you're well aware, we see after people get Covid a higher rate of autoimmune diseases crop up, which is really interesting because it seems to rev up self-directed immune response. And this I think many people haven't really noted yet, although obviously you're well aware of this, it's across all the different autoimmune diseases, connective tissue disease, not just one in particular. And it's, as you say, the idea that you could take the blood from a person suffering from Long Covid and give it to an experimental animal model and be able to recapitulate some of the abnormalities, it's really pretty striking. So the question I guess is if you were to do plasmapheresis and try to basically expunge these autoantibodies, wouldn't you expect people to have some symptomatic benefit pretty rapidly or is it just that the process is already far from the initiating step?
Akiko Iwasaki (07:54):
That's a great question. Plasmapheresis may be able to transiently improve the person if they're suffering from these autoantibody mediated diseases. People have reported, for example, IVIG treatment has dramatically improved their symptoms, but not in everybody. So it's really critical to understand who's suffering from this particular driver and appropriately treat those people. And there are many other very effective therapies in autoimmune disease field that can be repurposed for treating these patients as well.
Eric Topol (08:34):
The only clinical trial that has clicked so far, interestingly, came out of Hong Kong with different types of ways to manipulate the gut microbiome, which again, you know better than me is a major modulator of our immune system response. What are your thoughts about taking advantage of that way to somehow modulate this untoward immune response in people with this condition?
Akiko Iwasaki (09:07):
Yeah, so that is an exciting sort of development, and I don't mean to discount the importance of microbiome at all. It's just the drivers that are mentioning are something that can be directly linked to disease, but certainly dysbiosis and translocation of metabolites and microbiome itself could trigger Long Covid as well. So it's something that we're definitely keeping our eyes on. And as you say, Eric, the immune system is in intimate contact with the gut microbiome and also the gut is intimate contact with the brain. So there's a lot of connections that we really need to be paying attention to. So yeah, absolutely. This is a very exciting development.
Eric Topol (09:57):
And it is intriguing of course, the reactivation of viruses. I mean, we’ve learned in recent years how important EBV is in multiple sclerosis (MS). The question I have for you on that pathway, is this just an epiphenomena or do you actually think that could be a driving force in some people?
Akiko Iwasaki (10:19):
Yeah, so that's really hard to untangle in people. I mean, David Putrino and my team we're planning a clinical trial using Truvada. Truvada obviously is an HIV drug, but it has reported antiviral activity to Epstein-Barr virus (EBV) and others. So potentially we can try to interrogate that in people, but we're also developing mouse models that can sort of recapitulate EBV like viral reactivation and to see whether there's any sort of causal link between the reactivation and disease process.
Eric Topol (10:57):
Right now, recently there's been a bunch of anecdotes of people who get the glucagon-like peptide one (GLP-1) drugs which have a potent anti-inflammatory, both systemic and in the brain. I'd love to test these drugs, but of course these companies that make them or have other interests outside of Long Covid, do you think there's potential for a drug like that?
Akiko Iwasaki (11:23):
Yeah, so those drugs seem to have a lot of miraculous effects on every disease. So obviously it has to be used carefully because many people with Long Covid have issues with liver functions and other existing conditions that may or may not be conducive to taking those types of GLP-1 agonists. But in subset of people, maybe this can be tried, especially due to the anti-inflammatory properties, it may benefit again, a subset of people. I don't expect a single drug to cure everyone. That would be pretty amazing, but unlikely.
Eric Topol (12:09):
Absolutely. And it's unfortunate we are not further along in this whole story of clinical trials, testing treatments and applauding your efforts with my friend Harlan there to get into the testing which we had hoped RECOVER was going to do with their more than billion dollars or allocation, which didn't get us too far in that. Now before we leave Long Covid, which we could speak about for hours, I mean it's so darn important because so many people are really out there disabled or suffering on a daily basis or periodically they get better and then get worse again. There's been this whole idea that, oh, it's going away and that reinfections don't pose a threat. Maybe you could straighten that story out because I think there seems to be some miscues about the risk of Long Covid even as we go along with the continued circulating virus.
Akiko Iwasaki (13:11):
Right, so when you look at the epidemiological evidence of Long Covid, clearly in the beginning when we had no vaccines, no antivirals, no real good measure against Covid, the incident of developing Long Covid per infection was higher than a current date where we do have vaccines and Omicron may have changed its property significantly. So if you compare, let's say the Delta period versus Omicron period, there seems to be a reduced risk per infection of Long Covid. However, Omicron is super infectious. It's infected millions of people, and if you look at the total number of people suffering from Long Covid, we're not seeing a huge decline there at all because of the transmissibility of Omicron. So I think it's too early for us to say, okay, the rates are declining, we don't need to worry about it. Not at all, I think we still have to be vigilant.
(14:14):
We need to be up to date on vaccines and boosters because those seem to reduce the risk for Long Covid and whether Paxlovid can reduce the rate of Long Covid at the acute phase for the high risk individual, it seems to be yes, but for people who are not at high risk may or may not be very effective. So again, we just need to be very cautious. It's difficult obviously, to be completely avoiding virus at this time point, but I think masking and anything you can do, vaccination boosters is going to be helpful. And a reinfection does carry risk for developing Long Covid. So that prior infection is not going to prevent Long Covid altogether, even though the risk may be slightly reduced in the first infection. So when you think about these risks, again we need to be cognizant that reinfection and some people have multiple infections and then eventually get Long Covid, so we're just not safe from Long Covid yet.
Nasal Vaccines and Mucosal Immunity
Eric Topol (15:24):
Right. No, I think that's the problem is that people have not acknowledged that there's an ongoing risk and that we should continue to keep our guard up. I want to applaud you and your colleagues. You recently put out [Yale School of Public Health] this multi-panel about Covid, which we'll post with this podcast that gave a lot of the facts straight and simple diagrams, and I think this is what you need is this is kind of like all your threads on Twitter. . They're always such great educational ways to get across important information. So now let's go onto a second topic of great mutual interest where you've also been a leader and that's in the mucosal nasal vaccine story. I had the privilege of writing with you a nice article in Science Immunology back in 2022 about Operation Nasal Vaccine, and unfortunately we don't have a nasal vaccine. We need a nasal vaccine against Covid. Where do we stand with this now?
Akiko Iwasaki (16:31):
Yeah, so you're right. I mean nasal vaccines, I don't really know what the barrier is because I think the preclinical models all support the effectiveness against transmission and infection and obviously disease. And there is a White House initiative to support rapid development of next generation vaccine, which includes mucosal vaccine, so perhaps that's sort of pushing some of these vaccine candidates forward. You're probably more familiar than me about those kinds of events that are happening. But yeah, it's unfortunate that we don't have an approved mucosal booster vaccine yet, and our research has shown that as simple as a spray of recombinant spike protein without any adjuvants are able to restimulate immune response and then establish mucosal immunity in the nasal cavity, which goes a long way in preventing infection as well as transmission. So yeah, I mean I'm equally frustrated that things like that don't exist yet.
The Neomycin and Neosporin Surprise
Eric Topol (17:52):
Well, I mean the work that you and many other groups around the world have published on this is so compelling and this is the main thing that we don't have now, which is a way to prevent infection. And I think most of us would be very happy to have a spray that every three or four months and gave us much higher levels of protection than we're ever going to get from shots. And your whole concept of prime and spike, I mean this is something that we could have had years ago if there was a priority, and unfortunately there never has been. Now, the other day you came with a surprise in a paper on Neomycin as an alternate or Neosporin ointment. Can you tell us about that? Because that one wasn't expected. This was to use an antibiotic in a way to reduce Covid and other respiratory virus.
Akiko Iwasaki (18:50):
Right. So yeah, that's a little known fact. I mean, of course widespread use of antibiotics has caused some significant issues with resistance and so on. However, when you look at the literature of different types of antibiotics, we have reported in 2018 that certain types of antibiotics known as aminoglycoside, which includes Neosporin or neomycin, has this sort of unintended antiviral property by triggering Toll-like receptor 3 in specialized cell types known as conventional dendritic cell type 1. And we published that for a genital herpes model that we were working on at the time. But because it's acting on the host, the Toll-like receptor 3 on the host cell to induce interferon and interferon stimulated genes to prevent the replication of the virus, we knew that it could be pan-viral. It doesn't really matter what the virus is. So we basically leverage that discovery that was made by a postdoc Smita Gopinath when she was in the lab to see if we can use that in the nasal cavity.
(20:07):
And that's what Tianyang Mao, a former graduate student did, in fact. And yeah, little spray of neomycin in the nose of the mice reduce this infection as well as disease and can even be used to treat shortly after the infection disease progress and using hamster models we also showed that hamsters that are pretreated with neomycin when they were caged with infected hamsters, the transmission rate was much reduced. And we also did with Dr. Charles Dela Cruz, a small clinical trial, randomized though into placebo and Neosporin arms of healthy volunteers. We asked them to put in a pea size amount of Neosporin on a cotton swab into the nose, and they were doing that twice a day for seven days. We measured the RNA from the nose of these people and indeed see that more than half the participants in the Neosporin group had elevated interferon stimulated genes, whereas the control group, which were given Vaseline had no response. So this sort of shows the promise of using something as generic and cheap as Neosporin to trigger antiviral state in the nose. Now it does require a much larger trial making sure that the safety profiles there and effectiveness against viral infection, but it's just a beginning of a story that could develop into something useful.
New Frontiers in Immunology and Tx Cells
Eric Topol (21:51):
Yeah, I thought it was fascinating, and it does bring up, which I think has also been underdeveloped, is our approaches for interferon a frontline defense where augmenting that, just getting that exploiting the nasal mucosa, the entry site, whether it be through that means or of course through even more potent a nasal vaccine, it's like a missing, it's a hole in our whole defense of against this virus that's led to millions of people not just dying, but of course also sick and also with Long Covid around the world. So I hope that we'll see some progress, but I thought that was a really fascinating hint of something to come that could be very helpful in the meantime while we're waiting for specific nasal vaccines. Now added to all these things recently, like last week you published a paper in Cell with your husband who's in the same department, I think at Yale. Is that right? Can you tell us about that and this paper about the whole new perspectives in immunology?
Akiko Iwasaki (23:05):
Yeah, so my husband Ruslan Medzhitov is a very famous immunologist who's in the same department, and we've written four or five review and opinion pieces together over the years. This new one is in Cell and it's really exploring new perspectives in immunology. We were asked by the editors to celebrate the 50th anniversary of the Cell journal with a perspective on the immune system. And the immune response is just a beautiful system that is triggered in response to specific pathogens and can really provide long-term or even sometimes lifelong immunity and resistance against pathogens and it really saves our lives. Much has been learned throughout the last 20, 30 years about the innate and adaptive immune system and how they're linked. In this new perspective, we are trying to raise some issues that the current paradigm cannot explain properly, some of the mysteries that are still remaining in the immune system.
(24:22):
And we try to come up with new concepts about even the role of the immune system in general. For instance, is the immune system only good for fighting pathogens or can it be repurposed for conducting normal physiology in the host? And we came up with a new subset of T-cells known as, or we call it Tx cells, which basically is an interoceptive type of T-cells that monitor homeostasis in different tissues and are helping with the normal process of biology as opposed to fighting viruses or bacteria or fungi. But these cells, when they are not appropriately regulated, they are also the source of autoimmune diseases because they are by design reactive against auto antigens. And so, this is a whole new framework to think about, a different arm of the immune function, which is really looking inside of our body and not really fighting against pathogens, but we believe these cells exist, and we know that the counterpart of Tx cells, which is the T regulatory cells, are indeed well known for its physiological functions. So we're hoping that this new perspective will trigger a new set of approaches in the field to try to understand this interceptive property of T-cells.
Eric Topol (25:59):
Yeah, well, I thought it was fascinating, of course, and I wanted to get into that more because I think what we're learning is this immune system not only obviously is for cancer whole. We're only starting to get warmed up with immunotherapy where checkpoint inhibitors were just the beginning and now obviously with vaccines and all these different ways that we can take the CAR-T cells, engineered T-cells, take the immune system to fight cancer and potentially to even use it as a way to prevent cancer. If you have these, whether it's Tx or Tregs or whatever T-cells can do this. But even bigger than that is the idea that it's tied in with the aging process. So as you know, again, much more than I do, our senescent immune cells are not good for us. And the whole idea is that we could build immune resilience if we could somehow figure out these mysteries that you're getting at, whereby we get vulnerable just as we were with Covid. And as we get older, we get vulnerable to not just infections, but everything going wrong, whether it's the walls of our arteries or whether it's the cancer or the immunity that's going on in our brain for Alzheimer's and neurodegenerative diseases. How can we fix the immune system so that we age more healthily
The Immune System and Healthy Aging
Akiko Iwasaki (27:37):
Oh yeah. A lot of billionaires are also interested in that question and are pouring money into this question. It's interesting, but when you think about the sort of evolutionary perspective, we humans are only living so long. In the very recent decades, our life expectancy used to be much shorter and all we had to survive was to reproduce and generate the next progeny. But nowadays, because of this amazing wealth and health interventions and food and everything else, we're just living so much longer than even our grandparents. The immune system didn't evolve to deal with such one to begin with. So we were doing fine living up to 30 years of age or whatever. But now that we're living up to a hundred years, the immune system isn't really designed to keep up with this kind of stressors. But I think you're getting at a very important kind of more engineering questions of how do we manipulate the immune system or rejuvenate it so that we can remain healthy into the later decades? And it is well known that the immune system itself ages and that our ability to produce new lymphocytes, for example, decline over time and thymus that is important for T-cell development shrinks over time. And so anatomically it's impossible to help stop that process. However, is there a way of, for example, transferring some factors or engineering the immune cells to remain healthy and even like hematopoiesis itself can be manipulated to perhaps rejuvenate the whole immune system in their recent papers showing that. So this is a new frontier.
Eric Topol (29:50):
Do you think that some point in the future, we'll ex vivo inject Yamanaka factors into these cell lines and instead of this idea that you know get young plasma to old folks, and I mean since we don't know what's in there and it doesn't specifically have an effect on immune cells, who knows how it's working, but do you foresee that that might be a potential avenue going forward or even an in vivo delivery of this?
Akiko Iwasaki (30:22):
Yeah, it's not impossible, right? There are really rapidly evolving technologies and gene therapies that are becoming online. So it's not impossible to think about engineering in situ as you're suggesting, but we also have to be certain that we are living longer, but also healthy. So we do have to not only just deal with the aging immune system, but preventing neurodegenerative diseases and so on. And the immune system may have a role to play there as well. So there's a lot of, I mean, I can't think of a non-genetically mediated disease that doesn't involve the immune system.
Eric Topol (31:03):
Sure. No, I mean, it's just, when I think about this, people keep talking about the digital era of digital biology, but I actually think of it more as digital immunobiology, which is driving this because it's center stage and in more and more over time. And the idea that I'm concerned about is that we could rejuvenate the relevant immune cells or the whole immune response, but then it's such a delicate balance that we could actually wind up with untoward, whether it's autoimmune or overly stimulated immune system. It's not such a simple matter, as I'm sure you would agree. Now, this gets me to a broader thing which you've done, which is a profound contribution in life science and medicine, which is being an advocate for women in science. And I wonder if you could speak to that because you have been such a phenomenal force propelling the importance of women in science and not just doing that passively, but also standing up for women, which is being an activist is how you get things to change. So can you tell us about your thoughts there?
An Activist for Women in Science
Akiko Iwasaki (32:22):
Yeah, so I grew up in Japan, and part of the reason I left Japan at the age of 16 was that I felt very stifled because of the societal norm and expectation of what a woman should be. And I felt like I didn't have the opportunity to develop my skills as a scientist remaining in Japan. And maybe things have changed over the years, but at the time when I was growing up, that's how I felt. And so, I was very cognizant of biases in society. And so, in the US and in Canada where I also trained, there's a lot less barrier to success, and we are able to do pretty much anything we want, which is wonderful, and that's why I think I'm here. But at the same time, the inequity still exists, even in pay gaps and things like that that are easy to fix but are still kind of insidious and it's there.
(33:32):
And Yale School of Medicine has done a great job partly because of the efforts of women who spoke up and who actually started to collect evidence for pay gap. And now there's very little pay gap because there's active sort of involvement of the dean and everyone else to ensure equity in the medical school. But it's just a small segment of the society. We really need to expand this to other schools and making sure that women are getting paid equally as men in the same ranks. And also, I see still some sexual harassment or more just toxic environment for people in general in academia. Some PIs get away with a lot of behavior that's not conducive to a healthy environment, so I have written about that as well and how we can have antidotes for such toxic environments. And it really does require the whole village to act on it. It's not just one person speaking up. And there should be measures placed to make sure that those people who does have this tendency of abusive behavior that they can get training and just being aware of these situations and corrective behavior. So I think there's still a lot of work left in academia, but things have obviously improved dramatically over the last few decades, and we are in a very, very good place, but we just have to keep working to achieve true equity.
Why Don’t We Have Immunome Check-Ups?
Eric Topol (35:25):
Well applauding your efforts for that, and I'm still in touch with that. We got a ways to go, and I hope that we'll see steady and even more accelerated and improvement to get to parity, which is what it should be. And I really think you've been a model for doing this. It isn't like you aren't busy with everything else, so to fit that in is wonderful. In closing up, one of the things that I wonder about is our ability to assess back to the immune system for a moment isn't what it should be. That is we do a CBC and we have how many lymphocytes, how many this, why don't we have an immunome, why doesn’t everybody serially have an immune system checkup? Because that would tell us if we’re starting to go haywire and then maybe hunt for reactivated viruses or what’s going on. Do you foresee that we could ever get to a practical immunome as we go forward? Because it seems like it’s a big missing link right now.
Akiko Iwasaki (36:33):
Yeah, I think that’s a great idea. I mean, I’ll be the first one to sign up for the immunome.
Eric Topol (36:40):
But I’m depending on you to make it happen.
Akiko Iwasaki (36:44):
Well, interestingly, Eric, there are lots of amazing technologies that are developed even during the pandemic, which is monitoring everything from antibody reactivity to reactivated viruses to the cytokines to every cell marker you can imagine. So the technologies out there, it’s just I think a matter of having the right set of panels that are relatively affordable because some of these things are thousands of dollars per sample to analyze, and then of course clinical validation, something that’s CLIA approved, and then we can start to, I guess the insurance company needs to also cover this, right? So we need to demonstrate the benefit to health in the long run to be able to afford this kind of immunome analysis. But I think that very wealthy people can already get this done.
Eric Topol (37:43):
Yeah, well, we want to make it so it's a health equity story, not of course, only for the crazy ones that are out there that are taking 112 supplements a day and whatnot. But it's intriguing because I think we might be able to get ahead of things if we had such an easy means. And as you said during the pandemic, for example, my friends here in La Jolla at La Jolla Immunology did all kinds of T-cell studies that were really insightful and of course done with you and others around the country and elsewhere to give us insights that you didn't get just from neutralizing antibodies. But it isn't something that you can get done easily. Now, I think this immunome hopefully will get us to another level in the future. One of the most striking things I've seen in our space clinically before wrapping up is to take the CD19 CAR T therapies to deplete the B cells of people with lupus, systemic sclerosis and other conditions, and completely stop their autoimmune condition. And when the B cells come back, they're not fighting themselves. They're not self-directed anymore. Would you have predicted this? This seems really striking and it may be a clue to the kind of mastering approaches to autoimmune diseases in the future.
Akiko Iwasaki (39:19):
Yeah, absolutely. So for multiple sclerosis, for example, where B cells weren't thought to be a key player by doing anti-CD20 depletion, there's this remarkable clinical effects. So I think we can only find the answer experimentally in people when they do these clinical trials and show this remarkable effects. That's when we say, aha, we don't really understand immunology. You know what I mean? That's when we have to be humble about what we think we understand. We really don't know until we try it. So that's a really good lesson learned. And these may be also applicable to people with autoimmune phenotype in Long Covid, right? We may be able to benefit from similar kinds of depletion therapy. So I think we have a lot to learn still.
Eric Topol (40:14):
Yeah, that's why, again, going back to the paper you just had in Cell about the mysteries and about some new ideas and challenging the dogma is so important. I still consider the immune system most complex one in the body by far, and I'm depending on you Akiko to unravel it, not to put any weight on your shoulders. Anyway, this has been so much fun. You are such a gem and always learning from you, and I can't thank you enough for all the work. And the fact is that you've got decades ahead of you to keep building on this. You've already done enough for many people, many scientists in your career, and I know you'll keep going. So we're all going to be following you with great interest in learning from you on a frequent basis. And I hope we'll build on some of the things we've talked about like a Long Covid treatment, treatments that are effective nasal vaccines, maybe even some dab of Neosporin, and keep on the momentum we’ve had with the understanding of the immune system, and finally, someday achieving the true parity of gender and science. And so, thank you for all that you do.
Akiko Iwasaki (41:35):
Thank you so much, Eric.
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“Where do I think the next amazing revolution is going to come? … There’s no question that digital biology is going to be it. For the very first time in our history, in human history, biology has the opportunity to be engineering, not science.” —Jensen Huang, NVIDIA CEO
Aviv Regev is one of the leading life scientists of our time. In this conversation, we cover the ongoing revolution in digital biology that has been enabled by new deep knowledge on cells, proteins and genes, and the use of generative A.I .
Transcript with audio and external links
Eric Topol (00:05):
Hello, it's Eric Topol with Ground Truths and with me today I've really got the pleasure of welcoming Aviv Regev, who is the Executive Vice President of Research and Early Development at Genentech, having been 14 years a leader at the Broad Institute and who I view as one of the leading life scientists in the world. So Aviv, thanks so much for joining.
Aviv Regev (00:33):
Thank you for having me and for the very kind introduction.
The Human Cell Atlas
Eric Topol (00:36):
Well, it is no question in my view that is the truth and I wanted to have a chance to visit a few of the principal areas that you have been nurturing over many years. First of all, the Human Cell Atlas (HCA), the 37 trillion cells in our body approximately a little affected by size and gender and whatnot, but you founded the human cell atlas and maybe you can give us a little background on what you were thinking forward thinking of course when you and your colleagues initiated that big, big project.
Aviv Regev (01:18):
Thanks. Co-founded together with my very good friend and colleague, Sarah Teichmann, who was at the Sanger and just moved to Cambridge. I think our community at the time, which was still small at the time, really had the vision that has been playing out in the last several years, which is a huge gratification that if we had a systematic map of the cells of the body, we would be able both to understand biology better as well as to provide insight that would be meaningful in trying to diagnose and to treat disease. The basic idea behind that was that cells are the basic unit of life. They're often the first level at which you understand disease as well as in which you understand health and that in the human body, given the very large number of individual cells, 37.2 trillion give or take, and there are many different characteristics.
(02:16):
Even though biologists have been spending decades and centuries trying to characterize cells, they still had a haphazard view of them and that the advancing technology at the time – it was mostly single cell genomics, it was the beginnings also of spatial genomics – suggested that now there would be a systematic way, like a shared way of doing it across all cells in the human body rather than in ways that were niche and bespoke and as a result didn't unify together. I will also say, and if you go back to our old white paper, you will see some of it that we had this feeling because many of us were computational scientists by training, including both myself and Sarah Teichmann, that having a map like this, an atlas as we call it, a data set of this magnitude and scale, would really allow us to build a model to understand cells. Today, we call them foundational models or foundation models. We knew that machine learning is hungry for these kinds of data and that once you give it to machine learning, you get amazing things in return. We didn't know exactly what those things would be, and that has been playing out in front of our eyes as well in the last couple of years.
Spatial Omics
Eric Topol (03:30):
Well, that gets us to the topic you touched on the second area I wanted to get into, which is extraordinary, which is the spatial omics, which is related to the ability to the single cell sequencing of cells and nuclei and not just RNA and DNA and methylation and chromatin. I mean, this is incredible that you can track the evolution of cancer, that the old word that we would say is a tumor is heterogeneous, is obsolete because you can map every cell. I mean, this is just changing insights about so much of disease health mechanisms, so this is one of the hottest areas of all of life science. It's an outgrowth of knowing about cells. How do you summarize this whole era of spatial omics?
Aviv Regev (04:26):
Yeah, so there's a beautiful sentence in the search for lost time from Marcel Proust that I'm going to mess up in paraphrasing, but it is roughly that going on new journeys is not about actually going somewhere physically but looking with new eyes and I butchered the quote completely.[See below for actual quote.] I think that is actually what single cells and then spatial genomics or spatial omics more broadly has given us. It's the ability to look at the same phenomenon that we looked at all along, be it cancer or animal development or homeostasis in the lung or the way our brain works, but having new eyes in looking and because these new eyes are not just seeing more of something we've seen before, but actually seeing things that we couldn't realize were there before. It starts with finding cells we didn't know existed, but it's also the processes that these cells undergo, the mechanisms that actually control that, the causal mechanisms that control that, and especially in the case of spatial genomics, the ways in which cells come together.
(05:43):
And so we often like to think about the cell because it's the unit of life, but in a multicellular organism we just as much have to think about tissues and after that organs and systems and so on. In a tissue, you have this amazing orchestration of the interactions between different kinds of cells, and this happens in space and in time and as we're able to look at this in biology often structure is tightly associated to function. So the structure of the protein to the function of the protein in the same way, the way in which things are structured in tissue, which cells are next to each other, what molecules are they expressing, how are they physically interacting, really tells us how they conduct the business of the tissue. When the tissue functions well, it is this multicellular circuit that performs this amazing thing known as homeostasis.
(06:36):
Everything changes and yet the tissue stays the same and functions, and in disease, of course, when these connections break, they're not done in the right way you end up with pathology, which is of course something that even historically we have always looked at in the level of the tissue. So now we can see it in a much better way, and as we see it in a better way, we resolve better things. Yes, we can understand better the mechanisms that underlie the resistance to therapeutics. We can follow a temporal process like cancer as it unfortunately evolves. We can understand how autoimmune disease plays out with many cells that are actually bent out of shape in their interactions. We can also follow magnificent things like how we start from a single cell, the fertilized egg, and we become 37.2 trillion cell marvel. These are all things that this ability to look in a different way allows us to do.
Eric Topol (07:34):
It's just extraordinary. I wrote at Ground Truths about this. I gave all the examples at that time, and now there's about 50 more in the cardiovascular arena, knowing with single cell of the pineal gland that the explanation of why people with heart failure have sleep disturbances. I mean that's just one of the things of so many now these new insights it's really just so remarkable. Now we get to the current revolution, and I wanted to read to you a quote that I have.
Digital Biology
Aviv Regev (08:16):
I should have prepared mine. I did it off the top of my head.
Eric Topol (08:20):
It's actually from Jensen Huang at NVIDIA about the digital biology [at top of the transcript] and how it changes the world and how you're changing the world with AI and lab in the loop and all these things going on in three years that you've been at Genentech. So maybe you can tell us about this revolution of AI and how you're embracing it to have AI get into positive feedbacks as to what experiment to do next from all the data that is generated.
Aviv Regev (08:55):
Yeah, so Jensen and NVIDIA are actually great partners for us in Genentech, so it's fun to contemplate any quote that comes from there. I'll actually say this has been in the making since the early 2010s. 2012 I like to reflect on because I think it was a remarkable year for what we're seeing right now in biology, specifically in biology and medicine. In 2012, we had the beginnings of really robust protocols for single cell genomics, the first generation of those, we had CRISPR happen as a method to actually edit cells, so we had the ability to manipulate systems at a much better way than we had before, and deep learning happened in the same year as well. Wasn't that a nice year? But sometimes people only realize the magnitude of the year that happened years later. I think the deep learning impact people realized first, then the single cells, and then the CRISPR, then the single cells.
(09:49):
So in order maybe a little bit, but now we're really living through what that promise can deliver for us. It's still the early days of that, of the delivery, but we are really seeing it. The thing to realize there is that for many, many of the problems that we try to solve in biomedicine, the problem is bigger than we would ever be able to perform experiments or collect data. Even if we had the genomes of all the people in the world, all billions and billions of them, that's just a smidge compared to all of the ways in which their common variants could combine in the next person. Even if we can perturb and perturb and perturb, we cannot do all of the combinations of perturbations even in one cell type, let alone the many different cell types that are out there. So even if we searched for all the small molecules that are out there, there are 10 to the 60 that have drug-like properties, we can't assess all of them, even computationally, we can't assess numbers like that.
(10:52):
And so we have to somehow find a way around problems that are as big as that and this is where the lab in the loop idea comes in and why AI is so material. AI is great, taking worlds, universes like that, that appear extremely big, nominally, like in basic numbers, but in fact have a lot of structure and constraint in them so you can reduce them and in this reduced latent space, they actually become doable. You can search them, you can compute on them, you can do all sorts of things on them, and you can predict things that you wouldn't actually do in the real world. Biology is exceptionally good, exceptionally good at lab sciences, where you actually have the ability to manipulate, and in biology in particular, you can manipulate at the causes because you have genetics. So when you put these two worlds together, you can actually go after these problems that appear too big that are so important to understanding the causes of disease or devising the next drug.
(11:51):
You can iterate. So you start, say, with an experimental system or with all the data that you have already, I don't know from an initiative like the human cell atlas, and from this you generate your original model of how you think the world works. This you do with machine learning applied to previous data. Based on this model, you can make predictions, those predictions suggest the next set of experiments and you can ask the model to make the most optimized set of predictions for what you're trying to learn. Instead of just stopping there, that's a critical point. You go back and you actually do an experiment and you set up your experiments to be scaled like that to be big rather than small. Sometimes it means you actually have to compromise on the quality of any individual part of the experiment, but you more than make up for that with quantity.
The A.I. Lab-in-the-Loop
(12:38):
So now you generate the next data from which you can tell both how well did your algorithm actually predict? Maybe the model didn’t predict so well, but you know that because you have lab results and you have more data in order to repeat the loop, train the model again, fit it again, make the new next set of predictions and iterate like this until you're satisfied. Not that you've tried all options, because that's not achievable, but that you can predict all the interesting options. That is really the basis of the idea and it applies whether you're solving a general basic question in biology or you're interested in understanding the mechanism of the disease or you're trying to develop a therapeutic like a small molecule or a large molecule or a cell therapy. In all of these contexts, you can apply this virtual loop, but to apply it, you have to change how you do things. You need algorithms that solve problems that are a little different than the ones they solved before and you need lab experiments that are conducted differently than they were conducted before and that's actually what we're trying to do.
Eric Topol (13:39):
Now I did find the quote, I just want to read it so we have it, “biology has the opportunity to be engineering, not science. When something becomes engineering, not science, it becomes exponentially improving. It can compound on the benefits of previous years.” Which is kind of a nice summary of what you just described. Now as we go forward, you mentioned the deep learning origin back at the same time of CRISPR and so many things happening and this convergence continues transformer models obviously one that's very well known, AlphaFold, AlphaFold2, but you work especially in antibodies and if I remember correctly from one of your presentations, there's 20 to the 32nd power of antibody sequences, something like that, so it's right up there with the 10 to the 60th number of small molecules. How do transformer models enhance your work, your discovery efforts?
Aviv Regev (14:46):
And not just in antibodies, I'll give you three brief examples. So absolutely in antibodies it's an example where you have a very large space and you can treat it as a language and transformers are one component of it. There's other related and unrelated models that you would use. For example, diffusion based models are very useful. They're the kind that people are used to when you do things, you use DALL-E or Midjourney and so on makes these weird pictures, think about that picture and not as a picture and now you're thinking about a three-dimensional object which is actually an antibody, a molecule. You also mentioned AlphaFold and AlphaFold 2, which are great advances with some components related to transformers and some otherwise, but those were done as general purpose machines for proteins and antibodies are actually not general purpose proteins. They're antibodies and therapeutic antibodies are even further constrained.
(15:37):
Antibodies also really thrive, especially for therapeutics and also in our body, they need diversity and many of these first models that were done for protein structure really focused on using conservation as an evolutionary signal comparison across species in order to learn the model that predicts the structure, but with antibodies you have these regions of course that don't repeat ever. They're special, they're diverse, and so you need to do a lot of things in the process in order to make the model fit in the best possible way. And then again, this loop really comes in. You have data from many, many historical antibodies. You use that to train the model. You use that model in order to make particular predictions for antibodies that you either want to generate de novo or that you want to optimize for particular properties. You make those actually in the lab and in this way gradually your models become better and better at this task with antibodies.
(16:36):
I do want to say this is not just about antibodies. So for example, we develop cancer vaccines. These are personalized vaccines and there is a component in making a personalized cancer vaccine, which is choosing which antigens you would actually encode into the vaccine and transformers play a crucial role in actually making this prediction today of what are good neoantigens that will get presented to the immune system. You sometimes want to generate a regulatory sequence because you want to generate a better AAV-like molecule or to engineer something in a cell therapy, so you want to put a cis-regulatory sequence that controls gene expression. Actually personally for me, this was the first project where I used a transformer, which we started years ago. It was published a couple of years ago where we learned a general model that can predict in a particular system. Literally you throw a sequence at that model now and it will predict how much expression it would drive. So these models are very powerful. They are not the be all and end all of all problems that we have, but they are fantastically useful, especially for molecular therapeutics.
Good Trouble: Hallucinations
Eric Topol (17:48):
Well, one of the that has been an outgrowth of this is to actually take advantage of the hallucinations or confabulation of molecules. For example, the work of David Baker, who I'm sure you know well at University of Washington, the protein design institute. We are seeing now molecules, antibodies, proteins that don't exist in nature from actually, and all the things that are dubbed bad in GPT-4 and ChatGPT may actually help in the discovery in life science and biomedicine. Can you comment about that?
Aviv Regev (18:29):
Yeah, I think much more broadly about hallucinations and what you want to think about is something that's like constrained hallucination is how we're creative, right? Often people talk about hallucinations and they shudder at it. It sounds to them insane because if you think about your, say a large language model as a search tool and it starts inventing papers that don't exist. You might be like, I don't like that, but in reality, if it invents something meaningful that doesn't exist, I love that. So that constrained hallucination, I'm just using that colloquially, is a great property if it's constrained and harnessed in the right way. That's creativity, and creativity is very material for what we do. So yes, absolutely in what we call the de novo domain making new things that don't exist. This generative process is the heart of drug discovery. We make molecules that didn't exist before.
(19:22):
They have to be imagined out of something. They can't just be a thing that was there already and that's true for many different kinds of therapeutic molecules and for other purposes as well, but of course they still have to function in an effective way in the real world. So that's where you want them to be constrained in some way and that's what you want out of the model. I also want to say one of the areas that personally, and I think for the field as a whole, I find the most exciting and still underused is the capacity of these models to hallucinate for us or help us with the creative endeavors of identifying the causes of processes, which is very different than the generative process of making molecules. Thinking about the web of interactions that exist inside a cell and between cells that drives disease processes that is very hard for us to reason through and to collect all the bits of information and to fill in blanks, those fillings of the blanks, that's our creativity, that's what generates the next hypothesis for us. I'm very excited about that process and about that prospect, and I think that's where the hallucination of models might end up proving to be particularly impressive.
A.I. Accelerated Drug Discovery
Eric Topol (20:35):
Yeah. Now obviously the field of using AI to accelerate drug discovery is extremely hot, just as we were talking about with spatial omics. Do you think that is warranted? I mean you've made a big bet on that you and your folks there at Genentech of course, and so many others, and it's a very crowded space with so many big pharma partnering with AI. What do you see about this acceleration? Is it really going to reap? Is it going to bear fruit? Are we going to see, we've already seen some drugs of course, that are outgrowths, like Baricitinib in the pandemic and others, but what are your expectations? I know you're not one to get into any hyperbole, so I'm really curious as to what you think is the future path.
Aviv Regev (21:33):
So definitely my hypothesis is that this will be highly, highly impactful. I think it has the potential to be as impactful as molecular biology has been for drug discovery in the 1970s and 1980s. We still live that impact. We now take it for granted. But, of course that's a hypothesis. I also believe that this is a long game and it's a deep investment, meaning decorating what you currently do with some additions from right and left is not going to be enough. This lab in the loop requires deep work working at the heart of how you do science, not as an add-on or in addition to or yet another variant on what has become a pretty established approach to how things are done. That is where I think the main distinction would be and that requires both the length of the investment, the effort to invest in, and also the willingness to really go all out, all in and all out.
(22:36):
And that takes time. The real risk is the hype. It's actually the enthusiasm now compared to say 2020 is risky for us because people get very enthusiastic and then it doesn't pay off immediately. No, these iterations of a lab in the loop, they take time and they take effort and they take a lot of changes and at first, algorithms often fail before they succeed. You have to iterate them and so that is actually one of the biggest risks that people would be like, but I tried it. It didn't work. This was just some over-hyped thing. I'm walking away and doing it the old way. So that's where we actually have to keep at it, but also keep our expectations not low in magnitude. I think that it would actually deliver, but understanding that it's actually a long investment and that unless you do it deeply, it's not going to deliver the goods.
Eric Topol (23:32):
I think this point warrants emphasis because the success already we've seen has not been in necessarily discovery and in preliminary validation of new molecules, but rather data mining repurposing, which is a much easier route to go quicker, but also there's so many nodes on past whereby AI can make a difference even in clinical trials, in synthetic efforts to project how a clinical trial will turn out and being able to do toxic screens without preclinical animal work. There's just so many aspects of this that are AI suited to rev it up, but the one that you're working on, of course is the kind of main agenda and I think you framed it so carefully that we have to be patient here, that it has a chance to be so transformative. Now, you touched on the parallels to things like DALL-E and Midjourney and large language models. A lot of our listeners will be thinking only of ChatGPT or GPT-4 or others. This is what you work on, the language of life. This is not text of having a conversation with a chatbot. Do you think that as we go forward, that we have to rename these models because they're known today as language models? Or do you think that, hey, you know what, this is another language. This is a language that life science and biomedicine works with. How do you frame it all?
Large Non-Human Language Models
Aviv Regev (25:18):
First of all, they absolutely can remain large language models because these are languages, and that's not even a new insight. People have treated biological sequences, for example, in the past too, using language models. The language models were just not as great as the ones that we have right now and the data that were available to train models in the past were not as amazing as what we have right now. So often these are really the shifts. We also actually should pay respect to human language. Human language encodes a tremendous amount of our current scientific knowledge and even language models of human language are tremendously important for this scientific endeavor that I've just described. On top of them come language models of non-human language such as the language of DNA or the language of protein sequences, which are also tremendously important as well as many other generative models, representation learning, and other approaches for machine learning that are material for handling the different kinds of data and questions that we have.
(26:25):
It is not a single thing. What large language models and especially ChatGPT, this is an enormous favor for which I am very grateful, is that I think it actually convinced people of the power. That conviction is extremely important when you're solving a difficult problem. If you feel that there's a way to get there, you're going to behave differently than if you're like, nothing will ever come out of it. When people experience ChatGPT actually in their daily lives in basic things, doing things that felt to them so human, this feeling overrides all the intellectual part of things. It's better than the thinking and then they're like, in that case, this could actually play out in my other things as well. That, I think, was actually materially important and was a substantial moment and we could really feel it. I could feel it in my interactions with people before and after how their thinking shifted. Even though we were on this journey from before.
Aviv Regev (27:30):
We were. It felt different.
Eric Topol (27:32):
Right, the awareness of hundreds of millions of people suddenly in end of November 2022 and then you were of course going to Genentech years before that, a couple few years before that, and you already knew this was on the move and you were redesigning the research at Genentech.
Aviv Regev (27:55):
Yes, we changed things well before, but it definitely helps in how people embrace and engage feels different because they've seen something like that demonstrated in front of them in a way that felt very personal, that wasn't about work. It's also about work, but it's about everything. That was very material actually and I am very grateful for that as well as for the tool itself and the many other things that this allows us to do but we have, as you said, we have been by then well on our way, and it was actually a fun moment for that reason as well.
Eric Topol (28:32):
So one of the things I'm curious about is we don't think about the humans enough, and we're talking about the models and the automation, but you have undoubtedly a large team of computer scientists and life scientists. How do you get them to interact? They're of course, in many respects, in different orbits, and the more they interact, the more synergy will come out of that. What is your recipe for fostering their crosstalk?
Aviv Regev (29:09):
Yeah, this is a fantastic question. I think the future is in figuring out the human question always above all and usually when I draw it, like on the slide, you can draw the loop, but we always put the people in the center of that loop. It's very material to us and I will highlight a few points. One crucial thing that we've done is that we made sure that we have enough critical mass across the board, and it played out in different ways. For example, we built a new computational organization, gRED Computational Sciences, from what was before many different parts rather than one consolidated whole. Of course within that we also built a very strong AI machine learning team, which we didn't have as much before, so some of it was new people that we didn't have before, but some of it was also putting it with its own identity.
(29:56):
So it is just as much, not more, but also not less just as much of a pillar, just as much of a driver as our biology is, as our chemistry and molecule making is, as our clinical work is. This equal footing is essential and extremely important. The second important point is you really have to think about how you do your project. For example, when we acquired Prescient, at the time they were three people, tiny, tiny company became our machine learning for drug discovery. It's not tiny anymore, but when we acquired them, we also invested in our antibody engineering so that we could do antibody engineering in a lab in the loop, which is not how we did it before, which meant we invested in our experiments in a different way. We built a department for cell and tissue genomics so we can conduct biology experiments also in a different way.
(30:46):
So we changed our experiments, not just our computation. The third point that I think is really material, I often say that when I'm getting asked, everyone should feel very comfortable talking with an accent. We don't expect our computational scientists to start behaving like they were actually biology trained in a typical way all along, or chemists trained in a typical way all along and by the same token, we don't actually expect our biologists to just embrace wholeheartedly and relinquish completely one way of thinking for another way of thinking, not at all. To the contrary, we actually think all these accents, that's a huge strength because the computer scientist thinks about biology or about chemistry or about medical work differently than a medical doctor or a chemist or a biologist would because a biologist thinks about a model differently and sometimes that is the moment of brilliance that defines the problem and the model in the most impactful way.
(31:48):
We want all of that and that requires both this equal footing and this willingness to think beyond your domain, not just hand over things, but actually also be there in this other area where you're not the expert but you're weird. Talking with an accent can actually be super beneficial. Plus it's a lot of fun. We're all scientists, we all love learning new things. So that's some of the features of how we try to build that world and you kind of do it in the same way. You iterate, you try it out, you see how it works, and you change things. It's not all fixed and set in stone because no one actually wrote a recipe, or at least I didn't find that cookbook yet. You kind of invent it as you go on.
Eric Topol (32:28):
That's terrific. Well, there's so much excitement in this convergence of life science and the digital biology we've been talking about, have I missed anything? We covered human cell atlas, the spatial omics, the lab in the loop. Is there anything that I didn't touch on that you find important?
Aviv Regev (32:49):
There's something we didn't mention and is the reason I come to work every day and everyone I work with here, and I actually think also the people of the human cell atlas, we didn't really talk about the patients.
(33:00):
There's so much, I think you and I share this perspective, there's so much trepidation around some of these new methods and we understand why and also we all saw that technology sometimes can play out in ways that are really with unintended consequences, but there's also so much hope for patients. This is what drives people to do this work every day, this really difficult work that tends not to work out much more frequently than it works out now that we're trying to move that needle in a substantial way. It's the patients, and that gives this human side to all of it. I think it's really important to remember. It also makes us very responsible. We look at things very responsibly when we do this work, but it also gives us this feeling in our hearts that is really unbeatable, that you're doing it for something good.
Eric Topol (33:52):
I think that emphasis couldn't be more appropriate. One of the things I think about all the time is that because we're moving into this, if you will, hyper accelerated phase of discovery over the years ahead with this just unparallel convergence of tools to work with, that somebody could be cured of a condition, somebody could have an autoimmune disease that we will be able to promote tolerogenicity and they wouldn't have the autoimmune disease and if they could just sit tight and wait a few years before this comes, as opposed to just missing out because it takes time to get this all to gel. So I'm glad you brought that up, Aviv, because I do think that's what it's all about and that's why we're cheering for your work and so many others to get it done, get across the goal line because there's these 10,000 diseases out there and there's so many unmet needs across them where we don't have treatments that are very effective or have all sorts of horrible side effects. We don't have cures, and we've got all the things now, as we've mentioned here in this conversation, whether it's genome editing and ability to process massive scale data in a way that never could be conceived some years ago. Let's hope that we help the patients, and go ahead.
Aviv Regev (35:25):
I found the Proust quote, if you want it recorded correctly.
Eric Topol (35:29):
Yeah, good.
Aviv Regev (35:30):
It's much longer than what I did. It says, “the only true voyage, the only bath in the Fountain of Youth would be not to visit strange lands but to possess other eyes, to see the universe through the eyes of another, of a hundred others, to see the hundred universes that each of them sees, that each of them is; and this we do, with great artists; with artists like these we do fly from star to star.”—Marcel Proust
Eric Topol (35:57):
I love that and what a wonderful way to close our conversation today. Aviv, I look forward to more conversations with you. You are an unbelievable gem. Thanks so much for joining today.
Aviv Regev (36:10):
Thank you so much.
*************************************
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Professor Doudna was awarded the 2020 Nobel Prize in Chemistry with Professor Emmanuelle Charpentier for their pioneering work in CRISPR genome editing. The first genome editing therapy (Casgevy) was just FDA approved, only a decade after the CRISPR-Cas9 editing system discovery. But It’s just the beginning of a much bigger impact story for medicine and life science.
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Transcript with links to audio and relevant external links
Eric Topol (00:06):
This is Eric Topol with Ground Truths, and I'm really excited today to have with me Professor Jennifer Doudna, who heads up the Innovative Genomics Institute (IGI) at UC Berkeley, along with other academic appointments, and as everybody knows, was the Nobel laureate for her extraordinary discovery efforts with CRISPR genome editing. So welcome, Jennifer.
Jennifer Doudna (00:31):
Hello, Eric. Great to be here.
Eric Topol (00:34):
Well, you know we hadn't met before, but I felt like I know you so well because this is one of my favorite books, The Code Breaker. And Walter Isaacson did such a wonderful job to tell your story. What did you think of the book?
My interview with Walter Isaacson on The Code Breaker, a book I highly recommend
Jennifer Doudna (00:48):
I thought Walter did a great job. He's a good storyteller, and as you know from probably from reading it or maybe talking to others about it, he wrote a page turner. He actually really dug into the science and all the different aspects of it that I think created a great tale.
Eric Topol (01:07):
Yeah, I recommended highly. It was my favorite book when it came out a couple years ago, and it is a page turner. In fact, I just want to read one, there's so many quotes out of it, but in the early part of the book, he says, “the invention of CRISPR and the plague of Covid will hasten our transition to the third great revolution of modern times. These revolutions arose from the discovery beginning just over a century ago, of the three fundamental kernels of our existence, the atom, the bit, and the gene.” That kind of tells a big story just in one sentence, but I thought I’d start with the IGI, the institute that you have set up at Berkeley and what its overall goals are.
Jennifer Doudna (01:58):
Right. Well, let's just go back a few years maybe to the origins of this institute and my thinking around it, because in the early days of CRISPR, it was clear that we were really at a moment that was quite unique in the sense that there was a transformative technology. It was going to intersect with lots of other discoveries and technologies. And I work at a public institution and my question to myself was, how can I make sure that this powerful tool is first of all used responsibly and secondly, that it's used in a way that benefits as many people as possible, and it's a tall order, but clearly we needed to have some kind of a structure that would allow people to work together towards those goals. And that was really the mission behind the IGI, which was started as a partnership between UC Berkeley and UCSF and now actually includes UC Davis as well.
The First FDA Approved Genome Editing
Eric Topol (02:57):
I didn't realize that. That's terrific. Well, this is a pretty big time because 10 years or so, I guess starting to be 11 when you got this thing going, now we're starting to see, well, hundreds of patients have been treated and in December the FDA approved the first CRISPR therapy for sickle cell disease, Casgevy. Is that the way you say it?
Jennifer Doudna (03:23):
Casgevy, yeah.
Eric Topol (03:24):
That must have felt pretty good to see if you go from the molecules to the bench all the way now to actually treating diseases and getting approval, which is no easy task.
Jennifer Doudna (03:39):
Well, Eric, for me, I'm a biochemist and somebody who has always worked on the fundamentals of biology, and so it's really been extraordinary to see the pace at which the CRISPR technology has been adopted, and not just for fundamental research, but also for real applications. And Casgevy is sort of the crowning example of that so far, is that it's really a technology that we can already see how it's being used to, I think it's fair to say, effectively cure a genetic disease for the first time. Really amazing.
Genome Editing is Not the Same as Gene Therapy
Eric Topol (04:17):
Yeah. Now I want to get back to that. I know there's going to be refinements about that. And of course, there's beta thalassemia, so we've got two already, and our mutual friend Fyodor Urnov would say two down 5,000 to go. But I think before I get to the actual repair of the sickle cell defect molecular defect, I think one of the questions I think that people listeners may not know is the differentiation of genome editing with gene therapy. I mean, as you know, there was recently a gene therapy approval for something like $4.25 million for metachromatic leukodystrophy. So maybe you could give us kind of skinny on how these two fundamental therapies are different.
Jennifer Doudna (05:07):
Right. Well, it's a great question because the terminology sounds kind of the same, and so it could be confusing. Gene therapy goes back decades, I can remember gene therapy being discussed as an exciting new at the time, direction back when I was a graduate student. That was little while ago. And it refers to the idea that we can use a genetic approach for disease treatment or even for a cure. However, it fundamentally requires some mechanism of integrating new information into a genome. And traditionally that's been done using viruses, which are great at doing that. It's just that they do it wherever they want to do it, not necessarily where we want that information to go. And this is where CRISPR comes in. It's a technology allows precision in that kind of genetic manipulation. So it allows the scientist or the clinician to decide where to make a genetic change. And that gives us tremendous opportunity to do things with a kind of accuracy that hasn't been possible before.
Eric Topol (06:12):
Yeah, no question. That's just a footnote. My thesis in college at University of Virginia, 1975, I'm an old dog, was prospects for gene therapy in man. So it took a while, didn't it? But it's a lot better now with what you've been working on, you and your colleagues now and for the last decade for sure. Now, what I was really surprised about is it's not just of course, these hemoglobin disorders, but now already in phase two trials, you've got hereditary angioedema, which is a life-threatening condition, amyloidosis, cancer ex vivo, and also chronic urinary tract infections. And of course, there's six more others like autoimmune diseases like lupus and type 1 diabetes. So this is really blossoming. It's really extraordinary.
Eric Topol (07:11):
I mean, wow. So one of the questions I had about phages, because this is kind of going back to this original work and discovery, antimicrobial resistance is really a big problem and it's a global health crisis, and there's only two routes there coming up with new drugs, which has been slow and not really supported by the life science industry. And the other promising area is with phages. And I wonder, since this is an area you know so well, why haven't we put more, we're starting to see more trials in phages. Why haven't we doubled down or tripled down on this to help the antimicrobial resistance problem?
Jennifer Doudna (08:00):
Well, it's a really interesting area, and as you said, it's kind of one of those areas of science where I think there was interest a while ago and some effort was made for reasons that are not entirely clear to me, at least it fizzled out as a real focused field for a long time. But then more recently, people have realized that there's an opportunity here to take advantage of some natural biology in which viruses can infect and destroy microbes. Why aren't we taking better advantage of that for our own health purposes? So I personally am very excited about this area. I think there's a lot of fundamental work still to be done, but I think there's a tremendous opportunity there as well.
CRISPR 2.0
Eric Topol (08:48):
Yeah, I sure think we need to invest in that. Now, getting back to this sickle cell story, which is so extraordinary. This is kind of a workaround plan of getting fetal hemoglobin built up, but what about actually repairing, getting to fixing the lesion, if you will?
Eric Topol (09:11):
Yeah. Is that needed?
Jennifer Doudna (09:13):
Well, maybe it's worth saying a little bit about how Casgevy works, and you alluded to this. It's not a direct cure. It's a mechanism that allows activation of a second protein called fetal hemoglobin that can suppress the effect of the sickle cell mutation. And it's great, and I think for patients, it offers a really interesting opportunity with their disease that hasn't been available in the past, but at the same time, it's not a true cure. And so the question is could we use a CRISPR type technology to actually make a correction to the genetic defect that directly causes the disease? And I think the answer is yes. The field isn't there quite yet. It's still relatively difficult to control the exact way that DNA editing is occurring, especially if we're doing it in vivo in the body. But boy, many people are working on this, as you probably know. And I really think that's on the horizon.
Eric Topol (10:19):
Yeah. Well, I think we want to get into the in vivo story as well because that, I think right now it's so complicated for a person to have to go through the procedure to get ultimately this treatment currently for sickle cell, whereas if you could do this in vivo and you could actually get the cure, that would be of the objective. Now, you published just earlier this month in PNAS a wonderful paper about the EDVs and the lipid nanoparticles that are ways that we could get to a better precision editing. These EDVs I guess if I have it right, enveloped virus-like particles. It could be different types, it could be extracellular vesicles or whatnot. But do you think that's going to be important? Because right now we're limited for delivery, we're limited to achieve the right kind of editing to do this highly precise. Is that a big step for the future?
Jennifer Doudna (11:27):
Really big. I think that's gating at the moment. Right now, as you mentioned, somebody that might want to get the drug Casgevy for sickle cell disease or thalassemia, they have to go through a bone marrow transplant to get it. And that means that it's very expensive. It's time consuming. It's obviously not pleasant to have to go through that. And so that automatically means that right now that therapy is quite restricted in the patients that it can benefit. But we imagine a day when you could get this type of therapy into the body with a one-time injection. Maybe someday it's a pill that could be taken where the gene editors target the right cells in the body. In diseases like that, it would be the stem cells in the bone marrow and carry out gene editing that can have a therapeutic benefit. And again, it's one of those ideas that sounds like science fiction, and yet already there's tremendous advance in that direction. And I think over the next, I don't know, I'm guessing 5 to 10 years we're going to see that coming online.
Editing RNA, the Epigenome, and the Microbiome
Eric Topol (12:35):
Yeah, I'm guessing just because there's so much work on the lipid nanoparticles to tweak them. And there's four different components that could easily be made so much better. And then all these virus-like proteins, I mean, it may happen even sooner. And it's really exciting. And I love that diagram in that paper. You have basically every organ of the body that isn't accessible now, potentially that would become accessible. And that's exciting because whatever blossoming we're seeing right now with these phase two trials ongoing, then you basically have no limits. And that I think is really important. So in vivo editing big. Now, the other thing that's cropped up in recent times is we've just been focused on DNA, but now there's RNA editing, there's epigenetic or epigenomic editing. What are your thoughts about that?
Jennifer Doudna (13:26):
Very exciting as well. It's kind of a parallel strategy. The idea there would be to, rather than making a permanent change in the DNA of a cell, you could change just the genetic output of the cell and or even make a change to DNA that would alter its ability to be expressed and to produce proteins in the cell. So these are strategies that are accessible, again, using CRISPR tools. And the question is now how to use them in ways that will be therapeutically beneficial. Again, topics that are under very active investigation in both academic labs and at companies.
Eric Topol (14:13):
Yeah. Now speaking of that, this whole idea of rejuvenation, this is Altos. You may I'm sure know my friend here, Juan Carlos Belmonte, who's been pushing on this for some time at Altos now formerly at Salk. And I know you helped advise Altos, but this idea of basically epigenetic, well using the four Yamanaka factors and basically getting cells that go to a state that are rejuvenated and all these animal models that show that it really happens, are you thinking that really could become a therapy in the times ahead in patients for aging or particular ideas that you have of how to use that?
Jennifer Doudna (15:02):
Well, you mentioned the company Altos. I mean, Altos and a number of other groups are actively investigating this. Not I would say specifically regarding genome editing, although being able to monitor and probably change gene functions that might affect the aging process could be attractive in the future. I think the hard question there is which genes do we tweak and how do we make sure that it's safe? And better than me I mean, that's a very difficult thing to study clinically because it takes time for one thing, and we probably don't have the best models either. So I think there are challenges there for sure. But along the way, I feel very excited about the kind of fundamental knowledge that will come from those studies. And in particular, this question of how tissues rejuvenate I think is absolutely fascinating. And some organisms do this better than others. And so, understanding how that works in organisms that are able to say regrow a limb, I think can be very interesting.
Eric Topol (16:10):
And that gets me to that recent study. Well, as you well know, there's a company Verve that's working on the familial hypercholesterolemia and using editing with the PCSK9 through the liver and having some initial, at least a dozen patients have been treated. But then this epigenetic study of editing in mice for PCSK9 also showed results. Of course, that's much further behind actually treating patients with base editing. But it's really intriguing that you can do some of these things without having to go through DNA isn't it?
Jennifer Doudna (16:51):
Amazing, right? Yeah, it's very interesting.
Reducing the Cost of Genome Editing
Eric Topol (16:54):
Wild. Now, one of the things of course that people bring up is, well, this is so darn expensive and it's great. It's a science triumph, but then who can get these treatments? And recently in January, you announced a Danaher-IGI Beacon, and maybe you can tell us a bit about that, because again, here's a chance to really markedly reduce the cost, right?
Jennifer Doudna (17:25):
That's right. That's the vision there. And huge kudos to my colleague Fyodor Urnov, who really spearheaded that effort and leads the team on the IGI side. But the vision there was to partner with a company that has the ability to manufacture molecules in ways that are very, very hard, of course, for academic labs and even for most companies to do. And so the idea was to bring together the best of genome editing technology, the best of clinical medicine, especially focused on rare human diseases. And this is with our partners at UCSF and with the folks in the Danaher team who are experts at downstream issues of manufacturing. And so the hope there is that we can bring those pieces together to create ways of using CRISPR that will be cost effective for patients. And frankly, we'll also create a kind of roadmap for how to do this, how to do this more efficiently. And we're kind of building the plane while we're flying it, if you know what I mean. But we're trying to really work creatively with organizations like the FDA to come up with strategies for clinical trials that will maintain safety, but also speed up the timeline.
Eric Topol (18:44):
And I think it's really exciting. We need that and I'm on the scientific advisory board of Danaher, a new commitment for me. And when Fyodor presented that recently, I said, wow, this is exciting. We haven't really had a path to how to get these therapies down to a much lower cost. Now, another thing that's exciting that you're involved in, which I think crosses the whole genome editing, the two most important things that I've seen in my lifetime are genome editing and AI, and they also work together. So maybe before we get into AI for drug discovery, how does AI come into play when you're thinking about doing genome editing?
Jennifer Doudna (19:34):
Well, the thing about CRISPR is that as a tool, it's powerful not only as a one and done kind of an approach, but it's also very powerful genomically, meaning that you can make large libraries of these guide RNAs that allow interrogation of many genes at once. And so that's great on the one hand, but it's also daunting because it generates large collections of data that are difficult to manually inspect. And in some cases, I believe really very, very difficult to analyze in traditional ways. But imagine that we have ways of training models that can look at genetic intersections, ways that genes might be affecting the behavior of not only other genes, but also how a person responds to drugs, how a person responds to their environment and allows us to make predictions about genetic outcomes based on that information. I think that's extremely exciting, and I definitely think that over the next few years we'll see that kind of analysis coming online more and more.
Eric Topol (20:45):
Yeah, the convergence, I think is going to be, it's already being done now, but it's just going to keep building. Now, Demis Hassabis, who one of the brilliant people in the field of AI leads the whole Google Deep Mind AI efforts now, but he formed after AlphaFold2 behaving to predict proteins, 200 million proteins of the universe. He started a company Isomorphic Labs as a way to accelerate using AI drug discovery. What can you tell us about that?
Jennifer Doudna (21:23):
It's exciting, isn't it? I'm on the SAB for that company, and I think it's very interesting to see their approach to drug discovery. It's different from what I've been familiar with at other companies because they're really taking a computational lens to this challenge. The idea there is can we actually predict things like the way a small molecule might interact with a particular protein or even how it might interact with a large protein complex. And increasingly because of AlphaFold and programs like that, that allow accurate prediction of structures, it's possible to do that kind of work extremely quickly. A lot of it can be done in silico rather than in the laboratory. And when you do get around to doing experiments in the lab, you can get away with many fewer experiments because you know the right ones to do. Now, will this actually accelerate the rate at which we get to approved therapeutics? I wonder about your opinion about that. I remain unsure.
Editing Out Alzheimer’s Risk Alleles
Eric Topol (22:32):
Yeah. I mean, we have one great success story so far during the pandemic Baricitinib, a drug that repurposed here, a drug that was for rheumatoid arthritis, found by data mining that have a high prospects for Covid and now saves lives in Covid. So at least that's one down, but we got a lot more here too. But it, it's great that Demis recruited you on the SAB for Isomorphic because it brings in a great mind in a different field. And it goes back to one of the things you mentioned earlier is how can we get some of this genome editing into a pill someday? Wow. Now, one of the things that for personal interest, as an APOE4 carrier, I'm looking to you to fix my APOE4 and give me APOE2. How can I expect to get that done in the near future?
Jennifer Doudna (23:30):
Oh boy. Okay, we'll have to roll up our sleeves on that one. But it is appealing, isn't it? I think about it too. It's a fascinating idea. Could we get to a point someday where we can use genome editing as a prophylactic, not as a treatment after the fact, but as a way to actually protect ourselves from disease? And the APOE4 example is a really interesting one because there's really good evidence that by changing the type of allele that one has for the APOE gene, you can actually affect a person's likelihood of developing Alzheimer's in later life. But how do we get there? I think one thing to point out is that right now doing genome editing in the brain is, well, it's hard. I mean, it's very hard.
Eric Topol (24:18):
It a little bit's been done in cerebral spinal fluid to show that you can get the APOE2 switch. But I don't know that I want to sign up for an LP to have that done.
Jennifer Doudna (24:30):
Not quite yet.
Eric Topol (24:31):
But someday it's wild. It's totally wild. And that actually gets me back to that program for coronary heart disease and heart attacks, because when you're treating people with familial hypercholesterolemia, this extreme phenotype. Someday and this goes for many of these rare diseases that you and others are working on, it can have much broader applicability if you have a one-off treatment to prevent coronary disease and heart attacks and you might use that for people well beyond those who have an LDL cholesterol that are in the thousands. So that's what I think a lot of people don't realize that this editing potential isn't just for these monogenic and rare diseases. So we just wanted to emphasize that. Well, this has been a kind of wild ride through so much going on in this field. I mean, it is extraordinary. What am I missing that you're excited about?
Jennifer Doudna (25:32):
Well, we didn't talk about the microbiome. I'll just very briefly mention that one of our latest initiatives at the IGI is editing the microbiome. And you probably know there are more and more connections that are being made between our microbiome and all kinds of health and disease states. So we think that being able to manipulate the microbiome precisely is going to open up another whole opportunity to impact our health.
Can Editing Slow the Aging Process?
Eric Topol (26:03):
Yeah, I should have realized that when I only mentioned two layers of biology, there's another one that's active. Extraordinary, just going back to aging for a second today, there was a really interesting paper from Irv Weissman Stanford, who I'm sure you know and colleagues, where they basically depleted the myeloid stem cells in aged mice. And they rejuvenated the immune system. I mean, it really brought it back to life as a young malice. Now, there probably are ways to do that with editing without having to deplete stem cells. And the thought about other ways to approach the aging process now that we're learning so much about science and about the immune system, which is one of the most complex ones to work in. Do you have ideas about that are already out there that we could influence the aging process, especially for those of us who are getting old?
Jennifer Doudna (27:07):
We're all on that path, Eric. Well, I guess the way that I think about it is I like to think that genome editing is going to pave the way to make those kinds of fundamental discoveries. I still feel that there's a lot of our genetics that we don't understand. And so, by being able to manipulate genes precisely and increasingly to look at how genes interact with each other, I think one fundamental question it relates to aging actually is why do some of us age at a seemingly faster pace than others? And it must have to do at least in part with our genetic makeup and how we respond to our environment. So I definitely think there are big opportunities there, really in fundamental research initially, but maybe later to actually change those kinds of things.
Eric Topol (28:03):
Yeah, I'm very impressed in recent times how much the advances are being made at basic science level and experimental models. A lot of promise there. Now, is there anything about this field that you worry about that keeps you up at night that you think, besides, we talked about that we got to get the cost down, we have to bridge health inequities for sure, but is there anything else that you're concerned about right now?
Jennifer Doudna (28:33):
Well, I think anytime a new technology goes into clinical trials, you worry that things may get out ahead of their skis, and there may be some overreach that happens. I think we haven't really seen that so far in the CRISPR field, which is great. But I guess I remain cautious. I think that we all saw what happened in the field of gene therapy now decades ago, but that really put a poll on that field for a long time. And so, I definitely think that we need to continue to be very cautious as gene editing continues to advance.
Eric Topol (29:10):
Yeah, no question. I think the momentum now is getting past that point where you would be concerned about known unknowns, if you will, things that going back to the days of the Gelsinger crisis. But it's really extraordinary. I am so thrilled to have this conversation with you and to get a chance to review where the field is and where it's going. I mean, it's exploding with promise and potential well beyond and faster. I mean, it takes a drug 17 years, and you've already gotten this into two treatments. I mean, I'm struck when you were working on this, how you could have thought that within a 10-year time span you'd already have FDA approvals. It's extraordinary.
Jennifer Doudna (30:09):
Yeah, we hardly dared hope. Of course, we're all thrilled that it went that fast, but I think it would've been hard to imagine it at the time.
Eric Topol (30:17):
Yeah. Well, when that gets simplified and doesn't require hospitalizations and bone marrow, and then you'll know you're off to the races. But look, what a great start. Phenomenal. So congratulations. I'm so thrilled to have the chance to have this conversation. And obviously we're all going to be following your work because what a beacon of science and progress and changing medicine. So thanks and give my best to my friend there at IGI, Fyodor, who's a character. He's a real character. I love the guy, and he's a good friend.
Jennifer Doudna (30:55):
I certainly will Eric, and thank you so much. It's been great talking with you.
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Note: This podcast is a companion to the Ground Truths newsletter “A Big Week for GLP-1 Drugs”
Eric Topol (00:06):
It is Eric Topol with Ground Truths, and with me today is Dr. Daniel Drucker from the University of Toronto, who is one of the leading endocrinologists in the world, and he along with Joel Habener and Jens Juul Holst from the University of Copenhagen and Denmark, have been credited with numerous prizes of their discovery work of glucagon-like peptide-1 (GLP-1) as we get to know these family of drugs and he's a true pioneer. He's been working on this for decades. So welcome, Daniel.
Daniel Drucker (00:43):
Thank you.
Eric Topol (00:45):
Yeah, it's great to have you and to get the perspective, one of the true pioneers in this field, because to say it's blossom would be an understatement, don't you think?
Daniel Drucker (00:57):
Yeah, it's been a bit of a hectic three years. We had a good quiet 30 plus years of solid science and then it's just exploded over the last few years.
Eric Topol (01:06):
Yeah, back in 30 years ago, did you have any sense that this was coming?
Daniel Drucker (01:14):
Not what we're experiencing today, I think there was a vision for the diabetes story. The first experiments were demonstrating insulin secretion and patents were followed around the use for the treatment of GLP-1 for diabetes. The food intake story was much more gradual and the weight loss story was quite slow. And in fact, as you know, we've had a GLP-1 drug approved for people with obesity since 2014, so it's 10 years since liraglutide was approved, but it didn't really catch the public's attention. The weight loss was good, but it wasn't as spectacular as what we're seeing today. So this really has taken off just over the last three, four years.
Eric Topol (01:58):
Yeah, no, it's actually, I've never seen a drug class like this in my life, Daniel. I mean, I've obviously witnessed the statins, but this one in terms of pleiotropy of having diverse effects, and I want to get to the brain here in just a minute because that seems to be quite a big factor. But one thing just before we get too deep into this, I think you have been great to recognize one of your colleagues who you work with at Harvard, Svetlana Mojsov. And the question I guess is over the years, as you said, there was a real kind of incremental path and I guess was in 1996 when you said, well, this drug likely will inhibit food intake, but then there were gaps of many years since then, as you mentioned about getting into the obesity side. Was that because there wasn't much weight loss in the people with diabetes or was it related to the dose of the drugs that were being tested?
Why Did It Take So Long to Get to Obesity?
Daniel Drucker (03:11):
Well, really both. So the initial doses we tested for type 2 diabetes did not produce a lot of weight loss, maybe 2-3%. And then when we got semaglutide for type 2 diabetes, maybe we were getting 4-5% mean weight loss. And so that was really good and that was much better than we achieved before with any glucose lowering drug. But a lot of credit goes to Novo Nordisk because they looked at the dose for liraglutide and diabetes, which was 1.8 milligrams once daily for people with type 2 diabetes. And they asked a simple question, what if we increase the dose for weight loss? And the answer was, we get better weight loss with 3 milligrams once a day. So they learn that. And when they introduced semaglutide for type 2 diabetes, the doses were 0.5 and 1 milligrams. But in the back of their minds was the same question, what if we increased the dose and they landed on 2.4 milligrams once a week. And that's when we really started to see that the unexpected spectacular weight loss that we're now quite familiar with.
Eric Topol (04:16):
Was there also something too that diabetics don't lose as much weight if you were to have match dose?
Daniel Drucker (04:22):
Yeah, that's a general phenomenon. If one goes from either diet to bariatric surgery, and certainly with weight loss medicines, we tend to see maybe two thirds to three quarters of the amount of weight loss in people with type 2 diabetes. We don't really understand it. The brain pathways are probably resistant to some of the pathways that are activated that lead to weight loss, and it's really an interesting observation that needs further study.
The Brain Effect
Eric Topol (04:50):
Yeah, it's fascinating really. And it might've at least in part, held up this progress that has been truly remarkable. Now, recently you published a paper among many, you're a very prolific scientist, of course, physician scientist, but back in December in Cell Metabolism was a very important paper that explored the brain gut axis, the ability to inhibit inflammation and the mechanism through Toll-like receptors that you were seeing that. So maybe you could summarize the fact that you saw this, you were quoted in this Atlantic piece by Sarah Zhang, the science behind Ozempic was wrong. The weight loss effects of GLP-1 drugs have little to do with the gut and basically claiming that it's related to the effects on the brain, which of course could be reduced inflammation, reduced or inhibiting centers of addiction craving, that sort of thing. So how do you interpret your recent results and ongoing studies regarding GLP-1's effect on the brain?
Daniel Drucker (06:02):
Sure, so to be clear, I don't think that was a quote. I never would've said the science behind Ozempic was wrong. I think that was a headline writer doing what they do best, which is catching people's attention. I think what I was trying to say is that where this field started with insulin secretion first and then weight loss second, those are clearly very important pharmacological attributes of GLP-1. But physiologically, if we take GLP-1 away or we take the receptor away, you don't really develop diabetes without GLP-1. You don't really gain a lot of weight without GLP-1. So physiologically it's not that important. Why do we have GLP-1 in the distal gut? I think physiologically it's there to defend against infection and reduce gut inflammation. But we noticed that GLP-1 reduces inflammation in many different places in the heart and blood vessels and in the liver and many organs where you don't see a lot of GLP-1 receptors and you don't see a lot of GLP-1 receptors on immune cells.
Daniel Drucker (07:04):
So that really led us to the question, well, how does it work and affect all these organs where we don't see a lot of the receptors? And that's where we landed on the brain. Obviously the nervous system can communicate with many different cell types in almost every organ. And we identified neurons that expressed the GLP-1 receptor, which when blocked abrogated or completely eliminated the ability of GLP-1 to reduce inflammation in the periphery in white cells or in lungs. So it's been known for some time that the brain can control the immune system. So this is just the latest piece in the puzzle of how GLP-1 might reduce inflammation.
Eric Topol (07:49):
And just to be clear, I was quoting the Atlantic headline, not you that you were quoted within that article, but this is something that's really interesting because obviously GLP-1 is made in the brain in certain parts of the brain, it's transient in terms of its half-life made from the gut. But when we give these drugs, these agonists, how does it get in the brain? Because isn't there a problem with the blood brain barrier?
Daniel Drucker (08:22):
So I don't think the drugs get into the brain very well. We have a lot of data on this, so people have done the classic experiments, they either make radioactive ligands or fluorescent ligands, and they look how much gets in it and not very much gets in beyond the blood-brain barrier. And we also have big drugs that are immunoglobulin based and they work really well, so they don't get into the brain very much at all. And so, the way I describe this is that GLP-1 talks to the brain, but it doesn't directly get into the brain to meaningful extent, it does communicate somewhat there are areas obviously that are accessible in the area of the stream and circumventricular organs, but most of the time we have this communication that's not well understood that results in the magic that we see. And there are some discussions around for the neurodegenerative disease story where GLP-1 is being looked at in Parkinson's disease and in people with Alzheimer's disease. Would you be able to get more benefit if you could get the drugs into the brain to a greater extent, or would you simply increase the adverse event profile and the adverse response? So really important area for study as we begin to go beyond diabetes and obesity.
Eric Topol (09:41):
Yeah, I mean as you're pointing out, there's two ongoing trials, pretty large trials in Alzheimer's, early Alzheimer's, which may be a little bit too late, but at any rate, testing GLP-1 to see whether or not it could help prevent progression of the disease. And as you also mentioned, diseases and Parkinson's. But I guess, so the magic as you referred to it, the gut -brain axis so that when you give the GLP-1 family of drugs, we'll talk more about the double and triple receptor in a moment, but when you give these drugs, how does the message you get from the gut to the brain would you say?
Daniel Drucker (10:27):
So pharmacologically, we can give someone or an animal the drug, it does reach some of the accessible neurons that have GLP-1 receptors, and they probably transmit signals deeper into the brain and then activate signal transduction. So one way to look at it, if you use c-fos, the protein, which is an immediate early gene, which is increased when we activate neurons, we see rapid activation of c-fos in many regions that are deep within the brain within minutes. And we know that GLP-1 is not getting directly to those neurons, but it's activating pathways that turn on those neurons. And so, there's probably a very intricate set of pathways that sense the GLP-1 and the accessible neurons and then transmit those signals deeper into the brain.
Double and Triple Receptor Agonists
Eric Topol (11:18):
Okay, well that makes sense. Now, as this has been moving along in obesity from semaglutide to tirzepatide and beyond, we're seeing even more potency it appears, and we have now double and triple receptors adding into glucagon itself and the gastric inhibitory polypeptide, and there's mixed data. So for example, the Amgen drug has the opposite effect on GIP as does the dual receptor, but comes out with the same weight loss I guess. How do we understand, I mean you know these gut hormones inside and out, how do we get such disparate results when you're either blocking or revving up a peptide effect?
Daniel Drucker (12:13):
Yeah, it's a mystery. I always sort of joke that you've invited the wrong person because I don't fully understand how to reconcile this honestly. There are some theories you could say that tirzepatide may possibly desensitize the GIP receptor, and that would align with what the GIP receptor blocking component is. And so, I think we need a lot of research, we may actually never know in humans how to reconcile these observations. I think we can do the experiments in animals, we're doing them, other people are doing them to look at the gain and loss of function and use best genetics. But in humans, you'd have to block or activate these receptors in very specific populations for a long period of time with tools that we probably don't have. So we may not reconstruct. We may end up with Maritide from Amgen that's producing 15-20% plus weight loss and tirzepatide from Lilly, that's spectacular, that's producing more than 20% weight loss. And yet as you mentioned at the GIP level, they have opposite effect. So I don't think we fully understand. Maybe your next guest will explain it to you and invite me on. I'd be happy to listen.
Eric Topol (13:27):
Well, I don't know. I don't think anybody can explain it. You've done it as well as I think as possible right now. But then we have the triple receptor, which it seems like if you take that drug, you could just go kind of skeletal. It seems like there's no plateau and its effect, that is I guess is it retatrutide, is that the name of it?
Daniel Drucker (13:47):
Retatrutide, yeah.
Eric Topol (13:48):
Retatrutide, okay. And then of course we're going on with potentially oral drugs or drugs that last for a year. And where do you see all that headed?
Daniel Drucker (14:00):
So I think the way I describe innovation in this field is there are two buckets that we've talked about today. So one bucket is the new molecule, so we're going to have all kinds of different combinations that will be peptides, that will be small molecule orals, the NIH is funding innovative programs to see if we can develop cell-based factories that produce GLP-1. There are gene editing and gene therapy approaches. So there are going to be multiple different molecular approaches to delivering molecules that are better and hopefully easier to take maybe once monthly, maybe every six months. So that's really exciting. And the other obvious bucket is the disease that we're targeting, so we started off with type 2 diabetes. We're now firmly established in the obesity field. In your field, we've seen consistently positive cardiovascular outcome trials. We had a press release a few months ago in October - November saying that semaglutide reduces chronic kidney disease. We have trials underway with peripheral artery disease with Parkinson's disease, with Alzheimer's and a number of neuropsychiatric conditions. So I think we're going to see both innovation on the molecule side as well as expanding if the trials are positive, expanding clinical indication. So it's going to be a pretty exciting next couple of years.
Eric Topol (15:21):
Right, no question. And as you well know, just in the past week, the FDA gave the green light for using these drugs for heart failure with preserved ejection fraction, which was an important randomized trial that showed that. Now there's got to be some downsides of course there's no drug that's perfect. And I wanted to get your comments about muscle loss, potentially bone density reduction. What are the downsides that we should be thinking about with these drugs?
Side Effects
Daniel Drucker (15:54):
Sure, so the known side effects are predominantly gastrointestinal. So we have nausea, diarrhea, constipation and vomiting. And very importantly, if those side effects are severe enough that someone can't eat and drink for 24 hours, we need to tell them you have to seek medical attention because some people will get dehydrated and rarely get acute kidney injury. This is rare, but it's described in many of the outcome trials, and we definitely want to avoid that. Gallbladder events are probably one in several hundred to one in a thousand, and that can be anywhere from gallbladder inflammation to gallbladder stones to biliary obstruction. Don't fully understand that although GLP-1 does reduce gallbladder motility, so that may contribute. And then very rarely we're seeing reports of small bowel obstruction in some people difficult to sort out. We don't really see that in the large clinical trials, but we have to take people at there were, we haven't seen an imbalance in pancreatitis, we haven't seen an imbalance of cancer.
Daniel Drucker (17:01):
There is no evidence for clinically significant bone disease either at the level of reduced bone densities or more importantly at the level of fractures. And we have a lot of real world data that's looked at that. Now muscle losses is really interesting. So when the initial drugs were approved, they didn't produce much weight loss. We didn't think about it. Now that we're getting the 15 20% plus, the question is, will we see clinically significant sarcopenia? And I use the word clinically significant carefully. So we definitely see muscle lean mass loss on a DEXA scan, for example. But what we're not seeing so far are people who are saying, you know what my grip strength is weak. I can't get up off the chair. I have trouble reaching up into the cupboard. My exercise or walking capacity is limited. We’re not seeing that. In fact, we’re seeing the opposite.
Daniel Drucker (17:53):
As you might expect, people are losing weight, they’re less achy, they can move more, they can exercise more. So the question is buried within that data, are there some individuals with real clinical sarcopenia? And as we get to 25% weight loss, it’s very reasonable to expect that maybe we will see some individuals with clinical sarcopenia. So you’re very familiar. There are half a dozen companies developing medicines to promote fat mass loss and spare muscle with or without semaglutide or tirzepatide. And this is a really interesting area to follow, and I don’t know how it’s going to turn out. We really have to see if we are going to see enough clinically significant muscle loss and sarcopenia to merit a new drug category emerge, so fascinating to follow us.
Eric Topol (18:46):
No, I’m so glad you reviewed that because the muscle loss, it could be heterogeneous and there could be some people that really have some substantial sarcopenia. We’ll learn more about that. Now that gets me to what do we do with lifelong therapy here, Daniel, where are we going? Because it seems as though when you stop these drugs, much of the benefit can be not potentially all, but a substantial amount could be lost over time. Is this something that you would view as an insulin and other hormonal treatments or how do you see it?
The Question of Rebound
Daniel Drucker (19:26):
Yeah, so it’s fascinating. I think that traditional view is the one that you just espoused. That is you stop the drug, you regain the weight, and people are concerned about the rebound weight and maybe gaining more fat and having less favorable body composition. But if you look at the data, and it’s coming very fast and furious. A few months ago, we saw data for a tirzepatide trial, one of the surmount obesity trials, the first author was Louis Aronne in New York and they gave people tirzepatide or placebo for 38 weeks. And then they either continue the tirzepatide or stop the tirzepatide. One year later, so no tirzepatide for one year, more than 40% of the people still managed to keep at least 10% of their weight off, which is more than enough in many people to bestow considerable metabolic health. So I think there are going to be people that don't need to take the medicines all the time for weight loss, but we must remember that when we're excited about heart attacks and strokes and chronic kidney disease, there's no evidence that you can stop the medicines and still get the benefits to reduce those chronic complications.
Daniel Drucker (20:46):
So we're going to have to get much more sophisticated in terms of a personalized and precision medicine approach and ask what are the goals? And if the goals are to reduce heart attack strokes and death, you probably need to stay on the medicine if the goals are to achieve weight loss so that you can be metabolically healthy, there may be a lot of people who can come off the medicine for considerable amounts of time. So we're just learning about this. It's very new and it's really exciting.
Suppressing Inflammation as the Common Thread
Eric Topol (21:11):
Yeah, no question. And just going back to the inflammation story in heart disease, it was notable that there were biomarkers of reduced inflammation in the intervention trial before there was any evidence of weight loss. So the anti-inflammatory effects here seem to be quite important, especially with various end organ benefits. Would you say that's true?
Daniel Drucker (21:35):
Yeah, I think that's one of my favorite sort of unifying theories. If we step back for a minute and we come into this and we say, well, here's a drug that improves heart disease and improves liver inflammation and reduces chronic kidney disease and may have some effect on atherosclerosis and is being studied with promising results and neurodegenerative disease, how do we unify all that? And one way is to say all of these chronic disorders are characterized by a component of chronic inflammation. And Eric, it's fascinating. I get reports from random strangers, people who've been on tirzepatide or people who have been on semaglutide, and they tell me, and you'll be fascinated with this, they tell me, my post Covid brain fog is better since I started the drug. They send me pictures of their hands. These are people with chronic arthritis. And they say, my hands have never looked better since I started the drug. And they tell me they've had ulcerative colitis for years on biologics and all of a sudden it's in remission on these drugs. So these are case reports, they're anecdotes, but they're fascinating and quite consistent with the fact that some people may be experiencing an anti-inflammatory effect of these medicines.
Eric Topol (22:55):
And I think it's notable that this is a much more potent anti-inflammatory effect than we saw from statins. I mean, as you know, well they have an effect, but it's not in the same league, I don't think. And also the point you made regarding this is a very good candidate drug class for Long Covid and for a variety of conditions characterized by chronic inflammation. In fact, so many of our chronic diseases fit into that category. Well, this is fascinating, and by the way, I don't know if you know this, but we were both at Johns Hopkins at the same time when you were there in the early eighties. I was there as a cardiology fellow, but we never had a chance to meet back then.
Daniel Drucker (23:41):
So were you just ahead of Cricket Seidman and the whole team there, or what year was that?
Eric Topol (23:46):
Just before them, that's right. You were there doing, was it your internship?
Daniel Drucker (23:50):
I was doing an Osler internship. I think Victor McKusick loved to have a Canadian every year to recognize Osler, one of the great Canadians, and I was just lucky to get the slot that year.
Eric Topol (24:04):
Yeah, it's wild to have watched your efforts, your career and your colleagues and how much of a profound impact. If you were to look back though, and you were to put this into perspective because there were obviously many other hormones along the way, like leptin and so many others that were candidates to achieve what this has. Do you think there's serendipity that play out here or how do you kind of factor it all together?
Daniel Drucker (24:38):
Well, there there's always serendipity. I mean, for decades when people would write review articles on the neuropeptides that were important for control of hunger and satiety and appetite circuits, I would open the article, read it, and I'd say, darn, there's no GLP-1 on the figure. There's no GLP-1 or receptor on the figure, but there's leptin and agouti and the POMC peptides and all the melanocortin and so on and so forth, because physiologically, these systems are not important. As I mentioned, you don't see childhood obesity or genetic forms of obesity in people with loss of function mutations in the GLP-1 sequence or in the GLP-1 receptor. You just don't see a physiologically important effect for having low GLP-1 or having no GLP-1. And that's of course not the case for mutations in NPY or the melanocortin or leptin, et cetera.
Other Effects
Daniel Drucker (25:36):
But pharmacologically, it's been extraordinarily difficult to make drugs out of these other peptides and pathways that we talked about. But fortuitously or serendipitously, as you point out, these drugs seem to work and amazingly GPCRs are notoriously prone to desensitization. We use that in clinical medicine to turn off entire circuits. And thankfully what goes away with GLP-1 are the adverse effects. So nausea, vomiting, diarrhea, constipation, we see those during the first few weeks and then there’s tachyphylaxis, and they generally go away in most people, but what doesn't go away through good fortune are the ability of GLP-1 to talk to those brain circuits and say, you know what? You're not hungry. You don't need to eat. You don't need to think about food. And that's just good luck. Obviously pharmacologically that's benefited all of us working in this area.
Eric Topol (26:31):
It's extraordinary to be able to get desensitized on the adverse effects and not lose the power of the benefit. What about addiction that is, whether it's alcohol, cigarettes, gambling, addictive behavior, do you see that that's ultimately going to be one of the principal uses of these drugs over time?
Daniel Drucker (26:55):
The liver docs, when I give a talk at a metabolic liver disease meeting, they say we love GLP-1 because not only might it take care of liver disease, but there are still some people that we see that are having problems with alcohol use disorders and it might also reduce that. And obviously there are tons of anecdotes that we see. If you go on social media, and you'll see lots of discussion about this, and there's a hundred or so animal paper showing that addiction related dependence behaviors are improved in the context of these medicines. But we don't have the clinical data. So we have a couple of randomized clinical trials, small ones in people with alcohol use disorder, very unimpressive data. We had a trial in people with smoking, didn't really see much, although interestingly, they noted that people drank less alcohol than they did the smoking trial. So there are dozens and dozens of trials underway now, many investigator initiated trials looking at whether it's nicotine or cocaine or cannabinoids or all kinds of compulsive behaviors. I think in the next 12 to 24 months, we're going to start to learn are these real bonafide effects that are seen in large numbers of people or are these just the anecdotes that we won't get a very good complete response. So it's really exciting neuroscience and we're going to learn a lot over the next couple of years.
Eric Topol (28:20):
Yeah, no, it's a fascinating area which just extends the things that we've been discussing. Now, let's say over time, over the years ahead that these drugs become because of the competition and various factors, perhaps in pill form or infrequent dosing, they become very inexpensive, not like they are today.
Daniel Drucker (28:44):
That'd be great speaking as a non-pharmaceutical physician.
Eric Topol (28:48):
Yeah, yeah, no, these companies, which of course as you well know, it accounts for the number one economy in Denmark and is having a big impact in Europe. And obviously Eli Lilly is now the most valued biopharma company in the world from all these effects are coming from this drug class, but let's just say eventually it's not expensive and the drug companies are not gouging and pleasing their investors, and we're in a different world. With all these things that we've been discussing, do you foresee a future where most people will be taking one form or another of this family of drugs to prevent all these chronic conditions that we've just been discussing independent of obesity, type 2 diabetes, the initial frontier? Do you think that's possible?
Daniel Drucker (29:42):
Yeah, I'm a very conservative data-driven person. So today we don't have the data. So if I was in charge of the drinking water supply in your neighborhood and I had unlimited free cheap GLP-1, I wouldn't dump it in there just yet. I don't think we have the data, but we have trials underway, as you noted for Alzheimer's disease, a challenging condition for our society with a huge unmet need if like fingers crossed, if semaglutide does show a benefit for people living with early Alzheimer's disease, if it helps for Parkinson's, if it helps for metabolic liver disease, there are also studies looking at aging, et cetera. So it's possible one day if we have a lot more data that we will begin to think, okay, maybe this is actually a useful medicine that should deserve much more exposure, but today we just don't have the data.
Eric Topol (30:38):
Absolutely. I couldn't agree more, but just wanted to get you kind of speculate on that a bit off script if you will, but what your thoughts were, because this will take a long time, get to that point, but you just kind of wonder when you have an absence of chronic significant side effects overall with these diverse and relatively potent benefits that cut across many organ systems and as you just mentioned, might even influence the aging process, the biologic process.
Worsening Inequities
Daniel Drucker (31:10):
There's another related sort of angle to this, which is that the accessibility of these medicines is very challenging even in well-developed countries, the United States, Europe, et cetera, and we have hundreds of millions of people in the global south and less well-developed economies that are also challenged by heart disease and diabetes and obesity and chronic kidney disease and liver disease. And I think we need to start having conversations and I think they are happening just like we did for HIV and just like we did for hepatitis and certainly we did very quickly for the Covid vaccines. We need to think out of the box and say we need to help people in other parts of the world who may not have access to the medicines in their current form and at their current pricing. And I think these are really important moral and ethical discussions that need to be happening now because soon we will have small molecules and the price will come down and we need to make sure it's not just people in well-developed countries that can afford access to these medicines. I think this is a great opportunity for pharmaceutical companies and the World Health Organization and other foundations to really think broadly about how we can benefit many more people.
Eric Topol (32:29):
I couldn't agree with you more and I'm so glad you emphasize that because we can't wait for these prices to come down and we need creative ways to bridge, to reduce inequities in a vital drug class that's emerged to have far more applicability and benefit than it was initially envisioned, certainly even 5, 10 years ago, no less 30 years ago when you got on it. So Daniel, I can't thank you enough for this discussion. Really a candid discussion reviewing a lot of the things we do know, don't know will know someday perhaps. I just want to note, I know so many people are cheering for you and your colleagues to get recognized further like by the Nobel folks in the years ahead. I think it's pretty darn likely and hopefully when we get a chance to visit again in the years ahead, we'll unravel some of the things that we discussed today that we didn't know the answers and that you as a really an authority and pioneer in the field. Also, I could admit that there's a ways to go to really understand the boundaries if there are boundaries here for how these drugs are going to be used in the years ahead.
Daniel Drucker (33:51):
Yeah, it's another great story for basic science and bench to bedside, and it's just another story where none of us could have predicted the outcomes that we're talking about today to their full extent. And so to the extent that we can convince our governments and our funding agencies to really fund discovery science, the benefits are never apparent immediately. But boy, do they ever come in spades later on in an unpredictable manner. And this is just a great example.
Eric Topol (34:20):
Yeah, I also would say that this work cracking the case of obesity, which has been a stumbling block, I ran a big trial with Rimonabant, which was a failure with the neuropsychiatric side effects and suicidal ideation that had to get dropped. And there's many others like that as you know, very well Fen-Phen, and a long list. And the fact that this could do what it's doing and well beyond just obesity is just spectacular. And what I think it does, what you just mentioned, Daniel, is the basic science work that led to this is I think an exemplar of why we should put in these efforts and not expect immediate benefits, dividends of those efforts. Because look what's happened here. If you can break through with obesity, imagine what lies ahead. So thanks so much for joining and we'll look forward to continuing to follow your work. I know you're publishing the same pace, exceptional prolific pace over many, many years, and I'm sure that's going to continue.
Daniel Drucker (35:34):
Well, I have a great team and so it's a pleasure me to go into work and talk to them every day.
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Siddhartha Mukherjee is a Professor at Columbia University, oncologist, and extraordinary author of Emperor of All Maladies (which was awarded a Pulitzer Prize), The Gene, and The Song of the Cell, along with outstanding pieces in the New Yorker. He is one of the top thought leaders in medicine of our era.
“I have begun to imagine, think about what it would be to be a digital human..”—Sid Mukherjee
Eric Topol (00:06):
Well, hello, this is Eric Topol with Ground Truths, and I am delighted to have my friend Sid Mukherjee, to have a conversation about all sorts of interesting things. Sid, his most recent book, SONG OF THE CELL is extraordinary. And I understand, Sid, you're working on another book that may be cell related. Is that right?
Sid Mukherjee (00:30):
Eric, it's not cell related, I would say, but it's AI and death related, and it covers, broadly speaking, it covers AI, longevity and death and memory —topics that I think are universal, but also particularly medicine.
Eric Topol (00:57):
Well, good, and we'll get into that. I had somehow someone steered me that your next book was going to be something building on the last one, but that sounds even more interesting. You're going in another direction. You've covered cancer gene cells, so I think covering this new topic is of particularly interest. So let's get into the AI story and maybe we'll start off with your views on the healthcare side. Where do you think this is headed now?
A.I. and Drug Discovery
Sid Mukherjee (01:29):
So I think Eric, there are two very broad ways of dividing where AI can enter healthcare, and there may be more, I'm just going to give you two, but there may be more. One is on what I would call the deep science aspect of it, and by that I mean AI-based drug discovery, AI-based antibody discovery, AI-based modeling. All of which use AI tools but are using tools that have to do with machine learning, but may have to do less directly with the kind of large language models. These tools have been in development for a long time. You and I are familiar with them. They are tools. Very simply put, you can imagine that the docking of a drug to a protein, so imagine every drug, every medicine as a small spaceship that docks onto a large spaceship, the large spaceship being the target.
(02:57):
So if you think of it that way, there are fundamental rules. If anyone's watched Star Wars or any of these sci-fi films, there are fundamental rules by which that govern the way that the small spaceship in this case, a molecule like aspirin fits into a pocket of its target, and those are principles that are determined entirely by chemistry and physics, but they can be taught, you can learn what kind of spaceship or molecule is likely to fit into what kind of pocket of the mothership, in this case, the target. And if they can be learned, they're amenable to AI-based discovery.
Eric Topol (03:57):
Right. Well, that's, isn't that what you'd call the fancy term structure-based discovery, where you're using such tools like what AlphaFold2 for proteins and then eventually for antibodies, small molecules, et cetera, that you can really rev up the whole discovery of new molecules, right?
Sid Mukherjee (04:21):
That's correct, and that's one of the efforts that I'm very heavily involved in. We have created proprietary algorithms that allow us to enable this. Ultimately, of course, there has to be a method by which you start from these AI based methods, then move to physical real chemistry, then move to real biology, then move to obviously human biology and ultimately to human studies. It's a long process, but it's an incredibly fruitful process.
Eric Topol (04:57):
Well, yeah, as an example that recently we had Jim Collins on the podcast and he talked about the first new drug class of antibiotics in two decades that bind to staph aureus methicillin resistant, and now in clinical trials. So it’s happening. There’s 20 AI drugs in clinical trials out there.
Sid Mukherjee (05:18):
It’s bound to happen. It is an unstoppable bound to happen systematology of drug discovery. This is just bound to happen. It is unstoppable. There are kinks in it in the road, but those will be ironed out, but it’s bound to happen.
(05:41):
So that’s on the very discovery oriented end, which is more related to learning algorithms that have to do with AI and less to do with what we see in day-to-day life, the ChatGPT kind of day-to-day life of the world. On the very other end of the spectrum, just to move along on the very other end of the spectrum are what I would call patient informatics. So by patient informatics, I mean questions like who responds to a particular drug? What genes do they have? What environment are they in? Have they had other drug interactions in the past? What is it about their medical record that will allow us to understand better why or why they're not responding to a medicine?
(06:51):
Those are also AI, can also be really powered by AI, but are much more dependent and much more sensitive to our understanding of these current models, the large language models. So just to give you an example, let's say you wanted to enroll a clinical trial for patients with diabetes to take a new drug. You could go into the electronic medical record, which right now is a text file, and ask the question, have they or have they not responded to the standard agents? And what has their response been? Should they be on glucose monitoring? How bad is their diabetes based on some laboratory parameters, et cetera, et cetera. So that's a very different information rich, electronic medical record rich mechanism to understand how to develop medicines. One lies, the first lies way in the discovery end of the spectrum. The second lies way in the clinical trials and human drug exposure end of the spectrum. And of course, there are things in the middle that I haven't iterated, but those are the two really broad categories where one can imagine AI making a difference and to be fair through various efforts, I'm working on both of those, the two end spectrum.
A.I. and Cancer
Eric Topol (08:34):
Well, let's drill down a bit more on the person individual informatics for a moment, since you're an oncologist, and the way we screen for cancer today is completely ridiculous by age only. But if you had a person's genome sequence, polygenic risk scores for cancers and all the other known data that, for example, the integrity of their immune system response, environmental exposures, which we'll talk about in a moment more, wouldn't we do far better for being able to identify high risk people and even preventing cancer in the future?
Sid Mukherjee (09:21):
So I have no doubt whatsoever that more information that we can analyze using intelligent platforms. And I'm saying all of these words are relevant, more information analyzed through intelligent platforms. More information by itself is often useless. Intelligent platforms without information by themselves are often useless, but more information with intelligent platforms, that combination can be very useful. And so, one use case of that is just to give you one example, there are several patients, women who have a family history of breast cancer, but who have no mutations in the known single monogenic breast cancer risk genes, BRCA1, BRCA2, and a couple of others. Those patients can be at a high a risk of breast cancer as patients who have BRCA1 and BRCA2. It's just that their risk is spread out through not one gene but thousands of genes. And those patients, of course have to be monitored and their risk is high, and they need to understand what the risk is and how to manage it.
(10:57):
And that's where AI can, and first of all, informatics and then AI can play a big difference because we can understand how to manage those patients. They used to be called, this is kind of, I don't mean this lightly, but they used to be called BRCA3 because they didn't have BRCA1, they didn't have BRCA2, but they had a constellation of genes, not one, not two, but thousands of genes that would increase their risk of breast cancer just a little bit. I often describe these as nudge genes as opposed to shove genes. BRCA1 and BRCA2 are shoved genes. They shove you into having a high risk of breast cancer. But you can imagine that there are nudge genes as well in which they, in which a constellation of not one, not two, not three, but a thousand genetic variations, give a little push each one, a little push towards having a higher risk of breast cancer.
(12:09):
Now, the only way to find these nudge genes is by doing very clever informatic studies, some of which have been done in breast cancer, ovarian cancer, cardiovascular diseases, other diseases where you see these nudge effects, small effects of a single gene, but accumulated across a thousand, 2000, 3000 genes, an effect that's large enough that it's meaningful. And I think that we need to understand those. And once we understand them, I think we need to understand what to do with these patients. Do we screen them more assertively? Do we recommend therapies? You can get more aggressive, less aggressive, but of course that demands clinical trials and a deeper understanding of the biology of what happens.
A.I. And Longevity
Eric Topol (13:10):
Right, so your point about the cumulative effects of small variants, hundreds and hundreds of these variants being equivalent potentially, as we've seen across many diseases, it's really important and you're absolutely right about that. And I've been pushing for trying to get these polygenic risk scores into clinical routine use, and hopefully we're getting closer to that. And that's just as you say, just one layer of this information to add to the intelligence platform. Now, the next thing that you haven't yet touched on connecting the dots is, can AI and informatics be used to promote longevity?
Sid Mukherjee (13:55):
Yeah, so that's a very interesting question. Let me attack that question in two ways. One biological and one digital. The biological one is to understand, again, the biological one has to do with informatics. So we could use AI so that, imagine that there are thousands, perhaps tens of thousands of variables. You happen to live on a Mediterranean island, you happen to walk five miles a day, you happen to have a particular diet, you happen to have a particular genetic makeup, you happen to have a particular immunological makeup, et cetera, et cetera, et cetera. All of those you happen to have, you happen to have, you happen to have. Now, if we could collect all of this data across hundreds of thousands of individuals, we'd need a system to deconvolute the data and ask the question, what is it about these 750,000 individuals that predicted longevity? Was it the fact that they walked five miles a day? Was it their genetic makeup? Was it their diet? Was it their insulin level? Was it their, so you can imagine an n-dimensional diagram, as it were, and to deconvolute that n-dimensional diagram and to figure out what was the driving force of their longevity, you would need much more than conventional information analysis. You need AI.
(15:58):
So that's one direction that one could use. Again, informatics to figure out longevity. A second direction, completely independent of the first is to ask the question, what are the biological determinants of longevity in other animals? Is it insulin levels? Is it chronic? Is it the immune system? Is it the lack of, and we'll come back to this question, is it as you very well know, people with extreme longevity, the so-called supercentenarians. Interestingly, the supercentenarians don't generally die of cancer and heart disease, which are the two most common killers of people in their 70s and 80s in most countries of the western world. They die typically of what I would call regenerative failure. Their immune systems collapse. Their stem cells can't make enough skin, so they get skin infections, their skin collapses, they get bone defects, and they die of fractures. They get neurological defects, they die of neurodegenerative diseases and so forth. So they die of true degenerative diseases as opposed to cancer and heart disease, which have been the plagues of human biology since the beginning of time.
(17:49):
Again, I'm talking about the western world, of course, a different story with infectious diseases elsewhere. So a different way to approach the problem would be to say, what are the regenerative blockades that prevent regeneration at a biological level for these patients? And ask the question whether we can overcome these regenerative blockades using, again, the systems that I described before. What are they? What are the checkpoints? What are the mechanisms? And could we encourage the body to override those mechanisms? We still have to deal with heart disease and cancer, but once we had dealt with heart disease and cancer, we would have to ask the question. Okay, now we've dealt with those two things. What are the regenerative blockades that prevent people from having longevity once we've overcome those two big humps, heart disease and cancer?
Eric Topol (19:00):
Yeah, no, I think you're bringing up a really fascinating topic. And as you know, there's been many different ideas for how to achieve that, whether that's the senolytic drugs or getting rid of dead cells or using the transcription factors of cells instead of going into induced pluripotent stem cells, but rather to go to a rejuvenation of cells. Are you optimistic that eventually we're going to crack this case of better approach to regeneration?
Sid Mukherjee (19:33):
Oh, I'm extremely optimistic. I'm optimistic, but I'm optimistic to a point. And that brings me to the third place, which is I'm optimistic to a point, which is that you conquer in some, hopefully you conquer a major part of heart disease and cancer, and now you're up against cellular regeneration. You then conquer cellular regeneration. And I don't know what the next problem is going to be. It's going to be some new hurdle. So I think there are two solutions to that hurdle. One solution is to say, okay, there's a new hurdle. We'll solve that new hurdle and it's bit by bit extending longevity year by year, by year by year as it were. But a completely second solution occurs to me, and here I'm going completely off script, Eric, which is what I do in my life.
Going Off Script: Being A Digital Human
(20:45):
I have begun to imagine, think about what it would be to be a digital human and by a digital human I mean, it began with my father's death. My father passed away a few years ago, and I would sometimes enter a kind of psychic space, what I would call a psychomanteum, in which I would imagine myself asking him questions about critical moments in my life, make a critical decision. I would rely on my father to make that decision for me. He would give me advice. That advice had some stereotypical qualities about it. Think about this, think about that. My experience has been this. My life has been this. My life has been that. But of course, times change. And I began to wonder whether with the use of digital technologies and digital AI technologies in particular, what could create a simulacrum of a psychomanteum?
(22:06):
So in other words, your physical body would pass, but somehow your digital body, all the memories, the experiences, the learning, all of that, that you had, the emotional connections that you had formed in your lifetime would somehow remain and would remain in a kind of psychomanteum in which you could go into a room. And again, I'm not talking voodoo science here. I'm talking very particular ways of extracting information from a person's decision making, extracting information about a person's ideas about the word their sort of their schema, or as psychologists describe it, the schemata. So that in some universe, if my father downloaded passively or actively the kind of decision making, not the actual decisions, the form of decision making and the form of communication that he liked, that I could go back to him eternally. My grandchildren could go back to him eternally and ask the question, great grandpa, what would you do under these circumstances? And what's amazing about it is that this is not completely science fiction.
Eric Topol (23:45):
Not at all.
Sid Mukherjee (23:46):
It is within the realms of reality in the sense of there's no digital limitation to it. The main limitation to it is information. So Eric Topol, you make decisions I would imagine with some kind of stereotypical wisdom, you have accumulated wisdom in your life. You think about things in a particular critical way. When you read a book, you read a book in a particular way, it's whatever it might be. And Eric Topol psychomanteum would be, I would go into a space and see you and ask you a question, Eric, you read this book, what did you think about it? You found this piece of evidence. Read this scientific paper. What do you think about it? And so forth.
(24:49):
So again, let me just go back to my first point, which is number one, I think that regenerative medicine will have a regenerative moment itself, and we will discover new medicines, new mechanisms by which we can extend lifespan. Number two, that will involve getting over two big humps that we have right now, cancer and heart disease. Hopefully we'll get over both of those at some point of time. And number three, that in parallel, we will find a way to create digital selves that even when our physical bodies decay and die, that we will have a sense of eternal longevity based on digital selves, which is accessible or readily accessible through AI mechanisms. Yeah, this spectrum, I think will change our ideas of what longevity means.
The Environmental Factors
Eric Topol (26:10):
Well, I think your idea about the digital human and the brain and the decision making and that sort of thing is really well founded by the progress being made in the brain machine interface, as you know, with basically the mind is being digitized and you can get cells to talk, to speak to a person, and all sorts of things that are happening right now that are basically deconvoluting brain function at the cellular, even molecular neural level. So I don't think it's farfetched at all. I'm glad you went off script, Sid. That's great. Now this, I want to get back to something you brought up earlier because there are a lot of obstacles as you will acknowledge. And one of them is that we have in our environment horrible issues about pollution, about carcinogens, the focus of your recent New Yorker piece, plastics, microplastics, nanoplastics, now found in our arteries and brains and causing more, as we just recently saw, more heart attack, strokes and death, and of course the climate crisis. So with all this great science that we've just been discussing, our environment's going to hell, and I want to get your comments because you had a very insightful piece as always in the New Yorker in December about this, and I know you've been thinking about it, that the obstacles are getting worse to override the problems that we have today, don't you think?
Sid Mukherjee (27:55):
So you're absolutely right. If we go down this path, we are going to go to hell in eye baskets. What we haven't discounted for is really decades, if not possibly a century of research that shows that there are certain kinds of inflammatory agents that cause both cancer, heart disease, and inflammation that have to do with their capacity to be so foreign to the human body that they're recognized as alien objects and so alien that our immune systems can't handle them. And essentially send off what I would call a five-bell alarm, saying that here's something that the immune system can't handle. It's beyond the capacity. And that five-bell alarm, as we now know, unfortunately, causes a systemic inflammatory response. And that systemic inflammatory response can potentiate heart disease, cancer, and maybe many other diseases that we don't know about because we haven't looked.
Eric Topol (29:28):
Absolutely.
Sid Mukherjee (29:29):
So to connect this back to climate change, pollution is one of them. Air pollution is absolutely one of them. Microplastics, undegradable sort of forever plastics are one of them, or some of them. I think that there is no way around it except to really find a systematic way of assessing them. Look, it is wonderful to have new materials in the world. I'm wearing a jacket made out of God knows what, it's not cloth. I don't know what you are wearing, Eric, but it may not be cloth. These are great materials. This keeps the rain away. But on the other hand, it may be shedding something that I don't know. We need to find scientific ways of assessing the safety and the validity of some new materials that we bring into the world. And the way that we do that is to ask the question, is it inflammatory? Is there something that we are missing? Is there something about it that we should be thinking about that we haven't thought about?
Eric Topol (31:02):
Well, and to that end, you've been a very, I think, astute observer about diet as it relates to cancer. And we know similarly, as we just talked about with our environment, there's the issue of ultra-processed foods, and we've got big food, we got big plastics, big tobacco. I mean, we have all these counter forces to what the science is showing.
Sid Mukherjee (31:29):
Too many bigs.
Eric Topol (31:31):
Yeah, yeah. But I guess the net of it is, Sid, if I get it right, you think that the progress we're making in science, and that includes the things we've talked about and genome editing and accelerated drug discovery, these sorts of programs, the informatics, the AI can override this chasing of our tail with basically unchecked issues that are, whether it's from our nutrition, our air, what we ingest and breathe, these are some serious problems.
Preventing Diseases
Sid Mukherjee (32:06):
No, I don't think that. I don't think that cancer and cardiovascular disease prevention, as you very well know, Eric, because you've been in the forefront of it, is a pyramid. The base of the pyramid is prevention. Prevention is the most effective. It's the most difficult. It's the hardest to understand, the most difficult trials to incorporate, but it is the base of the pyramid. And so let it be said that I don't think that we're going to solve cancer, cardiovascular disease by better treatment using CRISPR. My laboratory, and one of my companies before I happened to be wearing the jacket, but was one of the first to use CRISPR and transplant CRISPR. CRISPR, human beings with or CRISPR bone marrow into human beings long before anyone else, we were actually among the first. These human beings, thankfully, and astonishingly remain completely alive. We deleted a gene from their bone marrow. They engrafted with no problem. They're still alive today, and we are treating them for cancer. Astonishing fact, there are 12 of them in the world.
(33:49):
And again, astonishing fact, wonderful, beautiful news, beautiful science. But there are 12, if we want to make a big change in the universe, we need to get not to 12, but to 12 million, potentially 120 million. And that's not going to happen because we're going to CRISPR their bone marrow. It's going to happen because we change their environments, their diets, their lifestyles, their exposures, we understand their risks, their genetics, et cetera, et cetera, et cetera, et cetera. It's not going to happen because we give them CRISPR bone marrow transplants that enable them to change their risk of cancer. So I'm very clear about this or clear eyed about it, I would say, which is to say that great progress in medicine is being made. There's no doubt about it. I'm happy about it. I'm happy to be part of it. I'm happy to be in the forefront of it.
(35:00):
We have now delivered one of the first cellular therapies for cancer in India at a price point that really challenges the price point of the west. We are now producing this commercially and or about to produce this commercially, so for lymphomas and leukemias, I'm so excited about the progress in science. But all of that said, let me be very clear, the real progress in cancer and cardiovascular disease is going to come from prevention. And if that's where we're going, we need to really rethink at a very fundamental level as you have Eric, at a very fundamental level, how do we rethink prevention, cancer prevention, cardiovascular disease prevention, and as a correlate, regenerative disease, regeneration, cancer prevention, cardiovascular disease prevention. The fundamentals are how do we find things that are in our exposome, things that we're exposed to environments, gene environment interactions that increase the risk of cancer and cardiovascular disease, and how do we take them out? And how do we do this without running 15-year trials so that we can get the results now? And that's what I'm really interested in in terms of information.
Eric Topol (36:55):
Yeah. Well, I'm with you there. And just to go along with those 12 patients you mentioned, as you know recently it was reported there were 15 patients with serious autoimmune diseases, and they got a therapy to knock out all their B cells. And when their B cells came back, they didn't make autoantibodies anymore. And this was dermatomyositis and lupus and systemic sclerosis, and it was pretty magical. If it can be extended, like you said, okay, 15 people, just like your 12, if you can do that in millions, well, you can get rid of autoimmune diseases, which would be a nice contribution. I mean, there's so many exciting things going on right now that we've touched on, but as you get to it, you've already approached this inequity issue by bringing potentially very expensive treatments that are exciting to costs that would be applicable in India and many countries that are not in the rich income category. So this is a unique time it seems like Sid, in our advances, in the cutting edge progress that's being made, wouldn't you say?
The Why on Cancer in the Young
Sid Mukherjee (38:14):
Well, I would say that the two advances have to go hand in hand. There will be patients who are recalcitrant to the standard therapies, your patients with severe lupus dermatitis, et cetera. Those patients will require cutting edge therapy, and we will find ways to deliver it to them. There are other patients, hundreds of not 12, not 15, but hundreds of thousands if not millions, who will require an understanding of why there is an increase, for instance, in asthmatics disease in India. Why is that increasing? Why is there an increase in non-smoking related lung cancer in some parts of the world? Why? What's driving that? Why is there an increase in young patients with cancers in the United States? Of all things that stand out, there is a striking increase in colorectal cancer in young men and women. There's an increase in esophageal cancer in young men and women. Why?
Eric Topol (39:58):
Yeah, why, why?
Sid Mukherjee (40:00):
Why? And so, the answer to that question lies in understanding the science, getting deeper information informatics, and then potentially understanding the why. So again, I draw the distinction between two broad classes of spaces where information science can make a big difference. On one hand, on the very left hand of the picture, an understanding of how to make new medicines for patients who happen to have these diseases. And on the way right hand of finding out why these patients are there in the first place, and asking the question, why is it that there are more patients, young men and women with colorectal cancer, are we eating something? Is it our diet? Is it our diet plus our environment? Is it the diet plus environment plus genetics? But why? There must be a why. When you have a trend like this, there's always a why. And if there's a why, there's always an answer. Why? And we have the best tools, and this is the positive piece of this. The positive piece of this is that we now have among the best tools that we've ever had to answer that why? And that's what makes me optimistic. Not a drug, not a medicine, not a fancy program, but the collective set of tools that we have that allow us to answer the question why? Because that is of course the question that every patient with esophageal and colorectal cancer is asking why.
Eric Topol (42:01):
I'm with you. What you're bringing up is fundamental. We have the tools, but we've noted this increase in colon cancer in the young for several years, and we're not any closer to understanding the why yet, right?
Sid Mukherjee (42:18):
Yes. We're not any closer to understanding the why yet. Part of the answer is that we haven't delved into the why properly enough. These are studies that take time. They have longitude because these are studies that have to do with prevention. They take time, they take patients. So the quick answer to your question is, I don't think we've made the effort and we haven't made the effort, especially with the technological advances that we have today. So imagine for a second that we launched a project in which, again, like the Manhattan project, the Apollo project, we advanced a project which said the colorectal cancer in young project in the United States, we brought the best science minds together and ask the question, go into a room, lock yourself up, and don't come out of the room until you have the answer to figuring out how and then why we have young men and women with colorectal cancer increasing. I would imagine you could nominate, I could nominate 10 people to that committee and they would willingly serve. They'd be willing to be locked up in a room and ask the question why? Because they want to answer that question. That why is extraordinarily important.
Eric Topol (44:14):
I'm with you on that too, because we have the tools, like you said, we can assess the gut microbiome, their genome, their diets, their environmental exposures and figure this out. But as you say, there hasn't been a commitment to doing it.
Sid Mukherjee (44:30):
And that commitment has to come centrally, right? That commitment has to come from the NIH, that has to come from the NCI, the National Cancer Institute, the National Institute of Health. It has to come as a mechanism that says, listen, let's solve this problem. So identifying the problem, there's an increase in colorectal cancer in young people. Important. Yes. Let's, let's figure out the answer why, and let's collect all the information for the next five years, seven years, whatever it might take to answer that question.
Eric Topol (45:18):
And as you said, the intelligent platforms will help analyze it.
Sid Mukherjee (45:23):
Yes. I mean, we have the tools. So if you have the tools and if you collect the information, the tools will analyze that information.
Eric Topol (45:36):
Right. Well, this has been inspiring and daunting at the same time, this discussion. What I love about you, Sid, is you're a big thinker. You're one of the great thinkers in medicine of our era, and you also of course are such an extraordinary writer. So we're going to look forward to your next book and your rejuvenation of the cancer Emperor of All Maladies book but I want to thank you. I always enjoy our discussions. They always get to areas that highlight where we're missing the opportunities that we have that we're not actualizing. That's one of the many things I really love about you and your work, so keep up the good stuff and I look forward to the next chance we get to visit and discuss all this stuff.
Sid Mukherjee (46:31):
And it's been a great pleasure knowing you for so many years, Eric. And then whenever we have dinner together, the dinner always begins with my asking you why. And so, the why question is the first question. The how question is a harder question. We can always answer the how question, but the why question is the first question. So the next time I have dinner with you, wherever it might be, San Diego, New York, Los Angeles, I'm going to ask you another why question. And you're going to answer the how question, because that's what you're good at. And it's been such a pleasure interacting with you for so many years.
Eric Topol (47:12):
Oh, thank you so much. What a great friend.
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Eric Topol (00:00:05):
This is Eric Topol from Ground Truths, and I am delighted to have with me Holden Thorp, who is the Editor-in-Chief of the Science journals. We're going to talk about Science, not just the magazine journal, but also science in general. This is especially appropriate today because Holden was just recognized by STAT as one of the leaders for 2024 because of his extraordinary efforts to promote science integrity, so welcome Holden.
Holden Thorp (00:00:36):
Thanks Eric, and if I remember correctly, you were recognized by STAT in 2022, so it's an honor to join a group that you're in anytime, that's for sure, and great to be on here with you.
Eric Topol (00:00:47):
Well, that's really kind to you. Let's start off, I think with the journal, because I know that consumes a lot of your efforts and you have five journals within science.
Holden Thorp (00:01:02):
Oh, we have six.
Eric Topol (00:01:03):
Oh six, I'm sorry, six. There's Science, the original, and then five others. Can you tell us what it's like to oversee all these journals?
Overseeing the Science Journals
Holden Thorp (00:01:16):
Yeah, we're a relatively small family compared to our commercial competitors. I know you had Magdalena [Skipper]on and Nature has I think almost ninety journals, so six is pretty small. In addition to Science, which most people are familiar with, we have Science Advances, which also covers all areas of science and is larger and is a gold open access journal and also is overseen by academic editors, not professional editors. All of our other journals are overseen by professional editors. And then the other four are relatively small and specialized areas, and probably people who listen to you and follow you would know about Science Translational Medicine, Science Immunology, Science Signaling and then we also have a journal, Science Robotics which is something I knew nothing about and I learned a lot. I've learned a lot about robotics and the culture of people who work there interacting with them.
Holden Thorp (00:02:22):
So we have a relatively small family. There's only 160 people who work for me, which is manageable. I mean that sounds like a lot, but in my previous jobs I was a provost and a chancellor, and I had tens of thousands of people, so it's really fun for me to have a group where I at least have met everybody who works for me. We're an outstanding set of journals, so we attract an outstanding group of professionals who do all the things that are involved in all this, and it's really, really fun to work with them. At Science, we don't just do research papers, although that's a big, and probably for your listeners the biggest part of what we do. But we also have a news and commentary section and the news section is 30 full-time and many freelancers around the world really running the biggest general news operation for science that there is. And then in the commentary section, which you're a regular contributor for us in expert voices, we attempt to be the best place in the world for scientists to talk to each other. All three of those missions are just really, really fun for me. It's the best job I've ever had, and it's one I hope to do for many years into the future.
Eric Topol (00:03:55):
Well, it's extraordinary because in the four and a half years I think it's been since you took the helm, you've changed the face of Science in many ways. Of course, I think the other distinction from the Nature Journals is that it's a nonprofit entity, which shows it isn't like you're trying to proliferate to all sorts of added journals, but in addition, what you've done, at least the science advisor and the science news and all these things that come out on a daily basis is quite extraordinary as we saw throughout the pandemic. I mean, just reporting that was unparalleled from, as you say, all points around the world about really critically relevant topics. Obviously it extends well beyond the concerns of the pandemic. It has a lot of different functions, but what I think you have done two major things, Holden. One is you medicalized it to some extent.
Eric Topol (00:04:55):
A lot of people saw the journal, particularly Science per se, as a truly basic science journal. Not so much applied in a medical sphere, but these days there's more and more that would be particularly relevant to the practice of medicine, so that's one thing. And the other thing I wanted you to comment on is you're not afraid to speak out and as opposed to many other prior editors who I followed throughout my career at Science, there were pretty much the politically correct type and they weren't going to really express themselves, which you are particularly not afraid of. Maybe you could comment about if you do perceive this medicalization of science to some extent, and also your sense of being able to express yourself freely.
Capturing the Breakthroughs in Structural Biology
Holden Thorp (00:05:48):
Yeah, well, you're kind to say both of those things are certainly things we have worked at. I mean, I do come from a background, even though I'm trained as a chemist, most of what I did towards the latter end of my career, I mean, I did very basic biochemistry when I was a researcher, but the last part of my research career I worked in on development of a drug called Vivjoa, which is an alternative to the fluconazole family that doesn't have the same toxicity and is currently on the market for chronic yeast infection and hopefully some other things in the future when we can get some more clinical trials done.
Holden Thorp (00:06:35):
And I've hung around biotech startups and drug development, so it is part of the business that I knew. I think the pandemic really gave us an opening because Valda Vinson, who's now the Executive Editor and runs all of life sciences for us and policies for the journal, she was so well known in structural biology that most of the first important structures in Covid, including the spike protein, all came to us. I mean, I remember crystal clear February of 2020, she came in my office and she said, I got the structure of the spike protein. And I said, great, what's the spike protein? Turned out later became the most famous protein in the world, at least temporarily. Insulin may be back to being the most famous protein now, but spike protein was up there. And then that kind of cascaded into all the main protease and many of the structures that we got.
Holden Thorp (00:07:45):
And we seized on that for sure, to kind of broaden our focus. We had the Regeneron antibodies, we had the Paxlovid paper, and all of that kind of opened doors for us. And we've also, now we have two clinical editors at Science, Priscilla Kelly and Yevgeniya Nusinovich, and then the Insights section, somebody that you work with closely, Gemma Alderton, she is very fluent in clinical matters. And then of course we've had Science Translational Medicine and we seek continue to strengthen that. Science Immunology was very much boosted by Covid and actually Science Immunology is now, I think probably if you care about impact factors, the second highest specialized immunology journal after Immunity. I've put some emphasis on it for sure, but I think the pandemic also really helped us. As far as me speaking out, a lot of people maybe don't remember, but Don Kennedy, who was the editor in the early 2000s who had been the Stanford president, he was similarly outspoken.
Confronting Controversies
Holden Thorp (00:09:15):
It's funny, sometimes people who disagree with me say, well, Don Kennedy would never say anything like that. And then I can dig up something that Don Kennedy said that's just as aggressive as what I might've said. But you're right, Bruce Alberts was very focused on education, and each one of us has had our own different way of doing things. When Alan Leshner hired me and Sudip Parikh reinforced this when he came on, I mean, he wanted me to liven up the editorial page. He explicitly told me to do that. I may have done more of it than he was expecting, but Alan and Sudip both still remain very supportive of that. I couldn't do what I do without them and also couldn't do it without Lisa Chong, who makes all my words sound so much better than they are when I start. And yeah, it kind of fed on itself.
Holden Thorp (00:10:21):
It started with the pandemic. I think there was an inflection when Trump first said that Covid was just the flu, and when he said some really ridiculous things about the vaccine, and that's where it started. I guess my philosophy was I was thinking about people who, they've got a spouse at home whose job might be disrupted. They got children they've got who are out of school, and somehow they managed to get themselves to the lab to work on our vaccine or some other aspect of the pandemic to try to help the world. What would those people want their journal to say when they came home and turned the news on and saw all these politicians saying all this ridiculous stuff? That was really the sort of mantra that I had in my head, and that kind of drove it. And now I think we've sort of established the fact that it's okay to comment on things that are going on in the world. We're editorially independent, Sudip and the AAAS board, treat us as being editorially independent. I don't take that for granted and it's a privilege to, as I sometimes tell people, my apartment's four blocks from the White House, sometimes I'm over there typing things that they don't like. And that tradition is still alive in this country, at least for the time being, and I try to make the most of it.
Eric Topol (00:12:11):
Well, and especially as you already touched on Holden, when there's a time when the intersection of politics and science really came to a head and still we're dealing with that, and that's why it's been so essential to get your views as the leader of such an important journal that is publishing some of the leading science in the world on a weekly basis. Now, one of the things I do want to get into this other track that you also alluded to. You went from a chemist, and you eventually rose to Dean and chancellor of University of North Carolina (UNC) and also the provost of Washington University, two of our best institutions academically in the country. I would imagine your parents who were both UNC grads would've been especially proud of you being the chancellor.
Holden Thorp (00:13:05):
It's true. Yeah. Unfortunately, my father wasn't there to see it, but my mother, as I always tell people, my mother very much enjoyed being the queen mother of her alma mater.
On Stanford University’s President Resignation
Eric Topol (00:13:16):
Yeah, I would think so, oh my goodness. That gives you another perspective that's unique having been in the senior management of two really prestigious institutions, and this past year a lot has been going on in higher education, and you have again come to the fore about that. Let's just first discuss the Stanford debacle, the president there. Could you kind of give us synopsis, you did some really important writing about that, and what are your thoughts looking back on the student who happens to be Peter Baker's and Susan's son, two incredible journalists at the New York Times and New Yorker, who broke the story at the Stanford Daily as a student, and then it led to eventually the President's resignation. So, what were your thoughts about that?
Holden Thorp (00:14:16):
Yeah, so it's a complicated and sad story in some ways, but it's also fascinating and very instructive. Two of the papers were in Science, two of the three main ones, the other one was in Cell. And we had made an error along the way because Marc had sent a correction in which for some reason never got posted. We searched every email server we had everything we had trying to find exactly what happened, but we think we have a website run by humans and there was something that happened when the corrections were transmitted into our operations group, and they didn't end up on the website. So, one of the things I had to do was to say repeatedly to every reporter who wanted to ask me, including some Pulitzer Prize winners, that we had looked everywhere and couldn't find any reason why somebody would've intentionally stopped those corrections from posting.
Holden Thorp (00:15:36):
And one thing about it was I didn't want, Marc had enough problems, he didn't need to be blamed for the fact that we botched that. So I think people were maybe impressed that we just came out and admitted we made a mistake, but that's really what this area needs. And those things happened before I became the editor in chief, but I was satisfied that where that error happened was done by people who had no idea who Marc Tessier-Lavigne even was, but because of all that, and because we had to decide what to do with these papers, I talked to him extensively at the beginning of this, maybe as much as anybody, now that I look back on it. And I think that for him, the error that happened is very common one. You have a PI with a big lab.
Holden Thorp (00:16:33):
There are many, many incentives for his coworkers and yours to want to get high profile publications. And what we see is mostly at the end when you kind of know what's happening, some corners get cut doing all the controls and all of the last things that have to be done to go into the paper. And someone in his lab did that, and he didn't notice when the jails were sent in. The committee that investigated it later found something that I was certain at the beginning was going to be true, which is he didn't have any direct involvement in and making the problematic images or know that they were there. Every time we see one of these, that's almost always the story.
Holden Thorp (00:17:32):
And if he hadn't been the president of Stanford, he probably would've, I mean, a couple of the papers that were attracted might even could have been just big corrections. That's another topic we can talk about in terms of whether that's the right thing to do but because he was the president of Stanford, it triggered all these things at the university, which made the story much, much more complicated. And it is similar to what we see in a lot of these, that it's the institution that does the most to make these things bigger than they need to be. And in this case, the first thing was that young Theo Baker who I've talked on the phone extensively with, and I just had a long lunch with him in Palo Alto a couple weeks ago, it's the first time we ever met in person. He's finishing up his book, which has been optioned for a movie, and I've told him that I want Mark Hamill to play me in the movie because I don't know if you saw this last thing he did, Fall of the House of Usher but he was a very funny curmudgeonly.
Holden Thorp (00:18:46):
And so, I think he would be a lot like me dealing with Theo, but Theo did great work. Did everything that Theo write add up precisely. I mean, he was teaching himself a lot of this biochemistry as he went along, so you could always find little holes in it, but the general strokes of what he had were correct. And in my opinion, and Marc would've been better served by talking to Theo and answering his questions or talking to other reporters who are covering this and there are many excellent ones. This is something I learned the hard way when I was at North Carolina. It's always better for the President to just face the music and answer the questions instead of doing what they did, which is stand up this long and complicated investigation. And when the institutions do these long investigations, the outcome is always unsatisfying for everybody because the investigation, it found precisely what I think anybody who understands our world would've expected that Marc didn't know about the fraud directly, but that he could have done more to create a culture in his laboratory where these things were picked up, whether that's making his lab smaller or him having fewer other things to do, or precisely what it is, people could speculate.
Managing a Crisis at a University
Holden Thorp (00:20:37):
But of course, that's what always happens in these. So the report produced exactly what any reporter who's covered this their whole lives would've expected it to produce, but the people who don't know the intimate details of how this works, were not satisfied by that. And he ended up having to step down and we'll never know what would've happened if instead of doing all of that, he just said, wow, I really screwed this up. I'm responsible for the fact that these images are in here and I'm going to do everything I can to straighten it out. I'd be happy to take your questions. That's always what I encourage people to do because I was in a similar situation at North Carolina with a scandal involved in athletics and an academic department, and we did umpteen investigations instead of me just saying, hey, everybody, we cheated for 30 years. It started when I was in middle school, but I'm still going to try to clean it up and I'll be happy to answer your questions. And instead, we get lawyers and PR people and all these carefully worded statements, and it's all prolonged. And we see that in every research integrity matter we deal with and there are a lot of other things in higher education that are being weighed down by all of that right now.
Eric Topol (00:22:06):
Yeah. One of the things that is typical when a university faces a crisis, and we're going to get into a couple others in a moment, is that they get a PR firm, and the PR firm says, just say you're going to do an investigation because that'll just pull it out of the news, take it out of the news. It doesn't work that way. And what's amazing is that the universities pay a lot of money to these PR companies for crisis management. And being forthright may indeed be the answer, but that doesn't happen as best as we can see. I think you're suggesting a new path that might be not just relevant, but the way to get this on the right course quickly.
Holden Thorp (00:22:58):
Just on that, there's a person in that PR space who I really like. There are a few of them that are really good, and he's the person who helped me the most. And he used to refer doing the investigation as putting it on the credit card.
Eric Topol (00:23:16):
Yeah. Yeah, exactly.
Holden Thorp (00:23:17):
Okay, because you still have to pay the credit card bill after you charge something.
Eric Topol (00:23:25):
Yeah, better to write a check.
Holden Thorp (00:23:27):
It's better to write a check. Yes, because that 18% interest can add up pretty quickly.
Resignations of the Presidents at Harvard and Penn
Eric Topol (00:23:32):
I like that metaphor entirely appropriate. That's a good one. Now, in the midst of all this, there's been two other leading institutions besides Stanford where the president resigned for different reasons, at least in part one was at Harvard and one at Penn. And this is just a crisis in our top universities in the country. I mean three of the very top universities. So, could you comment about the differences at Harvard and Penn related to what we just discussed at Stanford?
Holden Thorp (00:24:09):
Yeah, so I don't know Claudine Gay, but I've exchanged emails with her, and I do know Liz Magill and I know Sally Kornbluth even better. Our kids went to middle school together because she was at Duke. And I think Sally is in good shape, and she did a little bit better in the hearings because I think she was a little more forthcoming than Liz and Dr. Gay were but I think also Liz was in a pretty weakened state already when she went in there. And I think that what happened that day, and it was a devastating day for higher education. I cleared my calendar, and I watched the whole thing and I couldn't sleep that night. And it was, I thought, oh my goodness, my way of making a living has just taken a death blow. I just felt so much compassion for the three of them, two of whom I knew, one of whom I could imagine having been through similar things myself.
Holden Thorp (00:25:20):
And I think what my take on the whole thing about free speech and the war and all this stuff is that higher education has got a problem, which is that we have promised to deliver a product that we can't really deliver, and that is to provide individualized experiences for students. So, I'm back on the faculty now at GW. I have 16 people in my class, I know every single one of them. I was teaching during the fall, last fall. I teach on Monday nights, which Yom Kippur was on a Monday night, which was before October 7th. And so, I knew precisely how many Jewish kids I had in my class because they had to make up class for that Monday night.
Holden Thorp (00:26:18):
I was basically able to talk to each one of them and make sure. And then GW is a very liberal university, so I had a whole bunch that were all the way on the other side also. I was just able to talk to each of them and make sure they had what they needed from the university. But the institutions don't really have luxury. They don't have somebody who's been doing this for 35 years teaching 16 people who can make sure they're getting what they need, but they write letters to all their students saying, you're going to join a diverse student body where we're going to give you a chance to express yourself and explore everything, but there's too many of them to actually deliver that. And none of them want to say that out loud. And so, what happens in a situation like this?
Holden Thorp (00:27:19):
And everybody says, well, don't send out the statements, don't send out the statements, but how else are you going to communicate with all those people? I mean, because the truth is education is a hands-on individualized deal. And so, the students who are experiencing antisemitism at Harvard or Penn or anywhere else, were feeling distress. And the university wasn't doing what they promised and attending to that, and similarly to the students who wanted to express themselves in the other direction. And so, what really needs to happen is that universities need to put more emphasis on what goes on in the classroom so that these students are getting the attention that they've been promised. But universities are trying to do a lot of research and you're at a place that's got a little simpler mission but some of these big complicated ones are doing urban development and they're trying to win athletics competitions, and they're running hotels and fire departments and police departments, and it's really hard to do all and multi, multi-billion dollar investment vehicles.
Holden Thorp (00:28:47):
It's really hard to do all that and keep the welfare of a bunch of teenagers up at the top of the list. And so, I think really what we need around this topic in general is a reckoning about this very point. Now as far as how to gotten through the hearing a little better, I mean what they said was technically correct, no question about that. But where they struggled was in saying things that would cause them to admit that they had failed at doing what they promised for the people who are feeling distressed. And again, that's kind of my mantra on all these things, whether it's student affairs or research integrity or anything else, the universities have made massive commitments to do probably more things than they can, and rather than fessing up to that, they just bury the whole thing in legalistic bureaucracy, and it's time for us to cut through a lot of that stuff.
Eric Topol (00:30:09):
I couldn't agree more on that.
Holden Thorp (00:30:10):
And in Claudine's case, I think the plagiarism thing, I wrote a piece in the Chronicle that just kind of tried to remind people that the kinds of plagiarism that she was punished for, in my opinion, too much of a punishment is stuff that we routinely pick up now with authenticate and other tools in scholarly publishing, and people just get a report that says, hey, maybe you want to reward this, and that's it. If it doesn't change the academic content of the paper, we hardly ever even pay attention to that. She was being subjected to a modern tool that didn't exist when she wrote the stuff that she wrote. And it's same thing with image analysis, right? When Marc Tessier-Lavigne made his papers, Elisabeth Bik wasn't studying images, and we didn't have proof fig and image twin to pick these things up, so we're taking today's tools and applying them to something that's 20 years old that was produced when those tools didn't exist. You can debate whether that matters or not, but in my opinion it does.
Generative A.I. and Publishing Science
Eric Topol (00:31:31):
Yeah, that's bringing us to the next topic I wanted to get into you with, which is AI. You've already mentioned about the AI detection of image, which we used to rely on Elisabeth as a human to do that, and now it can be done through AI.
Holden Thorp (00:31:51):
Well, it doesn't get everything, so I keep telling Elisabeth she doesn't have to worry about being put out of business.
Eric Topol (00:31:58):
But then there's also, as you said about text detection, and then there's also, as you've written in Science, the overall submission of papers where a GPT may have had significant input to the writing, not just to check the spelling or check minor things. And so, I want to get your views because this is a moving target of course. I mean, it's just the capabilities of AI have just been outpacing, I think a lot of expectations. Where do you see the intersection of AI and Science publishing now? Because as you said, it changes the ground rules for picking up even minor unintended errors or self-plagiarism or whatever, and now it changes the whole landscape considerably.
Holden Thorp (00:32:54):
Yeah. So, I think you said the most important thing, which is that it's a moving target, and you've been writing about this for medicine for longer than just about anybody, so you've been watching that moving target. We started off with a very restrictive stance, and the reason we did that was because we knew it would keep moving. And so, we wanted to start from the most restrictive possible place and then sort of titrate in the things that we allowed because we didn't want to go through the same thing we went through with Photoshop when it first came along. Like all these altered images that we keep talking about by far the most papers that surface are from the period between when Photoshop became a tool and when we finally had sort of a consensus as a community in terms of what was okay and what wasn't okay to do with your gels when you process the images.
Holden Thorp (00:33:55):
And we didn't want the same thing with words where we allowed people to use ChatGPT to write, and then a few years later decided, oh, this thing wasn't permissible, and then we have to go back and re-litigate all those papers. We didn't want to do that again. So, we started off with a pretty restrictive stance, which we've loosened once and we'll probably loosen more as we see how things evolve. What we keep looking for is for entities that don't have a financial interest to issue guidelines, so if it's another journal, especially a commercial journal that makes money on the papers, well, you can imagine that these tools are going to give us even more papers. And for a lot of these entities that charge by the paper, they have a financial incentive for people to use ChatGPT to write papers. We look for societies and coalitions of academics who have come together and said these things are okay.
Holden Thorp (00:35:04):
And the first one of those was when we decided that it was okay, for example, if you are not an English speaker natively to have ChatGPT work on your prose. Now there are lots of people who disagree about that ChatGPT is good at that. That's a separate matter, but we felt we got to a point, I forgot when it was a couple months ago, where we could amend our policies and say that we were going to be more tolerant of text that had been done by ChatGPT. As long as the people who signed the author forms realize that if it makes one of these hallucinating errors that it makes and it gets into the paper that's on them, whether that actually saves you time or not, I don't know.
Holden Thorp (00:36:03):
I also have my doubts about that, but that's kind of where we're going. We're watching these things as they go. We're still very restrictive on images and there was this debacle in this Frontiers paper a couple of weeks ago with a ridiculous image that got through. So right now, we're still not allowing illustrations that were generated by the visual counterparts of ChatGPT. Will we loosen that in the future? Maybe, as things evolve, so when we did our first amendment, some of the reporters, they're just doing their jobs saying, well, you can't make your mind up about this. And I'm like, no, you don't want us to make up our mind once and for all. And by the way, science is something that changes over time also. So, we're watching this develop and we expect everybody jokes about how we spend too much time talking about this, but I think everybody's gotten to the point now where they're realizing we're going to talk about it for years to come.
Eric Topol (00:37:17):
Oh my goodness, yes because we're talking about truth versus fake and this is big stuff. I mean, it affects whether it's the elections, whether it's every sector of our lives are affected by this. And obviously publishing in the leading peer review journal, it couldn't be more important as to get this right and to adjust, as you said, as more evidence, performance and other issues are addressed systematically. That does get me to self-correcting science, something else you've written about, which is kind of self-correcting as to how we will understand the use of large language models and generative AI. But this, you get into science in many different ways, whether it's through the celebrity idea, how it has to adapt and correct that there's a miscue from the public about when evolves and it's actually that science. So maybe you could kind of give us your perspective about you are continuing to reassess what is science as we'll get into more about that in a moment. Where are you at right now on that?
Holden Thorp (00:38:40):
Yeah, so my general sort of shtick about science is to remind people that it's done by human beings. Human beings who have all different kinds of different brains who come from different backgrounds, who have all the human foibles that you see in any other profession. And I think that unfortunately a lot of, and we brought some of this on ourselves, we've kind of taken on an air of infallibility from time to time or as having the final answer when, if you go back just to the simplest Karl Popper and Thomas Kuhn early writings in the philosophy of science, it's crystal clear that science is something that evolves. It's something done by sometimes thousands or even hundreds of thousands of millions of people depending on the topic. And it's not the contributions of any individual person hardly ever.
Holden Thorp (00:39:54):
But yet we continue to give Nobel prizes and hold up various individual scientific figures as being representative. They're usually representative of many, many people. And it's a process that continues to change. And as always point out, if you want to get a paper in science, it's not good to say, hey, here's something everybody thought and we tested it and it's still correct. That's usually not a good way to get a science paper. The right thing to do is to say, hey, the W boson might weigh more than we expected it to, or it turns out that evolution occurs in ways that we didn't expect, or that's how you get a science paper and that's how you get on the cover of Science. Those are the things that we look for, things that change the way people think about science. And so that's what we're all actively looking for, but yet we sometimes portray to the public that we always have everything completely figured out, and the journalists sometimes don't help us because they like to write crisp stories that people can get something out of. And we like to go on TV and say, hey, I got the answer.
Holden Thorp (00:41:23):
Don't wear a mask. Do wear a mask. This is how much the temperature is going to go up next year. Oh, we refined our, and it turns out it's another 10th of a degree this way or that way. I mean, that's what makes what we do interesting and embedded in that is also human error, right? Because we make errors in interpretation. We might see a set of data that we think mean one thing, but then somebody else will do something that helps us interpret it another way. In my opinion, that's certainly not misconduct. We hardly ever publish corrections or retractions over interpretation. We just publish more papers about that unless it's some very egregious thing. And then we also have greed and ambition and ego and lots of other things that cause people to make intentional errors that get most of the attention. And we have errors that are unintentional, but still may relate to fundamental data in the paper.
Holden Thorp (00:42:36):
So when you put all this together, the answer isn't to try to catch everything because there's no way in the world we're going to catch everything and we wouldn't want to, even if we could for some of it, because as John Maddox, who ran my competitor journal for many years in a brilliant way at Nature, someone once asked him how many papers in Nature were wrong? And he said, all of them, because all of them are going to be replaced by new information. And so, what we'd be better off trying to convince the public that this is how science works, which is much harder than just going to them with facts. I mean, that takes a lot of work and doing a better job of telling each other that it's okay when we have to change the record because the biggest thing that erodes trust in science is not the fact that we make mistakes, is that when it turns into a drama over whether we are going to correct the record or not, that's what all these, the Stanford case is probably the biggest in people's minds. But if you look at, we've had this behavioral economic stuff at Harvard, I have this superconductivity at Rochester, Dana Farber's having a big event right now. All of these things don't have to be this dramatic if we would do a better job of collaborating with each other on maintaining an accurate scientific record rather than letting ambition and greed and ego get in the way of all of it.
Who Is A Scientist?
Eric Topol (00:44:21):
Well, you got some important threads in there. The one thing I just would also comment on is my favorite thing in Science is challenging dogma because there's so much dogma, and that's obviously part of what you were getting into and many other aspects as well. But that's the story of Science, that nothing stands. If it does, then you're not doing a good job of really interrogating and following up on whatever is accepted at any particular moment in time. But your writings, whether it's in Science and editorials or science forever, your Substack, which are always insightful but I think one of the most recent ones was about, who is a scientist? And I really love that one because I'll let you explain. There are some people who have a very narrow view and others who see it quite differently. And maybe you could summarize it.
Holden Thorp (00:45:23):
Well, I had the privilege to moderate a panel at the AAAS meeting that included Keith Yamamoto, who was our outgoing president, Willie May, who was our incoming president, Peggy Hamburg, who ran the FDA and many, many other things. Kaye Husbands Fealing who was a social scientist, and Michael Crow, who was the president of Arizona State. These are all extraordinary people. And I just asked him a simple question, so who was the scientist? Because I think one thing that I see in my work, and you probably see in the communication work and writing that you do, that not all of our colleagues who work in the laboratory think that the rest of this stuff is science.
Holden Thorp (00:46:17):
And the place that breaks my heart the most is when somebody says, one of our professional editors isn't qualified to reject their paper because they don't have their own lab. Alright, well you've interacted with a lot of our editors, they read more papers than either one of us. They know more about what's going on in these papers than anybody. They are absolute scholars in every sense of the word and if someone thinks they're not scientists, I don't know who a scientist is. And so, then you can extend that to science communicators. I mean, those are obviously the problems we've been talking about, the people we need the most great teachers. If someone's a great science teacher and they have a PhD and they worked in lab and they're teaching at a university, are they still a scientist even if they don't have a lab anymore?
Holden Thorp (00:47:11):
So in my opinion, an expansive definition of this is the best because we want all these people to be contributing. In fact, many of the problems we have aren't because we're not good in the laboratory. We seem to be able to do a good job generating that. It's more about all these other pieces that we're not nearly as good at. And part of what we need to do is value the people who are good at those things, so I pose this to the panel, and I hope people go on and watch the video. It is worth watching. Keith Yamamoto was in the group that said, it's only if you're doing and planning research that you're a scientist. He knew he was going to be outnumbered before we went out there. We talked about that. I said, Keith, you're my boss. If you don't want me to ask that question, I won't. But to his credit, he wanted to talk about this and then Michael Crow was probably the furthest on the other side who said, what makes humans different from other species is that we're all scientists. We all seek to explain things. So somewhere in the middle and the others were kind of scattered around the middle, although I would say closer to Michael than they were to Keith.
Holden Thorp (00:48:33):
But I think this is important for us to work out because we want everybody who contributes to the scientific enterprise to feel valued. And if they would feel more valued if we called them scientists, that suits me but it doesn't suit all of our academic colleagues apparently.
Eric Topol (00:48:54):
Well, I mean, I think just to weigh in a bit on that, I'm a big proponent of citizen scientists, and we've seen how it has transformed projects like folded for structural biology and so many things, All of Us program that's ongoing right now to try to get a million participants, at least half of whom are underrepresented to be citizen scientists learning about themselves through their genome and other layers of data. And that I think may help us to fight the misinformation, disinformation, the people that do their own research with a purpose that can be sometimes nefarious. The last type of topic I wanted to get to with you was the University of Florida and the state of Florida and the Surgeon General there. And again, we are kind of circling back to a few things that we've discussed today about higher education institutions as well as politics and I wonder if we get some comments about that scenario.
What’s Happening in Florida?
Holden Thorp (00:49:59):
Yeah. Well, I'm coming to you from Orlando, Florida where I have a home that I've had ever since I moved to a cold climate, and I spent the whole pandemic down here. I observed a lot of things going on in the state of Florida firsthand. And I think in a way it's two different worlds because Florida does make a massive investment in higher education more than many other states and that has really not changed that much under Governor DeSantis despite his performative views that seem to be to the contrary. And so, I think it's important to acknowledge that Florida State and Florida and UCF and USF, these are excellent places and many of them have thrived in terms of their budgets even in this weird climate, but the political performance is very much in the other direction. This is where the Stop WOKE Act happened. This is where, again, I live in Orlando. This is a company town that Ron DeSantis decided to take on the Walt Disney Corporation is the second biggest city in Orlando, and it's a company town, and he took on the employer.
Holden Thorp (00:51:32):
It doesn't make a whole lot of political sense, but I think it was all part of his national political ambitions. And down at the base of this was this all strange anti-vax stuff. Now I got my first vaccines down here. I went to public places that were organized by the Army Corps of Engineers that were at public properties. It was at a community college here in Orlando, was extremely well organized. I had no problem. I was there 10 minutes, got my vaccines. It was extremely well organized but at the same time, the guys on TV saying the vaccine's not any good. And he hires this person, Joseph Ladapo, to be his Surgeon General, who I think we would both say is an anti-vaxxer. I mean he just recently said that you didn't need to get a measles vaccine and then in the last couple of days said, if you're unvaccinated and you have measles, you don't have to quarantine for 21 days. Now really would be disastrous if measles came back. You know a lot more about that than I do but I'm a generation that had a measles vaccine and never worried about measles.
Holden Thorp (00:52:59):
So the part of it that I worry about the most is that this person, the Surgeon General, also has a faculty appointment at the University of Florida. And you can see how he got it because his academic resume has been circulated as a result of all of Florida's public records laws and he has a very strong, credible resume that would probably cause him to get tenure at a lot of places. The medical faculty at Florida have tried to assert themselves and say, we really need to distance ourselves from him, but the administration at the University of Florida has not really engaged them. Now, I did ask them last week about the measles thing. I was going to write about it again, and I wrote to them and I said, if you guys aren't going to say anything about what he is saying about the measles, then I'm going to have another editorial.
Holden Thorp (00:54:05):
And they sent me a statement, which I posted that you probably saw that they still didn't condemn him personally, but they did say that measles vaccination was very important, and it was a fairly direct statement. I don't know if that will portend more stronger words from the University of Florida. Maybe now that their president is somebody who's close to the governor, they'll feel a little more comfortable saying things like that. But I think the bigger issue for all of us is when we have academic colleagues who say things that we know are scientifically invalid, and this always gets to the whole free speech thing, but in my opinion, free speech, it is within free speech to say, yes, all these things about vaccines are true, but I still don't think people should be compelled to get vaccinated. That's an opinion. That's fine. But what's not an opinion is to say that vaccines are unsafe if they've been tested over and over again and proven to be effective.
Academic Freedom
Holden Thorp (00:55:24):
That's not an opinion. And I personally don't think that that deserves certainly to be weighted equally with the totality of medical evidence. I think that it's within bounds for academic colleagues and even institutions to call out their colleagues who are not expressing an opinion, but are challenging scientific facts without doing experiments and submitting papers and having lots of people look at it and doing all the stuff that we require in order to change scientific consensus. And this happens in climate change in a very parallel way. I mean, it's an opinion to say the climate is changing, humans are causing it, but I still don't think we should have government regulations about carbon. I think we should wait for the private sector to solve it, or I don't think it's going to have as bad of an effect as people say. Those are policy debates that you can have.
Holden Thorp (00:56:28):
But alleging that climate scientists are falsifying their projection somehow when they're not is in my opinion, not covered by free speech. And I think the best evidence we had of this is this recent verdict with Michael Mann, where it was the people who were criticizing him were found to be defamatory when they said that he committed research fraud. They could say he's exaggerating the threat. They could say they could dislike his style. He does have a very bombastic style. They can say all kinds of things about their opinions about him personally but if you accuse him of committing research fraud, and the paper that was in question was one of the most highly litigated papers of all time. It's been investigated more times than you can count. That's not something that's protected by free speech because it's defamatory to say that, and the jury found that. I think we have a lot of work to do to get within our own world, our colleagues, to get their arms around these two forms of debate.
Eric Topol (00:57:51):
Right. Well, I think this is, again, another really important point you're making during the pandemic parallel to the Michael Mann climate change case is that leading universities, as we recently reviewed in a podcast with Jonathan Howard, who wrote a book about this leading universities like Stanford, UCSF, Johns Hopkins and many others, didn't come out about the people that were doing things, saying things that were truly potential public harm. Not like you're saying, expressing an opinion with the truth, but rather negating evidence that was important to keep people protected from Covid. This is a problem which is thematic in our discussion I think Holden, is that universities have to get with it. They have to be able to help not put things on the credit card, be very transparent, direct quick respond, and not hide behind worried about social media or journalists or whatever else. This has been an incredible discussion, Holden, I got into even more than I thought we would.
Eric Topol (00:59:15):
You're a phenom to defend the whole science landscape that is challenging right now. I think you would agree for many reasons that we've discussed, and it affects education in a very dramatic, serious way. I want to thank you all that you're doing at Science with your team there to lead the charge and stand up for things and not being afraid to stimulate some controversies here and there. It's good for the field. And so, I hope I didn't miss anything and this exhaustive, this is the longest podcast I've done on Ground Truths, I want you to know that.
Holden Thorp (00:59:59):
Well, I'm flattered by that because you've had some great people on, that's for sure. And thank you for all you're doing, not just in science, but to spread the word about all these things and bring people together. It means a lot to all of us.
Eric Topol (01:00:15):
Oh, much appreciated. And we'll convene again soon to discuss so many dimensions of what we just have been reviewing and new ones to come. Thanks very much.
Holden Thorp (01:00:25):
Okay. Always good talking to you.
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Transcript
Eric Topol (00:06):
Well, hello, this is Eric Topol with Ground Truths and I am absolutely thrilled to welcome Daphne Koller, the founder and CEO of insitro, and a person who I've been wanting to meet for some time. Finally, we converged so welcome, Daphne.
Daphne Koller (00:21):
Thank you Eric. And it's a pleasure to finally meet you as well.
Eric Topol (00:24):
Yeah, I mean you have been rocking everybody over the years with elected to the National Academy of Engineering and Science and right at the interface of life science and computer science and in my view, there's hardly anyone I can imagine who's doing so much at that interface. I wanted to first start with your meeting in Davos last month because I kind of figured we start broad AI rather than starting to get into what you're doing these days. And you had a really interesting panel [←transcript] with Yann LeCun, Andrew Ng and Kai-Fu Lee and others, and I wanted to get your impression about that and also kind of the general sense. I mean AI is just moving it at speed, that is just crazy stuff. What were your thoughts about that panel just last month, where are we?
Daphne Koller (01:25):
I think we've been living on an exponential curve for multiple decades and the thing about exponential curves is they are very misleading things. In the early stages people basically take the line between whatever we were last year, and this year and they interpolate linearly, and they say, God, things are moving so slowly. Then as the exponential curve starts to pick up, it becomes more and more evident that things are moving faster, but it’s still people interpolate linearly and it's only when things really hit that inflection point that people realize that even with the linear interpolation where we'll be next year is just mind blowing. And if you realize that you're on that exponential curve where we will be next year is just totally unanticipatable. I think what we started to discuss in that panel was, are we in fact on an exponential curve? What are the rate limiting factors that may or may not enable that curve to continue specifically availability of data and what it would take to make that curve available in areas outside of the speech, whatever natural language, large language models that exist today and go far beyond that, which is what you would need to have these be applicable to areas such as biology and medicine.
Daphne Koller (02:47):
And so that was kind of the message to my mind from the panel.
Eric Topol (02:53):
And there was some differences in opinion, of course Yann can be a little strong and I think it was good to see that you're challenging on some things and how there is this “world view” of AI and how, I guess where we go from here. As you mentioned in the area of life science, there already had been before large language models hit stride, so much progress particularly in imaging cells, subcellular, I mean rare cells, I mean just stuff that was just without any labeling, without fluorescein, just amazing stuff. And then now it's gone into another level. So as we get into that, just before I do that, I want to ask you about this convergence story. Jensen Huang, I'm sure you heard his quote about biology as the opportunity to be engineering, not science. I'm sure if I understand, not science, but what about this convergence? Because it is quite extraordinary to see two fields coming together moving at such high velocity.
"Biology has the opportunity to be engineering not science. When something becomes engineering not science it becomes...exponentially improving, it can compound on the benefits of previous years." -Jensen Huang, NVIDIA.
Daphne Koller (04:08):
So, a quote that I will replace Jensen's or will propose a replacement for Jensen's quote, which is one that many people have articulated, is that math is to physics as machine learning is to biology. It is a mathematical foundation that allows you to take something that up until that point had been kind of mysterious and fuzzy and almost magical and create a formal foundation for it. Now physics, especially Newtonian physics, is simple enough that math is the right foundation to capture what goes on in a lot of physics. Biology as an evolved natural system is so complex that you can't articulate a mathematical model for that de novo. You need to actually let the data speak and then let machine learning find the patterns in those data and really help us create a predictability, if you will, for biological systems that you can start to ask what if questions, what would happen if we perturb the system in this way?
The Convergence
Daphne Koller (05:17):
How would it react? We're nowhere close to being able to answer those questions reliably today, but as you feed a machine learning system more and more data, hopefully it'll become capable of making those predictions. And in order to do that, and this is where it comes to this convergence of these two disciplines, the fodder, the foundation for all of machine learning is having enough data to feed the beast. The miracle of the convergence that we're seeing is that over the last 10, 15 years, maybe 20 years in biology, we've been on a similar, albeit somewhat slower exponential curve of data generation in biology where we are turning it into a quantitative discipline from something that is entirely observational qualitative, which is where it started, to something that becomes much more quantitative and broad based in how we measure biology. And so those measurements, the tools that life scientists and bioengineers have developed that allow us to measure biological systems is what produces that fodder, that energy that you can then feed into the machine learning models so that they can start making predictions.
Eric Topol (06:32):
Yeah, well I think the number of layers of data no less what's in these layers is quite extraordinary. So some years ago when all the single cell sequencing was started, I said, well, that's kind of academic interest and now the field of spatial omics has exploded. And I wonder how you see the feeding the beast here. It's at every level. It's not just the cell level subcellular and single cell nuclei sequencing single cell epigenomics, and then you go all the way to these other layers of data. I know you plug into the human patient side as well as it could be images, it could be past slides, it could be the outcomes and treatments and on and on and on. I mean, so when you think about multimodal AI, has anybody really done that yet?
Daphne Koller (07:30):
I think that there are certainly beginnings of multimodal AI and we have started to see some of the benefits of the convergence of say, imaging and omics. And I will give an example from some of the work that we've recently distributed on a preprint server work that we did at insitro, which took imaging data from standard histopathology slides, H&E slides and aligned them with simple bulk RNA-Seq taken from those same tumor samples. And what we find is that by training models that translate from one to the other, specifically from the imaging to the omics, you're able to, for a fairly large fraction of genes, make very accurate predictions of gene expression levels by looking at the histopath images alone. And in fact, because many of the predictions are made at the tile level, not at the entire slide level, even though the omics was captured in bulk, you're able to spatially resolve the signal and get kind of like a pseudo spatial biology just by making predictions from the H&E image into these omic modalities.
Multimodal A.I. and Life Science
Daphne Koller (08:44):
So there are I think beginnings of multimodality, but in order to get to multimodality, you really need to train on at least some data where the two modalities are simultaneously. And so at this point, I think the rate limiting factor is more a matter of data acquisition for training the models. It is for building the models themselves. And so that's where I think things like spatial biology, which I think like you are very excited about, are one of the places where we can really start to capture these paired modalities and get to some of those multimodal capabilities.
Eric Topol (09:23):
Yeah, I wanted to ask you because I mean spatial temporal is so perfect. It is two modes, and you have as the preprint you refer to and you see things like electronic health records in genomics, electronic health records in medical images. The most we've done is getting two modes of data together. And the question is as this data starts to really accrue, do we need new models to work with it or do you actually foresee that that is not a limiting step?
Daphne Koller (09:57):
So I think currently data availability is the most significant rate limiting step. The nice thing about modern day machine learning is that it really is structured as a set of building blocks that you can start to put together in different ways for different situations. And so, do we have the exact right models available to us today for these multimodal systems? Probably not, but do we have the right building blocks that if we creatively put them together from what has already been deployed in other settings? Probably, yes. So of course there's still a model exploration to be done and a lot of creativity in how these building blocks should be put together, but I think we have the tools available to solve these problems. What we really need is first I think a really significant data acquisition effort. And the other thing that we need, which is also something that has been a priority for us at insitro, is the right mix of people to be put together so that you can, because what happens is if you take a bunch of even extremely talented and sophisticated machine learning scientists and say, solve a biological problem, here's a dataset, they don't know what questions to ask and oftentimes end up asking questions that might be kind of interesting from machine learning perspective, but don't really answer fundamental biology questions.
Daphne Koller (11:16):
And conversely, you can take biologists and say, hey, what would you have machine learning do? And they will tell you, well, in our work we do A to B to C to D, and B to C is kind of painful, like counting nuclei is really painful, so can we have the machine do that for us? And it's kind of like that. Yeah, but that's boring. So what you get if you put them in a room together and actually get to the point where they communicate with each other effectively, is that not only do you get better solutions, you get better problems. I think that's really the crux of making progress here besides data is the culture and the people.
A.I. and Drug Discovery
Eric Topol (11:54):
Well, I'm sure you've assembled that at insitro knowing you, and I mean people tend to forget it's about the people, it's not about the models or even the data when you have all that. Now you've been onto drug discovery paths, there's at least 20 drugs that are AI driven that are in the clinic in phase one or two at some point. Obviously these are not only ones that you've been working on, but do you see this whole field now going into high gear because of this? Or is that the fact that there's all these AI companies partnering with big pharma? Is it a lot of nice agreements that are drawn up with multimillion dollar milestones or is this real?
Daphne Koller (12:47):
So there's a number of different layers to your question. First of all, let me start by saying that I find the notion of AI driven drugs to be a bit of a weird concept because over time most drugs will have some element of AI in them. I mean, even some of the earlier work used data science in many cases. So where do you draw the boundary? I mean, we're not going to be in a world anytime soon where AI starts out with, oh, I need to work on ALS and at the end there is a clinical trial design ready to be submitted to the FDA without anything, any human intervention in the middle. So, it's always going to be an interplay between a machine and a human with over time more and more capabilities I think being taken on by the machine, but I think inevitably a partnership for a long time to come.
Daphne Koller (13:41):
But coming to the second part of your question, is this real? Every big pharma has gotten to the point today that they realize they need some of that AI thing that's going around. The level of sophistication of how they incorporate that and their willingness to make some of the hard decisions of, well, if we're going to be doing this with AI, it means we shouldn't be doing it the old way anymore and we need to make a big dramatic internal shift that I think depends very much on the specific company. And some companies have more willingness to take those very big steps than others, so will some companies be able to make the adjustment? Probably. Will all of them? Probably not. I would say however, that in this new world there is also room for companies to emerge that are, if you will, AI native.
Daphne Koller (14:39):
And we've seen that in every technological revolution that the native companies that were born in the new age move faster, incorporate the technology much more deeply into every aspect of their work, and they end up being dominant players if not the dominant player in that new world. And you could look at the internet revolution and think back to Google did not emerge from the yellow pages. Netflix did not emerge from blockbuster, Amazon did not emerge from Walmart so some of those incumbents did make the adjustment and are still around, some did not and are no longer around. And I think the same thing will happen with drug discovery and development where there will be a new crop of leading companies to I think maybe together with some of the incumbents that we're able to make the adjustment.
Eric Topol (15:36):
Yeah, I think your point there is essential, and another part of this story is that a lot of people don't realize there's so many nodes of ways that AI can facilitate this whole process. I mean from the elemental data mining that identified Baricitinib for Covid and now being used even for many other indications, repurposing that to how to simulate for clinical trials and everything in between. Now, what seems like because of your incredible knack and this convergence, I mean your middle name is like convergence really, you are working at the level of really in my view, this unique aspect of bringing cells and all the other layers of data together to amp things up. Is that a fair assessment of where insitro in your efforts are directed?
Three Buckets
Daphne Koller (16:38):
So first of all, maybe it's useful to kind of create the high level map and the simplest version I've heard is where you divide the process into three major buckets. One is what you think of as biology discovery, which is the discovery of new therapeutic hypotheses. Basically, if you modulate this target in this group of humans, you will end up affecting this clinical outcome. That's the first third. The middle third is, okay, well now we need to turn that hypothesis into an actual molecule that does that. So basically generating molecules. And then finally there's the enablement and acceleration of the clinical development process, which is the final third. Most companies in the AI space have really focused in on that middle third because it is well-defined, you know when you've succeeded if someone gives you a target and what's called a target product profile (TPP) at the end of whatever, two, three years, whether you've been able to create a molecule that achieves the appropriate properties of selectivity and solubility and all those other things. The first third is where a lot of the mistakes currently happen in drug discovery and development. Most drugs that go into the clinic don't fail because we didn't have the right molecule. I mean that happens, but it's not the most common failure mode. The most common failure mode is that the target was just a wrong target for this disease in this patient population.
Daphne Koller (18:09):
So the real focus of us, the core of who we are as a company is on that early third of let's make sure we're going after the right clinical hypotheses. Now with that, obviously we need to make molecules and some of those molecules we make in-house, and obviously we use machine learning to do that as well. And then the last third is we discover that if you have the right therapeutic hypothesis, which includes which is the right patient population, that can also accelerate and enable your clinical trials, so we end up doing some of that as well. But the core of what we believe is the failure mode of drug discovery and what it's going to take to move it to the next level is the articulation of therapeutic hypotheses that actually translate into clinical outcome. And so in order to do that, we've put together, to your point about convergence, two very distinct types of data.
Daphne Koller (19:04):
One is data that we print in our own internal data factory where we have this incredible set of capabilities that uses stem cells and CRISPR and microscopy and single cell measurements and spatial biology and all that to generate massive amounts of in-house data. And then because ultimately you care not about curing cells, you care about curing people, you also need to bring in the clinical data. And again, here also we look at multiple high content data modalities, imaging and omics, and of course human genetics, which is one of the few sources of ground truth for causality that is available in medicine and really bring all those different data modalities across these two different scales together to come up with what we believe are truly high quality therapeutic hypotheses that we then advance into the clinic.
AlphaFold2, the Exemplar
Eric Topol (19:56):
Yeah, no, I think that's an extraordinary approach. It's a bold, ambitious one, but at least it is getting to the root of what is needed. One of the things you mentioned of course, is the coming up with molecules, and I wanted to get your comments about the AlphaFold2 world and the ability to not just design proteins now of course that are not extant proteins, but it isn't just proteins, it could be antibodies, it could be peptides and small molecules. How much does that contribute to your perspective?
Daphne Koller (20:37):
So first of all, let me say that I consider the AlphaFold story across its incarnations to be one of the best examples of the hypothesis that we set out trying to achieve or trying to prove, which is if you feed a machine learning model enough data, it will learn to do amazing things. And the space of protein folding is one of those areas where there has been enough data in biology that is the sequence to structure mapping is something that over the years, because it's so consistent across different cells, across different species even, we have a lot of data of sequence to structure, which is what enabled AlphaFold to be successful. Now since then, of course, they've taken it to a whole new level. I think what we are currently able to do with protein-based therapeutics is entirely sort of a consequence of that line of development. Whether that same line of development is also going to unlock other therapeutic modalities such as small molecules where the amount of data is unfortunately much less abundant and often locked away in the bowels of big pharma companies that are not eager to share.
Daphne Koller (21:57):
I think that question remains. I have not yet seen that same level of performance in de novo design of small molecule therapeutics because of the data availability limitations. Now people have a lot of creative ideas about that. We use DNA encoded libraries as a way of generating data at scale for small molecules. Others have used other approaches including active learning and pre-training and all sorts of approaches like that. We're still waiting, I think for a truly convincing demonstration that you can get to that same level of de novo design in small molecules as you can in protein therapeutics. Now as to how that affects us, I'm so excited about this development because our focus, as I mentioned, is the discovery of novel therapeutic hypotheses. You then need to turn those therapeutic hypotheses into actual molecules that do the work. We know we're not going to be the expert in every single therapeutic modality from small molecules to macro cycles, to the proteins to mRNA, siRNA, there's so many of those that you need to have therapeutic modality experts in each of those modalities that can then as you discover a target that you want to modulate, you can basically go and ask what is the right partner to help turn this into an actual therapeutic intervention?
Daphne Koller (23:28):
And we've already had some conversations with some modality partners as we like to call them that help us take some of our hypotheses and turn it into molecules. They often are very hungry for new targets because they oftentimes kind of like, okay, here's the three or four or whatever, five low hanging fruits that our technology uniquely unlocks. But then once you get past those well validated targets like, okay, what's next? Am I just going to go read a bunch of papers and hope for the best? And so oftentimes they're looking for new hypotheses and we're looking for partners to make molecules. It's a great partnership.
Can We Slow the Aging Process?
Eric Topol (24:07):
Oh yeah, no question about that. Now, we've seen in recent times some leaps in drugs that were worked on for decades, like the GLP-1s for obesity, which are having effects potentially well beyond obesity didn't require any AI, but just slogging away at it for decades. And you previously were at Calico, which is trying to deal with aging. Do you think that we're going to see drug interventions that are going to slow the aging process because of this unique time of this exponential point we are in where we're a computer and science and digital biology come together?
Daphne Koller (24:52):
So I think the GLP-1s are an incredible achievement. And I would point out, I know you said and incorrectly that it didn't use any AI, but they did actually use an understanding of human genetics. And I think human genetics and the genotype phenotype statistical associations that they revealed is in some ways the biological precursor to AI it is a way of leveraging very large amounts of data, admittedly using simpler statistical tools, but still to discover in a data-driven way, novel therapeutic hypothesis. So I consider the work that we do to be a progeny of the kind of work that statistical geneticists have done. And of course a lot of heavy lifting needed to be done after that in order to make a drug that actually worked and kudos to the leaders in that space. In terms of the modulation of aging, I mean aging is a process of decline over time, and the rate of that decline is definitely something that is modifiable.
Daphne Koller (26:07):
And we all know that external factors such as lifestyle, diet, exercise, even exposure to sun or smoking, accelerates the aging process. And you could easily imagine, as we've seen in the GLP-1s that a therapeutic intervention can change that trajectory. So will we be able to using therapeutic interventions, increase health span so that we live healthy longer? I think the answer to that is undoubtedly, yes. And we've seen that consistently with therapeutic interventions, not even just the GLP-1s, but going backwards, I mean even statins and earlier things. Will we be able to increase the maximum life span so that people habitually live past 120, 150? I don't know. I don't know that anybody knows the answer to that question. I personally would be quite happy with increasing my health span so that at the age of 80, I'm still able to actively go hiking and scuba diving at 90 and 100 and that would be a pretty good place to start.
Eric Topol (27:25):
Well, I'm with you on that, but I just want to ask though, because the drugs we have today that are highly effective, I mean statins is a good example. They work at a particular level of the body. They don't have across the board modulation of effect. And I guess what I was asking is, do you foresee we will have some way to do that across all systems? I mean, that is getting to, now that we have so many different ways to intervene on the process, is there a way that you envision in the future that we'll be able to here, I'm not talking about in expanding lifespan, I'm talking about promoting health, whether it's the immune system or whether it's through mitochondria and mTOR, caloric, I mean all these different things you think that's conceivable or is that just, I mean companies like Calico and others have been chasing this. What do you think?
Daphne Koller (28:30):
Again, I think it's a thing that is hard to predict. I mean, we know that different organ systems age at different rates, and is there a single bio even in a single individual, and it's been well established that you can test brain age versus muscle health versus cardiovascular, and they can be quite different in the same individual, so is there a single hub? No, that governs all forms of aging. I don't know if that's true. I think it's oftentimes different. We know protein folding has an effect, you know DNA damage has an effect. That's why our skin ages because it's exposed to sun. Is there going to be a single switch that reverts it all back? Certainly some companies are pursuing that single bullet approach. I personally would probably say that based on the biology that I've seen, there's at least as much potential in trying to find ways to slow the decline in a way that specific to say as we discussed the immune system or correcting protein, misfolding dysfunction or things like that. And I'm not dismissing there is a single magic switch, but let's just say I think we should be exploring multiple alternatives.
Eric Topol (29:58):
Yeah, no, I like your reasoning. I think it's actually like everything else you said here. It makes a lot of sense. The logic is hard to argue with. Well, I think what you're doing there at insitro is remarkable and it seems to be quite distinct from other strategies, and that's not at all surprising knowing your background and your aspiration.
Daphne Koller (30:27):
Never like to follow the crowd. It's boring.
Eric Topol (30:30):
Right, and I do know you left an aging directed company effort at Calico to do what you're doing. So that must have been an opening for you that you saw was much more diverse perhaps, or maybe I'm mistaken that Calico is not really age specific in its goals.
Daphne Koller (30:49):
So what inspired me to go found insitro was the realization that we are making medicines today in a way that is not that different from the way in which we were making medicines 20 or 30 years ago in terms of the process by which we go from a, here's what I want to work on to here's a drug is a very much an artisanal one-off each one of them is a snowflake. There is very little commonality and sharing of insights and infrastructure across those efforts except in relatively limited tool-based ways. And I wanted to change that. I wanted to take the tools of engineering and data and machine learning and build a very different approach of going from a problem definition to a therapeutic intervention. And it didn't make sense to build that within a company that's focused on any single biology, not just aging because it is such a broad-based foundation.
Daphne Koller (31:58):
And I will tell you that I think we are on the path to building the thing that I set out to build. And as one example of that, I will use the work that we've recently done in metabolic disease where based on the foundations that we've built using both the clinical machine learning work and the cellular machine learning work, we were able to go from a problem articulation of this is the indication that we want to work on to a proof of concept in a translatable animal model in one year. That is pretty unusual. Admittedly, this is with an SiRNA tool compound. Nice thing about things that are liver directed is that it's not that difficult of a path to go from an SiRNA tool compound to an actual SiRNA drug. And so hopefully that's a fairly linear journey from there even, which is great.
Daphne Koller (32:51):
But the fact that we were able to go from problem articulation to a proof of concept in a translatable animal model in one year, that is unusual. And we're starting to see that now across our other therapeutic areas. It takes a long time to build a platform because you're basically building a foundation. It's like, okay, where's the fruit of all of that? I mean, you're building and building and building and nothing comes out for a while because you're building so much of the infrastructure. But once you've built it, you turn the crank and stuff starts to come out, you turn the crank again, and it works faster and better than the previous time. And so the essence of what we've built and what has turned into the tagline for the company is what we call pipeline through platform, which is we're building a pipeline of therapeutic interventions that comes off of a platform. And that's rare in biopharma, the only platform companies that really have emerged by and larger therapeutic modality platforms, things like Moderna and Alnylam, which have gotten really good at a particular modality and that's awesome. We're building a discovery platform and that is a fairly unusual thing.
Eric Topol (34:02):
Right. Well, I have no doubt you'll be discovering a lot of important things. That one sounds like it could be a big impact on NASH.
Daphne Koller (34:14):
Yeah, we hope so.
Eric Topol (34:14):
A big unmet need that's not going to be fixed by what we have today. So Daphne, it's really a joy to talk with you and palpable enthusiasm for where the field is going as one of its real leaders and we'll be cheering for you. I hope we'll reconnect in the times ahead to get another progress report because you're definitely rocking it there and you've got a lot of great ideas for how to change the life science medical world of the future.
Daphne Koller (34:48):
Thank you so much. It's a pleasure to meet you, and it's a long and difficult journey, but I think we're on the right path, so looking forward to seeing that all that pan out.
Eric Topol (34:58):
You made a compelling case in a short visit, so thank you.
Daphne Koller (35:02):
Thank you so much.
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“A few years ago, I might have chuckled at the naiveté of this question, but now it's not so crazy to think that we will be able to take some sort of medicine to extend our healthy lifespans in the foreseeable future.”—Coleen Murphy
Transcript with external links
Eric Topol (00:06):
Hello, this is Eric Topol from Ground Truths, and I'm just so delighted to have with me Professor Coleen Murphy, who has written this exceptional book, How We Age: The Science of Longevity. It is a phenomenal book and I'm very eager to discuss it with you, Coleen.
Coleen Murphy (00:25):
Thanks for having me on.
Eric Topol (00:27):
Oh yeah. Well, just so everyone who doesn't know Professor Murphy, she's at Princeton. She's the Richard Fisher Preceptor in Integrative Genomics, the Lewis-Sigler Institute for Integrative Genomics at Princeton, and director of the Paul Glenn Laboratories for Aging Research. Well, obviously you've been in this field for decades now, even though you're still very young. The classic paper that I can go back to would be in Nature 2003 with the DAF-16 and doubling the lifespan of C. elegans or better known as a roundworm. Would that be the first major entry you had?
Coleen Murphy (01:17):
Yeah, that was my postdoctoral work with Cynthia Kenyon.
Eric Topol (01:20):
Right, and you haven't stopped since you've been on a tear and you’ve put together a book which has a hundred pages of references in a small font. I don't know what the total number is, but it must be a thousand or something.
Coleen Murphy (01:35):
Actually, it's just under a thousand. That's right.
Eric Topol (01:37):
That's a good guess.
Coleen Murphy (01:38):
Good guess. Yeah.
Eric Topol (01:39):
So, because I too have a great interest in this area, I found just the resource that you've put together as extraordinary in terms of the science and all the work you've put together. What I was hoping to do today is to kind of take us through some of the real exciting pathways because there's a sentence in your book, which I thought was really kind of nailed it, and it actually is aligned with my sense. Obviously don't have the expertise by any means that you do here but it says, “A few years ago, I might have chuckled at the naivety of this question, but now it's not so crazy to think that we will be able to take some sort of medicine to extend our healthy lifespans in the foreseeable future.” That's a pretty strong statement for a person who's deep into the science. First I thought we'd explore healthy aging health span versus lifespan. Can you differentiate that as to your expectations?
Coleen Murphy (02:54):
So, I think most people would agree that they don't want to live necessary super long. What they really want to do is live a healthy life as long as they can. I think that a lot of people also have this fear that when we talk about extending lifespan, that we're ignoring that part. And I do want to assure everyone that the people in the researchers in the aging field are very much aware of this issue and have, especially in the past decade, I think put a real emphasis on this idea of quality of life and health span. What's reassuring is actually that many of the mechanisms that extend lifespan in all these model organisms also extend health span as well and so I don't think we're going to, they're not diametrically opposed, like we'll get to a healthier quality of life, I think in these efforts to extend lifespan as well.
Eric Topol (03:50):
Yeah, I think that's important that you're bringing that up, which is there's this overlap, like a Venn diagram where things that do help with longevity should help with health span, and we don't necessarily have to follow as you call them the immoralists, as far as living to 190 or whatever year. Now, one of the pathways that's been of course a big one for years and studied in multiple species has been caloric restriction. I wonder if you could talk to that and obviously there's now mimetics that could simulate that so you wouldn't have to go through some major dietary starvation, if you will. What are your thoughts on that pathway?
Coleen Murphy (04:41):
Yeah, actually I'm really glad you brought up mimetics because often the conversation starts and ends with you should eat less. I think that is a really hard thing for a lot of people to do. So just for the background, so dietary restriction or caloric restriction, the idea is that you would have to take in up to 30% less than your normal intake in order to start seeing results. When we've done this with laboratory animals of all kinds, this works from yeast all the way up through mice, actually primates, in fact, it does extend lifespan and in most metrics of health span the quality of life, it does improve that as well. On the other hand, I think psychologically it's really tough to not eat enough and I think that's a part that we kind of blindly ignore when we talk about this pathway.
Coleen Murphy (05:30):
And of course, if we gave any of those animals the choice of whether they want to start eating more, they would. So, it's like that's not the experiment we ever hear about. And so, the idea for studying this pathway isn't just to say, okay, this works and now we know how it works, but as you pointed out, mimetics, so can we target the molecules in the pathway so that we can help people achieve the benefits of caloric restriction without necessarily having to do the kind of awful part of restriction? I think that's really cool, and especially it might be very good for people who are undergoing certain, have certain diseases or have certain impairments that it might make it difficult ever to do dietary restrictions, so I think that's a really great thing that the field is kind of getting towards now.
Eric Topol (06:15):
And I think in fact, just today, it's every day there's something published now. Just today there was a University of Southern California study, a randomized study report comparing plant-based fasting-mimicking diet versus controlled diet, and showed that many metabolic features were improved quite substantially and projected that if you stayed on that diet, you'd gain two and a half years of healthy aging or that you would have, that's a bit of an extrapolation, but quite a bit of benefit. Now, what candidates would simulate caloric restriction? I mean, what kind of molecules would help us do that? And by the way, in the book you mentioned that the price to pay is that the brain slows down with caloric restrictions.
Coleen Murphy (07:10):
There's at least one study that shows that.
Coleen Murphy (07:13):
Yeah, so it's good to keep in mind. One of the big things that is being looked at as rapamycin, looking at that TOR pathway. So that's being explored as one of these really good mimetics. And of course, you have things that are analogs of that, so rapalogs, and so people are trying to develop drugs that mimic that, do the same kind of thing without probably some of the side effects that you might see with rapamycin. Metformin is another one, although it's interesting when you talk to people about metformin who work on it, it's argued about what is exactly the target of metformin. There's thought maybe also acts in the TOR pathway could affect complex one of mitochondria. Some of the things we know that they work, and we don't necessarily know how they work. And then of course there's new drugs all the time where people are trying to develop to other target, other molecules. So, we'll see, but I think that the idea of mimetics is actually really good, and that part of the field is moving forward pretty quickly. This diet that you did just mention, it is really encouraging that they don't have to take a drug if you don't want to. If you eat the right kind of diet, it could be very beneficial.
Eric Topol (08:20):
Yeah, no, it was interesting. I was looking at the methods in that USC paper and they sent them a box of stuff that they would eat for three cycles, multiple weeks per cycle. It was a very interesting report, we'll link to that. Before we leave the caloric restriction and these mTOR pathway, you noted in the book that there some ongoing trials like PEARL, I looked that up and they finished the trial, but they haven't reported it and it's not that large. And then there's the FAME trial with metformin. I guess we'll get a readout on these trials in the not-too-distant future. Right?
Coleen Murphy (08:57):
Yeah, that's the hope that especially with the Metformin trial, which I think is going to be really large the FAME trial, that just to give the listeners a little background, one of the efforts in the field is not just to show that something works, but also to convince the FDA that aging could be a pharmaceutical, a disease that we might want to have interventions for. And to do that, we need to figure out the right way to do it. We can't do 30-year studies of safety and things to make sure that something's good, but maybe there are reasonable biomarkers that would tell us whether people are going to live a long time. And so, if we can use some of those things or targeting age-related diseases where we can get a faster readout as well. Those are reasonable things that companies could do that would help us to really confirm or maybe rule out some of these pharmaceuticals as effective interventions. I think that would be really great for consumers to know, is this thing really going to do good or not? And we just don't have that right now in the field. We have a lot of people saying something will work and it might and the studies in the lab, but when we get to humans, we really need more clinical studies to really tell us that things are going to be effective.
Eric Topol (10:12):
Right, I'm going to get to that in a bit too because I think you're bringing up a critical topic since there's an explosion of biopharma companies in this space, billions of dollars that have been put up for in capital and the question is what's going to be the ground rules to get these potential candidate drugs to final commercial approval. But before I leave, caloric restriction and insulin signaling and the homolog and the human to what your discovery of DAF-16, FOXO and all this, I just want you to comment, it wasn't necessarily developed in the book, but as you know, the GLP-1 drugs have become just the biggest drug class in medical history, and they do have some effects here that are very interesting. They are being tested as in Alzheimer's disease. Do you see that this is a candidate too that might promote healthy aging?
Coleen Murphy (11:12):
Yeah, I'm so glad you brought that up because my book, I finished writing it right before all this stuff came out, and it's looking really very compelling. People are on these drugs, they lose a ton of weight, but their blood biomarkers really become very good and on top of just the changes in weight and those kinds of effects. Let me just say, I think the biggest thing, the biggest risk actually for aging people right now are cardiovascular problems, cardiovascular disease, and these drugs, no doubt, it's going to basically make a huge dent in that. I'm absolutely sure of that. What I also find really interesting with those drugs is that the users report that they have fewer cravings for other things. So, this is not being looked at to treat alcoholism and drug addiction, other things, so it really opens up a whole new world of things that are bad for us that maybe we could avoid this with these peptides. It's almost staggering. I really think this going to be a huge, and as far as an aging drug, if you reduce your weight, you improve all your cardiovascular function, you don't feel like drinking all the time, all these things might be really great and I do think that people will live longer.
Eric Topol (12:32):
Yeah, no, it does have that look and you just have to wonder if as these will go on to oral drugs with triple receptors and very potent, maybe even avoiding peptides in the future too, that this could wind up being something that's exceedingly common to take for reasons far removed from the initial indication of type two diabetes and more recently of course, obesity. Now the next topic I wanted to get into with you were senolytics, these agents that basically are thought to reverse aging or slow aging. And again, since everything's coming out in a daily basis, there was a trial in diabetes macular edema where giving senolytic after people had failed their usual VEGF treatment was highly successful. So, we're starting to see, at least in the eye results. I wonder if you could describe how you conceive this field of senolytics?
Coleen Murphy (13:41):
Actually, I think they've made great progress in the past couple of years because there were some initial failures, like some of the things for osteoarthritis that went through I think phase two, but I think that one of the great things about the longevity biotech field is that they're starting to identify not just longevity, these age-related disorders that they could actually use. And so, it's kind of doubly beneficial. It tells us that the drugs actually do something and so maybe it'll be used for something else in the future and you get through, you can test safety, but also helping people actually have a very real problem that's acute that they really need to take care of. And so that's really exciting. Then in addition to the example you just mentioned, I was at a conference last summer where it was being explored whether some of these senolytics could be helpful for middle aged survivors of childhood cancers who do show various health effects from having gone through chemotherapies at a young age. So that's really exciting. Could you help people who are not aging, but they actually are showing having problems that we kind of associate with aging. And senolytics were at least the first thing I'd heard about that are actually being used for that, so there may be other approaches that help as well, but I think that's really great.
Eric Topol (15:05):
Well, and just to be clear the senolytics, I guess could be categorized at least one function might be to help clear dead cells. These senescent cells are bad actors and either they're taken out or they're somehow neutralized in their impact of secreting evil humors, if you will. Are there other forms of senolytics besides that way of dealing with these senescent cells?
Coleen Murphy (15:33):
I know that some people are exploring senomorphs, so things that make those cells just arrest but I do want to mention, of course, we lost a great Judith Campisi recently, and she was the one who discovered and described the senescent associated secretory phenotype, and she did amazing work in that field really opening that up. So, this idea that bad cells aren't just bad because they don't function, but they're actually toxic to other cells.
Coleen Murphy (16:04):
That's important for listeners to know. Yeah, so I don't know. I think that one of the things I'm excited about in the aging field is that it doesn't seem like there's one magic bullet. A lot of researchers will spend their time working on that one thing so if you only talk to that one person, you might get that impression, but there's a whole host of things that for bad or good, that things go wrong when we age, but those all end up being maybe targets that could help us live longer or at least in a healthier way. And so, we've already talked about a couple of them, but readers will see as we learn more, there might be more ways to help cells survive or to help us replace ourselves, for example.
Eric Topol (16:45):
I mean, I think what you're bringing up here is central because there's all these different, as I can see it, shots on goal that of course could be even used as combinations, no less senolytic interventions so we're getting closer as we started this conversation to fulfilling what you, I think is in store in the years ahead, which is extraordinary. Along with the senolytics, I wonder if you could just talk a little bit about these autophagy enhancers as a class of agents, maybe first explaining autophagy and then is this a realistic goal that we should be taking autophagy enhancers, or is this something that's too generalized that might have onward mTOR effects?
Coleen Murphy (17:39):
Well, it's interesting. Autophagy, so just for the listeners, autophagy literally means self-eating. So this is a pathway whereby proteins basically get degraded within the cell and those parts get recycled. And the idea is that if you have a cell or protein that's damaged in some way, or it can be renewed if you induce autophagy. I think I could be wrong here, but my sense is that the cancer field is really excited about autophagy enhancers. And so, I think that's probably where we'll see the biggest breakthroughs but along the way, of course we'll know because we'll know if they're safe and if there's other off-target effects. I think that that's largely being driven by the cancer field and the longevity field is kind of a little bit behind that, so we'll learn from them. It seems like a really exciting approach as well.
Eric Topol (18:34):
Yeah, it does. And then as you know, the idea of giving young blood, young plasma, which there already are places that do this, that it can help people who are cognitively impaired and have basically immediate effects, and sometimes at least with some durability. It's very anecdotal, but this idea, we don't know what's in the young blood or young plasma to some extent. How do you process that?
Coleen Murphy (19:10):
Okay. Well, so what we do know, and this is really work that a lot of people like Saul Villeda and Tony Wyss-Coray have done where they really have, they've taken that blood or plasma and then found the parts in the plasma that actually do specific jobs. And so, we actually are starting to learn a lot about that and that's exciting because of course, we don't really want to give people young blood. What we really would like to do is find out is there a particular factor in the blood? And there seems to be many that could be beneficial. And so, we really are getting close. We as a field, and specifically like the research I just mentioned and that's exciting because you can imagine, for example, if there's one factor that's in blood, that's in young blood, that's very helpful, manufacturing, a lot of that particular thing.
Coleen Murphy (20:01):
The other exciting thing, again, this is Saul Villeda’s lab that found that exercise mice. So even if they're the same age mice, if one of them is exercised, it makes factors that actually from the liver of the mouse upon exercise, that then gets secreted and then affect, improve cognitive function as well. So it seems like even within the blood, there's multiple different ways to get blood factors that are beneficial, whether they're from young blood or from exercise blood. And so, there's a lot of things we don't yet know, but I do think that field is moving very fast and they're identifying a lot of things. In fact, so I'm the director of Simons Collaboration Plasticity in the Aging Brain, and on that website we're developing basically a page that can tell you what are the factors and what has it been shown to be associated with, because we're very interested in slowing normal cognitive aging and blood factors seem to be one of the really powerful ways that might be available to us very soon to be able to improve that.
Eric Topol (21:03):
Yeah, no, I'm glad you mentioned that, Coleen. I think the point that you made regarding exercise, I certainly was struck by that because in the book, because we've known about this association with exercise and cognition, and this I think is certainly one potential link. An area that is also fascinating is epigenetics, so a colleague of mine here in the Mesa, Juan Carlos Belmonte, who was at Salk and left to go to Altos, one of these many companies that are trying to change the world in health span and lifespan. Anyway, he had published back several years ago.
Coleen Murphy (21:53):
Yeah, 2016.
Eric Topol (21:54):
Yeah, CRISPR basically modulation of the epigenome through editing and showed a number of through specific pathways, a number of pretty remarkable effects. I wonder if you could comment about epigenetics, and then I also want to get into this fascinating topic of transgenerational inheritance, which may be tied of course to that. So, what about this pathway? Is there something to it?
Coleen Murphy (22:29):
Well, absolutely. I just think we need to learn a lot more about it. So just for the listener, so epigenetics, we think about genetics that's basically based on DNA and chromosomes. And so, when we think about epigenetics, that could be either, we could be talking about modulation of the histone marks on the chromosomes that allow the genes to be expressed or be silenced. And then on the DNA itself, there are methylation marks. And so, people have used, of course, Steve developed a, sorry, I'm sorry. Steve Horvath developed a very nice, he was first to develop a DNA methylation clock. So this idea that you could, and that was really interesting because he based it on, he used this machine learning method to narrow down to the 353 marks that were actually predictive or correlated with age, but we don't understand how it biologically what that manifests in. I think that's not well understood. At the chromatin level, there's a lot of work on the specific histone marks that may change, for example, how genes are transcribed and so understanding that better will maybe help us understand what those changes. There's things called epigenetic drift, so genes stop being carefully regulated with age, and then how can we make that maintain better with age? It's one of the goals of the field in addition to basically understanding what's going on at the epigenetic level.
Eric Topol (24:01):
So now of course, could we alter that? Oh, it is fascinating as you say, that you could have the Horvath clock to so accurately predict a person's biological age. And by the way, just a few days ago, there was a review by all these clock aging folks in nature medicine about the lack of standards. There's so many clocks to basically determine biological age versus chronological age. Before we get into the transgenerational inheritance, what is your sense? Obviously, these are getting marketed now, and this field is got ahead of its skis, if you will, but what about these biologic age markers?
Coleen Murphy (25:02):
Yeah, I'm glad to hear that. I haven't seen that review. I should look it up. It's good to know that the players in the field are addressing those points. So just for the listeners, so these DNA methylation clocks so when Steve Horvath developed the first one, it was based on the controls from a very large number of cancer controls for other reasons, so he used a huge amount of information. It really depended on the, he was trying to develop a clock that was independent of which tissue, but it turned out there's more and more clocks that are tissue specific and really organism specific, species specific. It really depends on what you're looking at to make these, and whether you're looking at chronological age or trying to predict biological age. I think it's a little frustrating because what you'd really like to know as a consumer, if you send off for one of these clock kits, is it right?
Coleen Murphy (25:57):
What's the margin of error? If I took it every week, would I get the same number? And so, I think my sense is that people take it until they get a low number then, but you'd really like to know if they work, because if you want to take it, do a control and they start, get your clock number and then start taking some intervention and ask whether it works, right? Yeah. So, I think because the players in the field recognize these issues, they're going to straighten it out, but I think one part that drives a little bit of the problem is that we don't understand what that DNA methylation mark change translates into biologically. If we understood that better, I think we'd have a better feeling about it. Anne Brunet and Tony Wyss-Coray maybe a year and a half ago, they had a nice paper where two years ago where they looked at, they use a different type of clock, a transcriptional clock, and that worked really well. So they were looking at transcriptional clock in the subventricular zone, and they were able to actually see changes not just with age, but also when there was an intervention. I can't remember if they look at dietary restriction and then maybe an exercise in the mice. And so that's important for us to know how well those clocks work.
Coleen Murphy (27:13):
I think it'll get there. It'll get there.
Eric Topol (27:15):
You don't want to pay a few hundred dollars and then be told that you're 10 years older biologically than your chronologic age, especially if it's wrong. Right?
Coleen Murphy (27:25):
Yes. It'll get there. I think it may not be quite there yet.
Eric Topol (27:30):
And by the way, while we're on that, the organ clocks paper, in fact, just a recent weeks, I did interview Tony Wyss-Coray from Stanford, and we talked about what I consider really a seminal paper because using plasma proteins, they're able to basically clock each organ. And that seems like a promising approach, which could also help prove the case that you're changing something favorably with one of these various intervention classes or categories. Do you think that's true?
Coleen Murphy (28:05):
That feels more real directly looking at the proteins then.
Eric Topol (28:08):
Yeah, exactly. I thought that was really exciting work, and I'm actually going to visit with Tony in a few weeks to discuss it further. So excited about it.
Coleen Murphy (28:18):
That's great. He's doing great work, so it'll be a fascinating conversation.
Eric Topol (28:21):
Yeah, well this is also fascinating. Now, transgenerational inheritance is a very controversial topic in humans, which it is not so much in every other species. Can you explain why that is?
Coleen Murphy (28:38):
Well, there's a lot of, I would say emotional baggage attached here, right? Because that's what people are talking about, like transgenerational trauma. There's no doubt that traumatic experiences in childhood actually do seem to change the genome and change have very real biological effects. And that's been shown. So that's within the first generation. It's also no doubt that in other organisms, like in plants like DNA methylation, that's exactly how they regulate things, and that's multiple generations. So that's kind of the norm. And so, the question for humans is whether something like this, like a traumatic experience or starvation or thing, has an effect, not just on the person who's experiencing it, but also on their progeny, even on their grand progeny. And so, it's tough, right? Because the data that are out there are from pretty terrible experiences like the Dutch hunger winter. And so, there's a limited set of data, and some of those data look good, and some of them look weaker. Yeah, I think that we still need to figure out what's going on there, and if it's real, it'd be interesting to know. Are there ways, for example, with these epigenetic modulators, are there ways that you could help people be healthier by erasing some of those marks of trauma, generational trauma?
Eric Topol (30:03):
Yeah. So, I mean, the theory as you're getting to would be you could change the epigenome, whether it's through chromatin, acetylation, methylation, somehow through these experiences and it would be going through down through multiple generations. The reason I know it's controversial is when I reviewed Sid Mukherjee's book, the Gene, he had put in that it was real in humans, and the attack dogs came out all over the place. Now, we've covered a lot of these pathways. One that we haven't yet touched on is the gut microbiome, and the idea here, of course, it could be somewhat linked to the caloric restriction story, but it seems to be independent of that as well. That is there, our immunity is very much influenced by our gut microbiome. There's the gut brain axis and all sorts of interactions going on there, but what about the idea of using probiotics and particular bacterial species as a introducing the people as an idea in the future to promote health span?
Coleen Murphy (31:18):
Yeah, it's a great idea. So, I just want to back up and say the microbiome, the reason it's so fraught is because for a long time, people had confused correlation and causation. So, they would see that a person who has X disease has a difference in the microbiome from people who don't have that disease. And so, the question was always, do they have that disease because of a difference in the microbiome or the disease influence in the microbiome? And of course, even things that's eating different food. For example, if a child with autism doesn't want to eat certain range of food, it's going to have an effect on the microbiome. That does not mean the microbiome cause their autism. And so that's something where, and the same thing with Alzheimer's disease patients. I think that's often the source of some of this confusion. I think people wish that they could cure a lot of diseases by taking a probiotic.
Coleen Murphy (32:09):
On the other hand, now there's actually some really compelling data. Dario Valenzano's lab did a really nice experiment in killifish, which is my second favorite aging model research organism. So killifish, turquoise killifish, only live a few months. And so, you can do aging studies really quickly and what Dario's group did was they took the microbiome at middle aged fish, they wiped out their microbiome with antibiotics, and they added back either young or same age, and they saw a really nice extension of lifespan with the young microbiome. So that suggests, in that case where everything else is the same, it really does have a nice effect. John Cryan’s group in Ireland did something similar with mice, and they showed that there was a beneficial effect on cognitive function in older mice. So those are two examples of studies where it really does seem like there is an effect, so it could be beneficial. And then there's of course things like microbiome transfer for people who are in the hospital who have had other things, because your microbiome also helps you prevent other diseases. Those being there, if you wipe out all of your microbiome, you can actually get infected with other things. It's actually a protective barrier. There's a lot of benefits, I think in order to, we don't know a ton about how to control it. We know there are these, it's gross, but fecal microbiome transplantation.
Eric Topol (33:42):
FMT. Yeah, yeah.
Coleen Murphy (33:44):
Exactly. And so, I think that is kind of the extreme, but it can be done. I think in appropriate cases it could be a very good strategy.
Eric Topol (33:53):
It's interesting. There was a study about resilience of the immune system, which showed that women have a significant advantage in that they have just the right balance of not having a hyper inflammatory reaction to whether it's a pathogen or other stimulus. And they also have, of course, an immunocompetent system to respond, so unlike men overall, that although the problem of course with more prone to autoimmunity because of having two x chromosomes and exist or whatever other factors. But also, there's a balance that there's an advantage, in the immune system as a target for health span and lifespan, a lot of things that we've talked about have some interaction with the immune system. Is there anything direct that we can do to promote a healthier immune system and avoid immunosenescence and inflammaging or immuno aging or whatever you want to call it?
Coleen Murphy (35:04):
Sure, I will admit that immunology is a field that I want to learn more about, but I do not know enough about it to give a really great answer. I think it's one of the things I kind of shied away from when I wrote the book that if I were to rewrite it, I would add a whole new section on it. I think that's a really booming field, this interaction between immunology and aging. Obviously, there's immune aging, but what does that really mean?
Coleen Murphy (35:28):
I feel like I can't give you a really intelligent answer about that. Even though I'd like to, and I don't know how much of it's because there's just sort of this general idea that the immune system stops functioning well, but I do feel like the immune system is actually so mysterious. I have a peanut allergy, for example. We don't even really, I mean, we can prime ourselves against that now. We can give kids little bits of peanuts, but all the things that I feel like immunology is the one that's probably taking off the most, and we'll probably in a decade know way more about it than we do now, but I can't give you a very smart answer right now.
Eric Topol (36:09):
Yeah, no, I do think it's really provocative and the fact that if you have these exhausting T cells that are basically your backup system of your immune system, if they're not working, that's not good. And maybe they can be revved up without being problematic. We'll see.
Coleen Murphy (36:27):
And I guess the real question is do we need to do something independent or is that folded into everything else? If you were giving someone a drug that seemed very good systemically or some of these blood factors, would you have to do something special just for the immune system or is that something that would also be effective? I feel like that would be good to know.
Eric Topol (36:44):
Now the other area that I want to bring up, which is a little more futuristic is genome editing. So recently when I spoke to David Liu, he mentioned, well, actually it was Jennifer Doudna who first put it out there, but we discussed the idea of changing the people like me who are APOE4 carriers to APOE2, which is associated with longer life and all these other good things. Why don't we just edit ourselves to do that? Is that a prospect that you think ever could be actualized?
Coleen Murphy (37:20):
Well, I was just at a talk by Britt Adamson just moments ago, and that field is moving really fast, right? All the work that David Liu has done, and it's really exciting, this idea that you can now cure sickle cell anemia.
Coleen Murphy (37:35):
Fascinating. And I think Jennifer Doudna rightly proposed early on that what we should really be hitting first are like blood. Blood's really good because it's not hitting the germline. It's really something where we can help people at that stage. I was thinking about that while Britt was talking, what are the things we'd really want to address with CRISPR? I'm not sure how high up in the list aging related factors would be compared to a lot of childhood diseases, things that are really debilitating, but certainly is true since when we're looking at APOE4. I think that's the one exception because that is so strongly correlated with healthy lifespan and Alzheimer's and things, so we really want to do something about that. The question is how would we do that? That's not a blood factor. I think we'd have to think hard about that, but it is on the list of looming on the horizon.
Eric Topol (38:35):
I wouldn't be surprised if someday, and David, of course thought it's realistic, but it's not, obviously in the short term. Well, this has been enthralling to go through all these possibilities. I guess when you put it all together, there's just so many ways that we might be able to, and one of the things that you also pointed out in your book, which something that should not be forgotten, is the fact that all these things could even worsen the inequities that we face today. That is you have any one of these click, if not multiple, it isn't like they're going to be available to all. And the problem we have now, especially in this country without universal health and access issues, could be markedly exacerbated as we're seeing with the GLP-1 drugs too, by the way.
Coleen Murphy (39:27):
Absolutely.
Eric Topol (39:28):
So, I just want to give you a chance to reinforce what you wrote in the book, because I think this is where a lot of times science leads and doesn't realize the practical implications of who would benefit.
Coleen Murphy (39:42):
Yeah, I think actually for aging research often, even when I first started doing this work back in 2000, the first thing people would ask me if they're below a certain age was, don't you think that's terrible? Make the rich people just live the longest? And they're not wrong about that. I think what it can, we should raise awareness about the fact that even these things that we consider simple, like doing caloric restriction or getting exercise, even those things are not that straightforward if you're working two jobs or if you don't have access to excellent foods in your neighborhood, right? Fruits and vegetables. If we really want to not just extend longevity but raise life expectancy, then we should be doing a lot more that's for improving the quality of life of many people. And so there is that idea. On the other hand, I do want to point out that as we discover more and more of these things, like metformin is off patent, it's like it's really old. And so, it's more of these things get discovered and more broadly used. I do think that that may be a case where we could end up having more people might have access to things more easily. So that's my hope.
Coleen Murphy (40:57):
I don't want to discourage anyone from developing a longevity dry. I think eventually that could help a lot of people if it's not too absurdly expensive.
Eric Topol (41:04):
Yeah, no, I certainly agree. And one last footnote is that we did a study called The Wellderly here, about 1,400 people over age 85 who'd never been sick, so our goal here wasn't lifespan. It was to understand if there was genomics, which we did whole genome sequencing of this group. We didn't find much like the study that you cited in the book by the Calico group. And so just to give hope that people, if they don't have what they think are family genetics of short life or short health span, that may not be as much to that as a lot of people think. Any final thoughts about that point? Because it's one that's out there and data goes in different directions.
Coleen Murphy (41:55):
Yeah. The Calico study you mentioned, I think that's the one where they found that your health or lifespan mostly went with almost like your in-laws, which actually points again to your socioeconomic group probably you marry people, most people marry people are in a similar socioeconomic group. That's probably what that mostly had to do with. I do think if I'm going to say one thing because a lot of these drugs are on the horizon, they're not yet available, or there's nothing I can hang onto for an FDA approved drug to extend that. I do think the one thing that I would encourage people to do even more than the dietary restriction stuff, it is exercise because that's just generally beneficial in so many different ways. And so, if we can get people doing a little more exercise, I think that would be the one thing that probably could help a lot of people.
Eric Topol (42:40):
Well, I'm glad we are winding up with that because I think the data from lifestyle, which is exercise as you're pointing out, as well as nutrition and sleep.
Coleen Murphy (42:54):
All the boring things we already thought, right.
Eric Topol (42:55):
That we know about, but we don't necessarily put in our daily lives. There's a lot there. There's no question that studies, I think, really have reinforced that even recent one. Well, what a pleasure to talk to you about this and do this tour of the various exciting prospects. I hope I haven't missed anything. I know we can't go over all the pathways, and obviously there've been some bust in the past, which we don't need to review like the famous Resveratrol Sirtuin story, which you addressed in the book. I do want to encourage people that this book is extraordinary. Your work that you put into it had to be consumptive for I don't know how many years of work.
Coleen Murphy (43:37):
There was many years of work. My editor, we sat down to lunch right after it finished. She was like, so what are you going to work on for your next book?
Eric Topol (43:50):
Well, it's a scholarly approach to a very important field. If you can influence the aging process, you influence every part of our body function. The impact here is profound, and the contribution that you've made in your science as well as in your writing here is just so terrific. So thank you, Coleen. Thanks so much for joining us today.
Coleen Murphy (44:17):
Thank you so much. It's been a pleasure.
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Transcript with audio and relevant external links, recorded on 6 Feb 2024
Eric Topol (00:05):
Hello, this is Eric Topol with Ground Truths, and I have a remarkable guest with me today, Professor Michelle Monje, who is from Stanford, a physician-scientist there and is really a leader in neuro-oncology, the big field of cancer neuroscience, neuroinflammation, and she has just been rocking it recently with major papers on these fields, no less her work that's been on a particular cancer, brain cancer in kids that we'll talk about. I just want to give you a bit of background about Michelle. She is a National Academy of Medicine member, no less actually a National Academy of Medicine awardee with the French Academy for the Richard Lounsbery Award, which is incredibly prestigious. She received a Genius grant from the MacArthur Foundation and is a Howard Hughes Medical Institute (HHMI) scholar, so she is just an amazing person who I'm meeting for the first time. Michelle, welcome.
Michelle Monje (01:16):
Thank you. So nice to join you.
Long Covid and the Brain
Eric Topol (01:18):
Well, I just am blown away by the work that you and your colleagues have been doing and it transcends many different areas that are of utmost importance. Maybe we can start with Long Covid because that's obviously such a big area. Not only have you done work on that, but you published an amazing review with Akiko Iwasaki, a friend of mine, that really went through all the features of Long Covid. Can you summarize your thoughts about that?
Michelle Monje (01:49):
Yeah, and specifically we focused on the neurobiology of Long Covid focusing on the really common syndrome of cognitive impairment so-called brain fog after Covid even after relatively mild Covid. There has been this, I think really important and exciting, really explosion of work in the last few years internationally trying to understand this in ways that I am hopeful will be beneficial to many other diseases of cognition that occur in the context of other kinds of infections and other kinds of immune challenges. But what is emerging from our work and from others is that inflammation, even if it doesn't directly initially involve the nervous system, can very profoundly affect the nervous system and the mechanisms by which that can happen are diverse. One common mechanism appears to be immune challenge induced reactivity of an innate immune cell in the nervous system called microglia. These microglia, they populate the nervous system very early in embryonic development.
(02:58):
And their job is to protect the nervous system from infection, but also to respond to other kinds of toxic and infectious and immune challenges. They also play in healthy conditions, really important roles in neurodevelopment and in neuroplasticity and so they're multifaceted cells and this is some population of those cells, particularly in the white matter in the axon tracks that are exquisitely sensitive it seems to various kinds of immune challenges. So even if there's not a direct nervous system insult, they can react and when they react, they stop doing their normal helpful jobs and can dysregulate really important interactions between other kinds of cells in the brain like neurons and support cells for those neurons like oligodendrocytes and astrocytes. One common emerging principle is that microglial reactivity triggered by even relatively mild Covid occurring in the respiratory system, not directly infecting the brain or other kinds of immune challenges can trigger this reactivity of microglia and consequently dysregulate the normal interactions between cells and the brain.
(04:13):
So important for well-tuned and optimal nervous system function. The end product of that is dysfunction and cognition and kind of a brain fog impairment, attention, memory, ability to multitask, impaired speed of information processing, but there are other ways that Covid can influence the nervous system. Of course there can be direct infection. We don't think that that happens in every case. It may not happen even commonly, but it certainly can happen. There is a clear dysregulation of the vasculature, the immune response, and the reaction to the spike protein of Covid in particular can have very important effects on the vessels in the nervous system and that can trigger a cascade of effects that can cause nervous system dysregulation and may feed directly into that reactivity of the microglia. There also can be reactivation of other infections previous, for example, herpes virus infections. EBV for example, can be reactivated and trigger a new immune challenge in the context of the immune dysregulation that Covid can induce.
(05:21):
There also can be autoimmunity. There are many, we're learning all the different ways Covid can affect the nervous system, but autoimmunity, there can be mimicry of some of the antigens that Covid presents and unfortunate autoimmunity against nervous system targets. Then finally in severe Covid where there is cardiopulmonary compromise, where there is hypoxia and multi-organ damage, there can be multifaceted effects on the nervous system in severe disease. So many different ways, and probably that is not a comprehensive list. It is certainly not a mutually exclusive list. Many of these interactions can happen at the same time in the same individual and in different combinations but we're beginning to wrap our arms around all the different ways that Covid can influence the nervous system and cause this fairly consistent syndrome of impaired attention, memory, multitasking, and executive functions.
Homology with Chemo Brain
Eric Topol (06:23):
Yeah, well there's a lot there that you just summarized and particularly you highlighted the type of glia, the microglia that appear to be potentially central at least a part of the story. You also made analogy to what you've seen with chemotherapy, chemo brain. Maybe you could elaborate on that.
Michelle Monje (06:42):
Yeah, absolutely. So I've been studying the cognitive impairment that can happen after cancer therapies including chemotherapy, but also radiation and immunotherapy. Each time we develop a new model and dig in to understand what's going on and how these cancer therapies influence the nervous system, microglia emerge as sort of the unifying principle, microglial reactivity, and the consequences of that reactivity on other cell types within the nervous system. And so, understanding that microglia and their reactive state to toxic or immune challenges was central to chemotherapy induced cognitive impairment, at least in preclinical models in the laboratory and confirm by human tissue studies. I worried at the very beginning of the pandemic that we might begin to see something that looks a lot like chemotherapy induced cognitive impairment, this syndrome that is characterized by impaired attention, memory, executive function, speed of information processing and multitasking. When just a few months into the pandemic, people began to flood neurologists’ office complaining of exactly this syndrome. I felt that we needed to study it and so that was the beginning of what has become a really wonderful collaboration with Akiko Iwasaki. I reached out to her, kind of cold called her in the midst of the deep Covid shutdown and in 2020 and said, hey, I have this idea, would you like to work with me? She's as you know, just a thought leader in Covid biology and she's been an incredibly wonderful and valuable collaborator along the way in this.
Eric Topol (08:19):
Well, the two of you pairing up is kind of, wow, that's a powerful combination, no question. Now, I guess the other thing I wanted to get at is there've been many other studies that have been looking at Long Covid, how it affects the brain. The one that's frequently cited of course is the UK Biobank where they had CT or MRI scans before in people fortunately, and then once they had Covid or didn't get Covid and it had a lot of worrisome findings including atrophy and then there are others that in terms of this niche of where immune cells can be in the meninges, in the bone marrow or the skull of the brain. Could you comment on both those issues because they've been kind of coming back to haunt us in terms of the more serious potential effects of Covid on the brain?
Michelle Monje (09:20):
Yeah, absolutely and I will say that I think all of the studies are actually quite parsimonious. They all really kind of point towards the same biology, examining it at different levels. And so that UK Biobank study was so powerful because in what other context would someone have MRI scans across the population and cognitive testing prior to the Covid pandemic and then have paired same individual tests after a range of severity of Covid infection so it was just an incredibly important data set with control individuals in the same cohort of people. This longitudinal study has continued to inform us in such important ways and that study found that there were multiple findings. One is that there appears to be a small but significant atrophy in the neocortex. Two that there are also abnormalities in major white matter tracts, and three, that there is particular pathology within the olfactory system.
(10:30):
And we know that Covid induces as a very common early symptom, this loss of smell. Then together with those structural findings on MRI scans that individuals even with relatively mild acute disease, exhibited long-term deficits in cognitive function. That fits with some beautiful epidemiological studies that have been done across many thousands of individuals in multiple different geographic populations. Underscoring this consistent finding that Covid can induce lasting cognitive changes and as we begin to understand that biology, it fits with those structural changes that are observed. We do know that the olfactory system is particularly affected and so it makes sense that the olfactory system, which show those structural changes, the neocortical and white matter changes evident on MRI fit with what we found microscopically at the cellular and molecular level that highlighted a loss of myelinating oligodendrocytes, a loss of myelinated axons, a deficit in hippocampal new neuron production. All of those findings fit together with the structural changes that the UK Biobank study highlighted. So clearly this is a disease that has lasting impacts, and the challenge is to understand those better so that we can develop effective interventions for the many, many millions of people who are still struggling with decreases in their cognitive function long after Covid exposure affecting the world population.
The Brain’s Immune System
Eric Topol (12:17):
Yeah, that's a great summary of how the Biobank data UK aligned with the work that you've done and I guess the other question just to round this out is for years we didn't think the brain had an immune response system, right? Then there's been a wakeup call about that, and maybe you could summarize what we know there.
Michelle Monje (12:41):
Absolutely. Yes, the brain is not, we used to call the nervous system an immuno privilege site, and it is not hidden from the immune system. It has its own and distinct immune system properties, but it's very clear from work by Jony Kipnis and others that there are in fact lymphatics in the nervous system. These are in the meninges. It's also become increasingly clear that there is a unique bone marrow niche in the skull from which many of the lymphocytes and other kinds of immune cells that survey and surveil the brain and spinal cord, that's where they come from. That's where they develop and that's where they return and the lymphatic drainage of the nervous system goes to distinct places like the posterior cervical lymph nodes. We are now understanding the sort of trafficking in and out of the nervous system of cells, and certainly understanding how that changes in the context of Covid, how those cells may be particularly responsive to the immune challenge initiated in the respiratory system is something that is an area of deep importance and active exploration. In fact, some of my ongoing collaborations and ongoing lab work focuses on exactly this question, how does the trafficking from the brain borders into the nervous system change after Covid? And how does potentially cellular surveillance of immune cells contribute of the nervous system contribute to the persistent microglial reactivity that we observe?
Eric Topol (14:22):
And do you have any hunch on what might be a successful worthwhile therapy to a candidate to test prospectively for this?
Michelle Monje (14:30):
I think it's too early to nominate candidates, but I think that the biology, the molecular and cellular biology is underscoring a role for particular cytokines and chemokines that are initiated by the immune response in the lung. And clear cellular targets, the goal I think the central goal being to normalize the neurovasculature and normalize microglial reactivity and so the question in this disease context and in others becomes, how can we kind of molecularly coach these reactive cells to go back to doing their normal jobs to being homeostatic? That's the challenge, but it's a surmountable challenge. It's one that I think that the scientific community can figure out, and it will be relevant not only to Covid, but also to many other consequences of immune challenges, including other post-infectious syndromes. It's not only Covid that causes long-term cognitive and other kinds of neurological and neuropsychiatric consequences. We saw this after the influenza pandemic in 1918. We've seen it after many other kinds of infectious challenges and it's important as we prepare for the next pandemic for the next global health challenge that we understand how the long-term consequences of an immune response to a particular pathogen play out.
Eric Topol (15:58):
No question and that I guess also would include myalgic encephalomyelitis and all the other post-infectious post viral syndromes that overlap with this. Now to switch gears, because that work is just by itself extraordinary but now there's this other field that you are a principal driver, leader, and that is cancer neuroscience. I didn't even know they had boards in neuro-oncology. I thought neurology was enough, but you got board certified in that too. This field is just exploding of interest because of the ability for cancer to cells to hijack neurons and neural circuits, which I guess the initial work goes way back but more recently, the fact that gliomas were just electrically charged. And so maybe you can frame this because this has not just amazing biology, but it's also introducing all sorts of therapeutic opportunities, including many ongoing trials.
The Neuroscience of Cancer
Michelle Monje (17:08):
Yes, yes and thank you for asking me about it. It's certainly one of my favorite things to think about, and perhaps as a bridge between the cognitive impairment that occurs after Covid and other inflammatory challenges and the neuroscience of cancer. I'll just highlight that maybe the common theme is it's important to understand the way cells talk to each other and that these sort of molecular conversations are happening on multiple scales and in unexpected ways, and they shape pathophysiology in a very important way. So continuing on that theme, we've known for many, many years, for decades in fact, that the nervous system and its activity shapes the development of the nervous system and actually it doesn't just shape the development of the nervous system where perhaps it's intuitive that the activity within the nervous system might sculpt the way that it forms, but it turns out that innervation is critical for development broadly, that innervation is necessary for organogenesis and that this is becoming clear in every organ that's been studied.
(18:15):
And so it stands to reason given that kind of perspective on the role that neuronal activity plays in normal development, plasticity, homeostasis, and regeneration of many different tissues, that the activity of the nervous system and those principles can be hijacked in the context of cancer, which is in many ways a disease of dysregulated development and regeneration. And so, I'm a neuro oncologist, I take care of children with a very terrible form of brain cancer called high-grade glioma and the most common form of high-grade glioma in kids occurs in the brain stem, it's called diffuse intrinsic pontine glioma (DIPG). It's really the worst disease you can imagine and understanding it has been the need to understand and treat it has been a guiding principle for me. And so, taking a big step back and trying to wrap my arms around the biology of these terrible high-grade gliomas like glioblastoma, like diffuse intrinsic pontine glioma, I wondered whether nervous system activity might influence cancer the way that it influences normal development and plasticity.
(19:23):
And as soon as we started to leverage tools of modern neuroscience like optogenetics to ask those questions to modulate the activity of neurons in a particular circuit and see how that influences cancer proliferation and growth, it was clear how very important this was, that active neurons and various subtypes very robustly drives the growth of these brain cancers. And so trying to understand the mechanisms by which that occurs so that we can target them therapeutically, it's become clear that the tumors don't just respond to activity regulated growth signals. They do. There are those paracrine factors, but that in brain cancer, the cancers actually integrate into the neural circuits themselves. That there are bonafide electrophysiological functional synapses that form between various types of neurons and high-grade glioma cells. We're discovering the same can occur in brain metastases from different organs, and that this principle by which neuronal activity drives the cancer is playing out in other tissues.
(20:32):
So right when we made these discoveries about glioma within this few years, discoveries were made in prostate cancer, in gastric cancer, colon cancer, skin cancer, pancreatic cancer. It seems that innervation is critically important for those tumor, and not just for their growth, but also for invasion metastasis, even initiation in diseases that are driven by particular oncogenes. There's an intersection between the power of those oncogenes to cause the cancer and the necessary environment for the cancer to form and that appears to also be regulated by the nervous system in very powerful ways. So, the exciting thing about recognizing this relatively unsettling feature of cancers is that as we understand it, the neuroscience of cancer becomes an entirely new pillar for therapy to combine with immunotherapy and more traditional cytotoxic therapies and we've been missing it until now. And so the opportunity exists now to leverage medicines that were developed for other reasons, for indications in neurology and cardiology and psychiatry medicines that target neurotransmitter receptors and ion channels that it turns out have a role in some forms of cancer. Now, each cancer has its own biology, so different types of neurons, different neurotransmitters, different neuropeptides play specific roles in that tissue context, but the principle is the same and so as we understand each cancer, we can start to understand what neuroscience inspired medicines we might leverage to better treat these tumors.
Rewriting the Hallmarks of Cancer
Eric Topol (22:17):
Yeah, I mean it's amazing as a cardiologist to think that beta blockers could be used to help people with cancer and of course there are trials and some studies and particular cancers in that. One of the things that people maybe not outside of oncology don't follow these papers about hallmarks of cancer. There's been two editions, major editions of the hallmarks of cancer, and recently in the journal of cancer Cell, Douglas Hanahan and you wrote a classic about that the hallmarks need to be revised to include neuroscience. Maybe you could elaborate on that because it seems like this is a missing frontier that isn't acknowledged by some of the traditional views of cancer.
Michelle Monje (23:08):
Absolutely. So I think number one, I want to just give a shout out to Doug Hanahan and the role that the hallmarks of cancer, which is a review article that he wrote and has become sort of the Bible, if you will, of cancer biology really laying out common principles across cancer types that have provided a framework for us to understand this complex and diverse heterogeneous set of diseases. And so it was very exciting when he reached out and asked if I wanted to write this perspective, culminating nervous system interactions, neuroscience interactions as an emerging hallmark of cancer and as we examine them from that, we examine the neuroscience of cancer from that heuristic set of principles, this framework of principles of cancer biology, it's clear that there is a neural influence on the vast majority of them. We now understand from this exciting and burgeoning field that the nervous system can regulate cancer unregulated proliferation.
(24:17):
It promotes proliferation and growth. It promotes invasion and metastasis. It alters the immune microenvironment. It can both promote pro-tumour inflammation through neurotransmitter signaling. It can also help to modulate anti-tumor immunity. The crosstalk between immune cells, cancer cells and the nervous system are complex, profound, and I would argue incredibly important for immunotherapeutic approaches for cancer. At the same time that there are these diverse effects of the nervous system on cancer, cancer also influences the nervous system. And so, there's really this bidirectional crosstalk happening by which neurons in an activity dependent way, either in short range local neurons or in long range down a nerve or across a circuit, promote the pathophysiology of the cancer and you kind of know it's beneficial because the cancer does many different active things to increase innervation of the tumor. There is in a variety of different tissue context and disease states, elaboration of nervous system interactions through cancer derived either axonogenic or synaptogenic factors secretion, the nervous system remodels the nerves. It remodels the neural circuits to increase the connectivity of the nervous system with the cancer, and also to increase the activity of the nerves to increase the excitability of a neuron. And this contributes to not only driving the cancer, but to many of the really important symptoms that patients face with cancer, including tumor associated seizures as well as cancer associated pain.
Eric Topol (26:07):
Yeah, I mean this is actually so unusual to see a whole another look at what cancer is about. I mean, this is about as big a revision of thinking as I've seen at least in many, many years. The fact that you pulled this together about the new hallmarks also made me wonder because a number of years ago we went through this angiogenesis story whereby like this cancer can hijack blood vessels and promote it to growth. As you know very well, a lot of these anti-angiogenic efforts didn't go that well. That is they maybe had a small impact overall, but they didn't change the field in terms of success of therapy. I wonder if this is going to play out very differently. What are your thoughts about that? There's lots of shots on goal here and the trials have sprouted out very quickly to go after this.
Michelle Monje (27:12):
Yeah. I think it's important to recognize various microenvironmental effects on a cancer, including the nervous system effects as one piece of a puzzle that we need to put together in order to effectively treat the disease and I think to effectively treat a particularly very aggressive cancers, we need to hit this from multiple angles. Effective strategies will need to include targeting cell intrinsic vulnerabilities of the cancers as most traditional and targeted therapies are focused on doing right now together with decreasing the strong growth and metastasis influencing effects of the nervous system. I think that's one pillar of therapy that we really have been missing and that represents an important opportunity as well as leveraging the power of the immune system, which perhaps will only work optimally, particularly for solid tumors if you also address the nervous system influences on immune cells. And so I think that it's part of a holistic approach to effective therapy for tumors.
(28:21):
We have so far failed to treat with single agent or one dimensional kinds of approaches. We need to target not only the cell intrinsic vulnerabilities, the immunotherapeutic opportunities, and the nervous system mechanisms that are influencing all of that in really important ways. So I think it's important to design clinical research in the context of cancer neuroscience with that holistic view in mind. We don't think one strategy is going to be curative for difficult to treat tumors. I don't think that blocking neuron to glioma synapses in glioblastoma and DIPG will alone be sufficient but I do think it may be necessary for other therapies to work.
Eric Topol (29:01):
Yeah, I think that a perspective of in combination is extremely important. Now the overall, this a big fixation, if you will, about revving up immunotherapies various ways to do that. We'll talk about that in a moment, but without attention to the neurogenic side of this, that might be a problem. Now that gets me to the tumor type that you have put dedicated effort, which is this pediatric pontine tumor, which is horrendous, invading the brainstem and you've even done work with engineering T cells go after that. So you cover all the bases here. Can you tell us about where that stands? Because if you can prevail over that, perhaps that's one of the most challenging tumors of people there is.
Diffuse Intrinsic Pontine Glioma
Michelle Monje (29:54):
Yeah, absolutely. So just a few words about this tumor, for those who don't know, diffuse intrinsic pontine glioma and other related tumors that happen in the thalamus and the spinal cord are the leading cause of brain tumor related death in kids. This is a universally fatal tumor type that tends to strike school age children and it's the worst thing I've ever seen in medicine. I mean, it really has been something that since I saw in medical school, I just have not been able to turn away from. And so studying it from many different perspectives, both the cell intrinsic vulnerabilities, the microenvironmental opportunities for therapy, and also the immunotherapeutic opportunities, it became clear to me that for a cancer that diffusely infiltrates the nervous system forms synapses with a circuit that it is invading and integrates into those circuits in the brainstem and spinal cord, that the only way to really effectively treat it would be a very precise and powerful targeted approach.
(30:55):
So immunotherapy was a very attractive set of approaches because in the best case, you have an engineered T cell or other immune cell that can go in and kind of like a special forces agent just find the T cells and disintegrate them from this synaptically integrated circuit that has formed. And so I began to search for cell surface targets on this particular type of cancer and found that one of the antigens for which many immunotherapy tools had already been made because it's prevalent in other kinds of cancer, was very highly expressed on diffuse midline gliomas, including diffuse intrinsic pontine glioma. And so this target, which is a sugar, actually it's a disialoganglioside called GD2, is extraordinarily highly and uniformly expressed on DIPG because the oncogene that drives DIPG and other related tumors, which is actually a mutation in genes encoding histone H3, which causes broad epigenetic dysregulation, strongly upregulates the synthesis genes for GD2.
(32:05):
And so it's a really ideal immunotherapeutic target on every cell, and it's at extraordinarily high levels. Again, speaking to the importance of collaboration, right when we made this discovery, one of the leaders in chimeric antigen receptor T cell therapy, CAR-T cell therapy named Crystal Mackall at Stanford and her offices is in my building, so I walked over and knocked on the door and said, do you want to work on this together? And so, we've been working together ever since and found that indeed CAR-T cells targeting GD2 cure our mice models, which is something I have never seen. I develop these models and have never seen anything that's effective, but it's always easier to help a mouse than to help a person and so we knew that the clinical translation would be challenging. We also knew that it would require intentionally causing inflammation in the brainstem that's already compromised neurocritical care.
(33:07):
I'm going to not use the word nightmare, but it's a set of challenges that we had to think about really carefully. We spent a lot of time and collaborated with our neurocritical care colleagues, our neurosurgical colleagues, and developed a protocol that had many, we anticipated this neurotoxicity of causing inflammation in the brain stem and we had many safety measures built in an anticipatory way, gave the therapy only in the intensive care unit and had many safeguards in place to treat anticipated hydrocephalus and other consequences of inducing inflammation in this particular region of the nervous system. Over the last four years, we began this trial at the beginning of the pandemic in June 2020 so that was its own unique set of challenges. We've seen some really incredibly exciting promising results we've presented. We've published some of our early experience, we're getting ready to present the larger experience.
(34:14):
And we've presented this at meetings. We've seen some kids go from wheelchair bound to walking in a matter of weeks. It's been just incredible and reduced to nearly nothing. Other kids have had less robust responses the therapy has really helped some kids, and it's failed others. And so we're working very hard right now to understand what factors affect this heterogeneity and response so that we can achieve durable and complete responses for every kid. I will tell you that my leading hypothesis right now is that it is the intersection of the immuno-oncology with the neuroscience that is modulating the response. Certainly, there are immune suppressive mechanisms, but there's also, I think, really important influences of neurotransmitters and neuropeptides on the immune response against central nervous system cancers in the central nervous system and so we're working hard to understand that crosstalk and develop strategies to optimize this promising therapy.
(35:19):
But it really has been one of the highlights of my professional life to see kids with DIPG and spinal cord diffuse midline gliomas get better even for a while, something I hoped at some point in my career to ever see and having seen it now so frequently in our trial patients, I'm really hopeful that this approach will be part of the answer. I'm hopeful for the future of immuno-oncology for solid tumors in general. I think when we understand the tumor microenvironment, we will be able to leverage these really powerful therapies in a better way.
A Couple of Notable Neuroscientists!
Eric Topol (35:58):
Wow. Yeah, I mean, if anybody was to try to crack the case of one of the most challenging cancers ever seen, you would be that person. Now, speaking of collaboration, I didn't know this until I was getting ready to have the conversation with you, but your husband, Karl Deisseroth is like the optogenetics father. He is another exceptional rarefied leader in neuroscience. So, do you collaborate with him?
Michelle Monje (36:35):
We do collaborate, and in fact, so I met Karl when I was a medical student, and he was an intern in psychiatry so we go back a fair ways. We're both MD PhD students at Stanford, and we've been collaborating for many, many years in many different ways both in the clinic, I met him when I was a sub in neurology, and he was the psychiatry intern on neurology. We collaborated when he was a postdoc, and I was a graduate student on some neurobiological studies. We have four children. I have one stepson and four children that I can take full credit for and so we collaborated on five kids. For a while I really wanted, because he is such an amazing scientist, he's such a thought leader in neuroscience, as I started my own independent laboratory, I wanted to not be entirely in his shadow and so I did make it a point to do, I used optogenetics, but I took the course and bought the tools and did it all myself. I did the last questions at the dinner, but I really wanted to be kind of independent in the beginning. Now that my career and my laboratory is a bit more established, we are formally collaborating on some studies because he's a brilliant guy.
Eric Topol (38:01):
I think that you fit into that category too, and a bit more established is maybe the biggest understatement I've heard in a long time. The body of work you've done already at a young age is just beyond belief and you're on a tear to have big impact and many across the board. As you said, many things that you're learning about the brain with all of its challenges will apply to cancer, generally will apply to hopefully someday a treatment that's effective for Long Covid affects the brain and so many other things. So Michelle, I'm so grateful to have had this conversation. You are an inspirational force. You've covered a lot of ground in a short time and between you and your husband, I don't know that that's got to be the most dynamic duo of neuroscience that exists on the planet, in the human species, I guess. I just can't imagine what those kids of yours are going to do when they grow up.
Michelle Monje (39:07):
I'm biased, but they're pretty great kids.
Eric Topol (39:10):
Well, thank you for this and I think the folks that I get to listen to this will certainly get charged up. They'll realize the work that you're doing and the people you collaborate with and making cold calls to people. That's another story in itself that how you can get transdisciplinary efforts when you just approach somebody who's doing some good work. Another lesson just kind of hidden in our discussion. Thanks very much.
Michelle Monje (39:40):
Oh, thank you. It's wonderful to talk with you, Eric.
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Jim Collins is one of the leading biomedical engineers in the world. He’s been elected to all 3 National Academies (Engineering, Science, and Medicine) and is one of the founders of the field of synthetic biology. In this conversation, we reviewed the seminal discoveries that he and his colleagues are making at the Antibiotics-AI Project at MIT.
Recorded 5 February 2024, transcript below with audio links and external links to recent publications
Eric Topol (00:05):
Hello, it's Eric Topol with Ground Truths, and I have got an extraordinary guest with me today, Jim Collins, who's the Termeer Professor of Medical Engineering at MIT. He also holds appointments at the Wyss Institute and the Broad Institute. He is a biomedical engineer who's been making exceptional contributions and has been on a tear lately, especially in the work of discovery of very promising, exciting developments in antibiotics. So welcome, Jim.
Jim Collins (00:42):
Eric, thanks for having me on the podcast.
Eric Topol (00:44):
Well, this was a shock when I saw your paper in Nature in December about a new structure class of antibiotics, the one from 1962 to 2000. It took 38 years, and then there was another one that took 24 years yours, the structural antibiotics. Before I get to that though, I want to go back just a few years to the work you did published in Cell with halicin, and can you tell us about this? Because when I started to realize what you've been doing, what you've been chipping away here, this was a drug you found, halicin, as I can try to understand, it works against tuberculosis, c. difficile, enterobacter that are resistant, acinetobacter that are resistant. I mean, this is, and this is of course in mice models. Can you tell us how did you make that discovery before we get into I guess what's called the Audacious Project?
Jim Collins (01:48):
Yeah, sure. It's actually a fun story, so it is origins go broadly to institute wide event at MIT, so MIT in 2018 launched a major campus-wide effort focused on artificial intelligence. The institute, which had played a major role in the first wave of AI in the 1950s, 1960s, and a major wave in the second wave in the 1980s found itself kind of at the wheel in this third wave involving big data and deep learning and looked to correct that and to correct it the institute had a symposium and I had the opportunity to sit next to Regina Barzilay, one of our faculty here at MIT who specializes in AI and particularly AI applied to biomedicine and we really hit it off and realized we had interest in applying AI to drug discovery. My lab had focused on antibiotics to then close to 15 years, but primarily we're using machine learning and network biology to understand the mechanism of action of antibiotics and how resistance arise with the goal of boosting what we already had, with Regina we saw there was an opportunity to see if we could use deep learning to get after discovery.
(02:55):
And notably, as you kind of alluded in your introduction, there's really been a discovery void and the golden age of discovery antibiotics was in the forties, fifties and sixties before I was born and before you had the genomic revolution, the biotech revolution, AI revolution. Anyways, we got together with our two groups, and it was an unfunded project and we kind of cobbled together very small training set of 2,500 compounds that included 1,700 FDA approved drugs and 800 natural compounds. In 2018, 2019, when you started this, if you asked any AI expert should you initiate that study, they would say absolutely not, there's going to be two big data. The idea of these models are very data hungry. You need a million pictures of a dog, a million pictures of a cat to train a model to differentiate between the cat and the dog, but we ignored the naysayers and said, okay, let's see what we can do.
(03:41):
And we apply these to E. coli, so a model pathogen that's used in labs but is also underlies urinary tract infections. So it’s a look to see which of the molecules inhibited growth of the bacteria as evidence for antibacterial activity and we could have measured and we quantified each of their effects, but because we had so few compounds, we just discretized instead, if you inhibited at least 80% of the growth you were antibacterial, and if you didn't achieve that, you weren't antibacterial zero in ones. We then took the structure of each molecule and trained a deep learning model, specifically a graphical neural net that could look at those structures, bond by bond, substructure by substructure associated with whatever features you look to train with. In our case, making for good antibiotic, not for good antibiotic. We then took the train model and applied it to a drug repurposing hub as part of the Broad Institute that consists of 6,100 molecules in various stages of development as a new drug.
(04:40):
And we asked the model to identify molecules that can make for a good antibiotic but didn't look like existing antibiotics. So part of the discovery void has been linked to this rediscovery issue we have where we just keep discovering quinolones like Cipro or beta-lactams like penicillin. Well, anyways, from those criteria as well as a small tox model, only one molecule came out of that, and that was this molecule we called halicin, which was named after HAL, the killing AI computer system from 2001 Space Odyssey. In this case, we don't want it to kill humans, we want it to kill bacteria and as you alluded, it turned out to be a remarkably potent novel antibiotic that killed off multi-drug resistant extensively drugs, a pan-resistant bacteria went after to infections. It was affected against TB, it was affected against C. diff and acinetobacter baumannii and acted to a completely new mechanism of action.
(05:33):
And so we were very excited to see how AI could open up possibilities and enable one to explore chemical spaces in new and different ways. We took them model, then applied it to a very large chemical library of 1.5 billion molecules, looked at a subset of about 110 million that would be impossible for any grad student, any lab really to look at that experimentally but we looked at it in a model computer system and in three days could screen those 110 million molecules and identified several new additional candidates, one which we call salicin, which is the cousin of halicin that similes broad spectrum and acts to a novel mechanism of action.
Eric Topol (06:07):
So before we go further with this initial burst of discovery, for those who are not used to deep neural networks, I think most now are used to the convolutional neural network for images, but what you use specifically here as you alluded to, were graph neural networks that you could actually study the binding properties. Can you just elaborate a little bit more about these GNN so that people know this is one of the tools that you used?
Jim Collins (06:40):
Yeah, so in this case, the underlying structure of the model can actually represent and capture a graphical structure of a molecule or it might be of a network so that the underlying structure itself of the model will also look at things like a carbon atom connects to an oxygen atom. The oxygen atom connects to a nitrogen atom and so when you think back to the chemical structures we learned in high school, maybe we learned in college, if we took chemistry class in college, it was actually a model that can capture the chemical structure representation and begin to look at sub aspects of it, associating different properties of it. In this case, again, ours was antibacterial, but it could be toxic, whether it's toxic against a human cell and the model, the train model, the graph neural model can now look at new structures that you input them and then make calculations on those bonds so a bond would be a connection between two atoms or substructures, be multiple bonds, interconnecting multiple atoms and assign it a score. Does it make, for example, in our case, for a good antibiotic.
Eric Topol (07:48):
Right. Now, what's also striking as you set up this collaboration that's interdisciplinary with Regina, who I know of her work through breast cancer AI and not through drug discovery and so this was, I think that new effort and this discovery led to this, I love the name of it, Audacious Project, right?
Jim Collins (08:13):
Right. Yeah, so a few points on the collaboration then I'll speak to Audacious Project. In addition to Regina, we also brought in Tommi Jaakkola, another AI faculty member and marvelous colleague here at MIT and really we've benefited from having outstanding young folks who were multilingual. We had very rich, deep trained grad students from ML on Regina and Tommi's side who appreciated the biology and we had very richly, deeply trained postdocs, Jon Stokes in particular from the microbiology side on my side, who could appreciate the machine learning and so they could speak across the divide. And so, as I look out in the next few decades in this exciting time of AI coming into biomedicine, I think the groups will make a difference of those that have these multilingual young trainees and two who are well set up to also inject human intelligence with machine intelligence.
(09:04):
Brings the Audacious Project. Now, prior to our publication of halicin, I was invited by the Audacious Project to submit a proposal, the Audacious Project is a new philanthropic effort run by TED, so the group that does the TED Talks that's run by Chris Anderson, so Chris had the idea that there was a need to bring together philanthropists around the world to go for a larger scale in a collective manner toward audacious projects. I pitched them on the idea that we could use AI to address the antibiotic resistance crisis. As you can appreciate, and many of your listeners can appreciate that we're doomed if we don't actually address this soon, in that the number of resistance strains that are in our communities, in our hospitals has been growing decade upon decade, and yet the number of new antibiotics being developed and approved has been dropping decade upon decade largely because the antibiotic market is broken, it costs just as much to develop an antibiotic as it does a cancer drug or a blood pressure drug.
(09:58):
But antibiotic you take once or maybe over the course of three to five days, blood pressure, drug cancer drug you might take for months if not for the rest of your life. Pricing points for antibiotics are small dollars, cancer drugs, blood pressure drugs, thousands if not hundreds of thousands. We pitched this idea that we can maybe turn to AI and use the power of AI to address this crisis and see if we could use our wits to outcompete the genes of superbugs and Chris and his team really were taken with this, and we worked with them over the course of nine months and learned how to make the presentations and pulled this together. Chris took our pitches to a number of really active and fantastic philanthropists, and they got behind us and gave us a good amount of money to launch what we have now called the Antibiotics-AI Project at MIT and in conjunction with it and also using funding from the Audacious Project, we've launched a nonprofit called Phare Bio which is French for lighthouse, so our notion is that antibiotics are public good that we need to get behind his community and Phare Bio, which is run by Akhila Kosaraju, she's the CEO and President, is the mission of which is to take the most promising molecules out of the antibiotics AI project and advance them towards the clinic through partnerships with biotech, with pharma, with other nonprofits, with nation states as needed.
Eric Topol (11:18):
Well, before I get to the next chain of discovery and as explain ability features, which we all like to see when you can explain stuff with AI, did halicin because of this remarkable finding, did it get into clinical trials yet?
Jim Collins (11:36):
It's being advanced quite nicely and aggressively by Phare Bio. So Phare Bio is in discussions with the Department of Defense and BARDA, and actually on an interesting feature of halicin is that it acts like a flash bomb in the gut, meaning that when delivered orally to the gut, it only acts briefly and very quickly in a fairly narrow spectrum manner as well, so that it can go after pathogens sparing the commensals. One of the challenges our US military face is one of the challenges many militaries face are gut issues when soldiers are first deployed to a new location, and it can disable the soldiers for three to four weeks. And so, there's a lot of excitement that halicin might be effective as a treatment to help prevent gut dysbiosis resulting from new deployments.
Eric Topol (12:27):
Oh wow. That's another application that I would never have thought of. Interesting, so you then moved on to this really big report in Nature, which I think this is now involving a transformer model as I recall. So you can explain the difference and you made a discovery from a massive, again, number of potential compounds to staph aureus resistant methicillin resistant agents that were very potent in vivo. So how did you make this big jump? This is a whole new structural class of antibiotics.
Jim Collins (13:11):
Yeah, so we made this jump, this was an effort led by Felix Wong, who's a really talented postdoc in my lab, and we got intrigued of to what extent could we expand the utility of AI and biology of medicine. As you can appreciate that, that many of our colleagues are underwhelmed by the black box nature of many AI models and by black box I mean that when you train your model, you then largely use it as a filter where you'll provide the model with some input. You look at the output and the outputs, what's of interest to you, but you don't really understand in most cases, what guided the model to make the prediction of the output that you look at and that can be very unsatisfactory for biology, interested in mechanism. It can be very unsatisfactory for physicians interested in understanding the underlying disease mechanism.
(13:57):
It can be unsatisfactory for biotech and drug discoveries that want to understand how drugs act and what maybe underlies meaningful structural features. So with Felix, we decided it'd be interesting if you could open up the box. So could you look inside the model to see what was being learned? We are able to open up, in this case actually, we primarily focused on graph neural nets. We now have a new piece we're just about to submit on transformers, but in this case, we could open up and look to see what were the rationales, what were the chemical substructures that the model was pointing to in each compound that was leading to the high prediction that it could make for a good antibiotic and these rationales we then used as hooks, I should notably say, that we were able to identify the rationales from these large collections using algorithms that would develop by DeepMind as part of their AlphaGo program.
(14:51):
So AlphaGo was developed by DeepMind as a deep learning platform to play and win go the ancient Asian board game and we used similar approaches called Monte Carlo Tree Search that allowed us to identify these rationales that we effectively then used as hooks and kind of organizing hooks on screens where you can envision or appreciate that most exposed screens give you one-offs. This molecule does what you want and silico screens are similarly designed with these rationales. We could use them as organizing hooks to say, ah, these compounds that are identified as making for very good antibiotics all have the same substructure and thus they likely in the same class and act in similar mechanism and this led us to identify five novel classes, one of which we highlighted in this piece that acts very effectively against MRSA, so methicillin-resistant staph aureus you alluded, which is probably the most famous of the antibiotic resistant pathogens that we even outside infectious are quite familiar with. It be devil's athletes, so NFL players are often hit with MRSA, whether from scraping their limbs on AstroTurf or from actually surgeries to say, for example, correct something at their knee. This new class had great efficacy in animal models, again, acting through a new mechanism.
Eric Topol (16:12):
Will you bring that forward like halicin through this same entity?
Jim Collins (16:17):
Yes. We've now provided the molecules to Phare Bio and they're digging in to see which of these might be the most exciting and interesting to advance clinically.
Eric Topol (16:26):
I mean, it's amazing because this area is so neglected. Maybe you can help explain, since we're talking about existential threats as we get more and more resistant antibiotics and the biopharma industry is basically not into this and it relies on the work that you've been doing perhaps or other groups, I don't know of any that are doing more than you. I mean, it's incredible to me. Is it just because of the financial aspects that there's no business in the life science industry?
Jim Collins (17:03):
It's an interesting challenge. So I've thought about it. I really haven't come up with a great solution yet, but I think you've got multiple factors at play. One is that I think all of us, every one of your listeners has lost someone to a bacterial infection, but in most cases you don't realize you lost them to a bacterial infection. It might be that your elderly relative went into the hospital with a condition but acquired hospital-based infection and died subsequently from that and happened quite quickly. Another cases, again, it's secondary. Notably, during the pandemic, one out of seven individuals hospitalized for Covid had a bacterial infection and 50% of those who died had a bacterial co-infection. And noted by going back to the Spanish flu of over a hundred years ago. It was as deadly as it was because we didn't have antibiotics and most of the folks that died had a bacterial co-infection.
(17:56):
So you have this in the backdrop, you then have that, nobody's kind of gotten behind it, so we don't have any major foundation addressing antibiotic resistance. There are no charity walks, there are no charity runs, there is no month, there is no color, there are no ribbons, there are no celebrity behind it, there's just not known so it hasn't captured the public's imagination. AThen you come with that, this backdrop of the broken market where I said shared, it's really expensive to develop a new antibiotic, but if you develop a new antibiotic, the tendency now will be to shelve it until it's desperate so now even the young companies that had developed and gotten an antibiotic through to approval often went bankrupt because the model, the market couldn't provide them with revenue to go after the next one or sustain their efforts. And so you have pharma biotech jumping out. I think we need two-pronged effort going forward. I do think we need nation states to come forward and get behind this, and I think we increasingly need philanthropists to come forward and go after it. As I share your term of existential threat, I think if you speak with most educated individuals, antibiotic resistance broadly, antimicrobial resistance will be on everyone's existential threat list but notably of that list, it's the cheapest one that can be solved.
Eric Topol (19:09):
Well, you're showing that you've got the most extraordinary candidates that have been found in decades. So that says a lot right there.
Jim Collins (19:18):
Important step, yeah. So I think we've got additional innovation needed in the models to address this, and until we have that address, then this interesting discoveries we and others are making will not get to patients. So we need to have that additional next step to close this gap.
Eric Topol (19:32):
Now, obviously this has relied on AI and the progress that's occurring in AI to enable some of your work. I am fascinated by the use of AlphaGo. Most times we hear about using AlphaFold2, but you actually use AlphaGo the original game DeepMind work but there also was the progress of from deep neural networks to transformer models and your ability now to basically exemplify what can be achieved in drug discovery using the progress in multimodal AI. Is this something that is making a difference for you and your group?
Jim Collins (20:13):
It is, it’s huge. I think it's very early in terms of the introduction to these new tools extensively within drug discovery. Machine learning has been used for over two decades, both supervised learning and unsupervised learning. Now we're seeing groups coming in for the deep learning efforts. It's largely data-driven so in fact, with the exception of sequences, most of drug discoveries not yet big data in the big data phase, but it's beginning to change. It's truly been transformative for us, so we've used graph neural nets primarily for our discovery efforts. We're now beginning to incorporate language models as multimodal models along with the graph neural nets as well as to see to what extent pre-trained language models. For example, mobile form from IBM, which was trained on PubChem and the ZINC database could be fine-tuned with small amounts of training data, screening data from a resistant organism.
(21:06):
Third, and I made an indirect allusion already, we've been looking at using transformers and genetic algorithms in older form of AI tech for design of novel antibiotics so we've been now looking to see using fragments as a starting base, using trained models to build out novel antibiotics that can then be de novo designed. One of the big challenges in that space is how do you synthesize these molecules? So you have both the challenge of can you come up with a small number of steps that enable you to synthesize? And second is could you find somebody to synthesize them? And each of those remains very big challenges. My faculty colleague here at MIT, Connor Coley's probably one of the world leaders, easily, he's in using AI to calculate the synthesized ability of a molecule, but we still have gaps in that we don't have the community resources to make most of what we come up with.
Eric Topol (21:58):
Well, one of the features of large language models that David Baker at the Protein Design Institute exploited is its ability to hallucinate and come up with proteins that don't exist. Can you do the same thing in your design of antibiotic candidate molecules in a way that is not worrying about the synthesis, but just basically the hallucinatory behavior of large language models?
Jim Collins (22:28):
It's interesting, so yes and so David's work is marvelous and we're big fans and longtime friends of his work. Yes, so we've been driving these models truly to do de novo synthesis. So based on what has been learned, can you put together molecules that one's never seen before? We're doing it quite successfully. It becomes interesting from the hallucination in that it comes out really more of these models making stuff up and ours it's really more directing the hallucinations, right? Really looking to see can we harness the imagination of the models in order to move them forward in very creative design manners.
Eric Topol (23:08):
Yeah, I mean, I think most people have a negative connotation of hallucinations, but these are the smart variety potentially. This in many ways you could say there's so much crowded interest in the drug discovery AI world, but what you're doing now seems to be setting the pace in many respects for others to follow such remarkable advances in a short time. By the way, we'll link to that TED talk you gave in April 2020, where in seven minutes you went over what you're doing of course and who would've, and that was in 2020 that where you'd be three or four years later, and that was what you're going to do over the next seven years with seven new classes of new antibiotics. Now, before we wrap up, it isn't just that you're an AI antibiotic, you and your team antibiotic discover and doing compressing in time, what has taken decades that you're doing in months, but also you are a father of figure in the field of synthetic biology and I wonder if you, before we wrap up, can explain not only what synthetic biology is since a lot of people don't really know what that means, but how does that dovetail with your efforts in what we've been discussing?
Jim Collins (24:33):
Yeah, thanks. So synthetic biology is a relatively new field that's bringing together engineers with biologists to use engineering principles to model design and build synthetic gene networks and other molecular components that can be used to rewire and reprogram living cells and cell-free systems, endowing them with novel functions of a variety of applications. So the circuits, these programmable cells are impacting broad swats of the economy from food and water to health and sustainability of bioenergy to human health. Our focus is primarily human health and we've been advancing the idea that you can reprogram bacteria to detect and treat bacterial infections. So we've shown you can use this to go after cholera, we've shown you can use is to prevent antibiotic induced gut dysbiosis. We've also used synthetic biology to create whole new classes of diagnostics. For example, paper-based ones using RNA sensors for Ebola, for Zika and for Covid.
(25:33):
How it dovetails with what we talked about is that I think there's a great opportunity now to turn to AI to expand synthetic biology, both expanding the number of parts we have to re-engineer living systems as well as to better infer design principles that can be used to reprogram rewire living systems. We're beginning to advance, we're not yet at the SynBio AI project phase, but very early efforts and David's dominating the protein space and we and others are beginning to now movement to the RNA space. So to what extent can we create large libraries of RNA components, train language-based models, structure-based models that can both predict RNA structure more critically predict RNA function and as you know from your marvelous work and what's happening is that it's the exciting age of RNA of getting after RNA therapeutics, be it mRNA or CRISPR related and we still need to get better at our ability to design those therapeutics with certain functions in mind, and we think AI is going to help get us there faster.
Eric Topol (26:34):
Well, speaking of that, there was a paper this week in Cell by McCafferty and colleagues, and one of the sentences that struck me, we are standing on the cusp of a new era of biology, where the integration of multimodal structural datasets with multiscale physics-based simulation will enable the development of visible, virtual cells. This is yet another lineage or direction of where we're headed with AI, but this fusion of the advances that are occurring right now in biology with AI that extend in many different directions, it's so exciting and you are basically nailing it. I mean, you're putting points on the board, Jim, and I just have to say, I'm blown away by what you've been accomplishing in a time space that's so incredibly compressed.
Jim Collins (27:40):
Oh, well thanks. Well, you think back to the early days of molecular biology and physicists like Francis Crick and Max Delbrück played huge pioneering roles and then in the second wave in the eighties or so, you had other physicists like Walter Gilbert playing big roles. I do think physicists computer scientists are starting now to play big roles in this next phase where we need tools like AI in order to really grapple with and harness the complexity, both the biology and the chemistry that underlies living cells. They can kind of expand our intuitions both to understand and to really control these systems for good going forward.
Eric Topol (28:15):
Well, you're doing it and we're be cheering for the success of these drugs that you've come up with in the clinical trials as they go forward because they look so remarkably promising. You even highlighted ways that I wouldn't have envisioned where they could make a difference, so we'll follow your work, you and your colleagues with great interest. Thanks so much for joining,
Jim Collins (28:37):
Eric, thanks for having me. Enjoyed our conversation.
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Full video interview will post here
“The history of science, it turns out, is filled with stories of very smart people laughing at good ideas.”—Katalin Karikó
Ground Truths podcasts are now available on Apple and Spotify!
The list of obstacles that Kati Karikó faced to become a scientist, to make any meaningful discovery, to prevail over certain scientists and administrators who oppressed her, unable to obtain grants, her seminal paper rejected by all of the top-tier journals, demoted and dismissed, but ultimately to be awarded the 2023 Nobel Prize with Drew Weissman, is a story for the ages. We covered them in this conversation, which for me will be unforgettable, and hopefully for you an inspiration.
Recorded 30 January 2023, unedited transcript below
Eric Topol (00:06):
Well, hello, this is Eric Topol with Ground Truths, and I am really thrilled to have with me Kati Kariko, who I think everyone knows won the Nobel Prize with the Drew Weissman in 2023 and she has written a sensational book, it's called Breaking Through. I love that title because it's a play on words, a breakthrough and breaking through, and we have a lot to talk about Kati, so welcome.
Katalin Kariko (00:34):
Thank you very much for inviting me.
Eric Topol (00:36):
Yes, well I'd like to start off, as you did in the book with your background in Hungary where of course you started with a tough background in a one room house without running water and you never had exposures to scientists and somehow or other you became interested in science and you attributed some of these things like your biology teacher, Mr. Tóth and the book Stress of Life [by Hans Selye] Could you tell us a little bit more what stimulated you in a career of science?
Katalin Kariko (01:18):
I have to say that every child is interested in understanding the nature around them and so I was surrounded with nature because we had big garden, we had animals around and it was an exciting thing. The children ask questions and if they try to find an answer and teachers or parents might give the answer, but definitely the school, even elementary school was very stimulating. Teachers, chemistry teacher, figure out how we can make crystals and I was so excited to have my own crystals and things like that and in high school the teachers were so engaging and not like they tried to put all of the information into your brain, but they encourage you to think yourself, so that's all contributed. I think that most of the child in the first, I don't know, six, seven years of their life that’s how they can see their parents behaving, their friends, the school, classmates, and they shaped what kind of people they will be at the end and the rest of it is refining.
Eric Topol (02:41):
Right, right. Well one of the things I loved that you brought up in the book was how much you liked the TV show Columbo. That's one of my favorite TV shows of all time and one more thing, one more thing. Can you talk a little bit about Columbo? Because in some ways you were like the Peter Falk of mRNA in terms of one more thing.
Katalin Kariko (03:09):
Yes, so I realized that we as researchers, we are not called searchers, we researchers, so we are repeating things. Of course everybody knows who committed the crime in Columbo because this is how it starts and you don't have to figure out, but it seems always that things in a different direction you would lead but all the little clues and some of my colleagues said that they as a physician, they have this tunnel vision. So the patient comes and they can figure out probably from some clues that this is the disease and they get back the lab results and others. Then they realize that one or two things is not fitting, but they always so strongly believe their first instinct. What I taught them to focus on those which will not fit because that will lead to the real perpetrator in case of Columbo.
(04:23):
And so I like the simplicity. I know that what we are doing this research is very over complicated, but we can break down in very simple question, yes or no and then repeating things and many experiments. When I did one was the experiments really the question and the nine of them was like just controls always. I have to have a control for that, control for that and since I work most of the time with my own hands myself, so I had to make sure that I think through that what will be the experimental outcome and then think about that. Do I have a control for that? So that many times in my brain before I performed the experiment in my brain, I predicted that what will be the outcome, of course you never get the outcome what you expect, but at least you have the control that you can exclude a couple of things and so this is how I function usually in the end of the 20th century, 21st century people did not work like I did alone most of the time.
Eric Topol (05:35):
No, I see how you described it in the book was just so extraordinary and it really was in keeping with this relentless interrogation and that's what I want to get into is particularly the time when you came to the United States in 1985 and the labs that you worked in predominantly in Philadelphia through that period before leaving Penn to go on to BioNTech. So, you first kind of beached in at Temple University with a monster at least as you portray him in the book. I mean it was nice that he picked you up at the airport, you and your family. How do you say his name? Suhadolnik.
Eric Topol (06:31):
But not only was the lab kind of infested with cockroaches, but also after working there for a number of years, a few years, you then had gotten an offer to go to Johns Hopkins and when you informed him about that he threatened and did everything he could to ruin your career and get you deported. I mean this was just awful. How did you get through that?
Katalin Kariko (06:58):
As I mentioned later on, I went back and gave a lecture there and I have to say that I always put positivity in forefront, so I learned a lot from him, and he invited me to America. I was always very grateful, and he was kind, and we did very well, and we did a lot of publication. In one issue of biochemistry, we had three papers and two of them I was the first author, so I worked very hard and so he liked that, and he wanted me to stay there. I just learned that from this Selye book that this is what is given and then what I can do, I cannot change him. I cannot change the situation, how I can get out from it and that's what I focused on, so I am not bitter about him. I liked him and the same for other people. When I get an award, I usually thanks to all of these people who try to make my life miserable. They made me work harder.
Eric Topol (08:05):
Well, but you were very kind like you said when you went back to Temple many years later to give the lecture because what he did to you, I mean he was so vindictive about you potentially leaving his lab, which he demanded that he be called the boss and he was going to basically, he ruined the Johns Hopkins job. He called them and you were so nice and kind when you went back to give the lecture without saying a negative word about him, so I give you credit, when somebody goes low, you went high, which is nice.
Katalin Kariko (08:40):
It is important, which I learned from the Selye book, that you don't carry any grudge against anybody because it'll poison you and as Selye also said that when you are very frustrated and very upset, the quickest way you can think about how you can release the stress is revenge. He said, don't do that. It escalate. It hit you back. You have to think about how you can be grateful for the same person you were just ready to take some revenge and that's what you have to practice. Sometimes it is difficult to feel that, but I don't have any bad feeling against my chairman who put my stuff on the hallway.
Eric Topol (09:24):
Oh yeah, I was going to get to that. So then after a short stint at the Uniformed University of Health Science where you had to drive three hours from Philadelphia to go there and you would sleep on the floor. I mean, I have to say Kati, if I was driving three hours, all I'd be thinking about is how desperate situation I was put in by the prior PI you work with. Any rate, you work there and then finally you got a job with my friend Elliot Barnathan, a cardiologist at University of Pennsylvania. So here you are, you're very interested in mRNA and you hook up with Elliot who's interested in plasminogen activators, and you work in his lab and it's quite a story where one of the students in his lab, David Langer, ratted on you for being blunt about the experiments getting screwed up and then later you wind up working in his lab. Tell me a bit about the times with Elliot because he's a very gracious, I think he was very supportive of your efforts and you got him stimulated about the potential for mRNA, it seems like.
Katalin Kariko (10:41):
Yes, so I was desperate to be away from my family at Bethesda and try to get back and every day I sent out several applications. This was in 1989, so you had to send letters and then I called up usually the secretaries about what's going on and I called up also a secretary and she said that they were advertised because nobody was good enough. I said, can you ask him to look at again my application? Then half an hour later, Elliot called me back that come and bring your notebook. He wanted to know what kind of experiment I am doing, and he opened when I came a couple of days later and pulled up a northern blot and he said, you have done that? I said, yes, I did. He said, okay, you are hired and so that, because Elliot is just a couple of days younger than me, I convinced him that we should do kind of mRNA research and he agreed, and we did several experiments and he helped me to get all of these experiments ongoing and so it was a very exciting time and I listened. Elliot was there in many awards ceremony including the Nobel Prize. He was my guest because I was very grateful to him because I have to say that he tried to protect me and he get trouble for that because in higher up and when he was looking for tenure, somehow he get R01, several of them, but they did not put him tenure because he was standing up for me and he paid the price.
Eric Topol (12:42):
Do you think the reason in part that he went to Centocor, a biotech company who I worked with quite extensively was because he stood up for you?
Katalin Kariko (12:54):
He mentioned to the chairman that he's waiting for whether he will be tenured because he has a job offer with ReoPro what he was doing there in the lab and testing out and the chairman told him that, take that job.
Eric Topol (13:11):
Yeah. Well, that's interesting. I know Judy Swain very well, and she did everything she could to hurt your career. She demoted you, or actually she wanted you to leave, but you wound up taking a demotion and also Bill Kelley, who I know well, he was the Dean and CEO of the UPenn. Did he ever get any direct involvement with, because much later on he was advocating for your recognition, but during that time, he could have told Judy Swain to stop this, but did he ever get involved, do you know?
Katalin Kariko (13:45):
I was very low level of nobody, so he would not. It was interesting, we were hired on the same day in 1989. I was first, and I met him, Bill Kelley when the new faculty was hired, and I was so happy because my first project in Hungary was Lesch-Nyhan syndrome, and I know that he discovered the gene, and I was looking up to him very much always.
Eric Topol (14:15):
Well, you said in the book you were over the moon and I have to say, I worked with him. My first job was at University of Michigan, and I worked with him for six years before he left to go to Penn, and we've been friends all these years, but what happened with Judy Swain, as I read in the book, I got all it bristled. I really was upset to read about that. Anyway, somehow you stayed on, Elliot moved, by the way, during that time with Elliot, you were able to get mRNA to make urokinase plasminogen activator (uPA), and that was a step in the right direction. Before we leave, Elliot, if you had stayed there, if he had gotten tenure, do you think you would've ultimately together made the discovery that you did with Drew Weissman?
Katalin Kariko (15:05):
I couldn't be tenured because it is a clinical department and I had a PhD and nobody at the clinical department can be, but I could have been research associate professor if I can get a grant and in 1993, I already had submitted grant on circular RNA. When people in these days, they say that, oh, that's a novelty. Oh, in 1994, 1995, I had several grants on circular RNA I submitted for therapeutic purposes, and Elliot helped me with English and computer, everything what he could, but it is important that he was not an immunologist and I needed discovery. When I work with him, I did not realize the mRNA was inflammatory.
Eric Topol (16:02):
Right, right, exactly. We're going to get to that in a minute. Now, after Elliot left, then you needed someone else to support you, and you wound up with, as I mentioned earlier, David Langer, a neurosurgeon who you previously knew, and he also stood up for you, right?
Katalin Kariko (16:18):
Yes, yes. So at the beginning, every lab, when you have a medical student, they kind of know everything. One day he just told me that, Kati, I will want to learn everything you know, and I will know everything you know. I said, oh, by that time while you are learning, I learned so much more, you never catch me. That always I had to put him back, but kind of he liked how I worked, I concentrate, I didn't chitchat. Then he was just keep coming back when I was working, even with Elliot and he advanced from medical student to residency and so on, and then when he learned that I have no job because Elliot is leaving, then he went to a Eugene Flamm, the chairman of neurosurgery, and he convinced him that neurosurgery needs molecular biologics. That's what he was arguing and thanks to David and the chairman Eugene Flamm, then for 17 years I had a laboratory, and I had a financial support. Not much.
Eric Topol (17:36):
Yeah, I mean that was great, but again, you were not getting any real support from the university and then all of a sudden you show up one day and Sean has all your lab, everything that you worked on thrown in the hallway. I mean, that's just incredible story, right? At any rate, you then wound up because you were basically hawking mRNA as a path of science. It's going to be important. By the way, my favorite quote in the book, Kati. The history of science it turns out is filled with stories of very smart people laughing at good ideas. I just love that quote and it kind of exemplifies your career and your success, but you were steadfast and you ran in, of course, the famous story to Drew Weissman at the Xerox machine, and you were hawking trying to get anybody to believe it as you called it, led to the mRNA Believers Club, which only a handful of people in the world ever got there.
(18:38):
And here you have you take on something that obviously 1960 in your lifetime, early in your lifetime it was discovered, but everyone knew it was unstable, very difficult to work with, very challenging. Of course, you realized that could be beneficial, but you hooked up with Drew the immunologist that you mentioned, and I didn't know by the way, he had type one diabetes. I learned that from your book, and both of you worked so hard and it's just really incredible, but while you're at Penn, the famous or infamous Jesse Gelsinger case and his death occurred and he had the cytokine release syndrome, and you learned from that, right?
Katalin Kariko (19:25):
Yes. By that time, we also could see that the RNA could be inflammatory, but in his case, of course, because the virus was causing it or what certain condition caused that. I have to say that, people work at gene therapy at Penn and mostly of viral programs. When I mentioned I tried to make gene therapy with mRNA, of course everybody felt sorry for me. Poor Kati, hate RNA, it always degrade, but I have to say the degradation is coming mostly because the molecular biology laboratory, they use plasmid, and when they isolate plasmid, like the QIAGEN kit, they start with the RNAs. They add RNAs because you have to eliminate the bacterial RNA, and they contaminate the whole laboratory, the refrigerator door, the gel opera, everybody's RNAs and so that's what extra problem with working with RNA. So I could make RNA, and so it was working and kind of try to express that and I made a lot of RNA for people probably they still have in their freezer, never tested because I was a pusher.
Eric Topol (20:52):
Yeah, yeah. Well, what was fascinating of course is you had already learned in mice about this inflammation from putting mRNA in vivo, and then you made the remarkable discovery, which was the paper in Immunity that had been rejected by Nature and many other papers, even though you had been told if you could get a paper in Nature, maybe that could help your career, right. Back in 2021, the journal of Immunity, a very highly regarded self pressed journal, they asked me to comment on your discovery and I wrote, you may have seen it. Of course, several people wrote Tony Fauci and others. What I wrote was what began as a replacement for a uridine base to squash an inflammatory response in mice evolved into the basis for a broad therapeutic platform to fight both communicable and non-communicable diseases in people. So, this discovery that you made in that classic 2005 paper, which is the most important paper ever published in the journal Immunity, was the Toll-like receptor was mediating the inflammation.
(22:05):
And if you change the uridine to pseudouridine, you could essentially blunt or block the inflammation. This was a seminal discovery that opened up mRNA, but not just for Covid of course, but for so many pathogens and as we'll talk about when we wrap up about all these other things. So when you did this paper and Drew said when it's published, the phones are going to be ring off the hook and no one even acknowledged the paper, right? I mean no one realized how this was one of the most important discoveries in the history of biomedicine, right?
Katalin Kariko (22:43):
Yes. Especially knowing that Drew is not the person who is exaggerating things. Drew is very modest and would not say such things. I am more like daughter, maybe this happened, but he is not like that and I got the one invitation to go to the Rockefeller University for a meeting, and then I went to Japan from 2005 and it was 2006. Both of them that was invitation, and nothing happened in 2007, 2008 and 2009.
Eric Topol (23:24):
But those meetings that you went to, they were kind of obscure like microcosm groups. I mean they were relevant to your work, but they didn't realize this is a big deal. I mean, this is like a world changing type of finding because now you could deliver things in cells. Now of course, you worked on this for three decades and the people that think that you can do a flash in the pan science, but at the same time nanoparticles separately were being pursued. How important were the nanoparticles to make for the package for the ultimate success? When Covid hit in late 2019 and now you had been working at BioNTech, how would you rate the importance of the nanoparticles in the story?
Katalin Kariko (24:23):
For the vaccine it definitely is important because everybody ask the mRNA, if not immunogenic, where do you have the adjuvant? Where is the adjuvant? Then lipid nanoparticle contains an ionizable lipid, which was the adjuvant and why it is important that not the mRNA was inducing the response because the mRNA induced interferon, and if you have interferon, then follicular T-helper cells is not form, and then you get very low amount of antibodies, but if you do not induce interferon, but you induce IS6 and other cytokines is beneficial to have high level of antibodies, so that's what the ionizable lipid was causing and that's the adjuvant in the lipid nanoparticle. Yes, I always emphasize that it is very important and of course when we use the particle that was totalization, then it did not contain ionizable lipid.
Eric Topol (25:24):
Right? I think that's where there's a misconception because of the Nobel Prize recognition last year, a lot of people think, well, that's all tied only to the Covid vaccine. Actually no, your discovery was much bigger than that and it was applied for the Covid vaccine of course with the nanoparticle package, but yours is as we'll get to in a moment, much, much bigger. You left Penn, that was in 2013, and then you spent several years in Mainz, Germany working with the folks at BioNTech, and you really enjoyed that and they appreciated you then as opposed to what you dealt with at Penn where it was just that you kept hearing about the dollars per net square footage and all these ridiculous things and just extraordinary to go back there. Now I just want to mention about your own gene transfer, your daughter. Your daughter is a two-time gold medal Olympiad in rowing, which is incredible. So she didn't go down the path of science, but she also became a world leader in a field. Is that transmitted on a particular chromosome in the family?
Katalin Kariko (26:54):
I think that she just could see that you have to focus on something and then you give up many things and you focus and then achieve, and then you get the new goal, set up a new goal. I mean she get somewhat articulated at Penn, she get a master in science and later in UCLA, she get a MBA degree, but 10 years she was like, for me, it is a very boring thing, just rowing going backwards. Isn't that boring every day? She said, no, mom, it is fun. Every practice is different, I enjoy. The minute I don't enjoy, I will stop doing it.
Eric Topol (27:36):
Yeah. Well it's amazing story about Susan and of course the expansion of your family with a grandchild and everything else that you wrote about in the book. So now let's go to this story, the big story here, which is mRNA. Now you can get into cells, you can deliver just about anything. So now it can be used for genome editing, it can be used for all these different pathogens as vaccines and including not just pathogens but potentially obviously cancer, to rev up the immune system, neurodegenerative disease to prevent these processes and potentially even preventing cancer in a few years ahead. How do you see this platform evolving in the years ahead? You already have seen many vaccines getting approval or under intense study for pathogens, but that just seems like the beginning, right?
Katalin Kariko (28:38):
Yes, yes. When I came to Penn, the major advantage was going to lectures and when I went to the lectures, I always at the end of it think, mRNA would be good for it. So, I was collecting all of these different fields and then what happens is right now I can see the companies are making those RNA, which I thought that it will be useful and even many, many more things that they are applying and now it is up to those specialists to figure out they don't need me. They need experts on cardiology and other fields and allergies. There is also to tolerate allergies and there are so many fields scientists will be figuring out there what is useful for the mRNA, and they can just order now or create their own RNA and test it out.
Eric Topol (29:38):
It's actually pretty amazing because I don't know where we'd be right now if you had not been pushing this against all adversity. I mean just being suppressed and being told, put your stuff out in the hallway or being thrown out of the university and not being able to get any grants, which is amazing throughout all this time, not being able to get grants, it tells a big story and that's why the book is so sensational because it's obviously your autobiography, but it tells a story that is so important. It goes back to that memorable quote that I mentioned. You wrap up the book with your message of your life story, and I do want to read a bit of that and then get your reaction. My first message is this, we can do better. I believe we can improve how science has done at academic research institutions.
(30:38):
For one thing, we might create a clearer distinction between markers of prestige, titles, publication records, number of citations, grant funding, committee appointments, etiquette, dollars per net square footage, and those of quality science. Too often we conflate the two as if there's one in the same, but a person isn't a better scientist because she publishes more or first perhaps, she's holding back from publication because she wants to be absolutely certain of her data. Similarly, the number of citations might have little to do with the value of the paper and more to do with external events. When Drew and I published our landmark Immunity paper and indeed it was, it barely got any notice. It took a pandemic for the world to understand what we've done and why it mattered. I mean, that's profound, Kati, profound.
Katalin Kariko (31:42):
I have to tell you that what I could see as the science progress. Every scientist starts with understanding something to help the world but somehow they publish because they have something to say, but somehow, it's shifted. Now we want more money, more people would come, those people had to get publication because otherwise they cannot graduate. They need first to author a paper. They publish even when it is not finished or have nothing to say and then somehow the focus is promotion. You are advancing your position, and the tool is doing the experiments. If you see I was demoted, I was pushed out so if my goal would have been to see that I am advancing, then I would give up because that's what the problem is. So that focus is going away from the original thing that we want to understand the science because if you want to understand the science, you are even happy when you can see a publication doing half of that you have done already because you say, I wanted to understand, here's a paper they did, similar thing I did, but the people think, oh my god, my journal paper is out and my promotion is out because they discovered and they published before me, so that's the problem.
Eric Topol (33:12):
Well, I mean if I made a list of all the adversity that you faced from growing up in the Russian communist run Hungary to coming to the US not even knowing the language and also all the sacrifices you made along the way with your family and when you would go to Bethesda or when you moved to Mainz or I mean all along the whole time, no less what the university of Temple or Penn. I mean the list is very long and somehow you prevailed above all that, which is just so startling but another thing I want to just get into briefly, as you know, this has been a shocking counter movement to the vaccines and giving ridiculously the mRNA as a bad name. In the book, you kind of had a way to foreshadow this because back in the 1968 pandemic that you obviously experienced, here you talked about that.
(34:30):
You said we restricted our movement, limiting our contact with others. We scrubbed, we disinfected. I suppose the party encouraged this, but nobody complained about government overreach. This was a virus. It had no ideology, no political agenda. If we weren't careful, it would spread, then we would all suffer. These were just the facts. That's how viruses work. So how come we still don't know that? That was 1968 in Hungary and here we're go in the United States, and we have a huge movement, anti-vaccine, anti mRNA, Covid vaccines, and it's very worrisome because all the great science is threatened by this misinformation and disinformation. What are your thoughts about that?
Katalin Kariko (35:27):
Yes, I heard that viruses, they love democrats because everybody can do whatever they want, whereas in other countries give an order, everybody has to have vaccine and then that's different, but yes, I understand that the novelty the people were always against, even when X-ray was introduced, people thought that people will look through my clothes and seeing me naked because they take part of the truth and they don't say, maybe through the flesh is going through and I can see somebody's bone or something. Then they distort, and they create a fear and if you make fear, then you can control like Lord of the Flies, somebody you are afraid of and then you can control and you can be afraid of the virus or you can be afraid of the vaccine. Then that's what I don't understand exactly true said that when they investigated those who are spreading most of these news about against the vaccine is they are selling some kind of products benefiting just like a hundred years ago, those who were afraid that they can see through their clothes some they start to sell X-ray resistant underwear.
(36:57):
Of course people, they made money on the people's fear. I don't know that’s how to fight it or I think that the honesty when the scientists would say that, listen, we don't know today how it spread. This is how we suggest, be afraid, wash everything. Oh no, we know that it is in the air so that okay, you don't have to wash your clothes when you go out and come back but don't go to crowded places. In politics it's not working because it is like wishy-washy. Yesterday you said something and today, because we learn, they have to understand this is a science process constantly correcting. In politician, I know everything, this is how to do, they want to reflect this confidence. That's what it is and that's why politics everywhere mixed up with this. Some leaders want to reflect this confidence and they do things which helps the virus to spread.
Eric Topol (38:11):
Right. Well, I'm glad to get your perspective because obviously when you work so hard throughout your career and then you see the backlash, that's unwarranted. It's always good to be circumspect of course, but to say that this was done in a flash in the pan and it's never really, it's gene therapy and it's changing your DNA, I mean it's a lot of crazy things that of course that you brought out in the book as well. Now before wrapping up, you wrote the book before you were awarded the Nobel Prize and this recognition, you and Drew of course became fantastic, so richly deserved, but many things occurred and I wanted to ask you. For example, you did your PhD and your postdoc at the University of Szeged in Hungary, and you went back there, and I think you were celebrated in your university, perhaps the first Nobel laureate. I don't know, I would imagine perhaps. The second, oh okay but also the last thing that was recognized in the book it was a much different thing. It was like the Time 100 recognition but now that you have had many of these unanticipated awards, what are your thoughts about that? I mean, it is wonderful to get recognized by the university that you trained and the people that you grew up with.
(39:53):
Has this changed your life or is it really very much the same as it was?
Katalin Kariko (40:00):
My life is very much the same as it was. I am living in the same house. We moved in 1989 and okay, last year I get a new car. Up until then, I never had, only just some beat-up, last year I purchased my first new car but that's luxury when you are 68 years old, you could afford. Everything was a surprise because 40 years I never get any award and the first award I get in 2021. I tried to articulate to more people, life as a scientist is similar to mine. They are immigrant, they are not recognized and I try to tell them just not to focus something like the university is not grateful. Who is the university? Just they are walls. What administrator would tap your shoulder. You have to know that what you are doing is important and if you get pushed around, you always have to do what Selye said, figure out what you can do. Always that, not what they should do. The agency should give me the money, the boss, the superior should help me. No, I cannot make other people to do. I have to figure out what I can do. I can write better and better and rewrite, generate more data for a submitted grant application and always, that's why all of these naysayers made me better because I'm not focused on revenge or anger, but always, how can I be better.
Eric Topol (41:53):
So that gets me to what you do next. I know you're an avid reader. I know you read so much about science and your field and broader of course I take it you still are doing that, but what's in the next chapter for you? I can't imagine you're ever going to rest.
Katalin Kariko (42:16):
No, no. I will be six feet under when I can rest, I realize now. It is just that you are on a different field, and you understand like nucleotides, how naturally you make RNA, what is the transporters, what is happening in the mitochondria, different things that iron sulfur clusters and then you start to investigate like three months I was just reading one topic. I didn't even know about it or how in my life I was reading so many things. I realized there are so many diseases, I understand what is the reason, people don't. When I was at Penn I went to different people, professors about my idea for certain diseases but I was nobody and nobody listened. Now, I'm somebody. I have to be very careful because I say a name of the disease people will line up here and say, don't talk to Eric. Go and do something, help us and so that's what I try to help. I think that I understand certain disease, which is so enigmatic and nobody has a clue and maybe I have a solution for that. That's what I try to do now.
Eric Topol (43:38):
Do you ever go to Penn? Do you ever go to work in there?
Katalin Kariko (43:44):
No, I don't. When you are forced to retire, and I knew that they would throw me out because it was 2012, right before Christmas I was told that get out because you didn't get the 2012. Last time I submitted an mRNA for stroke therapy. Still very valid and good idea but anyway, I knew that I will be pushed out, but I don't have grudge, even the chairman. How can I expect the neurosurgeon who is doing the operation he just can see that I did not get the funding and those people who make the decision that my proposal is not good, they are expert. He's not an expert. He just can see that this is what the expert said. I talk to him, I don't blame anything.
Eric Topol (44:37):
Good for you. I mean I think it's much easier to be vindictive and you have to have the philosophy that you have, which is not to hold any grudges after all that has basically been done to you by many people along the way and I think we've covered that. I know this is a very different interview perhaps than many others that you've had. I didn't bring up the teddy bear and I didn't bring up a lot of things that others have brought up because they've already been covered. I wanted to get into what you had to endure, what you had to do to persevere and how it has changed the life science and medicine forever and now, still today, the mRNA package will be improved. I mean we've already learned, for example, the change of the two proline substitution that Andrew Ward at my place, along with Jason McLellan and others to make it to better immune response. It can be improved with a 6-P proline substitution. We can beat nature just like you did with the uridine substitution and the nanoparticles will improve and this whole package has got an incredible future but it's thanks to you, if it induced massive inflammation, it never would've been possible.
Katalin Kariko (46:02):
Yes, I always said that hundreds and thousands of scientists, every time I thanks them, those people, even not with us, I was reading their papers and it all contributed to this development and learning. So, I am not thinking that I was many, many other people together, we did that.
Eric Topol (46:30):
Well, I am so indebted to you as everyone who understands sciences, and it's of course a bigger story than mRNA. It's what you endured and how you persevered and against all odds, I mean truly against all odds, so thank you. Did I miss anything that I should have asked you about?
Katalin Kariko (46:51):
No. I have to say the book came out and now I can see in different social media that how other scientists get inspired. There was one who said that she quit doing PhD and she read my book and she cried, she laughed, and she went back. She realized that there is more to it because so many is expecting to do some work and then there will be some rewards. The rewards is this is not a short distance. This is a marathon to be scientist and you have to see the goals and it will one day and you might not the one that cross first the finish line, but you are helping others. That's what is important and that's what I am glad that I work with this and write this book so that other scientists more can associate because they feel the same way, that they are not appreciated. Things are not going as expected and then they might be inspired not to give up and that's what is also an important message.
Eric Topol (48:11):
Well, that's why I love the book because it is so inspirational and it will make people cry. It will make people commit to science or appreciate it more than ever. I don't know if you saw it, but I put it as my 10 favorite books for 2023 and indeed, I could have been the most favorite in many respects. So I hope more people listening or watching the video will read the book because it has a lot. I'm so glad you wrote it, Kati, because if we only knew you from papers and Nobel Prize, you wouldn't know the true story. We wouldn't know really what your life has been like over these many decades. So, thank you for that as well and thank you from the life science, the medical community, and for everyone, for all that you've done to change the future and the current state of medicine.
Katalin Kariko (49:10):
Yeah, thank you very much asking and I might add to the book that the book is published in many different languages is coming Italian and French, German, Thai, Japanese, Chinese. So scientists all over the world can read their native language and maybe they will be inspired.
Eric Topol (49:28):
Oh, I have no question about that. It's a story that it should be a movie so that the people that won't read the book will hopefully watch the movie. Has there already been a plan for that?
Katalin Kariko (49:40):
There was, but I don't think that you know they have this strike during the summer, and I don't know where it ends.
Eric Topol (49:52):
I wouldn't be surprised if it gets done in the future and I hope they'll consult with you, not just read the book and it'll be interesting who they get to play you in the movie, but thank you so much, Kati. What a joy and I look forward to future visits with you.
Jonathan Howard is a neurologist and psychiatrist who practices at NYU-Bellevue and posts frequently on Science Based Medicine.
Transcript, unedited, with links to audio
Eric Topol (00:05):
Well, hello, Eric Topol with Ground Truths and I'm really pleased to have the chance to talk with Jonathan Howard today, who is a neurologist and psychiatrist at NYU at Bellevue and has written quite an amazing book published a few months months ago called We Want Them Infected, so welcome Jonathan.
Jonathan Howard (00:27):
Hey, thanks so much for having me. I really appreciate it.
Eric Topol (00:30):
Yeah, I mean, there's so much to talk about because we're still in the throes of the pandemic with this current wave at least by wastewater levels and no reason to think it isn't by infections at least the second largest in the pandemic course. I guess I want to start off first with you being into the neuropsychiatric world. How did you become, obviously caring for patients with Covid, but how did you decide to become a Covidologist?
Jonathan Howard (00:59):
Well, I developed a strong interest in the anti-vaccine movement of all things about a decade ago when a doctor who I trained with here at NYU in Bellevue morphed into one of the country's biggest anti-vaccine doctors a woman by the name of Dr. Kelly Brogan. I knew her well and we were friends; She was smart and after she left NYU in Bellevue, she became one of the country's most outspoken anti-vaccine doctors and really started leaving off the wall things that germ theory didn't exist, that HIV doesn't cause AIDS. When Covid struck, she felt that SARS-CoV-2 was not killing people because she doesn't believe any virus kills people and so I became very fascinated about how smart people can believe strange, incorrect things and I dedicated myself to learning everything that I could about the anti-vaccine movement. In 2018, I wrote a book chapter on the anti-vaccine movement with law professor Dorit Reiss.
(02:01):
And so when the pandemic came around, I was really prepared for all of their arguments, but I got two very important things wrong. I thought the anti-vaccine movement would shrink. I was wrong about that and I was also really caught off guard by the fact that a lot of mainstream physicians started to parrot pandemic anti-vaccine talking points. So all of the stuff that I'd heard about measles and the HPV vaccine, these are benign viruses, the vaccines weren't tested, blah, blah, blah. I started hearing from professors at Stanford, Harvard, UCSF, Johns Hopkins, all about Covid and the Covid vaccine.
Eric Topol (02:40):
Yeah, we're going to get to some of the leading institutions and individuals within them and how they were part of this, and surprisingly too, of course. Before we do that in the title of your book, We Want Them Infected, it seems to bring in particularly the Great Barrington Declaration that is just protect the vulnerable elderly and don't worry about the rest. Can you restate that declaration and whether that's a core part of what you were writing about?
Jonathan Howard (03:21):
Yeah, the title of the book is to be taken literally. It comes from a quote by Dr. Paul Alexander, who was an epidemiologist in the Trump administration and he said in July 4th, 2020, before anyone had been vaccinated, infants, kids, teens, young people, young adults, middle age with no conditions, et cetera, have zero to little risk so we want to use them to develop herd, we want them infected. This was formalized in the Great Barrington Declaration, which was written by three doctors, our epidemiologist, none of whom cared for Covid patients, Jay Bhattacharya at Stanford, Martin Kulldorf who at the time was at Harvard, and Sunetra Gupta who is at Oxford. If I could state their plan in the most generous terms, it would be the following that Covid is much more dangerous for certain people, but we can relatively easily identify older people and people with underlying conditions.
(04:19):
It's much more benign for a healthy 10-year-old, for example and their idea was that you could separate these two groups, the vulnerable and the not vulnerable. If the not vulnerable people were allowed to catch the virus develop natural immunity that would create herd immunity. They said that this would occur in three to six months and in that time, once herd immunity had been achieved, the vulnerable people who have been in theory sheltering at home are in otherwise safe places could reenter society. So it was really the best of both worlds because lives would be saved and schools would be open, the economy would be open. It sounded very good on paper, kind of like my idea of stopping crime by locking up all the bad guys. What could go wrong? It was a very short document. It took about maybe an hour to write.
(05:17):
I imagine there were some nefarious forces behind it. One of the main instigators of it was a man by the name of Jeffrey Tucker, who sounds like a cartoon villain and he worked at the, I forget, is the American Enterprise Research Institute. It was some right-wing think tank and he is a literally pro child labor. He wrote an article in 2016 called Let the Kids Work, which suggested that children drop out of school to work at Walmart and Chick-fil-A I'm not making that up and he's overtly pro child smoking. He feels that children, teenagers should smoke because it's cool and then they can quit in their twenties before there are any bad harms from it. Needless to say, the Great Barrington's premises that one infection led to permanent immunity didn't really work out so well, but they were very influential. They had already met with President Trump in August of 2020 and the day after their Great Barrington Declaration was signed, they were invited to the White House. This was October 5th, 2020 to meet with Secretary Human Health and Secretary Services, Alex Azar, and they are advisors to Ron DeSantis. They became mini celebrities over the course of the pandemic and it was a very pro infectious movement. As I said, the title of the book, We Want Them Infected, and they did.
Eric Topol (06:42):
Right. In fact, I debated Martin Kulldorf, one of the three principals of the Great Barrington Declaration. It was interesting because if you go back to that debate we brought out, at least I tried to highlight the many flaws in this. You've mentioned at least one major flaw, which was to this virus. There's not a long-term immunity built by infections. It's just, as we say with vaccines the immunity for neutralizing antibody production and protection from infections and severe Covid is limited duration for four to six months, and at least for the antibodies and maybe the T-cell immunity is longer, but that doesn't necessarily kick in and quickly. So that was one major flaw, but there are many others, so maybe you could just take that apart further. For example, I like your analogy to lock up all the bad guys, but compartmentalizing people is not so easy in life and I think this is a significant concern of the idea that is, while you indicated there may be some merits if things went as planned, but what else was a flaw of that argument or proposition?
Jonathan Howard (08:11):
So yeah, this could be a 10-hour conversation and I think importantly, we don't have to speak hypothetically here. A lot of defenders of the Great Barrington Declaration will say, oh, we never tried it, but they promised that herd immunity would arrive in three to six months after lockdowns ended. So we don't have to speak theoretically about what would've happened had we done it. Lockdowns ended a while ago and we don't have herd immunity. They were very clear on this. Dr. Kulldorf tweeted in December 2020 that if we use focus protection, the pandemic will be over in three to six months. So, what could have gone wrong if about 250 million unvaccinated Americans contracted Covid simultaneously in October and November of 2020? A lot of things, as we said, they dichotomized people into vulnerable and not vulnerable, but of course it exists on this. The only bad outcome they recognized was death.
(09:11):
They felt that either you died or you had the sniffle for a few days and you emerged unscathed. Separating vulnerable people from not vulnerable people is a lot easier than it sounds and I think by way of comparison, look at the mRNA vaccine trials. You can read their protocols and the protocols for these trials were 300-400 pages of dense policies and procedures. The Great Barrington Declaration, if you go to their frequently asked questions section, they made some suggestions, which sound great, like older people should have food delivered at home during times of high transmission, but setting up a national or even statewide food delivery program, that's a lot harder than it sounds. When asked about that later, Dr. Bhattacharya has said they could have used DoorDash, for example. So it was just very clear that no serious thought went into this because it was really an unactionable thing.
(10:21):
It's not as if public health officials had billions of dollars at their disposal and they weren't many dictators. They couldn't set up home food delivery programs overnight like they suggested and two months after the Great Barrington Declaration was published, vaccines became available so it became obsolete. Not that vaccines have turned out to be the perfect panacea that we had hoped for, unfortunately, but the idea that young people should continue to try to get natural low immunity in favor instead of vaccination became at that point obscene, but they still are anti-vaccine for young people and for children, which I find despicable at this point.
Eric Topol (11:07):
Right, the data is unequivocal that there's benefit across the board. In fact, just last week in JAMA two senior people at FDA, Peter Marks and Robert Califf published the graphs of how across all ages there was reduction in mortality with the vaccines. That gets us to, as you say now into the vaccine era of Covid and one of the things that the anti-vax community jumped on was when we moved from Delta to Omicron where previous Omicron, there was exceptionally good protection from infections, 95%. It was rare for people to get to have spread with the up-to-date vaccine with the third original strain booster. But with Omicron that fell apart and if infections were breakthroughs were exceedingly common, this led to tremendous fodder for the anti-vax saying the vaccines don't work beyond the false claims that they were, whether they're killing people or gene therapy or microchips or all these other crazy notions. But can you talk to that? Because if you still protect against deaths, Long Covid and hospitalizations, that seems to be pretty important. It's disappointing, and obviously we need ways to prevent infections or otherwise we don't really have an effective exit strategy for the pandemic. This was used and still is used today as a reason that vaccines are worthless if indeed, they're not even dangerous.
Jonathan Howard (12:55):
The vaccines when they were initially came out, as we all know, were 95% effective, but the vaccines were brand new and the virus was brand new. All of this was less than a year old and what's interesting is, unfortunately, I realized this after I wrote my book, but I published an article about this on Science-Based Medicine where I've been blogging throughout the pandemic. So, if anyone can go there, I wrote an article on October 1st, 2023, called over-hyping vaccines it wasn’t pro-vaccine it was pro stop worrying about Covid. So almost all of the doctors that I mentioned in this book vastly overhyped vaccines as soon as they came out saying they were 100% effective against severe disease, that they completely blocked transmission and just really overselling the vaccine saying that they're going to definitely end the pandemic and mocking anyone who disagreed. Now these doctors are saying, oh, there's a lack of trust in the medical community.
(13:57):
We need to rebuild trust without holding a mirror to their statements. Dr. Bhattacharya, for example, participated in a round table discussion with Governor Ron DeSantis at the very end of July. On August 1st, 2021, Ron DeSantis tweeted out a quote by Dr. Bhattacharya that said, we have protected the vulnerable by vaccinating the older population. We have provided them with enormous protection against severe disease and death. That's why you see, even as the cases have risen in Sweden, blah, blah, blah, we've protected the vulnerable. The number of deaths have not risen proportionally and this was right when the Delta wave was taking off within. This is the one thing that was interesting, this pandemic, because you had people make prediction and within days their predictions were falsified. That was a tragic thing to see, but that's 20,000 Floridians or some number like that died during the Delta wave in Florida. More Floridians died after Dr. Bhattacharya said the vulnerable have been protected than before that. So I think there was a lot of over-hyping in the vaccines, and I get where this came from. We as doctors, we wanted everyone to get the vaccines. We wanted to encourage everyone to get the vaccine. I probably did this myself at some times, but I do think that that was a problem, but the same doctors who are now saying that the vaccines were overhyped and were often guilty of them.
Eric Topol (15:35):
Right. Well, I mean, I think as you said, we didn't know the virus is going to evolve with this Omicron event with well over 35 new mutations in the spike protein, no less other parts of the virus and then of course, recently we saw another superimposed Omicron event with this BA.2.86 or JN.1 variant. The problem with this of the vaccine takedown, and as you well know because you've been studying this for more than Covid, is that it extended to many other parts of the pandemic, such as masks, such as there's no such thing as Long Covid or it's exceptionally rare and it bleeds through other areas. So could you comment about that? That is the anti-science. It's not just anti-vax.
Jonathan Howard (16:30):
No, you're absolutely right. I don't talk a lot about Long Covid just because I think a lot of other people do a much better job of that. I have a hard time grasping the numbers myself. You'll read one study, it's one in a thousand, you'll read another study. Oh, 50% of people have Long Covid. My attitude towards Long Covid is I don't know exactly how many people have it, but some people are severely affected by it. We have a lot to learn about it, this is a brand new virus. We are going to be learning about this the rest of our lives, especially the consequences of repeat infections. A baby born today is going to be infected, what? 10 times by the time they go away to college. Who knows what are going to be the consequences of that? What does this mean for autoimmunity?
(17:15):
So my attitude with Long Covid and the long-term consequences are we just have to be very humble about this and again, all of the doctors who I discussed were very arrogant about this. They were writing in as early as March 2020 that school closures may prevent children from developing herd immunity. They spoke about infections as being beneficial for children, but you're right as well that these doctors cast doubt on all in any measures that were used to stop the virus masks, testing, ventilation, lockdowns. One of their core objections wasn't that they didn't feel that these were ineffective necessarily. They objected to lockdowns precisely because they stopped the spread of the virus, so you can read some articles from Scott Atlas in April 2020. He wrote several articles in the Hill, that publication saying it's time to stop the panic, et cetera. If people were as if panic was a bad reaction to Covid, as morgues were overflowing with dead bodies, panic was the right action. He said that the lockdowns have stopped Covid from spreading and stopped natural immunity from developing, which prevents us from reaching herd immunity. So again, these guys and the authors of the Great Barrington Declaration objected to lockdown saying they just postponed the inevitable, which there may be some truth to that. Probably everyone here has been infected by Covid at least one time, but postponing the inevitable, that's what I go to work every day trying to do.
Eric Topol (19:04):
And you could say a lot for putting off an infection, of course, as long as possible. And of course, even trying to put it off forever, because as you know very well, as we went on in the pandemic, we learned a lot then there was treatments such as paxlovid and far better treatments that were available for severe Covid, many randomized trials to help prevent deaths for people who were of high risk. The other thing that I guess I can't emphasize enough, and you had a whole chapter in the book, which is about children, kids, they're not so intrinsically protected. They can die, they can be hospitalized and there have been many deaths among them from Covid, even those who don't have coexisting conditions. So maybe you could talk a little bit about that, the flaw in that it's only people of advanced age or immunocompromised and that young people are bulletproof. That doesn't seem to be the case in reviewing all the data throughout the pandemic.
Jonathan Howard (20:12):
I mean, just to reemphasize the point that you made, that someone who gets Covid today, especially if they're vaccinated and boosted is in much better shape than someone who gets Covid, who got Covid in March or April 2020. The same way I hope someone who gets Covid in the year 2030 is going to be in better shape than we are today. But yes, back to pediatric Covid, the risk to any individual child is very small. So my kids have it, my nieces and nephews had it. I wasn't particularly worried and they fortunately had very mild disease, but there's 73 million children in the United States, and when you multiply a rare event by 73 million children, the numbers began to add up. So far around 2,000 children have died of Covid, which is comparable to what measles used to do before. In the pre pandemic days, hundreds of thousands of children have been hospitalized, and depending on the variant, about a third have needed ICU care.
(21:15):
And five to 10% have been intubated. Some children have had strokes, some children have had amputations. So it's not as bad, it's not as bad as car deaths. It's not as bad as bullets, but we don't have vaccines for those conditions and the vaccine is not a panacea for children. Some vaccinated children have died, but it's like wearing a seatbelt. You can die in a car crash wearing a seatbelt, but your odds are greatly enhanced if you are wearing a seatbelt, but all of these doctors who in 2020 state to their name, to the idea that we could get rid of Covid by spreading Covid be the purposeful infection of children, were unwilling to recognize that the vaccine can help them. They use many different techniques to minimize the benefits of the vaccine. One was to say that it never demonstrated efficacy against hospitalizations and deaths in randomized controlled trials, which is true in as far as it goes because it is very hard to detect rare events in randomized controlled trials unless you do a study of 200 to 300,000 children as was done with the polio vaccine.
(22:36):
And they suggested that this should have been done, that we should have re-enrolled hundreds of thousands of children in these trials, which would've taken, I don't know, five, ten years. So that's number one. We now have about 30 observational studies, and they all show the same. And by the way, there were six randomized controlled trials of the vaccine in children involving about 25,000 children. So they're not small trial. As I said, there are about 30 trials from around the world showing that the vaccine observational trials, so observational studies, I should say, showing that the vaccine is not perfect, but it's very good at preventing rare but serious side effects or serious harms of Covid. As you know, there was just a large study out of Penn a couple days ago showing that the vaccine during the Delta in the Omicron wave was extremely effective at preventing children from entering the ICU.
(23:36):
They also treated rare mild vaccine side effects as a fate worse than death and I mean that very literally, the vaccine in young men can cause myocarditis, which is mild in about 90-95% of people with it. I'm unaware of a single American who has been known to have died from vaccine myocarditis. These doctors made dozens of YouTube videos and editorials and commentaries all saying what a catastrophe vaccine myocarditis was. How dare you minimize vaccine myocarditis. When they also wrote editorials saying, young people should not fear death from Covid, and they spoke about death from Covid as milder than vaccine myocarditis when talking about deaths from Covid, they would say, oh, it's less than suicide. More children drown every year. They would just all sorts of crazy double standards.
Eric Topol (24:38):
Right. One of the things that's extraordinary in the book, Jonathan, is that you have, it isn't like you're just writing text about it. You have all the quotes, you have all the tweets, you have all the articles. I don't know how you did that. I mean, were you keeping an active list of everything that was, I mean, I liken it to remember during in the Trump administration, there was a guy in CNN, I'm trying to remember his name.
Jonathan Howard (25:09):
Dale something.
Eric Topol (25:10):
Dale, yeah. And he had a fact check every day, and he kept track of everything. That was his job full time, but it seemed like you were the only one that has this record of every statement written on the topics that we're discussing. How did you do it?
Jonathan Howard (25:35):
Well, I did it through the blog at Science-Based Medicine is that I'd been collecting these statements starting in May 2021, and it just grew out of that. And so basically, the book is sort of a reorganization, everything that I've been writing on that blog and I will say that the fact that I have so many direct quotes has made it impossible for these doctors to refute me, because if I'm wrong, then they're right. If they're right, then we'll have herd immunity in three to six months once the lockdowns are lifted, reinfections are very rare. Variants are nothing to worry about and so they have to make that case. What they've tried to do is they've tried to do some revisionist history. So, for example, Dr. Jerome Adams, who was Trump's surgeon general, and turned out to be very reasonable guy, recently posted on Twitter, I'll still call it that, that Scott Atlas wanted to, and he was right, wanted to infect people to achieve herd immunity.
(26:49):
And Dr. Jay Bhattacharya and Dr. Vinay Prasad, who's a misinformation oncologist at UCSF, we're a gas. They said, oh no, he didn't want to purposely infect children. We just wanted schools open. The harms of school closures were just so great. So they cast themselves as these very benevolent, we were just looking out for the children. We never wanted them infected. I never said that, I never thought that, but all you have to do is just read their own words. The ones who have responded to me have responded just by childish insults, really just calling me names. I'm a schmuck, I'm a grifter, I'm a B-list Covid influencer. None of them have ever tried to engage with any of the content and all that would require them to do is stand up for their own words, which they won't.
Eric Topol (27:46):
Alright. Now, we touched on it early in our conversation, but what was one of the surprising things on the one hand there are anti-vaxxers, like RFK Jr. and people, as you mentioned, the person that you knew at NYU who went on, but then there were these surprise people who were at top academic medical centers in the country that went into misinformation campaigns, whether it was deliberate because it was associated with all sorts of attention, or whether it was misinterpretation of data. I don't understand, but can you speculate what's going on there and whether or not the universities involved should have been somehow engaging with these individuals?
Jonathan Howard (28:39):
Yeah, so it's tough for me to understand their motives. I do think that what made them more dangerous than someone like Kelly Brogan or RFK Jr. By the way, these two worlds, which I kind of treated as separate, they're beginning to merge with people like Joseph Ladapo, for example. So they're not as separate as they once were and Dr. Vinay Prasad has praised RFK Jr. saying he would destroy Dr. Peter Hotez, a hero of vaccines in the debate. I mean, it's crazy, crazy stuff up, but I think the guys who I write about were more dangerous in that they mixed good advice with bad advice. So they would say very sensible things like, yes, you have to protect grandma. Grandma has to get vaccinated with bad advice, that the vaccine is more dangerous than Covid for children, for example. They also are very good, eloquent speakers who can speak in scientific jargon and use the language of evidence-based medicine, someone like Kelly Brogan, for example, would say that she uses intuition and higher ways of knowing, and crystals and tarot cards, these guys don't do that.
(29:51):
If we were to discuss our general approach to medicine, it would be no different than ours, than anyone's. They would say, we try to use science, evidence, data, logic, and reason to reach the best conclusions. So I think that that made them more dangerous. Again, what do I think their motivations were? I think a lot of it is some of these guys are natural born contrarians, which means that they just have to be a little bit different, that when everyone else is saying X, they got to say Y and that served them well in the beginning, in most of their careers and we need people like that In medicine, I would say that Nobel Prize winner, Katalin Kariko, I am probably butchering her name, but the Hungarian woman who developed the mRNA vaccines maybe fits that profile and so we need people like that in medicine.
(30:39):
I also think they had a hard time admitting air when they drastically underestimated Covid at the start of the pandemic, and all of them did predicting 10,000 people would die predicting that it would be less severe than the flu. They had a hard time saying, oops, I was wrong. Some doctors did that. Famously, Dr. Paul Offit, another vaccine hero, said at the beginning of March, I believe, or early February 2020, that he thought the flu was going to be more dangerous than Covid and when he turned out to be wrong, he said, oops, I was wrong. You might as well make an ass of yourself in front of a million people. But I think these guys couldn't admit air and once they had committed themselves to a policy, the purposeful mass infection of unvaccinated youth, it was hard to backtrack from that. What are you going to say?
(31:26):
Oops, I was wrong, and young people suffered and died because of what I said. No, I'm not going to say that. I'm going to say the vaccine is more dangerous than the virus so I think it was a lot of that. In terms of what universities should do, they're at a bind because if they speak out against these people, they're experts at weaponizing their delusions of self-persecution. I've been silenced, I've been censored. We need, even though, like I said, they became mini celebrities and met with Trump and DeSantis and advised Governor Glenn Youngkin and they were all over the news. They're huge social media presences. They were everywhere, but where I was in a hospital with Covid patient. So I think that if universities speak out, they run the risk of the Streisand effect. It's called amplifying people inadvertently and allowing them to claim their precious victim status, but if they don't speak out, which they really haven't done, they run the risk of what they're saying is this person carries the aperture. Am I pronouncing that word of our university, that we feel that this person is competent to speak on our behalf which is a problem.
Eric Topol (32:38):
No, I think we've just seen that, of course, with the three institutions that the presidents were brought in about a whole different matter, and how they didn't necessarily speak out as they could have a totally different matter, of course. This is a real tough one as you've outlined as to whether leaders of university, for example, at Stanford, the faculty did stand up and say, we're not supporting Scott Atlas or this or that. This didn't really happen at other universities that we've touched on at least. So the individuals now going forward here, there's a much bigger story than just Covid, and it's the anti-science, anti-vax movement, which is very dangerous. I think most people who are reasonable reviewing the data would say vaccines are just extraordinary for preserving health, but we're seeing now this movement has gotten legs, it's gotten funding, it's organized, and you're well familiar with Peter Hotez's book who gets through some of that substantiates where this has been with autism and where it's going.
(33:59):
So one of the problems is that there hasn't been much in the way of any antidote, any aggressive response to basically you have the corrections, the real time, the hall of shame, if you will, of this misinformation to have fact checkers, to get the story straight and perhaps not governmental sponsored because that's also an area of uncertainty of trust in public health, but some type of agency that could take on a corrective effort for the public to know what's fact and what's not. What are your thoughts of how we can get out of this mess?
Jonathan Howard (34:46):
Oh, I think it's going to get worse before it gets better. I think skepticism about the Covid vaccines, we're already seeing this as going to bleed into other vaccines. States are doing everything they can to get rid of what were once considered normal vaccine mandates. So I don't know how we're going to get out of it and I think any government agency designed to combat misinformation would become itself as, first of all, you got to be a little bit careful. We don't know who's going to be running that in 2025, right? I mean, Joseph Ladapo might be in charge of the government industry of misinformation depending on who wins election next. So we got to be careful with handing government that sort of, but I do think that more doctors need to do what I have done, what Dr. Peter Hotez has done, what you've done, what my mentor
(35:37):
Dr. David Gorski, who runs Science-Based Medicine and Steve Novella have done, which is to just speak out and to call out doctors. When we say, when we hear this misinformation, I think a lot of doctors are what we call shruggy, meaning they sort of shrug it off like, that person's kind of wacky. What are you going to do about it? But I think that we need to not tolerate it. We don't have to give them the victim status by saying, oh, you should be fired, you should be censored, this sort of thing, but just when these doctors make absurd statements by saying that the flu is more dangerous for children than Covid, we need to say no. Over the past three years, Covid has killed 2,000 children. The flu has killed about 300. 2,000 is bigger than 300. If I told you in 2019 virus A kills 2,000 people, virus B kills 300, you would not have a hard time answering that question and if you are trying to tell me now that the virus that killed 200 children is worse than the one that killed 2,000, that's absurd and we just shouldn't tolerate that sort of nonsense. I think that's the attitude that we need to have.
Eric Topol (36:51):
Yeah, I mean, I think it's very scary where we're headed, and it's ironic because we're seeing vaccine progression to pathogens never seen before, whether it's malaria, obviously, we have RSV vaccines and so many more that are coming. In addition, these same vaccines on the platform, whether it be mRNA and nanoparticles or proteins or whatnot, are being directed now to help amp up the immune response to cancer or to create vaccines that could help achieve tolerance to the immune system, an area that you work in multiple sclerosis and many other neurologic type one diabetes and on and on autoimmune conditions. So if we don't get this right, that if vaccines are trashed, we got some problems going forward.
Jonathan Howard (37:46):
We shouldn't call those vaccine. That's my suggestion number one. I'm half joking about that. We shouldn't. Sorry to cut you off, but yeah, we do have problems going forward, and like I said, I think it's going to get worse before it gets better and look at the Covid booster vaccination rates. I don't know what they are off the top of my head, but they're in the garbage.
Eric Topol (38:08):
19% in all Americans and we're one of the few countries that has it widely available for all adults, and only 35% in people 70 years and older, where there's a spike in hospitalizations right now that's comparable to the other waves of BA.2 and BA.5, and it's still rising. So yeah, the booster uptake has been very poor, especially in people at high risk. Absolutely right.
Jonathan Howard (38:37):
I think people have been influenced by the anti-vaccine movement, even when they don't recognize it. I think it's kind of permeated the culture because people have a very different attitude towards vaccines than they have to almost anything else in their life. I wouldn't say, for example, I don't need to go to the dentist again, because I went in 2020 and 2021 and 2022, I wouldn't say I don't need to go to the gym anymore because I went 10 times last year, for example. We recognize that there are certain things that we have to do for our health that have to be done on a frequent basis, and it's too bad that vaccination doesn't fit that bill. Again, I think one reason for this is that the vaccines were overhyped at the start of the pandemic, or at least in 2021, they were pitched as this panacea. This we're definitely going to solve things, and in retrospect, that was a mistake. We needed to proceed with a little bit more humility just about a brain. This is everyone's first pandemic, right?
Eric Topol (39:35):
Yeah. I mean, I think the unpredictability of the virus's evolution, which was very slow at first, and then of course it accelerated, was unforeseen and changed the entire profile of the protection forwarded by vaccines. I guess to wrap it up, Jonathan, I want to thank you for all the hard work you did to put this book together and your efforts to try to stand up for the evidence, the science that supports vaccines and the things that we can do to help preserve human health in a pandemic and beyond. I mean, in your practice of medicine that goes well, different and beyond a pathogen in caring for patients with neurologic conditions. I also, I guess would say I'm more hopeful that we will have oral nasal vaccines that do block infections, maybe just for a few months per spray or per inhalation and more durable vaccines that don't only last four to six months if we put our efforts and resources and priorities into it.
(40:44):
But I'm also worried that, as you say, the V word is a bad word now to many people. So I don't know that we've come up with any solution here outside of your idea of not calling vaccines, but it seems to me we have to be much more direct at dealing with the miss and disinformation movements that have grown so profoundly in the last few years and taking advantage of course of the pandemic fatigue and all the holes in our current tools that obviously there are no things that are fully protected, whether it's a vaccine or N95 mask or you name it. Any last comments about where are you headed? Are you still going to track this or are you had enough of it, or what's your next chapter in your work?
Jonathan Howard (41:42):
I'm going to still continue to write at Science-Based Medicine on this theme because I think that it's important as doctors that we regulate our own profession and that we hold our public communications to high standards, and I include myself in that. So in my book, I include several really stupid things that I said, and that might be the subject of a future article of dumb things I said, because I did say some dumb things. So I think we have to hold ourselves to a high standard when communicating with the public in a pandemic. So that's what I'm going to continue to do. I'm going to continue to do what I always do at Bellevue psych and NYU treat MS patients around on the inpatient service at Bellevue Hospital wouldn’t trade it for the world.
Eric Topol (42:29):
Well, I want to thank you, and Bellevue is a tough place to work. I know it well, and that in itself says a lot about you. You're a person who I had not met before, only having read your work, but I don't detect one scintilla of hubris. You come across as a genuine person who is really interested in facts and evidence. I want to thank you for all of your work and look forward to future conversation.
Jonathan Howard (42:58):
Well, thanks for the kind words. I really appreciate it. It means a lot, and I appreciate all you've done on your Twitter feed. Whenever there's a new story. I get it from you first, and so I appreciate it.
Eric Topol (43:08):
Thanks so much, Jonathan.
One of the untold stories of the COVID pandemic in the US is the role of medical and public health professionals in spreading disinformation, pushing for policies that exacerbate the virus’ spread, and drive people away from important interventions, particularly vaccines, which blunt the deadly effects of SARSCOV2. Because of professional courtesy, solidarity or just sheer cowardice, many inside the professions have refused to take on these frauds, egomaniacs, purveyors of sickness and suffering in white coats. Jonathan Howard’s book We Want Them Infected, though, names names. In painstaking detail, he builds an indictment of these men and women who have blood on their hands, abusing the trust of millions to peddle lies and falsehoods. This book is one for the ages, making it hard to sweep the complicity of these individuals with the virus under the carpet, leaving a record for the future, a cautionary tale for all of us.
is an award-wining entrepreneur and innovator in technology, especially A.I., a member of the editorial board of Harvard Business Review, and an outstanding communicator which makes him a frequent media guest and often featured in The Economist, WSJ, and Financial Times. is chock full of interesting analyses and podcasts on tech and A.I.
Here’s his summary of our extended and fun discussion
I hope you find our conversation interesting and informative.
A snippet of our conversation below
Transcript of our conversation 8 January 2023, edited for accuracy, with external links
Eric Topol
It’s a pleasure for me to have Liv Boeree as our Ground Truths podcast guest today. I met her at the TED meeting in October dedicated to AI. I think she's one of the most interesting people I’ve met in years and the first time I've ever interviewed a professional poker player who has won world championships and we're going to go through that whole story, so welcome Liv.
Liv Boeree
Thanks for having me, Eric.
Eric Topol
You have an amazing background having been at the University of Manchester in physics and astrophysics. Back around in 2005 you landed into the poker world. Maybe you could help us understand how you went from physics to poker.
From Physics to Poker
Liv Boeree
Ah, yeah. It's a strange story, I graduated as you said in 2005 and I had student debt and needed to get a job I had plans to continue in academia. I wanted to do a masters and then a PhD to work in astrophysics in some way, but I needed to make some money, so I started applying for TV game shows and it was on one of these game shows that I first learned how to play poker. They were looking for beginners and the loose premise of the show was which personality type is best suited for learning the game and even though I didn't win that particular show we were playing for a winner take all prize of £100,000 which was a life changing amount of money had I won it at the time. It was like a light bulb moment just the game and I’ve always been a very competitive person, but poker in particular really spoke to my soul. I always wanted to play in games where it was often considered a boy’s game and I could be a girl beating the boys at their own game. I hadn't played that much cards in particular, but I just loved any game that was very cutthroat which poker certainly is. From that point onwards I was like you know what I'm going to put physics on hold and see if I can make it in this poker world instead and then never really looked back.
Eric Topol
Well, you sure made it in that world. I know you retired back in about 2019, but that was after you won all sorts of world and European championships and beat a lot of men. No less. What were some of the things that that made you such a phenomenal player?
Liv Boeree
The main thing with poker is well the most important ingredient if you really want to make it as a professional is you have to be extremely competitive. I have not met any top pros who don't have that degree of killer instinct when it comes to the game that doesn't mean it means you're competitive in everything else in life, but you have to have a passion for looking someone in the eye, mentally modeling them, thinking how to outwit them and put them into difficult situations within the game and then take pleasure in that. So, there’s a certain personality type that tends to enjoy that. The other key facet is you have to be comfortable with thinking in terms of probability. The cards are shuffled between every hand so there's this inherent degree of randomness. On the scale of pure roulette which is all luck no skill to a game like chess which has almost no luck (close to 100% skill as you can get) poker lies somewhere in the middle and of course the more you play the bigger the skill edge and the smaller the luck factor. That's why professionals can exist. It's a game of both luck and skill which I think is what makes it so interesting because that's what life is really, right? We're trying to get our business off the ground, we're trying to compete in the dating market. Whatever it is. We're doing our strategy, the role of luck life can throw your curved balls that you can do everything right and still things don't go the way you intended them to or vice versa, but there's also strategies we can employ to improve our chances of success. Those are the sort of skills that poker players particularly this idea of gray scale probabilistic thinking that you really have to hone. I've always wondered whether having a background in science or at least you know studying having ah a scientific degree helped in that regard because of course the scientific method is about understanding variables and minimizing uncertainty as much as possible and understanding what cofounding factors can bias the outcome of your results. Again, that's always going on in a poker player's mind, you'll have concurrent hypotheses. Oh, this guy just made a huge bet into me when that ace came out, is it because he actually has an ace or is it because he's pretending to have an ace and so you've got to weigh all the bits of information up as unbiased as possible in an unbiased way as possible to come to a correct conclusion. Even then you can never be certain, so this idea of understanding biases understanding probabilities I think that’s why a lot of top poker players have backgrounds in scientific degrees a very good friend of mine he had a PhD in in physics. Especially over time poker has become a much more sort of scientific pursuit. When I first allowed to play it was very much a game of street smarts and intuition in part because we didn't have the technological tools to understand really the mechanics of the game as well. You couldn't record all your playing data if you were playing just in a casino unless you were writing down your hands. Otherwise, this information wasn't getting stored anywhere, but then online poker came along which meant that you could store all this data on your laptop and then build tools to analyze that data and so the game became a much more technical scientific pursuit.
Eric Topol
That actually gets to kind of the human side of poker. Not the online version —especially since we're going to be mainly talking about AI the term “poker face” the ability to bluff is that a big part of this?
Liv Boeree
Oh, absolutely. You can't be a good poker player if you don't ever bluff because your opponents will start to notice that so that means you're only ever putting your money on the line when you have a good hand so why would they ever pay you off. The point of poker is to maximize the deception to your opponents so you have to use strategies where some of the time you might be having a strong hand and some of the time you might be bluffing where you might have a weak hand. The key is this is getting into the technical sort of game theory side of it, but you want to be doing these bluffs versus what we call value bets as in betting with a good hand with the right to sort of frequency. You need these right ratios between them, so bluffing is a very core part of the game and yes having a poker face obviously helps because you want to be as inscrutable to your opponents as possible. At the same time online poker is an enormously popular game where you can't see your opponent's faces.
Eric Topol
Right, right.
Liv Boeree
Yet you can still bluff which could actually lead us into this topic of AI because now the best players in the world are actually AIs.
Eric Topol
Well, it's interesting because it takes out that human component of being able to bluff and it may be good for people who don't have a poker face. They can play online poker and be good at it because they don't have that disguise if you will.
Liv Boeree
Right.
Game Theory and Moloch Traps
Eric Topol
That gets me to game theory and a big part of the talk you gave at the TED conference about something that I think a lot of the folks listening aren't familiar with— Moloch traps. Could you enlighten us about that because what the talk which of course we’ll link to is so illuminating and apropos to the AI landscape that we face today?
Liv Boeree
Yeah, I’ll leave it for people to go and watch the TED talk because that's going to be much more succinct than me to explain the backstory of how it came to be called a Moloch trap because Moloch is a sort of biblical figure a demon and it seems strange that you would be applying such a concept to what's basically a collection of game theoretic incentives, but essentially what a Moloch trap is the more formal name for it is a multipolar trap which some of the listeners may be familiar with. Essentially a Moloch trap or a multipolar trap is one of those situations where you have a lot of competing different people all competing for 1 particular thing that say who can collect the most fish out of a lake. The trap occurs when everyone is incentivized to get as much of that thing as possible so to go for a specific objective, but if everyone ends up doing it then the overall environment ends up being worse off than before. What we're seeing with plastic pollution – It’s not like packaging companies want to fill the oceans with plastic. They don't want this outcome. It doesn't make them look good. They're all caught on the trap of needing to maximize profits and external and one of the most efficient ways of doing that is to externalize costs outside of their P&L by using cheap packaging that perhaps ends up in the lakes or the oceans and if everyone ends up doing this but well basically you're a CEO in a decision of I could do the more expensive selfless action, but if I don't do that then I know that my competitors are going to do the selfish thing. I might as well do it anyway because the world's going to end up in roughly the same outcome whether I do it or not because everyone ends up adopting this mindset they end up being trapped in this bad situation. Another way of thinking of it is if you're watching a football at a stadium or a concert and before the show starts everyone's sitting down, but then a few people near the front want to get a better view so they stand up. That now forces the people right behind them to make a decision. I don't really want to block the people behind me but I can't see anymore, so now I have to stand up. The whole thing sort of falls down until everyone is now stuck standing for the rest of the show. No one really actually has a comparative advantage anymore. No one's got a particularly better view than before because it's just the same that now everyone's standing, but overall everyone is net worse because now they have to stand for the whole thing and there's no easy way for everyone to coordinate. A Moloch trap is the result of a competitive landscape where the individual short-term incentives push people to take actions that from a God's eye view from the whole from the whole system's perspective makes everyone worse off than before and because there are so many people it's too hard for everyone to coordinate and really go back to the state before so it creates these kind of arms race dynamics these tragedy of the commons. These are all a result of these Moloch traps which is essentially just another name for bad short-term incentives that hurt the whole overall.
Eric Topol
No, that's great. You know someday you should write the book on competition because you have a deep understanding of that. You understand the whole range from healthy, sometimes we call managed competition. The kind that brings out the best in people to unhealthy, I might even call reckless competition, as I mentioned when we were together. Now let's go to as you say arms race nuclear, there's so many examples of this but in the AI world you were polite during your talk because you referred to one of the major CEOs without actually mentioning his name about making another one of the large AI companies titans. Make them dance as part of the competition and I think you came on to something very important which is we're interested in the safety of AI. As we move towards what seems to be inevitable artificial general intelligence, we'll talk more about that there's certainly concerns at least by a significant perhaps plurality of people that this is or can be dangerous. The fact that these this arms race if you will of AI is ongoing. What are your thoughts about that? How seriously bad is this competition?
“I hope with our [ChatGPT] innovation they will want to come out and show that they can dance. I want people to know we made them dance”—Satya Nadella, Microsoft CEO, on Google
The A.I. Arms Race
Liv Boeree
If it were the case that building powerful AI systems that it was trivially easy to align them with the best of humanity and minimize accidents then we would want more competition because more competition would encourage everyone to go faster and faster and we would want to get to that point as fast as possible. However, if we are in the world where it is not trivially easy to align powerful AI systems with what we want and make sure that they could not do reward hacking or create some kind of unintended consequence but fall into the wrong hands easily you know into the hands of people who want to use it for the various purposes then we wouldn't want as much competition as possible because that would make everything go faster. The thing is when your trajectory is pointing in the wrong direction the last thing you want is more speed, right? I have not yet seen a compelling argument that the current trajectory is sufficiently aligned with what is good for humanity and certainly not for the biosphere that we rely upon. This is not just with AI I mean it's the wider sort of techno capital system in many ways. Obviously, capitalism has been wonderful for us. We are living here speaking across the airwaves in a warm, comfortable environments. We have good food and God bless capitalism for providing us all with that. At the same time there are clearly externalities piling up in our biosphere whether it's through climate change whether it's through pollution and so on and so forth. One particular thing about AI is that if we're going to hack the process of intelligence itself it means intelligence by definition ubiquitous. It can be used to increase the process. It can be but can be used to make more of whatever you want to do. You can do it more efficiently faster more effectively. If you think the system is aligned with exactly what we want then that's a good thing, but I see lots of evidence of the ways it is not sufficiently aligned and I'm very concerned that if we're not thinking in more depth about which goals we should be optimizing for in the first place then we're going to just keep blindly going forward as fast as possible and create a bunch of unintended consequences or even in some cases intended ones with as I said it falling into the wrong hands of people.
Eric Topol
You're right on it, I think the issue is how to get the right balance of progress versus guardrails.
Liv Boeree
You mentioned this particular CEO that I quoted in the TED talk again I won't mention him by name, but anyone can go Google he basically said I want people to know we made our competitor dance and the reason why that resonated with me so much is because it reminded me of my old self in my early 20’s when I first learned to play poker and as I said you need this to win at poker which is by definition a 0 sum game you need this cutthroat almost bordering on psychopathic type willingness to like go after your opponents and get them by the throat metaphorically speaking to get their money, right? That mindset can be very useful when you're playing a game where the boundaries are clearly defined. Everyone is opting in and there's minimal externalities and harms to the wider world, but if you're using that same mindset to build something as powerful as artificial general intelligence which we don't know whether that's no one's certain whether it's going to be trivially easy whether it's impossible whether it will be controllable, whether it be completely uncontrollable, whether we're making a new species, whether it's just another tool or technology. No one really knows, but what I do know is that that is not the mindset or the impetus we want of the leaders building such incredibly powerful tools. Tools that couldn't be used to make them more powerful than any human and ever in history, tools that they may even lose control of themselves, we don't know That's really what alarms me the most is that first of all, we might have leaders who have that mindset in the first place but again even if they were all very wise and positive some mindset they weren't out there trying to just compete against each other and it's like pardon my French but like dick swinging contest even if they were perfectly enlightened they're still trapped in this difficult game theoretic dilemma this Moloch trap. I want to let my team build this safely as a priority, but I know that the other guys might not do it as safely, so if I go too slowly, they're going to get there ahead of me and deploy their really powerful systems first, so I have to go faster myself. Again, what suffers if everyone's trying to go as fast as possible the slow boring stuff like safety checks like evaluation testing etc. This is the real the fundamental nature of the problem that we need to be having more honest conversations about it's twofold. It's the mindset of the people building it. Now again some of them I know some of them personally, they're amazing people. Some of these CEOs I deeply respect and I think they understand the nature of the problem and they're really trying to do their best to not fall into this Moloch mindset, but there are others who truly are just wanting to I don't know solve some childhood trauma thing that they have through. I don't want to psychoanalyze them too much but whatever's going on there plus you have the game theoretic dilemma itself and we need to be tackling both of these because we're building something as powerful. Whether again it's AGI or not even narrow AI systems. LLMs are getting increasingly generalizable multimodal, they're starting to encroach into your area of expertise into biology I was reading about which I can't remember which chatbot it was but there's a really cool paper you guys could link to on archive talking about whether LLMs could be used to democratize access to use of technology like DNA synthesis. Is that something we want no safeguards on because that's sort of what we're careening towards and there are people actively pushing to be like no, that you can't deny anyone access to information. Google right now if you search if you Google how do I build a bomb. There's it's something like they just put it on front page. That information they don't give you the step-by-step recipe and yes, okay, you could go and get your chemistry degree and get some books and figure out how to build a bomb, but the point is there's a high barrier to entry and as these LLMs become more generalizable and more and more accessible we have this problem where the barrier to entry for anyone who is really murderous or omnicide or a terrorist mindset these are going to be falling into the hands of more and more of these people it’s going to be easier and easier for them to actually get hold of this information and there is no clear answer of what to do with this because how do we strike a balance between allowing free flow of information so that we're not stifling innovation which it also would be very terrible or even worse creating some kind of centrally controlled top-down tyrannical control of the internet saying who can read what that's an awful outcome, but then in and the other direction we can't have it widely available to but people like ISIS or whoever how to build a pathogen that makes COVID look like the common cold. How do we navigate this terrain where we don't end up in tyranny or self-terminating chaos. I don't know but that those are the problems. That's all we have to figure out.
Effective Altruism
Eric Topol
The idea that you conceptualize what's going on in AI as a Moloch trap I think is exceedingly important but now you also cited that there were a few companies that deserved at least credit with their words such as OpenAI where they're putting 20% of their resources towards alignment and Anthropic as well as DeepMind that's done a lot of great work with AlphaFold2 and life science, but as you said these are just words we haven't seen that actually translated into action. As we go forward one of the terms tossed around a lot that also was surrounding Sam Altman's temporary dismissal and brought back to OpenAI is effective altruism What is EA?
Liv Boeree
There's two ways of thinking about EA. There's the body of ideas, the principles which to summarize them as quickly as I can and as best as I understand them would be there are many different problems on earth there are only finite resources in terms of intellectual capital and actual capital in order to be spent on fixing these problems and so because of that we need to triage and figure out where is the most effective place to spend our time and money in order to solve these problems. How do we rank these problems in terms of scale and electiveness and so on and then how do we deploy our resources as efficiently and as effectively as possible in order to achieve these big problems. So those are the sort of principles and then. Out of those principles over time sprang up a community of people who adhere to those principles and in part have been very aligned with that I started a fundraising organization alongside some other poker players back in 2014 following these principles and encouraging poker players basically to donate to a range of different charities. Most of which were to do with because it if you want to save a life on average the most cost-effective way to do that averages out to people in sub-Saharan Africa dying from extreme poverty related illnesses particularly malaria turns out that providing bed nets on average will save a life for about $5000 from malaria there's vitamin A supplementation etc. That was my involvement I'm going off track, but that was my involvement in EA, but basically out of that sprang a movement and as that movement evolved then it became there were sort of different categories because it's very hard to concretely go well that's definitely problem number one because you have some which are well right now we know that there are this many people dying per day needlessly from this particular tropical disease or you could zoom out and go okay but over the next thirty years these are the kind of risks that civilization is facing so actually if we give that a 10 % probability then that could be 10 % of this many people so actually this is the biggest issue or you could go I care more about I don't just care about human lives I care about animal lives in which case then you. Then math would lead you to conclude that factory farming is actually the biggest issue particularly the amount of needless suffering that is going on factory farms like there's small rules changes that could be made in the way that these animals are treated during slaughter or raised pigs in gestation crates. Small changes there could have a huge positive impact on billions upon billions of animals' lives per year so out of these ideas sprung sort of different subcategories of EA of people focusing on different areas depending on what their personal calculations may led them to and of the category of sort of risks to humanity AI if you follow the if appreciate the game theoretic dilemmas that are going on and see just how fast things are going and how much safety is fallen by the wayside there's strong arguments that AI becomes a very important topic. Effective altrurists became from what I can see very concerned about AI long before almost the rest of the world did and so they became I guess kind of synonymous with the idea of AI safety measures and then I don't really understand well I mean there's reasons why I guess that that seems like the way the Sam Altman thing came up was because two members of the board have been associated with AI safety and effective altruism and they were 2 of the 3 that seems like they tried to you know, vote him out. Then this whole hooha drama came up about it and I wish I knew more I would love to know their reasons why they felt like Sam had to go. What it seems like again I'm purely speculating here but what I've heard through the grapevine was that it was more to do with him lying and misrepresenting them as opposed to a safety concern, but I don't know so that's the I guess Sam Altman EA drama.
The AGI Threat
Eric Topol
In many ways it's emblematic of what we've been talking about because you know there were a couple of board members that were there was a lot of angst regarding pushing hard on AGI. Whether or not there are other things of course that's a different story, but this is the tension we live in now that is we have on one hand some leaders like Yann LeCun, Andrew Ng who are not afraid who say you know still humans are going to be calling the shots as this gets more and more refined to whatever you want to call AGI, but more comprehensive abilities for machines to do things. The other the real concerns like Jeff Hinton and so many others have voiced which is we may not be able to control this, so we'll see how this plays out over time.
Liv Boeree
Look I hope that Andrew Ng and Yann LeCun turn out to be right. I deeply hope so, but I have yet to see them make compelling arguments because really the precautionary principle should apply here, right? When we're when we're playing such high stakes when we're gambling so high and there's a lot of people who don't have any skin in the game whose lives are on the line even if it's with a very small probability then you need to have real air type proof that your systems will do exactly what you want them to and even with ChatGPT-4 when it came out you know obviously there wasn't a threat to humanity in any explicit way, but that went through six months of testing before they released it. Six months and they got lots of different people. They put a lot of effort into testing it to make sure that it reliably did what they wanted it to when users used it. Within three days of it being available on the internet there were all kinds of unintended consequences coming up. It made the front page of The New York Times. Even with six months of testing I believe you know OpenAI really worked hard to make that be as bounded as possible and they thought they'd I'm sure they were expecting some things to slip through, but it was trivial once you got thousands of users on it figuring out ways to jail break it.
Liv Boeree
There hasn't been that's surely a data point to show that you know even with lots of testing this is not a trivially easy problem the people building a machine will always be able to control it and as systems get more and more powerful and more and more emerging properties come out of them as they increase in complexity that's what emergent seems to do. If anything is going to become harder to predict everything that they could do not easier and it's I don't know I as I say I would love for Yann and Andrew to be correct, but even I think even both of them when pushed for example on the topic of what about controlling access to LLMs that could be used for pathogen synthesis in some way or as a sort of put as a tool to help you figure out which DNA synthesis companies have the least stringent checks on their on their products and we'll just send you anything because that some really do have very low stringency there. They didn't have a good answer to that they couldn't answer it and they'll just sort of go back to yes, but you can't constrain information. It's still yeah, you have to give it all for free. It's like you can't be an absolutist here like there are tradeoffs. Yes, and we have to be very careful as a so civilization not to swing too much into censorship or to swing too much into just like letting all guardrails off. We have to navigate this, but it is not comforting to me as a semi layperson to see leaders who are building these technologies dismiss the concerns of alignment and unintended consequences as like trivially easy problems when they clearly aren't that's not filling me with confidence. They're hubris— I don't want a leader who's showing hubris and so that's end of my rant.
Eric Topol
It's really healthy to kind of vet the ideas here and that's what's really unique about you Liv is that you have this poker probabilistic thinking you know competition is fierce as it can be and how we are in such exciting times, but also in many ways daunting with respect to you know where we're headed where this could lead to and I think it's great. I also want to make a plug for your Win-Win that's perfect name for a podcast that you do and continue to be very interested in your ideas as we go forward because you have such a unique perspective.
Liv Boeree
Thank you so much, I really appreciate you plugging it. I remain optimistic there's a lot of well-intended people. Incredibly brilliant people working within the AI industry who do appreciate the nature of the problem. The question is I wish it was as simple as oh, just let the market decide just let profit maximization guide everything and that will always result in the best outcome I wish it was that simple that would make life much easier, but that's not the case externalities a real, misalignment of goals is real. We need people to reflect on just be honest, over the fact that move fast, and break things is not the solution to every problem and especially when the possible things you are breaking are the is the very biosphere or playing field that we all rely on and live on. Yeah, it's going to be interesting times.
Eric Topol
Well, we didn't solve it, but we sure heard a very refreshing insightful perspective. Liv, thanks for what you're doing to get us informed and to learn from other examples outside of the space of AI and your background and look forward to further discussions in the future.
Liv Boeree
Thank you so much. Really appreciate you having me on.
The science to advance our understanding of the aging process—and to potentially slow it down—has made important strides. One of the leading scientists responsible for this work is Professor Tony Wyss-Coray, whose work has particularly focused on brain aging but has implications for all organs. I believe his December 2023 Nature paper on blood proteins that can track aging for 11 of our organs is one of the most important aging reports yet.
Here is the audio and transcript of our conversation, recorded 20 December 2023, with a few relevant external links.
This is the last Ground Truths post for 2023 and I hope you’ll find it informative. I look forward to sharing many more exciting, cutting-edge biomedical advances with you in 2024!
00:10.38
Eric Topol
Hello this is Eric Topol and for this edition of Ground Truths. I'm so delighted to have with me Professor Tony Wyss-Coray of Stanford, a Distinguished Professor at Stanford and who directs the Knight Initiative for Brain Resilience. So welcome Tony.
00:30.19
Tony Wyss-Coray
Thank you, thank you for having me, Eric.
00:32.84
Eric Topol
Well, I've been following your career and your work for decades I have to say and what you just published a couple weeks ago in Nature. The cover paper about internal organ clocks. It blew me away. I mean it's a built on a foundation of extraordinary work. I thought we could start with that because to me that's really a breakthrough in that when we think of aging and how to gauge a person aging with things like the Horvath clock of methylation markers or telomeres or —not at all specific to any part of the body, just overall, l but you published an extraordinary work about plasma proteins for 11 organs that predicted the outcomes things like heart failure and Alzheimer's so maybe you could tell us about this. Seems to be a big deal to me.
01:28.41
Tony Wyss-Coray
Thank you so much I'm honored. Really, you know I think if you work on this stuff, especially for several years it feels sort of obvious to do it? But I think you know it is in a way. It is. Pretty simple. So what we argued is that the thousands of proteins that you know are present in our blood. They must originate from somewhere now a lot of proteins are you know, produced by cells throughout the body. But some proteins are very specifically produced. For example, only in the brain or only in the liver or only in the heart because they have specialized functions and we have you know being taking advantage of that in clinical medicine where you measure. Often you know one of these proteins to sort of diagnose pathology in a tissue, but we took this It's just a level further and said, well, let's just find out of thousands of proteins that we can measure assign them to specific organs and tissues. And then see whether they change with age and many of them turn out to change. We found you know about 1500 proteins or so in the study that we did although that number can grow dramatically if we you know keep.
03:01.11
Tony Wyss-Coray
Improving our technologies or techniques to measure them and many of them come from the brain or from other tissues and because they change with age. They tell us something about the aging of that organ. And as others have shown in the field including Steve Horvath is that that prediction of the age if it doesn't really match exactly your actual age contains information about the state the physiological state or the risk to develop. Organ-specific disease.
03:37.75
Eric Topol
Right. And you found that about 1 in 5 people had evidence of accelerated aging of 1 organ which of course is really starting to nail down ability to detect aging you know to localize it and um. What strikes me Tony is that now because we're seeing at the cusp of advancing in the science of aging a field that you have done so much to propel forward and one of the issues has been well, how are we going to prove it. We can't wait for 20 years to show that. Whatever intervention led to promotion of healthy aging. But when you have a marker like this of organ specificity, it seems like the chances of being able to show that intervention makes a difference is enhanced would you say so?
04:29.28
Tony Wyss-Coray
Yeah, absolutely I think that's one of the most exciting aspects of this that we can now start looking at interventions whether they are you know a specific intervention that tries to target the aging process, or you know just that. Let's say a cholesterol lowering drug or blood pressure lowering drug does that have a beneficial effect on the heart. For example, on the kidney or you can also start thinking of lifestyle interventions where they actually have an effect right? If you started exercising you collect your blood before and then a year after you have an exercise regimen does that actually change the age that we can measure with these different clocks.
05:22.55
Eric Topol
Right? Well I mean it's really a striking advance and by a marker of aging so that gets me to your other work. You've done well over 10 years which is that you could identify that given young blood. First of course in mice and then later verified in people could improve cognitive function in older whether it's experimental models or in people. So what are your thoughts about that is that if that's something you've been ruminating on for many years and I’m sure there are places around the world that are trying to do this sort of thing. What do you think of that potential?
06:11.40
Tony Wyss-Coray
Yeah, so there really this recent observation or study really came out of you know that finding that young blood can change the age of different organs and you know we. We were not the first to show this. We showed it for the brain but Tom Rando who studied muscle stem cell aging showed this you know a few years earlier in the muscle and we worked with Tom to explore this for the brain, but it shows sort of that this you know the composition of the blood. It is really not just reflecting the age of organs and tissues. But it actually also affects them. It directs them in a way and so you can speculate that you know if you had an organ that shows accelerated aging. Because some of the factors end up in the blood. They might actually induce aging in other tissues and so promote the aging process and people in the field have also shown that this is true for specific cells. We call them senescence cells. So these are a specific type of cell that seem to somehow stop dividing and assume the state that releases inflammatory factors these cells too. They seem to almost infect the neighborhood where they live in with an age promoting sort of.
07:41.95
Tony Wyss-Coray
The secretome , as we call it, so they release factors that seem to promote aging locally but potentially across the organism and interfering in that could potentially have rejuvenating effects and so that brings us back to this observation that.
08:01.23
Tony Wyss-Coray
Young blood could potentially rejuvenate organs We know old blood can accelerate it at least in mice. So could we neutralize the age promoting factors in people and could we deliver sort of the rejuvenating factors. Now what's been frustrating for me is that it has been incredibly challenging to identify the key factors.
08:33.30
Tony Wyss-Coray
I think we became to realize as a field that there is not 1 factor. There's not 1 magic factor that will keep us young or keep our organs young but rather different cells and different organs in our body seem to respond in different ways actually to this young blood. Can show this with molecular tools. We can show that every cell actually responds. So if you take a mouse an old mouse and you give it young blood every cell in that mouse shows a transcription of the response to the young blood.
09:10.80
Tony Wyss-Coray
Some of them may regenerate mitochondria and others activate other pathways. We see that stem cells respond particularly well the stem cells of the Immune system hematopoietic stem cells um while other cells show less of a response. And that to me suggests that they respond to different factors in the young blood and that you know they have very specific um receptors Probably that recognize some of these beneficial factors and then respond in a specific way. So that’s what we need to.
09:33.16
Eric Topol
Right.
09:48.63
Tony Wyss-Coray
Figure out I think as a field to translate this really to the clinic is what are the key factors and will it be possible to make a cocktail that sort of mimics Nature's you know elixir
10:06.13
Tony Wyss-Coray
I Said this before it's almost like the fountain of youth is within us, but it just dries out as we get older and if we could figure out what are the key factors that that make up this fountain. We could potentially you know either, as a treatment, deliver it again or reactivate that found and so that the body produces these factors again.
10:34.73
Eric Topol
Well, you know that's something that years ago I was very skeptical about and because of your work and others in the field. I've come a long way thinking that we're on the cusp of really identifying ways to truly promote healthy aging. And so this is a really you know extraordinary time in our lives I wonder you of course mentioned 2 critical paths that have been identified the senescent cells—removing them— or the infusion of young plasma. Would you say it's too simplistic to reduce this to decreasing inflammation or is that really the theme here, or is it much more involved than that.
11:28.48
Tony Wyss-Coray
I think inflammation has a big part in that but you know inflammation is such a broad term and such an ill-defined term that um yeah I can say yes to your question.
11:44.45
Tony Wyss-Coray
And I'm probably not going to be wrong. Um, but if we really want to know which molecular pathways in the inflammatory cascade are key to this detrimental process that seems to accelerate aging. Um, I think we have to work a bit harder and really so define what we're saying you can't just have thousands of proteins or genes that have something to do with immune and inflammatory process. It's called inflammation.
12:21.25
Tony Wyss-Coray
Then? yes, everything is inflammation. But I think we have to be more precise. Otherwise, you can't really target it. Having said that you know if we use sort of the conventional tools that biologists use these pathway analyses if we give young plasma. To an aged organism then the top pathway or one of the top pathways in almost every cell is inflammation, suggesting that we reduce the inflammatory process. But again, it's in a very broad sense and I want to know more? what? what. What we're finding? In fact, you know 1 of our first observations when Saul Villeda was in my lab and the first parabiosis study to look at factors that might promote brain aging. Yeah, he identified beta-two-microglobulin and eotaxin is a chemokine that is involved in a lot of you know, sort of inflammatory responses and has actually recently more recently again been implicated by Michelle Monje here at Stanford. To be a mediator of you know the chemobrain as people call it at least in animal models and we showed that it's part of the age plasma that causes sort of, an acute impairment of cognitive function in mice.
13:46.90
Tony Wyss-Coray
So that would be an example of a bad factor and that is part of an inflammatory cascade but we want to know what exactly is it. Um, we tried a small molecule that targets the receptor One of the main receptors for this chemokine. But unfortunately that compound had some side effects on the liver and we've never got to really test. The question is this you know potentially Important. It's one of the challenges you know for drug development.
14:20.82
Tony Wyss-Coray
You often don't get to test your questions because the drug has side effects that don't allow you to do that.
14:26.14
Eric Topol
Well, speaking of drugs out there this past week there was a very provocative paper from Daniel Drucker University of Toronto on the GLP-1 effects on brain inflammation and interestingly with. You may have seen it but with mice that were either knocked out of GLP-1 for their blood cells or their brain. It was clear that inflammation reduction with these drugs and they tested several different GLP-1s, all worked through the brain. Which is really fascinating and I wonder of course these drugs are now you know they craze for anti-obesity. But do you see something like that this this peptide agonist as a potential way to achieve some of the effects that you've been. Working on for a long time.
15:25.40
Tony Wyss-Coray
Yeah I think this is extremely fascinating. Um I mean these drugs. Um, we don't understand them exactly what they're doing as you know for many drugs.
15:31.24
Eric Topol
Right? right? right.
15:37.13
Tony Wyss-Coray
But it's really amazing the effects that you see and you know I'm very hopeful. There's a large phase 3 trial in Alzheimer's disease ongoing. The phase 2 looked very positive very promising so you know it. It is really possible that.
15:49.65
Eric Topol
Um, yeah.
15:56.49
Tony Wyss-Coray
Um, that there that there are key pathways that are responsible for you know cognitive decline and a cognitive impairment and inflammation is ah is a key aspect of that again inflammation in a broad term. We need to define it but it could be that it goes through. You know, um through these glib receptors and um and that ah might be ah, some regulator of a broader process but you know we see for example with aging just with normal aging you get um activation of.
16:35.96
Tony Wyss-Coray
Inflammatory pathways in the brain vasculature and young plasma reduces these changes acutely and maybe this is you know all part just dampening that inflammation gives you some additional brain power, if you will, for lack of a better word. That much of you know at least the early stages of cognitive impairment that lead to Alzheimer's disease.
17:12.10
Tony Wyss-Coray
Relatively transient and are more like a fog like we say you know the chemofog the chemobrain or brain fog but that you know with Covid that you also commented very prominently that. That suggests that it's not a structural damage early on but that that it might be some soluble factors that would go a long way if you could just suppress them.
17:35.83
Eric Topol
Right? right? Well, It's really fascinating to see and I'm glad you mentioned the Phase 3 trial in Alzheimer's for this one class because I think that's expected in 2025 to read out and that'll be really important. I Wanted to ask you because now there's many shots on goal to change the natural arc of aging at all these companies like Altos and Unity and Calico and I mean there's so many of them I can't even keep track. Um, they're all taking different strategies. I Have to think because they need to have their own intellectual property. What do you see as the alluring ways that we're going to be able to modulate this process.
18:25.65
Tony Wyss-Coray
That's a very tough question. I think it's hard to predict I would say you know like always in in biology. First of all, as you know what we discussed earlier. It could be that. Ah, drug that tries to test the pathway like you know one that Unity tried has side effects and you know you can't actually test your hypothesis. But I think one of the key sort of aspects of the aging process is really that it's both global across the organism but it's also very localized and so it's possible that targeting the aging process will first show benefits. In individual tissues if we target you know the aging process in 1 particular tissue that might show the first benefits. But then again it could be that if there is sort of a key inflammatory driver that to some extent responsible for overall aging of the organism and you manage to target that and slow it or block it. You may have an organism-wide effect. But I think we have to be we have to be realistic that.
19:51.88
Eric Topol
Um, yeah.
19:56.99
Tony Wyss-Coray
You know this is going to be an incremental process I think.
20:00.92
Eric Topol
So is there anything that you've seen that has grabbed you as having tremendous potential that is new or is it really you know the things that I've already been percolating that that we know about.
20:17.41
Tony Wyss-Coray
Yeah I mean just to the GLP-1 study I've been actually ah a bit involved in that. Um I find this really fascinating. Um, yeah.
20:23.98
Eric Topol
Um, yeah, yeah, well that I spoke.
20:29.81
Tony Wyss-Coray
I Mean not in that study that got published but in sort of more on the cognitive side.
20:33.78
Eric Topol
Where I right? I Thought that was especially welcome news because the drugs we have now for Alzheimer's seem to have you know some pretty serious side effects and somewhat low efficacy relative to the to the risk rssessment. So, this would be a drug that we know now as people have taken for years that I do want to get back to you with you on the durability. So if you give young blood to an old person who has let's say mild Cognitive impairment. Will you see a durable impact or is it just a very short lived one.
21:13.76
Tony Wyss-Coray
I think some of the effects will be durable and I'm saying that because of an experiment that James White and Vadim Gladyshev did they use this parabiosis model where you suture a young and an old mouse together.
21:32.10
Tony Wyss-Coray
Left these mice together for two months I mean or three months and then they separated them and let them live and looked at how long does an old mouse live that was paired with a young mouse for a few months. Compared to an old mouse that was paired with another old mouse and they saw that there is clear extension of lifespan if the mouse was exposed to young blood. Now this is in the context of you know, 2 major surgeries first suturing them together and then taking them apart. But I always note that when I present this experiment but I also say at the same time that this is the problem that a lot of older people have right when they have a surgery they don't recover from it as well. And it's often the beginning of cognitive decline if you ask families. You know when did it start? Oh they had heart surgery or they fell and had you know had a hip surgery or something like that or a major infection. That is often the trigger where it's almost like you know the organism is hanging in there and it's still functioning and then there's an injury and it collapses. Um and so you know what's remarkable with the rejuvenating intervention with.
23:02.11
Tony Wyss-Coray
With parabiosis is that it seems to overcome this to a very significant event and they also showed you know with many other tools including with the Horvath clock that tissues are actually getting younger through this process.
23:20.51
Tony Wyss-Coray
We have also found that you know stem cells are rejuvenated for a long period of time if you treat with young plasma infusions in mice and so I'm hopeful that some of the effects are going to be long-lasting. But. You know, practically you would probably still treat people on a regular basis like we do with all drugs. But maybe you would do an infusion every 3 months or every six months and you know we're still trying with um.
23:52.20
Eric Topol
Pray.
23:57.00
Tony Wyss-Coray
A company that I so that that I started Alkahest to you know, convince people to do a Phase 3clinical trial and see how far we can push this.
24:09.38
Eric Topol
Well, it'd be really interesting to see you get that done then going back to the senescent cells which is another leading prospect. It seems to be more difficult to get these cells out of the body. We know they're bad actors but it isn't like we can you know. Ah, very selectively remove them. But what are your thoughts about that approach.
24:35.76
Tony Wyss-Coray
I mean I'm always really puzzled and amazed at the effects that people show with you know, senescence cell removal in in animal models.
24:46.20
Eric Topol
Right.
24:49.57
Tony Wyss-Coray
There is something really almost magical there that you remove these few cells and you know the body is doing much better. Um, So I think you know we should. We should keep trying very hard to translate this to humans. But it's possible that again there are they're very likely different types of senescence cells and different tissues I mean in the brain you know there are no rapidly dividing cells. So. It's not the classic. You know arrest of cell cycle.
25:24.11
Tony Wyss-Coray
But it's probably more like an astrocyte type of cell that might mimic a senescent state. But I think it will be. It will be. You know very much organ specific. And may require very specific interactions or drug targets.
25:44.83
Eric Topol
Ah, right? right? Well then gets us back to kind of where we started before you're what I consider a landmark paper. Um, it would be difficult. To be able to go to a regulatory body like the FDA and say we show that this is affecting the aging process and we show in you know 3 organs 5 organs whatever the 11 organs you could track we are reversing the aging process. I mean you have that now as an extraordinary finding. Do you think that will help accelerate the field by having not having to have a whole-body aging story with an epigenetic clock but rather you know much more pinpointing. Organs that can be helped that they can be promoting healthy aging. It seems to me this is where not only advancing the theories of how to do it but the proof that you have done it. It seems like this is what. You know why I consider such an extraordinary finding.
26:59.90
Tony Wyss-Coray
I totally agree with you but I'm a bit biased I think you know this is what we need I mean this was always a criticism to me and you know we're very good friends with Steve Horvath and
Morgan Levine who you know came up with these remarkable aging clocks. But my my question was always how can you get information about the whole body aging. By looking at changes in blood cells or cheek cells. Um, that cannot contain information about how your pancreas ages or your heart ages at the high resolution. It will correlate. Admittedly, but it will not give you tissue-specific information most likely. So you know going more directly at molecules that are derived from cells across the organism is of course going to be much more informative. Um, we've just started to do this. You know it's a concept. Um, we are super excited about you know, looking now at large datasets like the UK biobank. We just get access. Through a collaboration to 50,000 individuals where we have 1500 protein measurements with a different proteomic platform and it seems most of the findings replicate.
28:47.45
Tony Wyss-Coray
You know, very strong risk for people who have older brains to develop the men in the future. Um, and you know we still see these extreme organ ages that I find very puzzling.
29:00.89
Eric Topol
Yeah, it's really striking and the fact that you could replicate the best biomarker for the brain.—Plasma pTau-181— through these proteins is exceptional. How much did machine learning AI help you? And deciphering this large data set of proteins was it really critical or was it just a small part.
29:26.23
Tony Wyss-Coray
Ah, oh it's certainly I mean this is I think you know the terminology are not so clear and I have to admit you know I'm not a computer scientist by any stretch. But I think this is classic machine learning using statistical elements of learning as you know. We use linear modeling. Um, but I think it will become more sophisticated. Um, and I think ai will help us to bring this. To a much higher level by but by basically learning from the relationship between proteins directly and then compare that in healthy people versus control similar to what Christina Theodoris recently did for gene expression. Ah, the single cell level. Um I think we will see that and we're trying this and I'm sure others are trying this too at the protein level but the current the current study uses really more traditional machine learning models. Um that you know are sophisticated but it's not.
30:42.33
Tony Wyss-Coray
You know I'm not sure we call this artificial intelligence.
30:44.85
Eric Topol
Sure. Well I think as you say it can build on that and you know putting in more models to more data sets and where the future goes. You'll get even more precision output as you know know, Tony.
30:51.58
Tony Wyss-Coray
Um, yeah. Absolutely.
31:02.44
Eric Topol
This This has been a real joy. I have to say congratulations to you and your team for such exceptional work. This has been a multi-decade run, you know one layer of after another of building on the science of aging particularly the brain aging I've learned so much from you and your team.
31:07.28
Tony Wyss-Coray
Thank you.
31:21.81
Eric Topol
I have to say the paper you just published you and your group got me excited I mean I really thought of all the things I’ve seen on aging this was the one that really opens it up for you know all the other possible ways to claim you're making a difference. You've got a metric that's emerging and so kudos to you and I know this is got to be of course that you probably just say oh, it's just 1 more thing we've been doing but I am so duly impressed.
31:52.30
Tony Wyss-Coray
Thank you so much. Thank you for the nice word means a lot.
31:58.94
Eric Topol
Well keep up the great stuff because we're all, we're all depending on you so that we can have a better arc of our healthy aging process and we'll keep in touch. If you can just stay on it!
32:10.30
Tony Wyss-Coray
Thank you! Thanks so much.
Thank you for listening, reading, subscribing to and sharing Ground Truths!
Happy new year,
Eric
David Liu is an gifted molecular biologist and chemist who has pioneered major refinements in how we are and will be doing genome editing in the future, validating the methods in multiple experimental models, and establishing multiple companies to accelerate their progress.
The interview that follows here highlights why those refinements beyond the CRISPR Cas9 nuclease (used for sickle cell disease) are vital, how we can achieve better delivery of editing packages into cells, ethical dilemmas, and a future of somatic (body) cell genome editing that is in some ways is up to our imagination, because of its breadth, over the many years ahead.
Recorded 29 November 2023 (knowing the FDA approval for sickle cell disease was imminent)
Annotated with figures, external links to promote understanding, highlights in bold or italics, along with audio links (underlined)
Eric Topol (00:11):
Hello, this is Eric Topol with Ground Truths and I'm so thrilled to have David Liu with me today from the Broad Institute, Harvard, and an HHMI Investigator. David was here visiting at Scripps Research in the spring, gave an incredible talk which I'll put a link to. We're not going to try to go over all that stuff today, but what a time to be able to get to talk with you about what's happening, David. So welcome.
David Liu (00:36):
Thank you, and I'm honored to be here.
Eric Topol (00:39):
Well, the recent UK approval (November 16, 2023) of the first genome editing after all the years that you put into this, along with many other colleagues around the world, is pretty extraordinary. Maybe you can just give us a sense of that threshold that's crossed with the sickle cell and beta thalassemia also imminently [FDA approval granted for sickle-cell on 8 December 2023] likely to be getting that same approval here in the U.S.
David Liu (01:05):
Right? I mean, it is a huge moment for the field, for science, for medicine. And just to be clear and to give credit where credit is due, I had nothing to do with the discovery or development of CRISPR Cas9 as a therapeutic, which is what this initial gene editing CRISPR drug is. But of course, the field has built on the work of many scientists with respect to CRISPR Cas9, including Emmanuel Charpentier and Jennifer Doudna and George Church and Feng Zhang and many, many others. But it is, I think surprisingly rapid milestone in a long decade’s old effort to begin to take some control over our genetic features by changing DNA sequences of our choosing into sequences that we believe will offer some therapeutic benefit. So this initial drug is the CRISPR Therapeutics /Vertex drug.
Now we can say it's actually a drug approved drug, which is a Crispr Cas9 nuclease programmed to cut a DNA sequence that is involved in silencing fetal hemoglobin genes. And as you know, when you cut DNA, you primarily disrupt the sequence that you cut. And so if you disrupt the DNA sequence that is required for silencing your backup fetal hemoglobin genes, then they can reawaken and serve as a way to compensate for adult hemoglobin genes like the defective sickle cell alleles that sickle cell anemia patients have. And so that's the scientific basis of this initial drug.
Eric Topol (03:12):
So as you aptly put— frame this—this is an outgrowth of about a decade's work and it was using a somewhat constrained, rudimentary form of editing. And your work has taken this field considerably further with base and prime editing whereby you're not just making a double strand cut, you're doing nicks, and maybe you can help us understand this next phase where you have more ways you can intervene in the genome than was possible through the original Cas9 nucleases.
David Liu (03:53):
Right? So gene editing is actually a several decades old field. It just didn't quite become as popular as it is now until the discovery of CRISPR nucleases, which are just much easier to reprogram than the previous programmable zinc finger or tail nucleases, for example. So the first class of gene editing agents are all nuclease enzymes, meaning enzymes that take a piece of DNA chromosome and literally cut it breaking the DNA double helix and cutting the chromosome into two pieces. So when the cell sees that double strand DNA break, it responds by trying to get the broken ends of the chromosome back together. And we think that most of the time, maybe 90% of the time that end joining is perfect, it just regenerates the starting sequence. But if it regenerates the starting sequence perfectly and the nuclease is still around, then it can just cut the rejoin sequence again.
(04:56):
So this cycle of cutting and rejoining and cutting and rejoining continues over and over until the rejoining makes the mistake that changes the DNA sequence at the cut site because when those mistakes accumulate to a point that the nuclease no longer recognizes the altered sequence, then it's a dead end product. That's how you end up with these disrupted genes that result from cutting a target DNA sequence with a nuclease like Crispr Cas9. So Crispr Cas9 and other nucleases are very useful for disrupting genes, but one of their biggest downsides is in the cells that are most relevant to medicine, to human therapy like the cells that are in your body right now, you can't really control the sequence of DNA that comes out of this process when you cut a DNA double helix inside of a human cell and allow this cutting and rejoining process to take place over and over again until you get these mistakes.
(06:03):
Those mistakes are generally mixtures of insertions and deletions that we can't control. They are usually disruptive to a gene. So that can be very useful when you're trying to disrupt the function of a gene like the genes that are involved in silencing fetal hemoglobin. But if you want to precisely fix a mutation that causes a genetic disease and convert it, for example, back into a healthy DNA sequence, that's very hard to do in a patient using DNA cutting scissors because the scissors themselves of course don't include any information that allows you to control what sequence comes out of that repair process. You can add a DNA template to this cutting process in a process called HDR or Homology Directed Repair (figure below from the Wang and Doudna 10-year Science review), and sometimes that template will end up replacing the DNA sequence around the cut site. But unfortunately, we now know that that HDR process is very inefficient in most of the types of cells that are relevant for human therapy.
(07:12):
And that explains why if you look at the 50 plus nuclease gene editing clinical trials that are underway or have taken place, all but one use nucleases for gene disruption rather than for gene correction. And so that's really what inspired us to develop base editing in 2016 and then prime editing in 2019. These are methods that allow you to change a DNA sequence of your choosing into a different sequence of your choosing, where you get to specify the sequence that comes out of the editing process. And that means you can, for the first time in a general way, programmable change a DNA sequence, a mutation that causes a genetic disease, for example, into a healthy sequence back into the normal, the so-called wild type sequence, for example. So base editors work by actually performing chemistry on an individual DNA base, rearranging the atoms of that base to become a different base.
(08:22):
So base editors can efficiently and robustly change A's into G's G's, into A's T's into C's or C's into T's. Those four changes. And those four changes for interesting biochemical reasons turn out to be four of the most common ways that our DNA mutates to cause disease. So base editors can be used and have been used in animals and now in six clinical trials to treat a wide variety of diseases, high cholesterol and sickle cell disease, and T-cell leukemia for example. And then in prime editors we developed a few years later to try to address the types of changes in our genomes that caused genetic disease that can't be fixed with a base editor, for example. You can't use a base editor to efficiently and selectively change an A into a T. You can't use a base editor to perform an insertion of missing DNA letters like the three missing letters, CTT, that's the most common cause of cystic fibrosis accounting for maybe 70% of cystic fibrosis patients.
(09:42):
You can't use a base editor to insert missing DNA letters like the missing TATC. That is the most common cause of Tay-Sachs disease. So we develop prime editors as a third gene editing technology to complement nucleases and base editors. And prime editors work by yet another mechanism. They don't, again, they don't cut the DNA double helix, at least they don't cause that as the required mechanism of editing. They don't perform chemistry on an individual base. Instead, prime editors take a target DNA sequence and then write a new DNA sequence onto the end of one of the DNA strands and then sort of help the cell navigate the DNA repair processes to have that newly written DNA sequence replace the original DNA sequence. And in the process it's sort of true search and replace gene editing. So you can basically take any DNA sequence of up to now hundreds of base pairs and replace it with any other sequence of your choosing of up to hundreds of base pairs. And if you integrate prime editing with other enzymes like recombinase, you can actually perform whole gene integration of five or 10,000 base pairs, for example, this way. So prime editing's hallmark is really its versatility. And even though it's the newest of the three ways that have been robustly used to edit mammalian cells and rescue animal models of genetic disease, it is arguably the most versatile by far,
Eric Topol (11:24):
Right? Well, in fact, if you just go back to the sickle cell story as you laid out the Cas9 nuclease, that's now going into commercial approval in the UK and the US, it's more of a blunt instrument of disruption. It's indirect. It's not getting to the actual genomic defect, whereas you can do that now with these more refined tools, these new, and I think that's a very important step forward. And that is one part of some major contributions you've made. Of course, there are many. One of the things, of course, that's been a challenge in the field is delivery whereby we'd like to get this editing done in many parts of the body. And of course it's easy, perhaps I put that in quotes, easy when you're taking blood out and you're going to edit those cells and them put it back in. But when you want to edit the liver or the heart or the brain, it gets more challenging. Now, you did touch on one recent report, and this is of course the people with severe familial hypercholesterolemia. The carriers that have LDL cholesterol several hundred and often don't respond to even everything we have on the shelf today. And there were 10 people with this condition that was reported just a few weeks ago. So that's a big step forward.
David Liu (13:09):
That was also a very exciting milestone. So that clinical trial was led by scientists at Verve Therapeutics and Beam Therapeutics, and it was the first clinical readout of an in vivo base editing clinical trial. There was previously at the end of 2022, the first clinical readout of an ex vivo base editing clinical trial using CAR T cells, ex vivo base edited to treat T-cell leukemia in pediatric patients in the UK. Ffigure from that NEJM paper below). But as you point out, there are only a small fraction of the full range of diseases that we'd like to treat with gene editing and the types of cells we'd like to edit that can be edited outside of the body and then transplanted back into the body. So-called ex vivo editing. Basically, you can do this with cells of some kind of blood lineage, hematopoietic stem cells, T-cells, and really not much else in terms of editing outside the body and then putting back into the body as you point out.
(14:17):
No one's going to do that with the brain or the heart anytime soon. So what was very exciting about the Verve Beam clinical trial is that Verve sought to disrupt the function of PCSK9 storied, gene validated by human genetics, because there are humans that naturally have mutations in PCSK9, and they tend to have much lower incidences of heart disease because their LDL, so-called bad cholesterol, is much lower than it would otherwise be without those mutations. So Verve set out to simply disrupt PCSK9 through gene editing. They didn't care whether they used a nuclease or a base editor. So they compared side-by-side the results of disrupting PCSK9 with Cas9 nuclease versus disrupting it by installing a precise single letter base edit using an adenine base editor. And they actually concluded that the base editor gave them higher efficacy and fewer unwanted consequences.
(15:28):
And so they went with the base editor. So the clinical trial that just read out were patients treated in New Zealand, in which they were given a lipid nanoparticle mRNA complex of an adenine base editor programmed with a guide RNA to install a specific A to G mutation in a splice site in PCSK9 that inactivates the gene so that it can no longer make functional PCSK9 protein. And the exciting result that read out was that in patients that receive this base editor, a single intravenous injection of the base editor lipid nanoparticle complex, as you know, lipid nanoparticles very efficiently go to the liver. In most cases, PCSK9 was edited in the liver and the result was substantial reduction in LDL cholesterol levels in these patients. And the hope and the anticipation is that that one-time treatment should be durable, should be more or less permanent in these patients. And I think while the patients who are at highest risk of coronary artery disease because of their genetics that give them absurdly high LDL cholesterol levels, that makes the most sense to go after those patients first because they are at extremely high risk of heart attacks and strokes. If the treatment proves to be efficacious and safe, then I think it's tempting to speculate that a larger and larger population of people who would benefit from having lower LDL cholesterol levels, which is probably most people, that they would also be candidates for this kind of therapy.
Eric Topol (17:22):
Yeah, no, it's actually pretty striking how that could be achieved. And I know in the primates that were done prior to the people in New Zealand, there was a very durable effect that went on well over I think a year or even two years. So yeah, that's right. Really promising. So now that gets us to a couple of things. One of them is the potential for off-target effects. As you've gotten more and more with these tools to be so precise, is the concern that you could have off-target effects just completely, of course inadvertent, but potential for other downstream in time known unknowns, if you will. What are your thoughts about that?
David Liu (18:15):
Yeah, I have many thoughts on this issue. It's very important the FDA and regulatory bodies are right to be very conservative about off-target editing because we anticipate those off targets will be permanent, those off-target edits will be permanent. And so we definitely have a responsibility to minimize adding to the mutational burden that all humans have as a function of existing on this planet, eating what we eat, being bombarded by cosmic rays and sunlight and everything else. But I think it's also important to put off-target editing into some context. One context is I think virtually every substance we've ever put into a person, including just about every medicine we've ever put into a person, has off-target effects, meaning modulates the function of biological molecules other than the intended target. Of course, the stakes are higher when those are gene editing agents because those modifications can be permanent.
(19:18):
I think most off-target edits are very likely to have no consequence because most of our genome, if you mutate in the kinds of small ways like making an individual base pair change for a base editor are likely to have no consequence. We sort of already know this because we can measure the mutational burden that we all face as a function of living and it's measurable, it's low, but measurable. I've read some papers that estimate that of the roughly 27 trillion [should be ~37] cells in an adult person, that there are billions and possibly hundreds of billions of mutations that accumulate every day in those 27 [37] trillion cells. So our genomes are not quite the static vaults that we'd like to think that they are. And of course, we have already purposefully given life extending medicines to patients that work primarily by randomly mutating their genomes. These are chemotherapeutic agents that we give to cancer patients.
(20:24):
So I think that history of giving chemotherapeutic agents, even though we know those agents will mess up the genomes of these patients and potentially cause cancer far later down the road, demonstrates that there are risk benefit situations where the calculus favors treatment, even if you know you are causing mutations in the genome, if the condition that the patient faces and their prognosis is sufficiently grave. All that said, as I mentioned, we don't want to add to the mutational burden of these patients in any clinically relevant way. So I think it is appropriate that the early gene editing clinical candidates that are in trials or approved now are undergoing lots and lots of scrutiny. Of course, doing an off-target analysis in an animal is of limited value because the animal's genome is quite different than the human genome. So the off targets won't align, but doing off-target analysis in human cells and then following up these patients for a long time to confirm hopefully that there isn't clinical evidence of quality of life or lifespan deterioration caused by off-target editing, that's all very, very important.
(21:55):
I also think that people may not fully appreciate that on target editing consequences also need to be examined and arguably examined with even more urgency than off-target edits. Because when you are cutting a chromosome at a target site with a nucleus, for example, you generate a complex mixture of different products of different DNA sequences that come out, and the more sequences you sequence, the more different products you realize are generated. And I don't think it's become routine to try to force the companies, the clinical groups that are running these trials to characterize the top 1000 on target products for their biological consequence. That would be sort of impractical to do and would probably slow down greatly the benefit of these early nuclease clinical trials for patients. But those are actually the products that are generated with much higher frequency typically than the off-target edits. And that's part of why I think it makes more sense from a clinical safety perspective to use more precise gene editing methods like base editing and prime editing where we know the products that are generated are mostly the products that we want are not uncontrolled mixtures of different deletion and insertion products.
(23:27):
So I think paying special attention to the on-target products, which are generated typically 70 to 100% of the time as opposed to the off targets which may be generated at a 0.1 to 1% level and usually not that many at that level once it reaches a clinical candidate. I think that's all important to do.
Eric Topol (23:51):
You've made a lot of great points there and thanks for putting that in perspective. Well, let's go on to the delivery issue. You mentioned nanoparticles, viral vectors, and then you've come up with small virus-like neutered viruses if you will. I think a company Nvelop that you've created to push on that potential. What are your thoughts about where we stand since you've become a force for coming up with much better editing, how about much better and more diverse delivery throughout the body? What are your thoughts about that?
David Liu (24:37):
Yeah, great. Great question. I think one of the legacies of gene editing is and will be that it inspired many more scientists to work hard on macromolecular delivery technologies. All of these gene editing agents are macromolecules, meaning they’re proteins and or nucleic acids. None of them are small molecules that you can just pop a pill and swallow. So they all require special technologies to transfer the gene editing agent from outside of the cell into the cell. And the fact that taking control of our genetic features has become such a popular aspiration of medicine means that there's a lot of scientists as measured, most importantly by the young scientists, by the graduate students and the postdocs and the young professors of which I'm no longer one sadly, who have decided that they're going to devote a big part of their program to delivery. So you summarized many of the clinically relevant, clinically validated delivery technologies already, somewhat sadly, because if there were a hundred of these technologies, you probably wouldn't need to ask this question. But we have lipid nanoparticles that are particularly good at delivering messenger RNA, that was used to deliver the covid vaccine into billions of people. Now also used to deliver, for example, the adenine base editor mRNA into the livers of those hypercholesterolemia patients in the Verve/Beam clinical trial.
(26:20):
So those lipid nanoparticles are very well matched for gene editing delivery as long as it's liver. And they also are particularly well matched because their effect is transient. They cause a burst of gene editing agents to be produced in the liver and then they go away. The gene editing agents can't persist, they can't integrate into the genome despite what some conspiracy theorists might worry about. Not that you've had any encounter with any of those people. I'm sure that's actually what you want for a gene editing agent. You ideally want a delivery method that exposes the cell only for the shortest amount of time needed to make the on-target edit at the desired level. And then you want the gene editing agent to disappear and never come back because it shouldn't need to. DNA edits to our genome for durable cells should be permanent. So that's one method
.
(27:25):
And then there are a variety of other methods that researchers have used to deliver to other cells, but they each carry some trade-offs. So if you're trying to edit hematopoietic stem cells, you can take them out of the body. Once they're out of the body, you have many more methods you can use to deliver efficiently into them. You can electroporated messenger, RNA or even ribonuclear proteins. You can treat with lipids or viruses, you can edit and then put them back into the body. But as you already mentioned, that's sort of a unique feature of blood cells that isn't applicable to the heart or the brain, for example, or the eyes. So then that brings us to viral vectors. There are a variety of clinically validated viral methods for delivery. AAV— adeno associated virus— is probably the most diverse, most relevant, and one of the best tolerated viral delivery methods. The beauty of AAV is that it can deliver to a variety of tissues. AAV can deliver into spinal cord neurons, for example, into retinal cells, into the heart, into the liver, into a few other tissues as well.
(28:48):
And that diversity of being able to choose AAV capsids that are known to get into the types of tissues that you're trying to target is a great strength of that approach. One of the downsides of AAV for gene editing agents is that their delivery tends to be fairly durable. You can engineer AAVs into next generation capsids that sort of get rid of themselves or the gene editing agents get rid of themselves. But classic AAV tends to stay around in patients for a long time, at least months, for example, and possibly years. And we also don't yet have a good way, clinically validated way of re-dosing AAV. And once you administer high doses of AAV in a patient that tends to provoke high-titer, neutralizing antibodies against those AAVs making it difficult to then come back six months or a year later and dose again with an AAV.
(29:57):
So researchers are on the bright side, have become very good at engineering and evolving in the laboratory next generation AAVs that can go to greater diversity issues that can be more potent. Potency is important because if you can back off the dose, maybe you can get around some of these immunogenicity issues. And I think we will see a renaissance with AAV that will further broaden its clinical scope. Even though I appreciate that the decisions by a couple large pharma companies to sort of pull out of using AAV for gene therapy seemed to cause people to, I think prematurely conclude that AAV has fallen out of favor. I think for gene therapy, it's quite different than gene editing. Gene therapy, meaning you are delivering a healthy copy of the gene, and you need to keep that healthy copy of the gene in the patient for the rest of the patient's life.
(30:59):
That's quite different than gene editing where you just need the edit to take place over days to weeks, and then you want the editing agent to actually go away and you never want to come back. I think AAV will used to deliver gene editing agents will avoid some of the clinical challenges like how do we redose? Because you shouldn't need to redose if the gene editing clinical trial proceeds as you hope. And then you mentioned these virus-like particles. So we became interested in virus-like particles as other labs have because they offer some of the best strengths of non-viral and viral approaches like non-viral approaches such as LMPs. They deliver the transient form of a gene editing agent. In fact, they can deliver the fully assembled protein RNA complex of a base editor or a prime editor or a CRISPR nuclease. So in its final form, and that means the exposure of the cell to the editing agent is minimized.
(32:15):
You can treat with these virus-like particles, deliver the protein form of these gene editing agents, allow the on-target site to get edited. And then since the half-life of these proteins tends to be very small, roughly 24 hours for example, by a week later, there should be very little of the material left in the animal or prospectively in the patient virus-like particles, as you call them, neutered viruses, they lack viral DNA or RNA. They don't have the ability to integrate a virus's genome into the human genome, which can cause some undesired consequences. They don't randomly introduce DNA into our genomes, therefore, and they disappear more transiently than viruses like AAV or adenoviruses or other kinds of lentiviruses that have been used in the clinic. So these virus-like particles or VLP offer really some of the best strengths on paper at least of both viral and non-viral delivery.
(33:30):
Their limitation thus far has been that there really haven't been examples of potent in vivo delivery of cargoes like gene editing agents using virus-like particles. And so we recently set out to figure out why, and we identified several bottlenecks, molecular bottlenecks that seemed to be standing in the way of virus-like particles, doing a much more efficient job at delivering inside of an animal. (Figure from that paper below.) And we engineered solutions to each of these first three molecular bottlenecks, and we've identified a couple more since. And that resulted in what we call VLPs engineered virus-like particles. And as you pointed out, Keith Joung and myself, co-founded a company called Nvelop to try to bring these technologies and other kinds of molecular delivery technologies, next generation delivery technologies to patients.
Eric Topol (34:28):
Well, that gets me to the near wrapping up, and that is the almost imagination you could use about where all this can go in the future. Recently, I spoke to a mutual friend Fyodor Urnov, who talked about wouldn't it be amazing if for people with chronic pain you could just genome edit neurons their spinal cord? As you already touched on recently, Jennifer Doudna, who we both know talked about editing to prevent Alzheimer's disease. Well, that may be a little far off in time, but at least people are talking about these things that is not, we're not talking about germline editing, we're just talking about somatic cell and being able to approach conditions that have previously been either unapproachable or of limited success and potential of curing. So this field continues to evolve and you and all your colleagues are a big part of how this has evolved as quickly as it has. What are your thoughts about, are there any bounds to the potential in the longer term for genome editing? Right.
David Liu (35:42):
It's a great question because all of the early uses of gene editing in people are appropriately focused on people who are at dire risk of having shorter lives or very poor quality of life as it should be for a new kind of therapeutic because the risks are high until we continue to validate the clinical benefit of these gene editing treatments. And therefore we want to choose patients the highest that face the poorest prognosis where the risk benefit ratio favors treatment as strongly as possible. But your question, I think very accurately highlights that our genome and changes to it determine far more than whether you have a serious genetic disorder like Sickle Cell Disease or Progeria or Cystic Fibrosis or Familial Hypercholesterolemia or Tay-Sachs disease. And being able to not just correct mutations that are associated with devastating genetic disorders, but perhaps take control of our genomes in more sophisticated way that you pointed out two examples that I think are very thought provoking to treat chronic pain permanently to lower the risk of horrible diseases that affect so many families devastating to economies worldwide as well, like Alzheimer's disease, Parkinson's disease, the genetic risk factors that are the strongest genetic determinants of diseases like Alzheimer's disease are actually, there are several that are known already.
(37:36):
And an interesting possibility for the future, it isn't going to happen in the next few years, but it might happen within the next 10 or 20 years, might be to use gene editing to precisely change some of those most grievous alleles that are risk factors for Alzheimer's disease like a apoE4, to change them to the genetic forms that have normal or even reduced risk for Alzheimer's disease. That's a very tough clinical trial to run, but I'd say not any tougher than the dozens of most predominantly failed Alzheimer's clinical trials that have probably collectively accounted for hundreds of billions of dollars of investment
Eric Topol (38:28):
Easily.
David Liu (38:31):
And all of that speaks to the fact that Alzheimer's disease, for example, is enormous burden on society by every measure. So it's worth investing and major resources and taking major risks to try to create perhaps preventative treatments that just lower our risk globally. Getting there will require that these pioneering early clinical trials for gene editing are smashing successes. I'm optimistic that they will be, there will be bumps in the road because there always are bumps in the road. There will be patients who have downturns in their health and everyone will wonder whether those patients had a downturn because of a gene editing treatment they received. And ascertaining whether that's the case will be very important. But as these trials continue to progress, and as they continue hopefully on this quite positive trajectory to date, it's tempting to imagine a future where we can use precise gene editing methods. For example, you can install a variety using prime editing, a variety of alleles that naturally occur in people that reduce the risk of Alzheimer's disease or Parkinson's disease like the mutation that 0.1% of Icelandic people and almost nobody else has in amyloid precursor protein changing alanine 673 to threonine (A673T).
(40:09):
It is very thought provoking, and I don't think society is ready now to take that step, but I think if things continue to proceed on this promising trajectory, it's inevitable because arguably, the defining trait of our species is that we use every ounce of our talents and our gifts and our resources and our creativity to try to improve our lives and those of our children. And I don't think if we have ways of treating genetic diseases or even of reducing grievous genetic disease risk, that we will be able to sit on our hands and not take steps towards that kind of future solon as those technologies continue to be validated in the clinic as being safe and efficacious. It's, I teach a gene editing class and I walk them through a slippery slope at the end of five ethics cases, starting with progeria, where most people would say having a single C of T mutation in one gene that you, by definition didn't inherit from mom or dad.
(41:17):
It just happened spontaneously. That gives you an average lifespan of 14 and a half years and strongly affects other aspects of the quality of your life and your family's life that if you can change as we did in animals that T back into a C and correct the disease and rescue many of the phenotypes and extend lifespan, that that's an ethical use of gene editing. Treating genetic deafness is the second case. It's a little bit more complicated because many people in the deaf community don't view deafness as a disability. It's at least a more subjective situation than progeria. But then there are other cases like changing apoE4 to apoE3 or even apoE2 with the lower than normal risk of Alzheimer's disease, or installing that Icelandic mutation and amyloid precursor protein that substantially lowers risk of Alzheimer's disease. And then finally, you can, I always provoke a healthy debate in the class at the end by pointing out that in the 1960s, one of the long distance cross country alpine skiing records was set by a man who had a naturally occurring mutation in his EPO receptor, his erythropoietin receptor, so that his body always thought he was on EPO as if he were dosing on EPO, although that was of course before the era of EPO dosing was really possible, but it was just a naturally occurring mutation in this case, in his family.
(42:48):
And when I first started teaching this class, most students could accept using gene editing to treat progeria, but very few were willing to go even past that, even to genetic deafness, certainly not to changing a ApoE risk factors for Alzheimer's. Nowadays, I'd say the 50% vote point is somewhere between case three and case four, most people are actually say, yeah, especially since they have family members who've been through Alzheimer's disease. If they are a apoE4, some of them are a apoE4/apoE4 [homozygotes], why not change that to a apoE3 or even an ApoE2 or as one student challenged the class this year, if you were born with a apoE2, would you want to change it to a ApoE3 so you could be more normal? Most people would say, no, there's no way I would do that.
(43:49):
And for the first time this year, there were one or two students who actually even defended the idea of putting in a mutation in erythropoietin receptor to increased increase their endurance under low oxygen conditions. Of course, it's also presumably useful if you ever, God forbid, are treated with a cancer chemotherapeutic. Normally you get erythropoietin to try to restore some, treat some of the anemia that can result, and this student was making a case, well, why wouldn't we? If this is a naturally occurring mutation that's been shown to benefit certain people doing certain things. I don't think that's a general societal view. And I am a little bit skeptical we'll ever get widespread acceptance of case number five. But I think all of it is healthy stimulates a healthy discussion around the surprisingly gentle continuum between disease treatment, disease prevention, and what some would call human improvement.And it used to be that even the word human improvement was sort of an anathema. I think now at least the students in my class are starting to rethink what does that really mean? We improving ourselves a number of ways genetically and otherwise by virtue of our lifestyles, by virtue of who we choose to procreate with. So it's a really interesting debate, and I think the rapid development and now clinical progression and now approval, regulatory approval of gene editing drugs will play a central role in this discussion.
Eric Topol (45:38):
No question. I mean, also just to touch on the switch from a apoE4 to apoE2, you would get a potential 2-fer of lesser risk for Alzheimer's and a longer lifespan. So I mean, there's a lot of things here. The thing that got me years ago, I mean, this is many years ago at a meeting with George Church and he says, we're going to just edit 60 genes and then we can do all sorts of xeno-pig transplants and forget the problem of donors. And it's happening now.
David Liu (46:11):
Yeah, I mean, he used a base editor to edit hundreds of genes at once, if not thousands of
Eric Topol (46:16):
That's why it's just, yeah, no, it's just extraordinary. And I think people need to be aware that opportunities here, as you say, with potential bumps along the way, unquestionably, is almost limitless. So this has been a masterclass thanks to you, David, in where we are, where we're headed in genome editing at a very extraordinary time where we've really seeing things click. And I just want to also add that you're going to be here with a conference in La Jolla in January, I think, on base and prime editing. Is that right? So for those who are listeners who are into this topic, maybe they can also hear the latest, I'm sure there'll be more between now and next. Well, several weeks from now at your, it's a
David Liu (47:12):
Conference on, it's the fifth international conference on base and prime editing and associated enzymes, the somewhat baroque name. And I will at least be giving a virtual talk there. It actually overlaps with the talk I'm giving at Rockefeller that time. Ah, okay, cool. But I'm speaking at the conference either in person or virtually.
Eric Topol (47:34):
Yeah. Well, anytime we get to hear from you and the field, of course it's enlightening. So thanks so much for joining. Thank you
David Liu (47:42):
For having me. And thank you also for all of your, I think, really important public service in connecting appropriately the ground truths about science and vaccines and other things to people. I think that's very much appreciated by scientists like myself.
Eric Topol (48:00):
Oh, thanks David.
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Recorded 4 December 2023
Transcript below with external links to relevant material along with links to the audio
ERIC TOPOL (00:00):
This is for me a real delight to have the chance to have a conversation with Geoffrey Hinton. I followed his work for years, but this is the first time we've actually had a chance to meet. And so this is for me, one of the real highlights of our Ground Truths podcast. So welcome Geoff.
GEOFFREY HINTON (00:21):
Thank you very much. It's a real opportunity for me too. You're an expert in one area. I'm an expert in another and it's great to meet up.
ERIC TOPOL (00:29):
Well, this is a real point of conversion if there ever was one. And I guess maybe I'd start off with, you've been in the news a lot lately, of course, but what piqued my interest to connect with you was your interview on 60 Minutes with Scott Pelley. You said: “An obvious area where there's huge benefits is healthcare. AI is already comparable with radiologists understanding what's going on in medical images. It's going to be very good at designing drugs. It already is designing drugs. So that's an area where it's almost entirely going to do good. I like that area.”
I love that quote Geoff, and I thought maybe we could start with that.
GEOFFREY HINTON (01:14):
Yeah. Back in 2012, one of my graduate students called George Dahl who did speech recognition in 2009, made a big difference there. Entered a competition by Merck Frost to predict how well particular chemicals would bind to something. He knew nothing about the science of it. All he had was a few thousand descriptors of each of these chemicals and 15 targets that things might bind to. And he used the same network as we used for speech recognition. So he treated the 2000 descriptors of chemicals as if they were things in a spectrogram for speech. And he won the competition. And after he'd won the competition, he wasn't allowed to collect the $20,000 prize until he told Merck how he did it. And one of their questions was, what qsar did you use? So, he said, what's qsar? Now qsar is a field, it has a journal, it's had a conference, it's been going for many years, and it's the field of quantitative structural activity relationships. And that's the field that tries to predict whether some chemical is going to bind to something. And basically he'd wiped out that field without knowing its name.
ERIC TOPOL (02:46):
Well, it's striking how healthcare, medicine, life science has had somewhat of a separate path in recent AI with transformer models and also going back of course to the phenomenal work you did with the era of bringing in deep learning and deep neural networks. But I guess what I thought I'd start with here with that healthcare may have a special edge versus its use in other areas because, of course, there's concerns which you and others have raised regarding safety, the potential, not just hallucinations and confabulation of course a better term or the negative consequences of where AI is headed. But would you say that the medical life science AlphaFold2 is another example of from your colleagues Demis Hassabis and others at Google DeepMind where this is something that has a much more optimistic look?
GEOFFREY HINTON (04:00):
Absolutely. I mean, I always pivot to medicine as an example of all the good it can do because almost everything it's going to do there is going to be good. There are some bad uses like trying to figure out who to not insure, but they're relatively limited almost certainly it's going to be extremely helpful. We're going to have a family doctor who's seen a hundred million patients and they're going to be a much better family doctor.
ERIC TOPOL (04:27):
Well, that's really an important note. And that gets us to a paper preprint that was just published yesterday, on arXiv, which interestingly isn't usually the one that publishes a lot of medical preprints, but it was done by folks at Google who later informed me was a model large language model that hadn't yet been publicized. They wouldn't disclose the name and it wasn't MedPaLM2. But nonetheless, it was a very unique study because it randomized their LLM in 20 internists with about nine years of experience in medical practice for answering over 300 clinical pathologic conferences of the New England Journal. These are the case reports where the master clinician is brought in to try to come up with a differential diagnosis. And the striking thing on that report, which is perhaps the best yet about medical diagnoses, and it gets back Geoff to your hundred million visits, is that the LLM exceeded the clinicians in this randomized study for coming up with a differential diagnosis. I wonder what your thoughts are on this.
GEOFFREY HINTON (05:59):
So in 2016, I made a daring and incorrect prediction was that within five years, the neural nets were going to be better than radiologists that interpreting medical scans, it was sometimes taken out of context. I meant it for interpreting medical scans, not for doing everything a radiologist does, and I was wrong about that. But at the present time, they're comparable. This is like seven years later. They're comparable with radiologists for many different kinds of medical scans. And I believe that in 10 years they'll be routinely used to give a second opinion and maybe in 15 years they'll be so good at giving second opinions that the doctor's opinion will be the second one. And so I think I was off by about a factor of three, but I'm still convinced I was completely right in the long term.
(06:55):
So this paper that you're referring to, there are actually two people from the Toronto Google Lab as authors of that paper. And like you say, it was based on the large language PaLM2 model that was then fine-tuned. It was fine-tuned slightly differently from MedPaLM2 I believe, but the LLM [large language model] by themselves seemed to be better than the internists. But what was more interesting was the LLMs when used by the internists made the internists much better. If I remember right, they were like 15% better when they used the LLMs and only 8% better when they used Google search and the medical literature. So certainly the case that as a second opinion, they're really already extremely useful.
ERIC TOPOL (07:48):
It gets again, to your point about that corpus of knowledge that is incorporated in the LLM is providing a differential diagnosis that might not come to the mind of the physician. And this is of course the edge of having ingested so much and being able to play back those possibilities and the differential diagnosis. If it isn't in your list, it's certainly not going to be your final diagnosis. I do want to get back to the radiologist because we're talking just after the annual massive Chicago Radiologic Society of North America RSNA meeting. And at those meetings, I wasn't there, but talking to my radiology colleagues, they say that your projection is already happening. Now that is the ability to not just read, make the report. I mean the whole works. So it may not have been five years when you said that, which is one of the most frequent quotes in all of AI and medicine of course, as you probably know, but it's approximating your prognosis. Even now
GEOFFREY HINTON (09:02):
I've learned one thing about medicine, which is just like other academics, doctors have egos and saying this stuff is going to replace them is not the right move. The right move is to say it's going to be very good at giving second opinions, but the doctor's still going to be in charge. And that's clearly the way to sell things. And that's fine, just I actually believe that after a while of that, you'll be listening to the AI system, not the doctors. And of course there's dangers in that. So we've seen the dangers in face recognition where if you train on a database that contains very few black people, you'll get something that's very good at recognizing faces. And the people who use it, the police will think this is good at recognizing faces. And when it gives you the wrong identity for a person of color, then the policemen are going to believe it. And that's a disaster. And we might get the same with medicine. If there's some small minority group that has some distinctly different probabilities of different diseases, it's quite dangerous for doctors to get to trust these things if they haven't been very carefully controlled for the training data.
ERIC TOPOL (10:17):
Right. And actually I did want to get back to you. Is it possible for the reason why in this new report that the LLMs did so well is that some of these case studies from New England Journal were part of the pre-training?
GEOFFREY HINTON (10:32):
That is always a big worry. It's worried me a lot and it's worried other people a lot because these things have pulled in so much data. There is now a way round that at least for showing that the LLMs are genuinely creative. So he's a very good computer science theorist at Princeton called Sanjeev Arora, and I'm going to attribute all this to him, but of course, all the work was done by his students and postdocs and collaborators. And the idea is you can get these language models to generate stuff, but you can then put constraints on what they generate by saying, so I tried an example recently, I took two Toronto newspapers and said, compare these two newspapers using three or four sentences, and in your answer demonstrate sarcasm, a red herring empathy, and there's something else. But I forget what metaphor. Metaphor.
ERIC TOPOL (11:29):
Oh yeah.
GEOFFREY HINTON (11:29):
And it gave a brilliant comparison of the two newspapers exhibiting all those things. And the point of Sanjeev Arora’s work is that if you have a large number of topics and a large number of different things you might demonstrate in the text, then if I give an topic and I say, demonstrate these five things, it's very, anything in the training data will be on that topic demonstrating those five skills. And so when it does it, you can be pretty confident that it's original. It's not something it saw in the training data. That seems to me a much more rigorous test of whether it generates new stuff. And what's interesting is some of the LLMs, the weaker ones don't really pass the test, but things like GPT-4 that passes the test with flying colors, that definitely generates original stuff that almost certainly was not in the training data.
ERIC TOPOL (12:25):
Yeah. Well, that's such an important tool to ferret out the influence of pre-training. I'm glad you reviewed that. Now, the other question that most people argue about, particularly in the medical sphere, is does the large language model really understand? What are your thoughts about that? We're talking about what's been framed as the stochastic parrot versus a level of understanding or enhanced intelligence, whatever you want to call it. And this debate goes on, where do you fall on that?
GEOFFREY HINTON (13:07):
I fall on the sensible side. They really do understand. And if you give them quizzes, which involve a little bit of reasoning, it's much harder to do now because of course now GPT-4 can look at what's on the web. So you are worried if I mention a quiz now, someone else may have given it to GPT-4, but a few months ago when you did this before, you could see the web, you could give it quizzes for things that it had never seen before and it can do reasoning. So let me give you my favorite example, which was given to me by someone who believed in symbolic reasoning, but a very honest guy who believed in symbolic reasoning and was very puzzled about whether GT four could do symbolic reasoning. And so he gave me a problem and I made it a bit more complicated.
(14:00):
And the problem is this, the rooms in my house are painted white or yellow or blue, yellow paint fade to white within a year. In two years’ time, I would like all the rooms to be white. What should I do and why? And it says, you don't need to paint the white rooms. You don't need to paint the yellow rooms because they'll fade to white anyway. You need to paint the blue rooms white. Now, I'm pretty convinced that when I first gave it that problem, it had never seen that problem before. And that problem involves a certain amount of just basic common sense reasoning. Like you have to understand that if it faded to yellow in a year and you're interested in the stage in two years’ time, two years is more than one year and so on. When I first gave it the problem and didn't ask you to explain why it actually came up with a solution that involved painting the blue rooms yellow, that's more of a mathematician solution because it reduces it to a solved problem. But that'll work too. So I'm convinced it can do reasoning. There are people, friends of mine like Jan Leike, who is convinced it can't do reasoning. I'm just waiting for him to come to his sense.
ERIC TOPOL (15:18):
Well, I've noticed the back and forth with you and Yann (LeCun) [see above on X]. I know it's a friendly banter, and you, of course, had a big influence in his career as so many others that are now in the front leadership lines of AI, whether it's Ilya Sutskever at OpenAI, who's certainly been in the news lately with the turmoil there. And I mean actually it seems like all the people that did some training with you are really in the leadership positions at various AI companies and academic groups around the world. And so it says a lot about your influence that's not just as far as deep neural networks. And I guess I wanted to ask you, because you're frequently regarded to as the godfather of AI, and what do you think of that getting called that?
GEOFFREY HINTON (16:10):
I think originally it wasn't meant entirely beneficially. I remember Andrew Ng actually made up that phrase at a small workshop in the town of Windsor in Britain, and it was after a session where I'd been interrupting everybody. I was the kind of leader of the organization that ran the workshop, and I think it was meant as kind of I would interrupt everybody, and it wasn't meant entirely nicely, I think, but I'm happy with it.
ERIC TOPOL (16:45):
That's great.
GEOFFREY HINTON (16:47):
Now that I'm retired and I'm spending some of my time on charity work, I refer to myself as the fairy godfather.
ERIC TOPOL (16:57):
That's great. Well, I really enjoyed the New Yorker profile by Josh Rothman, who I've worked with in the past where he actually spent time with you up in your place up in Canada. And I mean it got into all sorts of depth about your life that I wasn't aware of, and I had no idea about the suffering that you've had with the cancer of your wives and all sorts of things that were just extraordinary. And I wonder, as you see the path of medicine and AI's influence and you look back about your own medical experiences in your family, do you see where we're just out of time alignment where things could have been different?
GEOFFREY HINTON (17:47):
Yeah, I see lots of things. So first, Joshua is a very good writer and it was nice of him to do that.
(17:59):
So one thing that occurs to me is actually going to be a good use of LLMs, maybe fine tune somewhat differently to produce a different kind of language is for helping the relatives of people with cancer. Cancer goes on a long time, unlike, I mean, it's one of the things that goes on for longest and it's complicated and most people can't really get to understand what the true options are and what's going to happen and what their loved one's actually going to die of and stuff like that. I've been extremely fortunate because in that respect, I had a wife who died of ovarian cancer and I had a former graduate student who had been a radiologist and gave me advice on what was happening. And more recently when my wife, a different wife died of pancreatic cancer, David Naylor, who you know
ERIC TOPOL (18:54):
Oh yes.
GEOFFREY HINTON (18:55):
Was extremely kind. He gave me lots and lots of time to explain to me what was happening and what the options were and whether some apparently rather flaky kind of treatment was worth doing. What was interesting was he concluded there's not much evidence in favor of it, but if it was him, he'd do it. So we did it. That's where you electrocute the tumor, being careful not to stop the heart. If you electrocute the tumor with two electrodes and it's a compact tumor, all the energy is going into the tumor rather than most of the energy going into the rest of your tissue and then it breaks up the membranes and then the cells die. We don't know whether that helped, but it's extremely useful to have someone very knowledgeable to give advice to the relatives. That's just so helpful. And that's an application in which it's not kind of life or death in the sense that if you happen to explain it to me a bit wrong, it's not determining the treatment, it's not going to kill the patient.
(19:57):
So you can actually tolerate it, a little bit of error there. And I think relatives would be much better off if they could talk to an LLM and consult with an LLM about what the hell's going on because the doctors never have time to explain it properly. In rare cases where you happen to know a very good doctor like I do, you get it explained properly, but for most people it won't be explained properly and it won't be explained in the right language. But you can imagine an LLM just for helping the relatives, that would be extremely useful. It'd be a fringe use, but I think it'd be a very helpful use.
ERIC TOPOL (20:29):
No, I think you're bringing up an important point, and I'm glad you mentioned my friend David Naylor, who's such an outstanding physician, and that brings us to that idea of the sense of intuition, human intuition, versus what an LLM can do. Don't you think those would be complimentary features?
GEOFFREY HINTON (20:53):
Yes and no. That is, I think these chatbots, they have intuition that is what they're doing is they're taking strings of symbols and they're converting each symbol into a big bunch of features that they invent, and then they're learning interactions between the features of different symbols so that they can predict the features of the next symbol. And I think that's what people do too. So I think actually they're working pretty much the same way as us. There's lots of people who say, they're not like us at all. They don't understand, but there's actually not many people who have theories of how the brain works and also theories of how they understand how these things work. Mostly the people who say they don't work like us, don't actually have any model of how we work. And it might interest them to know that these language models were actually introduced as a theory of how our brain works.
(21:44):
So there was something called what I now call a little language model, which was tiny. I introduced in 1985, and it was what actually got nature to accept our paper on back propagation. And what it was doing was predicting the next word in a three word string, but the whole mechanism of it was broadly the same as these models. Now, the models are more complicated, they use attention, but it was basically you get it to invent features for words and interactions between features so that it can predict the features of the next word. And it was introduced as a way of trying to understand what the brain was doing. And at the point at which it was introduced, the symbolic AI peoples didn't say, oh, this doesn't understand. They were perfectly happy to admit that this did learn the structure in the tiny domain, the tiny toy domain it was working on. They just argued that it would be better to learn that structure by searching through the space of symbolic rules rather than through the space of neural network weights. But they didn't say this is an understanding. It was only when it really worked that people had to say, well, it doesn't count.
ERIC TOPOL (22:53):
Well, that also something that I was surprised about. I'm interested in your thoughts. I had anticipated that in Deep Medicine book that the gift of time, all these things that we've been talking about, like the front door that could be used by the model coming up with the diagnoses, even the ambient conversations made into synthetic notes. The thing I didn't think was that machines could promote empathy. And what I have been seeing now, not just from the notes that are now digitized, these synthetic notes from the conversation of a clinic visit, but the coaching that's occurring by the LLM to say, well, Dr. Jones, you interrupted the patient so quickly, you didn't listen to their concerns. You didn't show sensitivity or compassion or empathy. That is, it's remarkable. Obviously the machine doesn't necessarily feel or know what empathy is, but it can promote it. What are your thoughts about that?
GEOFFREY HINTON (24:05):
Okay, my thoughts about that are a bit complicated, that obviously if you train it on text that exhibits empathy, it will produce text that exhibits empathy. But the question is does it really have empathy? And I think that's an open issue. I am inclined to say it does.
ERIC TOPOL (24:26):
Wow, wow.
GEOFFREY HINTON (24:27):
So I'm actually inclined to say these big chatbots, particularly the multimodal ones, have subjective experience. And that's something that most people think is entirely crazy. But I'm quite happy being in a position where most people think I'm entirely crazy. So let me give you a reason for thinking they have subjective experience. Suppose I take a chatbot that has a camera and an arm and it's being trained already, and I put an object in front of it and say, point at the object. So it points at the object, and then I put a prism in front of its camera that bends the light race, but it doesn't know that. Now I put an object in front of it, say, point at the object, and it points straight ahead, sorry, it points off to one side, even though the object's straight ahead and I say, no, the object isn't actually there, the object straight ahead. I put a prism in front of your camera and imagine if the chatbot says, oh, I see the object's actually straight ahead, but I had the subjective experience that it was off to one side. Now, if the chatbot said that, I think it would be using the phrase subjective experience in exactly the same way as people do,
(25:38):
Its perceptual system told it, it was off to one side. So what its perceptual system was telling, it would've been correct if the object had been off to one side. And that's what we mean by subjective experience. When I say I've got the subjective experience of little pink elephants floating in front of me, I don't mean that there's some inner theater with little pink elephants in it. What I really mean is if in the real world there were little pink elephants floating in front of me, then my perceptual system would be telling me the truth. So I think what's funny about subjective experiences, not that it's some weird stuff made of spooky qualia in an inner theater, I think subjective experiences, a hypothetical statement about a possible world. And if the world were like that, then your perceptual system will be working properly. That's how we use subjective experience. And I think chatbots can use it like that too. So I think there's a lot of philosophy that needs to be done here and got straight, and I didn't think we can lead it to the philosophers. It's too urgent now.
ERIC TOPOL (26:44):
Well, that's actually a fascinating response and added to what your perception of understanding it gets us to perhaps where you were when you left Google in May this year where you had, you saw that this was a new level of whatever you want to call it, not AGI [artificial general intelligence], but something that was enhanced from prior AI. And you basically, in some respects, I wouldn't say sounded any alarms, but you were, you've expressed concern consistently since then that we're kind of in a new phase. We're heading in a new direction with AI. Could you elaborate a bit more about where you were and where your mind was in May and where you think things are headed now?
GEOFFREY HINTON (27:36):
Okay, let's get the story straight. It's a great story. The news media puts out there, but actually I left Google because I was 75 and I couldn't program any longer because I kept forgetting what the variables stood for. I took the opportunity also, I wanted to watch a lot of Netflix. I took the opportunity that I was leaving Google anyway to start making public statements about AI safety. And I got very concerned about AI safety a couple of months before. What happened was I was working on trying to figure out analog ways to do the computation so you could do these larger language models for much less energy. And I suddenly realized that actually the digital way of doing the computation is probably hugely better. And it's hugely better because you can have thousands of different copies of exactly the same digital model running on different hardware, and each copy can look at a different bit of the internet and learn from it.
(28:38):
And they can all combine what they learned instantly by sharing weights or by sharing weight gradients. And so you can get 10,000 things to share their experience really efficiently. And you can't do that with people. If 10,000 people go off and learn 10,000 different skills, you can't say, okay, let's all average our weight. So now all of us know all of those skills. It doesn't work like that. You have to go to university and try and understand what on earth the other person's talking about. It's a very slow process where you have to get sentences from the other person and say, how do I change my brain? So I might've produced that sentence, and it's very inefficient compared with what these digital models can do by just sharing weights. So I had this kind of epiphany. The digital models are probably much better. Also, they can use the back propagation algorithm quite easily, and it's very hard to see how the brain can do it efficiently. And nobody's managed to come up with anything that'll work in real neural nets as comparable to back propagation at scale. So I had this sort of epiphany, which made me give up on the analog research that digital computers are actually just better. And since I was retiring anyway, I took the opportunity to say, Hey, they're just better. And so we'd better watch out.
ERIC TOPOL (29:56):
Well, I mean, I think your call on that and how you back it up is really, of course had a big impact. And of course it's still an ongoing and intense debate, and in some ways it really was about what was the turmoil at OpenAI was rooted with this controversy about where things are, where they're headed. I want to just close up with the point you made about the radiologists, and not to insult them by saying they'll be replaced gets us to where we are, the tension of today, which is our humans as the pinnacle of intelligence going to be not replaced, but superseded by the likes of AI's future, which of course our species can't handle that a machine, it's like the radiologist, our species can't handle that. There could be this machine that could be with far less connections, could do things outperform us, or of course, as we've, I think emphasized in our conversation in concert with humans to even take it to yet another level. But is that tension about that there's this potential for machines outdoing people part of the problem that it's hard for people to accept this notion?
GEOFFREY HINTON (31:33):
Yes, I think so. So particularly philosophers, they want to say there's something very special about people. That's to do with consciousness and subjective experience and sentience and qualia, and these machines are just machines. Well, if you're a sort of scientific materialist, most of us are brain's just a machine. It's wrong to say it's just a machine because a wonderfully complex machine that does incredible things that are very important to people, but it is a machine and there's no reason in principle why there shouldn't be better machines than better ways of doing computation, as I now believe there are. So I think people have a very long history of thinking. They're special.
(32:19):
They think God made them in his image and he put them at the center of the universe. And a lot of people have got over that and a lot of people haven't. But for the people who've got over that, I don't think there's any reason in principle to think that we are the pinnacle of intelligence. And I think it may be quite soon these machines are smarter than us. I still hope that we can reach a agreement with the machines where they act like benevolent parents. So they're looking out for us. They have, we've managed to motivate them, so the most important thing for them is our success, like it is with a mother and child, not so much for men. And I would really like that solution. I'm just fearful we won't get it.
ERIC TOPOL (33:15):
Well, that would be a good way for us to go forward. Of course, the doomsayers and the people that are much worse at their level of alarm tend to think that that's not possible. But we'll see obviously over time. Now, one thing I just wanted to get a quick read from you before we close is as recently, Demis Hassabis and John Jumper got the Lasker Award, like a pre Nobel Award for AlphaFold2. But this transformer model, which of course has helped to understand the structure 3D of 200 million proteins, they don't understand how it works. Like most models, unlike the understanding we were talking about earlier on the LLM side. I wrote that I think that with this award, an asterisk should have been given to the AI model. What are your thoughts about that idea?
GEOFFREY HINTON (34:28):
It's like this, I want people to take what I say seriously, and there's a whole direction you could go in that I think Larry Page, one of the founders of Google has gone in this direction, which is to say there's these super intelligences and why shouldn't they have rights? If you start going in that direction, you are going to lose people. People are not going to accept that these things should have political rights, for example. And being a co-author is the beginning of political rights. So I avoid talking about that, but I'm sort of quite ambivalent and agnostic about whether they should. But I think it's best to stay clear of that issue just because the great majority of people will stop listening to you if you say machines should have rights.
ERIC TOPOL (35:28):
Yeah. Well, that gets us course of what we just talked about and how it's hard the struggle between humans and machines rather than the thought of humans plus machines and symbiosis that can be achieved. But Geoff, this has been a great, we've packed a lot in. Of course, we could go on for hours, but I thoroughly enjoyed hearing your perspective firsthand and your wisdom, and just to reinforce the point about how many of the people that are leading the field now derive a lot of their roots from your teaching and prodding and challenging and all that. We're indebted to you. And so thanks so much for all you've done and we'll continue to do to help us, guide us through the very rapid dynamic phase as AI moves ahead.
GEOFFREY HINTON (36:19):
Thanks, and good luck with getting AI to really make a big difference in medicine.
ERIC TOPOL (36:25):
Hopefully we will, and I'll be consulting with you from time to time to get some of that wisdom to help us
GEOFFREY HINTON (36:32):
Anytime.
“A.I. is not the problem; it’s the solution.”—Andrew Ng at TED, 17 October 2023
Recorded 21 November 2023
Transcript with relevant links and links to audio file
Eric Topol (00:00):
Hello, it's Eric Topol with Ground Truths, and I'm really delighted to have with me Andrew Ng, who is a giant in AI who I've gotten to know over the years and have the highest regard. So Andrew, welcome.
Andrew Ng (00:14):
Hey, thanks Eric. It's always a pleasure to see you.
Eric Topol (00:16):
Yeah, we've had some intersections in multiple areas of AI. The one I wanted to start with is that you've had some direct healthcare nurturing and we've had the pleasure of working with Woebot Health, particularly with Alison Darcy, where the AI chatbot has been tested in randomized trials to help people with depression and anxiety. And, of course, that was a chatbot in the pre-transformer or pre-LLM era. I wonder if you could just comment about that as well as your outlook for current AI models in healthcare.
Andrew Ng (01:05):
So Alyson Darcy is brilliant. It's been such a privilege to work with her over the years. One of the exciting things about AI is a general purpose technology. It's not useful for one thing. And I think in healthcare and more broadly across the world, we're seeing many creative people use AI for many different applications. So I was in Singapore a couple months ago and I was chatting with some folks, Dean Chang and one of his doctors, Dr. M, about how they're using AI to read EHRs in a hospital in Singapore to try to estimate how long a patient's going to be in the hospital because of pneumonia or something. And it was actually triggering helpful for conversations where a doctor say, oh, I think this patient will be in for three days, but the AI says no, I'm guessing 15 days. And this triggers a conversation where the doctor takes a more careful look. And I thought that was incredible. So all around the world, many innovators everywhere, finding very creative ways to apply AI to lots of different problems. I think that's super exciting.
Eric Topol (02:06):
Oh, it's extraordinary to me. I think Geoff Hinton has thought that the most important application of current AI is in the healthcare/ medical sphere. But I think that the range here is quite extraordinary. And one of the other things that you've been into for all these years with Coursera starting that and all the courses for deep learning.AI —the democratization of knowledge and education in AI. Since this is something like all patients would want to look up on whatever GPT-X about their symptoms different than of course a current Google search. What's your sense about the ability to use generative AI in this way?
Andrew Ng (02:59):
I think that instead of seeing a doctor as a large language model, what's up with my symptoms, people are definitely doing it. And there have been anecdotes of this maybe saving a few people's lives even. And I think in the United States we're privileged to have some would say terrible, but certainly better than many other country’s healthcare system. And I feel like a lot of the early go-to market for AI enabled healthcare may end up being in countries or just places with less access to doctors. The definitely countries where you can either decide do you want to go see if someone falls sick? You can either send your kid to a doctor or you can have your family eat for the next two weeks, pick one. So with families made these impossible decisions, I wish we could give everyone in the world access to a great doctor and sometimes the alternatives that people face are pretty harsh. I think any hope, even the very imperfect hope of LLM, I know it sounds terrible, it will hallucinate, it will give bad medical advice sometimes, but is that better than no medical advice? I think there's really some tough ethical questions are being debated around the world right now.
Eric Topol (04:18):
Those hallucinations or confabulation, won't they get better over time?
Andrew Ng (04:24):
Yes, I think LLM technology is advanced rapidly. They still do hallucinate, they do still mix stuff up, but it turns out that I think people still have an impression of LLM technology from six months ago. But so much has changed in the last six months. So even in the last six months, it is actually much harder now to get an LMM, at least many of the public ones offered by launch companies. It's much harder now compared to six months ago to get it to give you deliberately harmful advice or if you ask it for detailed instructions on how to commit a crime. Six months ago it was actually pretty easy. So that was not good. But now it's actually pretty hard. It's not impossible. And I actually ask LLMs for strange things all the time just to test them. And yes, sometimes I can get them when I really try to do something inappropriate, but it's actually pretty difficult.
(05:13):
But hallucination is just a different thing where LLMs do mix stuff up and you definitely don't want that when it comes to medical advice. So it'll be an interesting balance I think of when should we use web search for trust authoritative sources. So if I have a sprained ankle, hey, let me just find a webpage on trust from a trusted medical authority on how to deal with sprained ankle. But there are also a lot of things where there is no one webpage that just gives me an answer. And then this is an alternative for generating a novel thing that's need to my situation. In non-healthcare cases, this has clearly been very valuable in just the healthcare, given the criticality of human health and human life. I think people are wrestling with some challenging questions, but hallucinations are slowly going down.
Eric Topol (05:59):
Well, hopefully they'll continue to improve on that. And as you pointed out the other guardrails that will help. Now that gets me to a little over a month ago, we were at the TED AI program and you gave the opening talk, which was very inspirational, and you basically challenged the critics of the negativism on AI with three basic issues: amplifying our worst impulses, taking our jobs and wiping out humanity. And it was very compelling and I hope that that will be posted soon. And of course we'll link it, but can you give us a skinny of your antidote to the doomerism about AI?
Andrew Ng (06:46):
Yeah, so I think AI is a very beneficial technology on average. I think it comes down to do we think the world is better off or worse off with more intelligence in it, be it human intelligence or artificial intelligence? And yes, intelligence can be used for nefarious purposes and it has been in history, I think a lot of humanity has progress through humans getting smarter and better trained and more educated. And so I think on average the world is better off with more intelligence in it. And as for AI wiping oiut humanity, I just don't get it. I’ve spoken with some of the people with this concern, but their arguments for how AI could wipe up humanity are so vague that they boil down to it could happen. And I can't prove it won't happen any more than I can prove a negative like that. I can't prove that radio wave is being emitted from earth won't cause aliens to find us and space aliens to wipe us out. But I'm not very alarmed about space aliens, maybe I should be. I don't know. And I find that there are real harms that are being created by the alarmist narrative on AI. One thing that's quite sad was chatting with they're now high school students that are reluctant to enter AI because they heard they could lead to human extinction and they don't want any of that. And that's just tragic that we're causing high school students to make a decision that's bad for themselves and bad for humanity because of really unmerited alarms about human extinction.
Eric Topol (08:24):
Yeah, no question about that. You had, I think a very important quote is “AI is not the problem, it's the solution” during that. And I think that gets us to the recent flap, if you will, with OpenAI that's happened in recent days whereby it appears to be the same tension between the techno-optimists like you and I would say, versus the effective altruism (EA) camp. And I wonder what your thoughts are regarding, obviously we don't know all the inside dynamics of this, with probably the most publicized interactions in AI that I can remember in terms of its intensity, and it's not over yet. But what were your thoughts about as this has been unfolding, which is, of course, still in process?
Andrew Ng (09:19):
Yeah, honestly, a lot of my thoughts have been with all the employees of OpenAI, these are hundreds of hardworking, well-meaning people. They want to build tech, make available others, make the world better off and out of the blue overnight. The jobs livelihoods and their levers to make a very positive impact to the world was disrupted for reasons that seem vague and at least from the silence of the board, I'm not aware of any good reasons for really all these wonderful people's work and then livelihoods and being disrupted. So I feel sad that that just happened, and then I feel like OpenAI is not perfect, no organization in the world is, but frankly they're really moving AI forward. And I think a lot of people have benefited from the work of OpenAI. And I think the disruptions of that as well is also quite tragic. And this may be—we will see if this turns out to be one of the most dramatic impacts of unwarranted doomsaying narratives causing a lot of harm to a lot of people. But we'll see what continuously emerges from the situation.
Eric Topol (10:43):
Yeah, I mean I think this whole concept of AGI, artificial general intelligence and how it gets down to this fundamental assertion that we're at AGI, the digital brain or we're approximating or the whole idea that the machine understanding is that at unprecedented levels. I wonder your thoughts because obviously there still is the camp that says this is a sarcastic parrot. It's all anything that suggests understanding is basically because of pre-training or other matters and to try to assign any real intelligence that's at the level of human even for a particular task no less beyond human is unfounded. What is your sense about this tension and this ongoing debate, which seemed to be part of the OpenAI board issues?
Andrew Ng (11:50):
So I'm not sure what happening in the OpenAI board, but the most widely accepted definition of AGI is AI to do any intellectual tasks that the human can. And I do see many companies redefining AGI to other definitions. So for the original definition, I think we're decades away. We're very clearly not there, but many companies that, let's say alternative definitions and yeah, you have an alternative definition, maybe we're there already. One of my eCommerce friends looked at one of the alternative definitions. He said, well, for that definition, I think we got AGI 30 years ago.
(12:29):
And looking on the more positive side. And I think one of the signs that the companies reach AGI frankly would be if they're rational economic player, they should maybe let go all of their employees that do maybe intellectual work. So until that happens, I just don't, not to joke about it, that would be a serious thing. But I think we're still many decades away from that original definition of AGI. But on the more positive side in healthcare and other sectors, I feel like there's a recipe for using AI that I find fruitful and exciting, which is it turns out that jobs are made out of tasks and I think of AI as automating tasks rather than jobs. So a few years ago, Geoff Hinton had made some strong statements about AI replacing radiologists. I think those predictions have really not come true today, but it turns out as Eric, I enjoy your book, which is very thoughtful about AI as well.
(13:34):
And I think if you look at say the job of radiologists, they do many, many different things, one of which is read x-rays, but they also do patient intakes, they operate X-ray machines. And I find that when we look at the healthcare sector or other sectors and look at what people are doing, break jobs down into tasks, then usually there can often be a subset of tasks. There's some that are amenable to AI automation and that recipe is helping a lot of businesses create value and also in some cases make healthcare better. So I'm actually excited and because healthcare, so many people doing such a diverse range of tasks, I would love to see more organizations do this type of analysis.
(14:22):
The interesting thing about that is we can often automate, I'm going to make up a number, 20% or 30% or whatever, have a lot of different jobs tasks. So one, there's a strong sign we're far from AGI because we can't automate a hundred percent of the intellectual tasks, but second, many people's jobs are safe because when we automate 20% of someone's job, they can focus on the other 80% and maybe even be more productivity and causes the marginal value of labor and therefore maybe even salaries that go uprooted and down. Actually recently, a couple weeks ago, few weeks ago, released a new course on Coursera “Generative AI for Everyone” where I go deeper into this recipe for finding opportunities, but I'm really excited about working with partners to go find these opportunities and go build to them.
Eric Topol (15:15):
Yeah, I commend you for that because you have been for your career democratizing the knowledge of AI and this is so important and that new course is just one more example. Everyone could benefit from it. Getting back to your earlier point, just because in the clinician doctor world, the burdensome task of data clerk function of having to be slave to keyboards and entering the visit data and then all the post- visit things. Now, of course, we're seeing synthetic notes and all this can be driven through an automated note that is not involving any keyboard work. And so, just as you say, that comprises maybe 20, 30% of a typical doctor's day, if not more. And the fact is that that change could then bring together the patient and doctor again, which has been a relationship that suffered because of electronic records and all of the data clerk functions. That's just a really, I think, a great example of what you just pointed out. I love “Letters from Andrew” which you publish, which as you mentioned, one of your recent posts was about the generative AI for everyone. And in those you recently addressed loneliness, which is as associated with all sorts of bad health outcomes. And I wonder if you could talk about how AI could help loneliness.
Andrew Ng (16:48):
So this is a fascinating case study where, so AI fund, we had wanted to do something on AI and relationships, kind of romantic relationships. And I'm an AI guy, I feel like, what do I know about romance? And if you don't believe me, you can ask my wife, she'll confirm I know nothing about romance, but we're privileged to partner with the former CEO of Tinder, Renata Nyborg, who knows about relationships in a very systematic way far more than anyone I know. And so working with her with a deep expertise about relationships, and it turns out she actually knows a lot about AI too. But then my team's knowledge about AI we're able to build something very unique that she launched that she announced called me. Now I've been playing around with it on my phone and it's actually interesting, remarkably good. I think relationship mentor, frankly, I wish I had Meeno back when I was single instead, I've asked my dumb questions to, and I'm excited that maybe AI, I feel like tech maybe has contributed to loneliness. I know the data is mixed, that social media contributes to social isolation. I know that different opinions are different types of data, but this is one case where hopefully AI can clearly not be the problem, but be part of the solution to help people gain the skills to build better relationships.
Eric Topol (18:17):
Yeah, now, it's really interesting here again, the counterintuitive idea that technology could enhance human bonds, which are all too short that we want to enhance. Of course, you've had an incredible multi-dimensional career. We talked a little bit about your role in education with the founding of the massive online courses (MOOCs), but also with Baidu and Google. And then of course at Stanford you've seen the academic side, you've seen the leading tech titan side, the entrepreneurial side with the various ventures of trying to get behind companies that have promised you have the whole package of experience and portfolio. How do you use that now going forward? You're still so young and the field is so exciting. Where do you try to just cover all the bases or do you see yourself changing gears in some way? You haven't had a foot in every aspect?
Andrew Ng (19:28):
Oh, I really like what I do. I think these days I spend a lot of time at AI fund builds new companies using AI and deep learning.ai is an educational arm. And one of the companies that AI fund has helped incubate does computer vision work than AI. We actually have a lot of healthcare users as well using, I feel like with the recent advances in AI at the technology layer, things like large language models, I feel like a lot of the work that lies ahead of the entire field is to build applications on top of that. In fact, a lot of the media buzz has been on the technology layer, and this happens every time this technology change. When the iPhone came out, when we shifted the cloud, it's interesting for the media to talk about the technology, but it turns out the only way for the technology suppliers to be successful is if the application builders are even more successful.
(20:26):
They've got to generate enough revenue to pay the technology suppliers. So I've been spending a lot of my time thinking about the application layer and how to help either myself or support others to build more applications. And the annoying and exciting thing about AI is as a general purpose technology, there's just so much to do, there's so many applications to build. It's kind of like what is electricity good for? Or what is the cloud good for? It's just so many different things. So it is going to take us, frankly, longer than we wish, but it will be exciting and meaningful work to go to all the corners of healthcare and all the corners of education and finance and industrial and go find these applications and go help them.
Eric Topol (21:14):
Well, I mean you have such a broad and diverse experience and you predicted much of this. I mean, you knew somehow or other that when the graphic processing unit (GPU) would go from a very low number to tens of thousands of them, what might happen. And you were there, I think, before and perhaps anyone else. One of the things of course that this whole field now gets us to is potential tech dominance. And by what I mean there is that you've got a limited number of companies like Microsoft and Google and Meta and maybe Inflection AI and a few others that have capabilities of 30,000, 40,000, whatever number of GPUs. And then you have academic centers like your adjunct appointment at Stanford, which maybe has a few hundred or here at Scripps Research that has 150. And so we don't have the computing power to do base models and what can we do? How do you see the struggle between the entities that have what appears to be almost, if you will, if it's not unlimited, it's massive computing power versus academics that want to advance the field. They have different interests of course, but they don't have that power base. Where is this headed?
Andrew Ng (22:46):
Yeah, so I think the biggest danger to that concentration is regulatory capture. So I've been quite alarmed over moves that various entities, some companies, but also governments here in the US and in Europe, especially US and Europe, less than other places have been contemplating regulations that I think places a very high regulatory compliance burden that big tech companies have the capacity to satisfy, but that smaller players will not have the capacity to satisfy. And in particular, the definitely companies would rather not have the computer open source. When you take a smaller size, say 7 billion parameters model and fine tune it for specific to, it works remarkably well for many specific tasks. So for a lot of applications, you don't need a giant model. And actually I routinely run a seven or 13 billion parameters model on my laptop, more inference than fine tuning. But it's within the realm of what a lot of players can do.
(23:51):
But if inconvenient laws are passed, and they've certainly been proposed in Europe under the EU AI Act and also the White House Executive Order, if I think we've taken some dangerous steps to what putting in place very burdensome compliance requirements that would make it very difficult for small startups and potentially very difficult for less smaller organizations to even release open source software. Open source software has been one of the most important building blocks for everyone in tech. I mean, if you use a computer or a smartphone that because open, that's built on top of open source software, TCP, IP, internet, just how the internet works, law of that is built on top of open source software. So regulations that pamper people just wanting to release open source, that would be very destructive for innovation.
Eric Topol (24:48):
Right? In keeping with what we've been talking about with the doomsday prophecies and the regulations and things that would slow up things, the whole progress in the field, which we are obviously in touch with both sides and the tension there, but overregulation, the potential hazards of that are not perhaps adequately emphasized. And another one of your letters (Letters from Andrew), which you just got to there, was about AI at the edge and the fact that we can move towards, in contrast to the centralized computing power at a limited number of entities as you, I think just we're getting at, there's increasing potential for being able to do things on a phone or a laptop. Can you comment about that?
Andrew Ng (25:43):
Yeah, I feel like I'm going against many trends. It sounds like I'm off in a very weird direction, but I'm bullish about AI at the edge. I feel like if I want to do grammar checking using a large language model, why do I need to send all my data to a cloud provider when a small language model can do it just fine on my laptop? Or one of my collaborators at Stanford was training a large language model in order to do electronic health records. And so at Stanford, this actually worked done by one of the PhD students I've been working with. But so Yseem wound up fine tuning a large language model at Stanford so that he could run inference over there and not have to ship EHR and not have to ship private medical records to a cloud provider. And so I think that was an important thing to, and if open source were shut down, I think someone like Yseem would have had a much harder time doing this type of work.
Eric Topol (27:04):
I totally follow you the point there. Now, the last thing I wanted to get to was a multimodal AI in healthcare. When we spoke 5 years ago, when I was working on the Deep Medicine book, multimodal AI wasn't really possible. And the idea was that someday we'll have the models to do it. The idea here is that each of us has all these layers of data, our various electronic health records, our genome, our gut microbiome, our sensors and environmental data, social determinants of health, our immunome, it just goes on and on. And there's also the corpus of medical knowledge. So right now, no one has really done multimodal. They've done bimodal AI in healthcare where they take the electronic health records and the genome, or usually it's electronic health records and the scan, medical scan. No one has done more than a couple layers yet.
(28:07):
And the question I have is, it seems like that's imminently going to be accomplished. And then let's then get to will there be a virtual health coach? So unlike these virtual coaches like Woebot and the diabetes coaches and the hypertension coaches, will we ultimately have with multimodal AI, your forecast on that, the ability to have feedback to any given individual to promote their health, to prevent conditions that they might be at risk for having later in life or help managing all their conditions that they actually have already been declared. What's your sense about where we are with multimodal AI?
Andrew Ng (28:56):
I think there's a lot of work to be done still at unimodal, a lot of work to be done in text. LLM AI does a lot of work on images, and maybe not to talk about Chang's work all the time, but just this morning, I was just earlier, I was chatting with him about he's trying to train a large transformer on some time series other than text or images. And then semi collaborative, Stanford, Jeremy Irvin, Jose kind of poking at the corners of this. But I think a lot of people feel appropriately that there's a lot of work to be done still in unimodal. So I'm cheering that on. But then there's also a lot of work to be done in multimodal, and I see work beyond text and images, maybe genome, maybe some of the time series things, maybe some the HR specific things, which maybe is kind of textbook kind of not, I think it was just about a year ago that check GP was announced. So who knows? Just one more year of progress, who knows where it will be.
Eric Topol (29:55):
Yeah. Well, we know there will be continued progress, that's for sure. And hopefully as we've been discussing, there won't be significant obstacles for that. And hopefully there will be a truce between the two camps of the doomerism and optimism or somehow we're meet in the middle. But Andrew, it's been a delight to get your views on all this. I don't know how the OpenAI affair will settle out, but it does seem to be representative of the times we live in because at the same TED AI that you and I spoke at Ilya, spoke about AGI and that was followed onlhy a matter by days by Sam Altman talking about AGI and how OpenAI was approaching AGI capabilities. And it seems like this is, even though as you said, that there's a lot of different definition for AGI, the progress that's being made right now is extraordinary.
(30:57):
And grappling with the idea that there are certain tasks, at least certain understandings, certain intelligence that may be superhuman via machines is more than provocative. And I know you are asked to comment about this all the time, and it's great because in many respects, you're an expert, neutral observer. You're not in one of these companies that's trying to assert that they have sparks of AGI or actual AGI or whatever. So in closing, I think we look to you as , not just an expert, but one who has had such broad experience in this field and who has predicted so much of its progress and warned of the reasons that we would not continue to make that type of extraordinary progress. So I want to thank you for that. I'll keep reading Letters from Andrew. I hope everybody does, as many people as possible, should attend your “Generative AI for Everyone” course. And thank you for what you've done for the field, Andrew, we're all indebted to you.
Andrew Ng (32:17):
Thank you, Eric. You're always so gracious. It's always such a pleasure to see you and collaborate with you.
Thanks for listening and reading Ground Truths. Please share this podcast if you found it informative.
If you care about what you eat, you won’t want to miss this conversation! Chris Van Tulleken is an infectious disease physician-scientist in the UK’s National Health Service who has written a deeply researched masterpiece book on food—ULTRA-PROCESSED PEOPLE. It’s not just about these synthetic and artificial UPF substances, that carry many health hazards, but also about our lifestyle and diet, challenging dogma about low carbs/glycemic index and the impact of exercise.
Chris ate an 80% UPF diet for a month with extensive baseline and follow-up assessments including MRI brain scans. He has an identical twin brother who at times is 20 kg heavier than him. Why? What can be done to get limit pervasive UPF ingestion and its multitude of adverse effects on our health?
For additional background to the book, here are some Figures and a Table from a recent BMJ piece by Mathilde Touvier and colleagues.
Consumption of UPFs are highest in the USA and UK
A Table summarizing some of the health hazards and magnitude of increased risk
In his book Chris gets into the evidence for risks that are much broader than cardio- metabolic, including cancer, dementia, inflammatory bowel disease, and other chronic conditions.
A schematic for how UPFs increase the risk of cardiometabolic diseases
Here is the transcript of our conversation, unedited, with links to the audio podcast.
Recorded October 20, 2023.
Eric Topol (00:00):
It's Eric Topol here with Ground Truths. And what a delight for me to welcome Chris van Tulleken, who has written a masterpiece. It's called Ultra-processed People, and it's actually much more beyond ultra-processed food as I learned. We're going to get into how it covers things like exercise, nutrition in general, all sorts of things. Welcome, Chris.
Christoffer van Tulleken (00:27):
It's such a pleasure to be here. And there's no one I would rather say that about my book than you, so that means a huge amount.
Eric Topol (00:35):
Well, I was kind of blown away, but I have to tell you, and it's probably going to affect my eating behavior and other things as we'll discuss for years to come. You're going to be stuck in my head. So what's interesting, before we get into the thick of it, your background, I mean as a molecular virologist turned into a person that devoted so much to food science, and you go through that in the book, how you basically got into rigorous reviews of papers and demand for high quality science and then somehow you migrated into this area. Maybe you could just give us a little bit of background on that.
Christoffer van Tulleken (01:20):
So I suppose it feels a tenuous thing. I'm an infectious diseases clinician, but the only people who get infections are disadvantaged people. For the most part, rich people well off people get cardiometabolic disease. And so I worked a lot in very low income settings in South Asia and Pakistan in the hills and in Central and West Africa. And the leading cause of death in the kids I was seeing in the infants was the marketing of food companies. So food, particularly formula, but also baby food was being made up with filthy water. And so these children were getting this triple jeopardy where they were having bugs, they were ingesting bugs from filthy water. Their parents were becoming poor because they couldn't afford the food and they lacked the immune system of breast milk in the very young. And so it sort of presented itself, although I was treating infections that the root of the problem was the food companies. And now my work has sort of expanded to understanding that poor diets has overtaken tobacco or it's depending on the number set you look at, but the Lancet Global health data shows that poor diets overtaken tobacco is the leading cause of early death globally. And so we need to start thinking about this problem in terms of the companies that cause it. So that's how I still treat patients with infections, but that was my route into being interested in what we call the commercial determinants of health.
Eric Topol (02:52):
Yeah, well you've really done it. I have 15 pages of highlights and notes that I got from the book and book. I mean, wow. But I guess the summary statement that somebody said to you during the course of the book, because you researched it heavily, not just through articles, but talking to experts that ultra-processed foods is not food, it's an industrial produced edible substance, and really it gets graphic with the bacteria that's slime and anthem gum and I mean all this stuff, I mean everywhere I look, I see. And I mean all these, I mean just amazing stuff. So before we get into the nitty gritty of some of these additives and synthetic crap, you did an experiment and with the great University College in London where you took I guess 80% of your diet for a month of up pfs. So can you tell us about that experiment, what it did for you, what you learned from it?
Christoffer van Tulleken (04:04):
Yeah, so it wasn't just a stunt for the book. I was the first patient in a big study that I'm now running. It's a clinical trial of ultra-processed food. And so I was a way of gathering data. I mean, you know how these things work, Eric. I was teaming up with my neuroscience colleagues to do MRI scans my metabolic colleagues instead of going, look, if we put patients on this diet, how would it all look and what should we be investigating if we do MRI scans, will we see anything? And so I ate various news outlets have portrayed this as kind of me heroically putting my body on the line for science. I ate a completely normal diet for many American adults. About one in five Americans eats the diet of 80% of their calories. It's a very typical diet for a British or an American teenager or young person.
(04:52):
So it wasn't arduous. And I was really looking forward to this diet because like most 45 year old doctors, I have started because of my marriage and my children, you start to eat in a rather healthy way. And this was amazing opportunity to go back to eating the garbage that I'd eaten as a teenager. I was going back to these foods I loved. So I guess there were kind of four things that happened. There were these three physical effects on my body. I gained a huge amount of weight and I wasn't force-feeding myself. And that really chimes with the epidemiological data that we have and from the clinical trial data run by Kevin Hall at the NIH, that this is food that gets around your body's evolved mechanisms that say, stop eating, you're full. Now the second thing that happened is we did some brain scans and I thought, well, the brain scan we're not going to see anything in a month of normal food.
(05:43):
So I switched from about 20% to 80% and we saw enormous changes in connectivity between the habit, automatic behavior bits at the back in the cerebellum and the reward addiction bits in the middle in the limbic system and associated regions. So that was very significant in me. And we did follow-up scans and those changes were robust and we really have no idea what is happening in children who are eating this stuff from birth to their brains, but it's concerning. And then the most intriguing thing was I ate a standard meal at the beginning of the diet and we measured my hormonal response to the food. And I think people are more and more familiar with some of these hormones because we've got drugs like semaglutide or wegovy that are interrupting these fullness or these hunger hormone pathways. And what we saw was that my hunger hormone response to a standard meal, my hunger hormones remain sky high at the end of the diet.
(06:41):
So this is food that is fiddling with your body's ability to say I'm done. But the most amazing thing was that this experience I had where the food became disgusting, there was this moment talking to a friend in Brazil called Fernanda Rabu. She's an incredible scientist, and she was the one who said, it's not food, Chris. It's an industrially produced edible substance. And I sat down that night to eat, I think it was a meal of fried chicken. And I was reading the ingredients and I could barely finish it. And so the invitation in my book is, please keep eating this food, read your ingredients lists and ask yourself why are you eating maltodextrin? What is it? Why are you eating xantham gum? What is diacetyl tartaric acid esters of monoglycerides of fatty acids? Why is that in your bread?
Eric Topol (07:31):
Yeah. Well, and then the other thing that the experiment brought out was the inflammatory response with the high C-reactive protein, fivefold leptin. So I mean, it really was extraordinary. Now the other thing that was fascinating is you have an identical twin. His name is, is it Xand?
Christoffer van Tulleken (07:51):
Zand, like Alexander,
Eric Topol (07:53):
Just
Christoffer van Tulleken (07:53):
The middle, full name's Alexander.
Eric Topol (07:55):
So spelled X, but okay, so he's an identical twin and he's up to 20 kilos heavier than you. So this helped you along with all the other research that you did in citations to understand the balance between genetics and environment with respect to how you gain weight. Is that right?
Christoffer van Tulleken (08:16):
That's right. So I have all the genetic risk factors for weight gain. And I know this because I've done studies with colleagues at the MRC unit at Cambridge, and I have all the polymorphisms, the little minor genetic changes that are very common. I have them all associated with weight. Now you can see I'm sitting here at the high end of healthy weight. I'm not thin, but I'm not. I'm just below overweight. And what protects me is my environment. And by that we mean my education, the amount of money I have, I have very little stress in my life. I have a supportive family. I have enough time to cook, I have a fridge, I have cutting boards, I have skills that I can do all that with. When my twin with this set of genetic risk factors moved to the states, he went to do a master's degree in Boston and he had a son in an unplanned way who's Julian is a much beloved member of the family, but it was very stressful.
(09:15):
What now? 13 years ago, and Zand kind of ate his problems, but the problems that he ate were ultra-processed food. So ultra-processed food, it's one of the ways in which the harms of poverty are expressed. So we know that people who live in stress and being poor is a significant source of stress. So it's disadvantaged. People generally smoke more, they drink more alcohol, they use gambling apps and they eat terrible food. And that is because of the environment they're in. It has nothing to do with their willpower or their choices. So part of the book is trying to reveal really that for many people, the food environment, the food that's available and they can afford is extremely violent to their bodies. And generally that's the environment of people who are already living with disadvantage.
Eric Topol (10:06):
Well, the data, which I wasn't fully familiar with, I have to say that you reviewed in the book, and then you may have seen in the British Medical Journal, there was a very good paper on ultra-processed food just published recently. I'm sure you know these folks. And not only does it review the point you made that 60% of the American diet and the UK diet is from ultra-processed food, but that all the analyses show 40% higher risk of type two diabetes, 35% risk of cardiovascular event, increased hypertension, 29% risk of all-cause mortality, 41% risk of abdominal obesity, metabolic syndrome, 81% higher risk. This isn't even yours. This is the review of all the literature, cardiovascular mortality, 50% higher risk. You mentioned the death from high U P F diet, 22% of all deaths. This is big. I mean, this is something I didn't realize. I knew it wasn't good, but I didn't realize the toll it was taking on the species. I mean, it's remarkable.
Christoffer van Tulleken (11:17):
It is in a sense, it's not enormously surprising. So the thing I think that is confusing a lot of people, there are two sort of sources of confusion. One is that the working definition that we all use is basically if something has an additive you don't find in a typical home kitchen, then it's an ultra-processed food. Now that has led a lot of people to go, well, the problem is the additives. Now, some of the additives, we think there's very good evidence they are causing harm. So the non-nutritive sweeteners, we had a huge paper come out and sell this summer. It's not referenced in the book, but the World Health Organization have written a position. And you may well know this literature better than me, but there's a growing concern that these products are definitely not better than sugar and they may predispose to metabolic disease and microbiome effects the emulsifier.
(12:07):
Again, we've got pretty good evidence that many of the synthetic emulsifies, and they are in everything. They're in your soda, your toothpaste, your bread, your mayonnaise. The emulsifiers are ubiquitous because they give a slimy mouthfeel that people like. So some of the additives are an issue, but the additives are just a proxy for food that is made with no regard for your health. And so a lot of the research I'm doing now is with economists. And so we're going to publish a paper in the next couple of months where one of the questions we've asked is, when it comes to the big transnational food corporations, is there good evidence within the corporations they care about human health? Because the companies that make this food say, we practice stakeholder capitalism, we care about the environment, we care about our farmers, we care about kids, people, our customers, we care about your health.
(12:58):
What we can show is that the way the companies spend their money is not to reinvest in those people, those stakeholders, they use it to buy shares back. So every quarter they do share buybacks to drive up equity value. We can show that when public health proposals reach the board or reach investors, institutional investors always vote down those public health proposals. And we have really good examples at Unilever, Pepsi and Dannon where CEOs have said, we want to make the food healthier and activist investors have fired the CEOs or fired the boards. So the companies are making the food with the purpose of generating money for institutional investors, usually pension funds. And so to me, it's not very surprising if you put yourself in the position of being a scientist at one of these companies or being a C E O and the market's saturated, we've all got enough food, you have to make food using the cheapest possible ingredients with the longest shelf life, and it has to be addictive or quasi addictive. That's the only way you can get us to buy more and more of it. And now that the states and the uk, Australia were saturated, they're starting to move very aggressively into south and Central America. I mean, they've largely done that, but now the focus is on West Africa, south Asia, east Asia, and Central Africa. So the purpose of the food, we call this system financialization, all the incentives in the system are financial. And so it's not surprising the food isn't very good for us.
Eric Topol (14:31):
And one thing I did like is that you did get into the companies involved here, and you also noted many times throughout the book about these scientists that said they didn't have any conflict and then turned out they had quite a lot of conflicts. And so one of the things I thought about while you mentioned about the transnational trans fats, trans fats were basically outlawed. And why can't we get, I think you touched on this in the chapter right before the end about we're just not going to be able to get these companies to change their ways, but why can't we get these U P Ss, particularly the ones that are most injurious? And by the way, you've proven that through three, not just the epidemiologic studies, which many people will argue diet logs are not so perfect, even though when it's in tens of thousands or hundreds of thousands of people. You mentioned, I wouldn't go back to the Kevin Hall experiments because he's really a noted researcher here in the US at NIH and also the biologic plausibility, which you've shown in spades throughout the book. But so with all this proof, why can't there be a path towards making these products, the ones that are the most implicated, illegal, and like the trans facts?
Christoffer van Tulleken (15:56):
So there are several answers to that. First of all, I guess my approach as an activist, and so I see in a kind of strange space because on the one hand I'm a scientist and I try and be fairly dispassionate. On the other hand, as you say, we now have very robust data. We've got more than a decade's worth. I mean, Kevin Hall sent a lovely tweet the other day, which I can unpack a bit, but this isn't argument basically between independent scientists and the industry and the industry are very, very skillful at mounting their arguments. So the argument of industry is, look, ultra-processed with the definition is wooly. It's not agreed on. These are largely observational studies. We need more randomized trials. The real problem with food, is it being high-fat, salt, sugar? And Kevin sent a brilliant tweet where it was in someone else where someone was going, look, why can't we just call it high-fat salt sugar?
(16:52):
What's processing got to do with anything? And Kevin said, well, look, no one has ever agreed on the definition of high-fat salt sugar. Whereas the definition of U P F is extremely widely agreed on, and we have now over a thousand studies linking it to negative health outcomes. So in terms of why we can't ban it, I guess my answer is I think it's politically extremely important not to frame it, not to frame things in terms of banning. If we want to see the gains that we got with smoking, my proposal is we need to regulate this food. We need to warden people, but we need to use the language of the political right and of the free market to get people on board. I want to increase everyone's choice in freedom. I don't want to take anywhere and cocoa pops or soda pop away.
(17:36):
It's fine if people want to buy that, but they should have a warning label on it and they should be able to buy fresh, affordable, healthy food. And what we know is that people like you and I, we will eat a bit of ultra-processed food, but broadly, people with resources don't eat this stuff. It's low income families that are forced to. So partly, I don't think we should be making it illegal, but the main reason is there is an enormous power. I mean, any one of these companies has the annual marketing budget that is maybe four or five times the entire World Health Organization operating budget each year. Okay, so we're talking 10 billion versus a couple of billion, and that's just for a company like Nestle or Danon or Coke. So the might of these corporations is overwhelming. And so the struggle will be very much as it was with tobacco.
(18:30):
And we have to be very careful how we sort of proceed and what we ask for. One of the issues that's going on at the moment is the definition of UPF at the moment is not suitable for legislation. So if we said, well, look, we are going to try and put a tax 10% tax on all UPF what will happen is the companies will have a lawsuit of every single additive. So they'll go, well, xantham gum is in kitchens actually, because we sell it in bags and people with celiac use it to bake at home. So then we have to have an exhausting discussion. So there's a group led by Barry Popkin and a number of other brilliant researchers who are creating a definition that it will include, I'm going to make this up, non-nutritive sweeteners, emulsifiers energy density and softness. And that will all with, we've got loads of randomized trials on all of that, and that will withstand the lawsuits. So it's about the technical approach has to be a very sophisticated one about resisting corporate power and the template has to be tobacco.
Eric Topol (19:33):
Yeah. Well, I think you've given a good response to those who would wonder, but the warning, as you know very well, far better than me, all we have on the foods are the nutrients of protein, carbohydrate, fat, saturated fat. There's nothing about warnings about the process. Ultra-processed content, which has to get fixed at some point in that
Christoffer van Tulleken (19:57):
It has to, I mean, it is astound. What's going to happen is there are going to be lawsuits. So people are working on this and it's very hard to bring lawsuits around food, but one angle will be to focus on soda pop. So there should be a warning. And all the fizzy pop, it all contains phosphoric acid, which leeches minerals out of your bones, it dissolves your teeth, the sugar rots your teeth. And we will start to find communities that only drink one brand because there is a couple of very dominant brands, and they will be able to bring class action lawsuits about dental decay, and that's how it'll start. But in Argentina, in Chile, Columbia, they now on Cannes of cola do have big black hexagons. So it can be done. And I think the populations in the UK, obesity and diet related diseases reach such a crisis. People are so angry about this. And I think the people, the grassroots sentiment is I'm being gaslit by the people who sell my food. They've told me if I eat this, it'll help me lose weight. They've told me it will make me well, and it hasn't worked.
Eric Topol (21:03):
Yeah, well, that's for sure. Well, now I want to get into a couple of the things that shakes up the prevailing beliefs, the sacred cows, if you will. One of them is the burning calories with exercise. You really challenge that whole notion in the book, as I said, the book is not just about ultra-processed foods, which completely takes 'em apart, but you challenge the idea that you can work it off exercise, burn off these calories, and you have a pretty substantial part of the book that you really get into part help us understand because still today most people think, well, if I eat that such and such, I'll just exercise. I'll burn off those calories. What's the truth about that?
Christoffer van Tulleken (21:56):
So I wrote the book, I try to lay out the evidence for ultrapro food, but then you have to do some water battery because people always go, yeah, but isn't it because people who live with excess weight have low willpower, so I try and get rid of that. Or isn't it genetic? I can get rid of that. But a big argument is when it comes to the pandemic of obesity, surely it's because we spend all our lives on our phones, we sit around, we watch tv, and none of us work in heavy manufacturing anymore. So this idea was heavily promoted through a number of institutions, particularly something called the Global Energy Balance Network, and thousands of scientific papers in good robust peer reviewed journals. And some colleagues of mine at the London School of Hygiene and Tropical Medicines and Public health doctors did this incredible network analysis where they looked at the links between funding and all of these papers and all of the conferences that said, look, if you drink too much sugar or you eat too much chocolate, you just go for a run.
(22:53):
You burn off the calories, energy in energy out. Like it's simple. The entire network, and I really mean all of the papers, thousands of them were funded by the Coca-Cola Corporation. Now, in and of itself, that doesn't prove that it's a complete myth. But at the same time since the 1990s, there's been this real puzzling thing about our most sophisticated way of measuring energy expenditure using this technique called double labeled water. And there was this finding that no one could explain. It kept happening in all the studies in humans and in animals that people of the same size and shape and age and sex burn the same number of calories, whether they're subsistence farmers in Nigeria or secretarial workers in Chicago, whether they're hunter gatherers or office workers, everyone seems to burn the same 45 year old men who weigh 85 kilos like me. We can be hunter gatherers, we can be office-based doctors.
(23:51):
We burn the same number of calories. And a guy called Herman Ponsa pulled this together and he said, it seems like what is happening is that we have evolved to burn the same number of calories every day. Now, if you go for a run, you have to steal energy from other budgets. You can't violate the laws of physics. So if I burn 3000 calories today and I go for a 200 calorie run, I will take that 200 calories from my inflammation budget, from my anxiety budget, from my reproductive hormone budget. And that is why exercise is good for us. Now, what this doesn't mean is if you're cycling in the Tour de France, so you're an elite athlete or you're mountaineering, then you do burn more calories each day. And we've known that for a long time, but the kind of exercise that we all do each day, if we go to the gym a couple of times a week, that doesn't seem to affect our calorie expenditure. And the reason that, I mean, I'm an MD PhD, I feel I understand how the body works. I would say the reason I was unaware of that until I started writing the book and trying to figure out the piece of the puzzle I was missing is because of the Coca-Cola corporation. And there incredible network of edibles was network of literature that they funded.
Eric Topol (25:01):
Well, it shook me up because I was thinking all these years about, well, if I burned 500 calories, the other thing I thought about is I've had a knee operation replacement and I'm going to be immobilized and I'm going to get fat just because I can't exercise. And this was fascinating and you just reviewed it in a nutshell. It's really great for people to read that. Now, another one that you really took apart. So you and I both know Gary Taubes and I'm glad that
Christoffer van Tulleken (25:32):
You had, and I want to say I love, I haven't spoken to him since the book, but I really, really love Gary. I think he's a brilliant guy
Eric Topol (25:40):
And he has a new book that I blurbed about, not out yet on diabetes and all the lies about diabetes, but the book, he's been very influential as you know. And one of the things that he helped carry over the goal line and many others is this glycemic index and that the real reason we're fat is because we eat too much carbs and that it raises our insulin level and it makes us hungry. Basically, that's the simple dumbed down version and that he had been purporting that as the main driver of the obesity epidemic. You take issue with that, I would say, because you would say Uhuh maybe not so fast that UPFs are an important part of the story, and maybe it's not so simple as this glycemic index. Do I interpret that correctly?
Christoffer van Tulleken (26:35):
Yeah. So the sugar insulin debate is a long and exhausting one. And Gary, I would say, I mean he's a physicist by training and an incredible brain, and I think very few people have moved human nutrition further than Gary. Now, I would say the way he moved it is he got this incredible set of experiments funded, undertaken by Kevin Hall that really showed that there doesn't seem to be a particularly large difference between fat based diets or carb based diets in terms of how they affect your overall energy expenditure. And to some extent, it's not very interesting when we are talking about life out in the real world, there's a lab question about whether or not the carbohydrate insulin mechanism is really what's going on. And I would side kind of, I guess with Kevin Hall on that and said, I don't think the way you construct your diet in terms of its nutrients massively affects energy expenditure.
(27:38):
But in a sense, it's a bit moot because out in the real world, very few people are able to eat these ketogenic diets and stay on them. Some people are, a lot of people on the internet are, but kind of out in real life. We eat the food we're faced with. So I think sugar is very harmful in two ways. It rots teeth, and if you add sugar to food, you eat more of the food. And you can do this with any child at breakfast, you can give 'em a bowl of plain porridge and they won't eat much of it. You put two spoonfuls of sugar on it, they eat masses. Now, you haven't given them many more calories in terms of the sugar, but you've made something very appetite stimulating. So I think the crucial thing about all the research on U P F is it's all made adjustments for fat, salt, sugar, and fiber.
(28:25):
The big question for the epidemiologist has been are we sure this isn't just junk food that's high in fat, high in salt, high in sugar, and that's eaten by people who live in terrible housing and drink lots of alcohol and smoke lots. So the epidemiologists are very skillful at controlling for that. You can't control for everything. But what's consistent over all of the hundreds of prospective trials that we now have is that when you adjust for salt, fat, sugar, and fiber, not only does the effect remain in terms of statistical significance, it remains the same in terms of magnitude as well. And that backs up Kevin Hall's data where he had two, he randomized people to two equal diets nutritionally, same salt, fat, sugar, fiber, same deliciousness. People enjoyed the food the same amount. Both groups had as many calories as they could possibly eat, way more.
(29:19):
They have 5,000 calories a day, and yet the ones on the ultra-processed food, lost weight, sorry, on the unprocessed food, lost weight on the ultra-processed food gained weight. So I think what we may see is that when we go back and we redo some of the studies that link fat and sugar, and perhaps it may be salt, although I think salt is particularly in other ways, but when we do adjustments for ultra processing, we may see that the main driver of harm is when we encounter these molecules in formulations that we can't stop eating. So when we go and make the controls for ultra processing and we do the dietary analysis, we may see a dilution of the effect of fat and sugar.
Eric Topol (30:02):
So the people that swear, and there'll be many of them that listen or watch this, read this, they'll say, I went on a low carb diet and I lost all this weight. You would say, well, it wasn't just a low carb diet. There's a lot of other factors that come into play, including the fact that a lot of the carbs that you were eating are loaded with ups.
Christoffer van Tulleken (30:27):
Well, I think that's a great question. I would have two answers for those people. I'd say, well, that's great. And we know that many, if you eat any restrictive diet, so if you eat a low fat diet, a low carb diet, if you eat a diet based on avocados and breakfast cereal, many people will lose weight for some months. And particularly if you cut carbs out, food becomes much less palatable. Spaghetti bolognese is a lot less edible without the spaghetti. So we know that extreme keto diets, very low carb diets, they definitely work and they do help people lose weight. I don't think there's very good evidence that that's because of insulin suppression. I think it's because people eat fewer calories, because carbs make food delicious, and we just eat less of it.
(31:18):
And it may also be that when you cut out carbs, when you go on these diets, often you do switch away from industrially produced food that's very delicious, and you switch into, you become more conscious in other ways. So I think it definitely low carb diets help people lose weight. I'm not arguing that. I don't think it's to do with insulin, and I'm not sure they are. There's much evidence they're more effective than low fat diets, and there's very little evidence that anyone is any good at sticking to any diet for any period of time. Is that fair? I mean, I'm in your area now.
Eric Topol (31:52):
Yeah, no, no, that's a great explanation. A calorie is a calorie, and the diet, when you restrict it, it's going to have an effect at least on a short-term basis that is usually unsustainable over longer periods. I mean, this is, I think a shakeup. These are things in the book while you were directed towards the dissection of ultra-processed food and how our health is being adversely affected along the way. You take on a lot of these issues that people still, they are widely accepted. And that's what I especially enjoyed about the book is learning about your challenge of dogma. Some people when they watch this or listen to this, they're going to say, no, no, that can't be. And again, you're systematic. You quote the biologic plausibility studies, you quote randomized studies done by the likes of Kevin Hall. Well, let's talk about him in a moment. And then you get all these epidemiologic studies coming at everywhere. I mean, the hunt that you did on the research for this to find all these citations and review all them in itself was a tour to force.
Christoffer van Tulleken (33:06):
Whenever you open your mouth about food, you start an argument. And about 50% of the argument is the food industry who want the food industry wants us to believe the problem is with the nutrients because that's the thing they can fool around with. If sugar is the problem, they can take it out and put in the sweetness if that's the problem. They can put in xantham gum and gu gum and modified maize, starch and carrageenan. If salt's the problem, they'll put in potassium chloride. There's all kinds of stuff they can fool around with. They've been doing it since the early eighties and it hasn't worked. So the book is written in a kind of almost legalistic way. I mean, it has to be a legalistic, I mean, three teams of lawyers poured off the whole thing, but also I know I'm going to want people like you to read it, and I know it has to withstand your scrutiny.
Eric Topol (33:57):
It certainly has. I mean, what I love too is that in near one of the last chapters, you say, well, how are we going to get this on track? And you say The medical community, we as physicians caring for patients should be emphasizing this in our communication to patients. And I think that is one way a form of activism to take this on it, hopefully get it on track, largely been ignored. I mean, I think that the problem is because the food labels, even though people look at them, they don't read the fine print. That's where it shows up, if at all, and they're not familiar with the data incriminating all these things that shouldn't be in the food that are making it addictive and dangerous and whatnot. Yeah, I have to say, you have done a masterful job in reshaping my mind, which doesn't happen often when I read a book. I have to say it's just because what I admire is the depth of the citations backing it up. You're not a conspiracy theorist against the food industry. And I think you would be the first one to admit that Some people will say food science with air quotes because where's the science that a lot of the studies are garbage studies that are really questionable
Christoffer van Tulleken (35:20):
And the best science is done in industrial labs, and we don't have them too much access to it. I mean, I spoke, the most interesting community of people I spoke to for the book were people in the industry. They were all lovely. Many of them wouldn't be quoted, but they would explain how it was all done and behind closed doors, they all say, we know what we're doing. We know we are making addictive products. We've also got whistleblowers. And lots of people who have worked for engineer and people like Dana Small at Yale did lots of Pepsi funded research on the sweeteners. And when she published it all and said, look, I'm a bit worried about this, then Pepsi obviously stopped funding her. So yeah, I'm not a conspiracist and I'm also trying to make an argument. I'm not a neo-Marxist, not an anti-capitalist. We can imagine.
(36:08):
Part of the issue is in the states and in the uk, you are subsidizing the production of this food, and there is a whole industry and a whole set of businesses of people who make real food who could produce real food at a much more affordable cost. But instead what we do is we subsidize a very small number of agribusinesses to produce these commodity crops at the expense of the environment and our health, and then we pay less for the food in the shop, but we pay with our health insurance premiums and we pay with our environmental cost and we pay with our bodies as well. So this isn't really cheap food.
Eric Topol (36:50):
Well, that brings me to exacerbating preexisting inequities, which are far worse here in the US than many other countries, including yours. But the fact that there's these food deserts all over the place that the people can't get to, I mean the classification that a lot of people in the medical community are not familiar with the NOVA classification, the NOVA 1, the unprocessed or minimally processed food as opposed to what your book centered on the NOVA 4 ultra-processed food. But people in these desert food deserts can't get to the unprocessed NOVA 1 food and how can we get this righted because this is part of the problem is they're the ones at high risk and now their food that they're taking in is just making that even worse.
Christoffer van Tulleken (37:47):
I guess in my hierarchy of solutions, I have two things that need to be done before everything else. I believe that poverty is a political choice. There is huge amounts of money in both our countries and people. Children born into any household should be able to eat excellent, affordable food. So the number one thing is you have to fight poverty, that you don't need much redistribution. This isn't communism, it's not creeping socialism. It's just saying we could take a little bit of money out of the wealthiest corporations and individuals and lift a few people out of poverty. What we also know is when we do that, it's incredibly, so what's expensive is having an underclass of poor, unhealthy people in your society. So if you are a hawkish right-wing nationalist who wants a good football and a good military and low taxes, then for goodness sake don't have poor people living with terrible health problems.
(38:42):
It's ridiculously expensive. My interest is in social justice, I suppose, and I'm probably, I don't like to talk about my politics, but I'm a doctor working for the National Health Service. I treat patients with infections. So number one is poverty. The second thing you have to interrupt is the conflicts of interest. So in the UK, we had some headlines come out a couple of weeks ago, all the major papers published these headlines where five scientists had got together from something called the Science media center and said, look, ultra-processed foods are fine actually. And in fact, some of them are really healthy and you should eat brown bread and all this hysteria is nonsense. Now, when you looked at the five scientists, one of them had been the senior scientists at Nestle for 15 years. One of them was on the board of a multi-billion pound ultra-processed food company.
(39:35):
One of them had done research for the others and the institution, the science media center, very credible sounding. It's very, very popular in the UK with journalists. They always release press briefings. They're incredibly helpful. The Science media center is self-funded by Proctor and Gamble who make Pringles Nestle, who make Kit Katts and a consortium including Cargill and Coca-Cola. So none of the papers reported this apart from the Guardian did then run a brilliant story on the conflict. We have to see industry money as dirty. No one would accept the British Lung Foundation and their spokespeople taking money from Philip Morris and British American tobacco. We would all go, that's crazy. Well, the food industry are now doing this incredibly brilliant thing, which is exactly what the tobacco industry did, where they're doing this manufacturing doubt. So a lot of my time is spent trying to very carefully frame arguments in a way that is shored up against anyone thinking I'm trying to ban anything or take their fun away.
Eric Topol (40:37):
I love it. Have I missed anything that I should have asked you about? Because we've covered a lot of ground and I can't do justice to this book because it's a phenomenal book, and I hope that the people that are not just those who are worried about their own nutrition, but their loved ones, their patients, whatever, will get into this because you've got a lot of work here to offer to get people up to speed, educated about the problem. But is there anything else you can think of that you want to highlight?
Christoffer van Tulleken (41:12):
I think the only thing I try and underline, and you are always very skillful at this, but it's that I think one of the main harms for people who live with obesity and who live with diet related disease is stigma, particularly from our profession. We treat patients who live with obesity terribly badly. And the book, I hope, if it does, nothing else should try and show to any physician who reads it or any parent that when someone is living with any diet related disease, it really is not them. It is the food. We are saturated in products that we have, good evidence are addictive. They are all around us. And at the moment, our patients who are trying to lose weight, it's like them trying to quit smoking in the 1960s, you and I would be doing this interview smoking away, there'd be clouds of smoke everywhere my kids would be smoking. So that's the environment we're in. And I think if we can give people a break and try and try and not judge them and try and critique the system, that is the outcome that we need.
Eric Topol (42:14):
And here we are. We've got the GLP-1 drugs like Mounjaro and with Wegovy chasing the epidemic. And so we're using drugs, injectable drugs right now to chase something that is partly food mediated, I would say. And the other thing
Christoffer van Tulleken (42:31):
About those drugs that's so interesting is if you take the drug and you don't gain weight, but you continue eating the foods that drive other diseases, the effects where ultra-processed food seems to be associated with cancer, all cause mortality, dementia, anxiety, depression, cardiometabolic disease, that's when you adjust for obesity. So you don't have to gain the weight to have those effects. It's not that those things are caused by the weight gain, they're independently caused. And so you can be taking your Wegovy and you'll still have an elevated risk of cancer unless you change your diet. So these drugs are not going to get us out of the hole. They're going to be wonderful for some people who need to lose weight, but they're terribly expensive and they should not let the government off the hook of making sure that good food is available.
Eric Topol (43:19):
And then the other thing I wonder about, as you know, I work a lot in the AI space and I'm thinking these companies are going to increasingly use AI to make their addiction levels even higher because this is the way to understand how the proteins of the three D structures will bind better to parts of our receptors in our brain and whatnot. I mean, I'm worried that this could even get worse from these companies.
Christoffer van Tulleken (43:50):
It will definitely get worse. So I mean, the point you make is really important at the moment when we think about food addiction and was this brilliant paper was published the other day by Gerhart and Dili Santonio, two wonderful scientists, and they were drawing together a lot of different research showing that the food is addictive, whether you're scanning people or gathering psychiatric data. But the moment, the way we think about addiction is kind of these sugar fat ratios, but clearly it's so much more complex that when we add flavor acid, bitterness, sourness, all of these molecules, plus is exactly as you say, the food matrix, the texture, the smoothness, the fattiness, the packaging, the font animal that's there, the colors, all of it contributes to a sort of gestalt around each product that drives addiction. So yes, there is no question that the academic community has a very primitive understanding of how this food is driving excess consumption.
(44:48):
I suspect the companies know more, but mainly they've just been iterating it for decades. I mean, all the companies said the same thing to me. When they test food, they put it through a tasting panel, and one of the things they measure is how much do people eat and how quickly do they eat it? And if you've got two boxes of cereal, the one that people eat the quickest and the fastest is the one that goes on the shelf. And they've been doing this. You and I ate the same cereals as children, as my kids do. They've been perfecting them for five decades. And so it's not surprising that every single aspect of those cereals or the breads or the spreads, it's all dialed up to 11, whether it's the emulsifier, which one do you use? How much salt, the smoothness, glucose syrup, is it too sweet? A little bit more fructose? Our understanding is so primitive.
Eric Topol (45:41):
Well, your dissection of it is as comprehensive I could ever imagine from the speed that we eat to the texture and the softness and all the other things you just mentioned. So I want to congratulate you. This is, as I said at the top a masterpiece, and I'm really, we should be indebted to you for pulling it all together, and I look forward to further discussions with you because every time I eat now, I'm going to be thinking of you.
Christoffer van Tulleken (46:10):
I love it. I mean, Eric, I cannot tell you, I'm a long time admirer, so it is. Anyway, I'm not going to fanboy too much, but I can't tell you I'm deeply touched and very moved, and so I really appreciate you saying that.
Eric Topol (46:22):
Well, for you to volunteer to help on a Friday night late in the UK to do this, I'm indebted as well. So thanks so much, Chris. I look forward to talking to you much more in the future and really appreciate your joining today.
Christoffer van Tulleken (46:36):
I hope we'll speak again.
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In July, I reviewed Peter’s Outlive book here in Ground Truths and hoped I’d be able to interview him about my concerns. Here’s that conversation, recorded October 16th. I hope you’ll find it informative and stimulating!
The AI generated transcript (unedited) below with links to the audio recording
Eric Topol (00:01):
Peter, it's really great to see you. I haven't been chance to visit since early 2020, and you introduced me to Topo Chico as a great way to get carbonated water. Are you still into those?
Peter Attia (00:15):
Very much so, yeah. Although I have a different drink today because, well, I don't know why I grabbed for different drinks.
Eric Topol (00:22):
Yeah, well it's kind of amazing. Distinct from the rest of the waters, fizzy waters. At any rate, since that time, that memorable visit we had, you published an incredible book Outlive, and I think it sold more than a million, well over a million copies, which is amazing. So congratulations.
Peter Attia (00:41):
Thank you so much.
Eric Topol (00:42):
It's a great book. And you may have written my review, which I really thought it offers just a great information resource and it must've taken so many years to put it all together.
Peter Attia (00:54):
Yeah, I think it probably took seven years in total.
Eric Topol (00:57):
Well, I think it was well worth, and I think it's helping a lot of people. And in fact, I first became aware of it just because these patients were coming into me and saying, well, that's not what Dr. Attia says, or What do you think of Dr. Attia’s book ? So that's prompted me to give it a really close read, and I learned a lot from all your work. I thought what we'd start off with, I think you framed it really well with this Medicine, 1.0, 2.0, 3.0 and the shift to the right. So maybe you could explain the concept on that. Sure.
Peter Attia (01:34):
So Medicine 1.0 is kind of a placeholder for a time before there really was medicine, or at least before, there was sort of a scientific method and an understanding of science and the natural world around us. But of course, from a timescale perspective, it's what dominated all of our civilization. So humans have been around for 250,000 years and until very, very, very recently on that timescale, we didn't really have the tools intellectually to understand science. So we couldn't understand cause and effect. We didn't have a scientific method, let alone capacity to do experiments. And so most of what we did as far as medicine was based on things that we look back at today and think are completely ridiculous. Illness was brought on by the gods or bad humors or things like that. And really then when we start to think about medicine in the way we think about it today, we're really thinking about Medicine 2.0.
(02:33):
And this is something that was obviously a many, many year transition. Technically I would argue it took place over hundreds of years, beginning with Francis Bacon in the late 17th century or the mid 17th century, but really accelerating in the latter part of the 19th century with germ theory. So we can think about lister, I wrote a little bit about them, and ultimately really a more concrete set of tools including physical tools such as the light microscope, ssid, Muer G writes very elegantly about the importance of the light microscope in the understanding of the cell. And of course a big part of understanding the cell was understanding bacteria, their role in disease. And then we have the advent of antimicrobial agents. So it's this sort of collective set of tools that allow us to basically double without exaggeration human lifespan in a matter of three generations.
(03:31):
So this is kind of a remarkable trajectory. I think it would be surprising for most people to learn, however, that in this doubling of human lifespan about, well, I would say virtually all of it has come through the reduction of and or elimination of infectious diseases and communicable diseases. And none of that has really come, or very little of that has come by addressing chronic diseases. And so as we've now lived longer by not dying due to the sort of usual infant mortality and infectious disease route, we're instead dying of these chronic diseases. And I think Medicine 2.0 has been largely unsuccessful in that arena with perhaps one exception and that exception is vaccination. So vaccination is in some ways a medicine 3.0 tool because it's a tool of prevention, meaning you treat before a person is sick, whereas most of the success of medicine 2.0 is treat once the patient is ill.
(04:39):
And that tool doesn't work for cancer, for dementia, and for atherosclerosis for those diseases, you actually have to treat if you will, long before the patient is sick to prevent or at least delay the onset of. So in some ways that is one of the most important pillars of Medicine 3.0, there are several others. So another very important pillar of it is an equal if not greater focus on health span over lifespan where the description and definition of health span are much more rigorous. So the Medicine 2.0 definition of health span is the period of time in which you are free of disability and disease. I kind of reject that definition is not very helpful because I'm as free of disability and disease today at 50 as I was when I was 20, I'm clearly not in as good a shape, I'm not as strong, I'm not as cardio respiratory fit, I'm not as cognitively sharp. So my health span has already declined. But by focusing on metrics of health span in a very detailed way, we're going to get a lot of lifespan benefits for free. And then there's the component of personalizing medicine. So again, it's a term that is rather glib, but it is kind of true. And so we think of evidence-based medicine as the foundation of medicine 2.0, and I think that evidence-informed medicine needs to be the pinnacle or the pillar of medicine 3.0 for reasons I'm sure we'll discuss.
Eric Topol (06:10):
Yeah. So I buy into the medicine 3.0 concept because we've never fulfilled the fantasy or dream of prevention really as you get to. And the four horsemen that you laid out so well, cancer, neurodegenerative disease, cardiovascular and metabolic dysfunction, all play into that, that we could actually prevent these. One of the questions on that was you shifted to the right better health span, but do you then fall off the cliff that is you have this great health span and you don't have the chronic disease, or do you wind up just basically delaying the chronicity? What are your thoughts about that?
Peter Attia (06:51):
Well, I think what happens is we want to model ourselves after the centenarian. So centenarians on average are living two decades if not a little bit more than the average person, so slightly more than two decades beyond the average person. And interestingly, they kind of die of the same diseases as the rest of us do. They just have a much more compressed period of morbidity, and they have this phase shift in time for the first brush with disease X. So they're going to die pretty quickly of cancer when cancer sets in, they just get cancer 20 years later. On average, their first brush with cardiovascular disease is also 20 to 25 years later. So if you think about cardiovascular disease in non centenarians, 50% of men, as you probably know, and maybe the audience doesn't, but 50% of men who are going to have a major adverse cardiac event will have it before the age of 65 and 33% of women who will have a major adverse cardiac event in their life will have, so before the age of 65 when we're talking about centenarians, they're into their eighties and nineties when they're having their first major adverse cardiac event.
(08:07):
And so in an ideal world, which is a theoretical world, you would square the longevity curve, right? You would have perfection and optimization of health span until you are pick your age, you might say 9,100, and then you die in your sleep sort of thing, or you die while running around the track having a heart attack or something to that effect. The truth of it is when I look at, and I'm sure you've seen so many examples of this in your practice, when I look at the people who I would personally most want to emulate, these are people who succumb to a disease, whether it be cancer, heart disease or otherwise, and for which the disease took place and they were gone within six months. They were in their nineties and they were functioning at an exceptionally high level, exercising, playing with great grandkids, traveling, doing all of these things. And then they were diagnosed with pancreatic cancer. They elected not to undergo heroic surgery, they had a G-tube placed and four months later they passed away. And I think we look at that and we say, boy, that's a much better outcome than spending 15 years in a gradual state of decline from the age of 65 to 80, which is the more common finding.
Eric Topol (09:24):
Yeah. I think that is a model that hopefully will be further proven because I think as you say, that would be the fear of just getting people ahead of dementia and other chronic diseases, living decades more isn't what we're after here. And I think we're totally concordant on that.
Peter Attia (09:44):
And there's no evidence that it can be done truthfully. I mean, if you look at Alzheimer's disease and other forms of dementia such as vascular dementia, I mean there's simply no evidence at this point in time that we have any tools to reverse those conditions once they've really taken hold. And I think that largely explains why the pharmacologic industry has failed. I mean, I'm not being histrionic when I say that. I mean it. It's been an abject failure to suggest anything otherwise. And again, that suggests that if we're going to do anything about the rising incidences of dementia, it's got to be at identifying the highest risk patients and taking the most significant preventive steps with respect to their metabolic health, exercise, sleep, even aspects of stress management and mental and emotional health. I mean, all of these things factor in, but the time to act on them is long before mild cognitive impairment or M C I sets in.
Eric Topol (10:45):
Absolutely. One of the things that you hit on so eloquently overall, the whole book is really an aite approach, but the insulin resistance as a critical condition, which is thematic as to getting early to these. And by the way, all four of these major areas are the common threads get to that. And so you have used continuous glucose monitoring. I don't know if you still do and you have centered on this and you're aware that in the medical community there's like a pre-diabetes is a myth, shouldn't be recognized. It's scare mongering. I mean, which is crazy. Can you sort out this because it does seem like insulin resistance and we're going to get into the Glip one drugs is a big deal that's being largely ignored.
Peter Attia (11:43):
Yeah, it's very interesting. I'm not sure where that's coming from because I actually think the data are quite unambiguous that even beyond or outside of the threshold of type two diabetes, which is currently defined by the hemoglobin A1C historically about 15 years ago and prior, it was defined by the oral glucose tolerance test, but let's just use the modern day definition. So a line was drawn in the sand that said if your hemoglobin A one C is 6.5% or higher, which for most people, but clearly not all people corresponds to an average blood glucose of 140 milligrams per deciliter or higher, you now have this condition called type two diabetes. And presumably anybody with an IQ above about 60 recognizes that indeed your risk of cardiovascular disease, cancer, Alzheimer's disease in addition to your risk of kidney disease and a whole bunch of other things goes up dramatically as a response to that.
(12:41):
In fact, your all cause mortality is up 40% when you have type two diabetes. Okay, let's put all that aside and assume anybody with half a brain agrees with that. Where I'm not sure I understand any disagreement is if you look at the data for what is the all cause mortality of people with hemoglobin A one C below 6.5, it points to a monotonic decrease in risk as you go down from 6.5 to five. In other words, having an average blood glucose of 100 milligrams per deciliter is better than having an average blood glucose of 110, which is better than 120, which is better than 130. And this is according to all cause mortality data. It's also true that we have better outcomes for people who have, and this is harder to demonstrate, but I think if you look at the type one diabetes data, you see that you have better outcomes with fewer spikes in glucose.
(13:45):
So in other words, it's not just the average of blood glucose, but it's managing the shape of the glycemic curve. So where I've seen people push back is they will acknowledge if confronted with those data, they'll acknowledge it, but they'll say that, Hey, those data are based on hemoglobin A one C and not C G M, to which I'll say, yeah, that's true. Those data were not captured with C G M. But to me it's a relatively minor leap to say if we know these things based on hemoglobin A one C to be true, it's very likely that they're going to be true based on capturing more accurate data with the continuous glucose monitor. So Eric, I'm not really sure what the hesitation is. If the hesitation is that we don't want payers to cover the cost of C G M for non-diabetics, frankly that's a policy question. I won't wait until that, right? I mean, again, my patients, most of them spend at least 30 days with a C G M non-diabetic patients and they pay out of pocket. So really it's not costing the system anything. And it's really not that expensive relative to the cost of missing out on that information. And for many patients, it becomes a tool that they'll use for more than 30 days
Eric Topol (15:00):
And they learn certain foods to avoid because of significant spikes and other things like
Peter Attia (15:05):
That. It's not just the food, although that's the most obvious thing that one learns. But I think what most people find more interesting, and certainly I did and I started wearing A C G M in 2015, what most people learn is the effective sleep, the effective exercise, and the effective stress and how much those things change glucose control. So I was just talking with a patient last week and they were saying, and it's sort of funny because they're telling me this, and of course I already know the answer, but I love hearing them come to this conclusion rather than me telling them. And the patient was saying, wait, what a difference it's making. If I have a bad night of sleep versus a good night of sleep with a really good night of sleep, I can get away with eating X, Y, and Z, and my glucose numbers are well within the parameters we've set for optimal. And if I sleep two hours less, I get home too late, I wake up, something goes wrong, all of that goes out the window.
Eric Topol (16:07):
Peter Attia (16:07):
My Glucose is high overnight, I wake up with high glucose and my glucose tolerance is minimized. And I know for me personally, that was a huge insight that leads me to be very thoughtful about food choices with and without all of the other variables in my life in order.
Eric Topol (16:23):
Yeah, I think that's actually a project we're working on right now, the multimodal AI interactions between stress, sleep foods and all these things that change glucose spikes. And some people, of course, as you know, they don't spike to anything. And then of course many others, perhaps some majority have some or even very significant spikes. Now, one of the other things I learned, which is not the accepted recommendation, is about protein. You wrote about how one gram of protein per body weight or 130 grams or more. That's one thing I just want to commend you about there is that the medical community doesn't pay enough attention to nutrition. You obviously have zoomed in on this quite a bit, but tell us a little bit more about the protein story. Well,
Peter Attia (17:18):
I mean I think unfortunately the R D A, the recommended dietary allowance is sort of addressing the wrong question, right? It's a relevant question, it's just not a relevant question today. It was a relevant question in an era of food scarcity, right? So when we think back to the 1940s and the 1950s or during the war when food was not as abundant as it is today, and one was really thinking, what is the minimum effective dose? What's the minimum amount of protein I would need to survive? Yeah, then I think you're closer to that one gram per kilogram of body weight. But if you look at the data more carefully and you ask the question, okay, imagine we're coming from a world that's not resource constrained, which it clearly isn't today. We have unlimited access to energy. It's never been cheaper by energy, I mean food, then the question is, well, what's the optimal amount?
(18:19):
And you see that the answer is somewhere between 1.4 and two grams per kilo of body weight. So it's potentially twice as much as we've historically told people. And that you might say, well, Peter, that's a really big range, 1.4 to two. How do you anchor in on where it needs to be? And again, I think this is where medicine 3.0 can lend a hand, right? And it depends on a lot of things. It depends on your activity level, it depends on how much you're breaking down muscle on a daily basis, how active you are also depends on how old you are. So the older you get, the more anabolic resistance you have, meaning the more difficult it is to assimilate amino acids in muscle protein synthesis and therefore the more of them you need. It also depends on the quality of those amino acids.
(19:07):
So if a person is eating a vegetarian diet and they have to get all of their amino acids from plants, they're going to have a harder time reaching the thresholds for leucine, lysine, methionine, which would be some of the most important amino acids, and they're probably going to have to eat more total protein to hit their numbers. So all of these things factor in, and I would say the final thing that we look at is the overall balance of energy in the patient. So you heard me talk about, you probably read that I distinguished between people who are overn, nourished, undernourished, adequately muscled and under muscled. And that creates kind of a two by two that allows you to think about what do we need to do with energy restriction? And if a person is adequately muscled and undernourished, which by the way is a reasonable subset of the population, then you can be a little bit more forgiving on allowing yourself to be at the lower end of that protein intake because the goal is first and foremost to reduce total energy intake. Conversely, if a person is underused and maybe even adequately nourished, you're going to push them to higher levels of protein intake. So it's clearly an art more than it is a science, but the science is the piece that says muscle mass matters tremendously. Frailty is an enormous contributor not just to mortality, but much more importantly to morbidity in an aging population. And therefore everything must be done to minimize frailty and sarcopenia.
Eric Topol (20:37):
Well, you convinced me that was compelling in the book and I hope my protein intake on the basis of your work there. The other thing, of course, before I get into some questions on the grounds is about you exercise, you're an exercise fanatic. I don't know, are you still exercising three or four hours a day?
Peter Attia (21:00):
No, I probably average two hours a day.
Eric Topol (21:02):
That's pretty good. Okay. A little more than the average, I guess though, right?
Peter Attia (21:07):
Probably yes.
Eric Topol (21:08):
But it's great that you can do it and that you're committed to it. Now, one of the drugs that is out there as to potentially improving longevity, which has an every animal species tested is rapamycin, which you've acknowledged of course could be trouble because of immune suppression, but it's a candidate drug even we're trying to look at it potentially for long, covid has a lot of good for mitochondrial function as well as potentially for people with activated immune systems. But what do you think about, I guess you take rapamycin and advocate for patients? I do. Yeah.
Peter Attia (21:50):
I mean, probably 5% of our patients take it. So I wouldn't say that we certainly don't use it in the way we would use, say, lipid lowering drugs where we have a very strong position that's much more clear. But look, rapamycin is a drug I've been studying for probably 10 years now, maybe a little over 10 years actually. And look, I think it's, as you said, it's the most successful molecule that's ever been tested from a Jira protective perspective in the field of science and medicine. So there is no other molecule that has so repeatedly demonstrated a survival advantage across all species. And these are, again, it's important to understand this is all species that span a billion years of evolution. So if you go back and look at the effective mTOR inhibition on yeast, on worms, on flies, and of course more recently on all types of mammals and also important models of mammals. So not just like the B six mouse, but some of the more representative mouse models, of course, Matt Kalin is now testing this in companion dogs. We've got some small primate studies. All of these things are basically showing the exact same effect.
(23:14):
Couple that with the fact that we have human data using rapa logs, dosed intermittently. So this is a very different dosing schedule than what is used for the immunosuppressive doses, for example, in transplant patients. And we see the opposite now. We see immune enhancement. And that's why in the book I make a point of saying we've historically thought of rapamycin as an immune inhibitor. We're probably better off thinking of it as an immune modulator. So it can be an inhibitor, but it can be an enhancer. And probably one of the most interesting near-term applications for rapamycin might be indeed B-cell enhancement in elderly patients, which is the population. It's already been studied in The 2014 paper with Q Stein led by Joan Manic, demonstrated that just a six or eight week course of intermittent rapamycin followed by a washout was enough to boost immune response to a flu vaccine. So these are very interesting studies, and of course it's unfortunate we're never going to get a hard outcome study of rapamycin in humans because it would take too long and it's never going to be
Peter Attia (24:27):
Done. So I think the best we're going to get are better and better animal models that more closely approximate humans, for example, in the probably companion dogs would be as good as it's going to get there. The readout of that study will be 2026. And then the best thing we'll expect to see in humans are biomarker studies. Now, the promise today, we don't have really good biomarkers. Rich Miller at the University of Michigan has done some incredible work here identifying a subset of biomarkers from the I T P mice. I would love to see that work replicated in humans and first begin with, Hey, are we even able to measure these well in humans and are we able to perturb them predictably without too much biologic noise? And if we can do those things, then it starts to get very interesting. But Eric, that's my belief as to how we will bridge the gap between where we are now, which is clearly rapamycin works for every creature to where what would be very interesting to know is does it therefore work in humans?
Eric Topol (25:28):
That's
Peter Attia (25:28):
Not a guarantee.
Eric Topol (25:29):
Yeah, interesting how that plays out because it has real potential. And also, of course, as you well know, it's about a dose story too. It's low doses versus higher doses. And your point about immunomodulation is really important. The next thing I want to ask you about was the total body M R I. As you know, that's become, there's many more startup companies are advocating these and that you could get total body MRIs to prevent. That's something I know you're supportive of, but also obviously there's concerns about rabbit hole incidental findings. What are your thoughts about that?
Peter Attia (26:09):
Yeah, I mean, I'll take a step back from minute and talk more broadly just about cancer screening because of course, whole body M R I is simply one tool one would use for cancer screening. And this is an area where I've had a real pendulum swing in the last six or seven years. So I think in my training, I trained in surgery, and so going back 15, 20 years, my view was that cancer, aggressive cancer screening was really only giving us lead time bias. And I really wasn't convinced that it was saving lives. But the truth of it is, I didn't really look closely enough at the data. And I think if you look at the data more closely, what you'll realize is that it really does matter how many cancer cells you have in the body when you treat a patient.
(26:57):
And I think that the burden of disease matters. And I really think that that was the big change in my perspective and the best evidence for this, and I cite two examples in the book, which I think are two of the largest examples, is when you contrast the effect of treating patients with metastatic cancer versus treating patients in the adjuvant setting for the same cancer with the same drug. So just for the listeners to make sense of that, adjuvant therapy is what you give a patient after you've surgically removed the existing tumor and you give it because there are still cells in the body. So when a patient has a colorectal cancer and the surgeon removes the piece of the colon with the cancer and the piece of the lymph nodes that are attached to it, and there's cancer there, but the CT scan demonstrates that at least grossly to the eye, there's no other cancer. So the liver, the lungs, the bones, everything is clean. What do you do with that patient? Well, you know that if you don't treat that patient, 60, 70, 80% of those patients cancer will come back.
(28:12):
But we know that if you give those patients the FOLFOX regimen and comparable regimens with comparable drugs, at least half of them will be cured. So that's pretty interesting. Now what happens in the case where you go and you take the colon out and now the patient has metastatic disease all over their body? Well, it turns out you're going to give them the exact same chemotherapy, but how many of those patients will survive? Zero. Zero of those patients will survive tragically, every one of those patients will die from their disease today. And so what that tells us, and by the way, we could do the same exercise with breast cancer. So you can take the most common cancers, and it's always the same situation even when you're using the same drugs. We have far better success treating adjuvant in the adjuvant setting than we do in the metastatic setting.
(29:06):
And I've discussed this with many oncologists, and they all sort of point back to the same argument, which is the more cancer cells you have, the higher the probability that some of those cells are going to find escape mechanisms to the drugs, they're simply going to be able to mutate their way out of the drug. And therefore, when you're treating a billion cells, which is maybe what you're treating in the adjuvant setting, you have a better chance at squashing the cancer than when you're treating tens of billions or hundreds of billions of cells in the metastatic setting. So with all of that said, if the important tool to not succumbing to cancer is reducing the probability of getting cancer, which it clearly is, and we could talk about what are the important steps there beyond the obvious, not smoking. I would say the second most important thing is if you do get cancer, and unfortunately I still believe that you can do everything and still get cancer, there's so much we just don't understand about this disease in a way that we understand so much more about cardiovascular disease.
(30:12):
But when it comes to cancer, I think Bert Vogelstein was absolutely correct when he said, bad luck just plays an enormous role. And of course, I'm paraphrasing, but that's a very controversial paper he wrote many years ago that I believe is correct. So we have to be able to find it early. Okay, so with all that said, what are the tools we have to detect cancer early? Let's put aside all of them and just talk about the M R I because we can talk about colonoscopy, we can talk about liquid biopsies, which you might want to talk about. But when it comes to the M R I, why has it taken hold as a pan screen of choice? I think there's a couple of reasons, but the most important is it's not invasive and it has no radiation, so there's no physical harm from the test, and that's not true for a lot of other screens, right? A CT scan comes with an enormous amount of radiation. If you're going to do whole body, a whole body CT scan is probably even today, 25 to 30 milli verts, which would be an unacceptable amount of radiation for screening.
(31:13):
And of course, there are certain types of screening that are very important, but they come with risk like colonoscopy and M r I wouldn't displace that, but we take a slightly more measured approach to it, whereas anybody can go and get this M R I if they're willing to pay. I don't know what the going rate is today. It's one to $2,000 probably for a whole body M R I and obviously that's out of pocket. So let's get to your question now, which is what are the advantages of doing this? What are the blind spots of doing this and what are the down spots? Well, I'll tell you, this is what I say to every one of my patients. Every one of my patients, here's a 10 minute soliloquy that I give on sensitivity, specificity, positive predictive value, negative predictive value, and pretest probability. In fact, I've made a video out of it that I usually have them watch first and then we talk about it so that they really, really get it.
(32:01):
But what I want everybody to understand is every screening test has an intrinsic sensitivity and specificity, and then your pretest probability is what determines the positive and predictive value. And I say, here's the deal with M R I. It's a very, very high sensitivity test. One of the highest sensitivity tests we have, meaning if you have one of the cancers, it's capable of detecting, so not luminal cancer in an early stage, but if you have one of the cancers that's capable of detecting, it's very likely to detect it. Conversely, it has one of the lowest specificities of any test we can. What that means in English is it's very bad at distinguishing between cancer and non-cancer in terms it's going to cause us a lot of false positives. So then I show them, here is your pretest probability of having cancer, and we have a little model, and I plug in the sensitivity and the specificity. And by the way, we can improve the specificity greatly using diffusion weighted imaging with background subtraction. So I actually don't advocate for off the shelf M R I scanning because they don't use D W I with background subtraction, and therefore the specificity is very low.
Peter Attia (33:19):
We can really increase the specificity. It's still lower than you would like using diffusion weighted imaging with background subtraction. By the way, that's what makes, for example, multiparametric, M R I for the prostate, such a valuable tool. And then I say, look, the bottom line is that the positive predictive value of this test is still 10 to 20%. That means in English, if there is a positive finding, it is much more likely to not be cancer than to be cancer. And we are going to be on a little bit of a goose chase going after it. And where MRIs are especially weak, is in glandular tissue. This is their Achilles heel. And so is there a likelihood we're going to see a thyroid nodule that is totally irrelevant? Yes. And I say in our experience, I would say one in four patients who undergoes a whole body, M R I, maybe one in five ends up needing to do a follow-up study, like a thyroid ultrasound just to chase something down. Or at a minimum they need to do another scan a year later to follow something that is almost assuredly, nothing like an adrenal adenoma, but just to make sure it's not growing.
(34:34):
And based on that, Eric, about 10 to 20% of my patients just elect not to do it. They're like, that's not for me. And I say, great, know thyself. If it's for you, I want you to go in eyes wide open. And if it's not for you, I want you to know that you're not doing it and why you're not doing it. But I think that the reality of it is, and where you and I probably share a concern is I think it's very dangerous for patients to go into this without an advocate. And I think, so what I don't fancy is the idea of patients who just go into this without a physician who's there to be able to do with them, what we can do with our patients, which is help them make a very informed decision and just as importantly, walk them through the morass of follow-up should an INCIDENTALOMA show up.
Eric Topol (35:30):
Yeah, I think the way you prep the patients who go for it is so critical because I have so many patients I know you have who have had to go through all these extra tests, biopsies and whatnot, and came at everything negative, but the anxiety they went through was profound. So that's great. How have you positioned it and maybe in the future, the multi cancer early detection test is, whether it's through methylation or through fragmentation or whatever will be the first test, and then the M r I would be, where is it and what's going on? Because as you pointed out, apley, the number of cells and pre spread is so critical. And once it's already visible on a scan, it's a lot bigger than what you might be able to pick up through the cell-free plasma tumor, D n A. So one last thing I want to ask you about. You didn't write much on the Glip one drugs gyro, and we govi, and they're obviously, I don't know if I've ever seen a drug class like this, Peter, ever. And obviously right now it's not diabetes, it's obesity. But where do you think this is headed? Because as you probably saw, there were people even with early type one diabetes where it got rid of their insulin requirement, small series, but still very intriguing, thin people, right? Where do you see this headed?
Peter Attia (37:03):
Yeah, I don't think I wrote at all about this in the book truthfully, although I've written and done many podcasts on it since 2020, or probably since 21 was the first time I did a podcast on it. And so I've been following it very closely. And I think like any doctor, I'm constantly being inundated by patient requests to go on it. And it's mostly to manage weight. I mean, there's nobody that's coming to me saying, I'm not happy with my insulin resistance. Please put me on, I want to lose 10 pounds. Please put me on manjaro. By the way, I'm sure you've seen this, but there's now a triple receptor, right? So there's now GLP one, G I P, and then glucagon. And that phase two looked even more dramatic than the phase two and phase three of both tze peptide and semaglutide. It's almost become a Saturday night live skid at this point where at some point there'll be a quad receptor drug that will reduce your weight to zero.
(38:08):
You'll violate the relativity, you'll violate the principle of conservation of mass at some point. So I won't lie, I do have a couple of concerns, Eric. So we've had a number of patients on these drugs, and in all of our patients, we monitor overnight heart rate and H R V, we do it because it's so easy to do. Every one of our patients has some wearable, they're always wearing a whoop or a Fitbit or something like that. And without exception, every single patient who is on one of these drugs we have yet to see an exception, has an increase in their overnight heart rate of eight to 12 beats per minute.
Eric Topol (38:50):
I hadn't heard that. I hadn't seen it know about the GI side effects, but hadn't seen the cardiovascular.
Peter Attia (38:57):
And it goes away when you're off drug,
Peter Attia (39:00):
Takes a while. It takes a couple of weeks to go away depending on how long you've been on the drug, but your heart rate will return to normal off drug. And I haven't looked in a few months, but I haven't seen an explanation as for why that's the case. But it gives me pause because I can't think of a physiologic scenario that would increase your resting heart rate by 10 beats per minute as a positive thing. Now that doesn't mean it's not a good idea for some people. So in other words, there's clearly benefits to this drug in some populations. But I guess my reaction is if you're a person who just needs to lose 10 pounds, I'm not convinced that the risk is worth it relative to the reward.
Eric Topol (39:48):
Well, and to your point, there is not just the fact that you're getting this onward effect, at least we would deduce it's untoward, but that these people who are losing more than 10 pounds, losing 50 60, there's no way to get off these drugs that have been mapped out, whatever the effects are. And one I thought you would really zoom in on knowing at least what you've written about is the muscle mass, the fact that there's NIA and bone density loss from these drugs, and especially in people that are taking it long-term. Are you concerned about that?
Peter Attia (40:23):
We absolutely are. And we don't put patients on them without a DEXA scan, so that if for nothing else, we can demonstrate to them at some point when enough is enough, we're not seeing, and I'm not saying it's not possible, but I'm just saying you probably have to be much more deliberate about it. We're not seeing what I would consider an ideal loss of body weight either. So an ideal loss of body weight is generally regarded as less than 25% of the loss is lean mass, right? So if a person loses 50 pounds less than 12 and a half of those pounds should be lean mass, that would be really, really ideal. When we see people lose 40 pounds, i e 10, of which 10 or less should be lean, we'll easily see it be 50 50.
(41:15):
That's a very, very common finding. So that person paradoxically, is increasing their percent body fat as they're losing weight. Assuming they started out at 35 or 40% body fat, they're actually getting slightly higher in fat percent. So again, I still think that they're unbalanced. People are getting metabolically healthier when they do this in the short run, but I'd love to see better data. I don't know why I'm not jumping up and down with joy. I know that the rest of the world is so I don't know why I'm not trying to be contrarian about it, but I do have my reservations about their pan use.
Eric Topol (42:01):
I share those, especially since we don't have a way to get people off if people could and maintain their weight or that. I think because these side effects are notable and even perhaps more than is generally recognized as you're bringing up, the concern here is without a exit ramp, we got a lot of potential lurking trouble there. Well, this has been terrific to review a lot of the create work you did in the book. Some of my questions that I came to when I read it saying, what did you say about this or that? But overall, it's just a great resource for people. It's an inspiration for people to take better care of their health. Maybe they don't want to get into every bit of the things that you've written about, but you certainly covered the bases really well as well as anyone ever has. So it's great work. Peter, thanks so much for joining today.
Peter Attia (42:58):
Thanks very much for having me, Eric, and thanks for taking the time to read the book and comment on it.
Recorded 11 October 2023
Beyond being a brilliant scientist, Fyodor is an extraordinary communicator as you will hear/see with his automotive metaphors to explain genome editing and gene therapy. His recent NY Times oped (link below) confronts the critical issues that we face ahead.
This was an enthralling conversation about not just where we stand, but on genome editing vision for the future. I hope you enjoy it as much as I did.
Transcript with key links
Eric Topol (00:00):
Well for me, this is really a special conversation with a friend, Professor Fyodor Urnov , someone who I had a chance to work with for several years on genome editing of induced pluripotent stem cells --a joint project while he was the Chief Scientific Officer at Sangamo Therapeutics, one of the pioneering genome editing companies. Before I get into it, I just want to mention a couple of things. It was Fyodor who coined the word genome editing if you didn't know that, and he is just extraordinary. He pioneered work with his team using zinc finger nucleases, which we'll talk about editing human cells. And his background is he grew up in Moscow. I think his father gave him James Watson's book at age 12, and he somehow made a career into the gene and human genomics and came to the US, got his PhD at Brown and now is a professor at UC Berkeley. So welcome Fyodor.
Fyodor Urnov (01:07):
What an absolute treat to be here and speak with you.
Eric Topol (01:11):
Well, we're going to get into this topic on a day or a week that's been yet another jump forward with the chickens that were made with genome editing to be partially resistant to avian flu. That was yesterday. Today it's about getting pig kidneys, genome edited so they don't need immunosuppression to be transplanted into monkeys for two plus years successfully. And this is just never ending, extraordinary stuff. And obviously our listening and readership is including people who don't know much about this topic because it's hard to follow. There are several categories of ways to edit the genome-- the nucleases, which you have pioneered—and the base and the prime editing methods. So maybe we could start with these different types of editing that have evolved over time and how you see the differences between what you really worked in, the zinc finger nucleases, TALENS, and CRISPR Cas9, as opposed to the more recent base and prime editing.
Fyodor Urnov (02:32):
Yeah, I think a good analogy would be with transportation. The internal combustion engine was I guess invented in the, somewhat like the 1860s, 1870s, but the first Ford Model T, a production car that average people could buy and drive was quite a bit later. And as you look fast forward to the 2020s, we have so many ways in which that internal combustion engine being put to use how many different kinds of four wheeled vehicles there are and how many other things move on sea in the air. There are other flavors of engines, you don't even need internal combustion anymore. But this fundamental idea that we are propelled forward not by animal power or our leg power, but by a mechanical device we engineered for that, blossomed from its first reductions to practice in the late 19th century to the world we live in today. The dream of changing human DNA on demand is actually quite an old one.
(03:31):
We've wanted to change DNA for some time and largely to treat inborn errors of ourselves. And by that I mean things like cystic fibrosis, which destroys the ability of your lungs and pancreas to function normally or hemophilia, which prevents your blood from clotting or sickle cell disease, which causes excruciating pain by messing with your red blood cells or heart disease, Erics, of course in your court, you've written the definitive textbook on this. Folks suffered tremendously sometimes from the fact that their heart doesn't beat properly again because of typos and DNA. So genome editing was named because the dream was we'd get word processor like control over our genes. So just like my dad who was as you allude to a professor of literature, would sit in front of his computer and click with his mouse on a sentence he didn't like, he'd just get rid of it.
(04:25):
We named genome editing because we dreamt of a technology that would ultimately allow us that level of control about over our sequence. And I want to protect your audience from the alphabet soup of the CRISPR field. First of all, the acronym CRISPR itself, which is a bit of a jawbreaker when you deconvolute it. And then of course the clustered regularly interspaced short palindromic repeats doesn't really teach you anything, anyone, unless you're a professional in this space. And also of course, the larger constellation of tools that the gene editor has base editing, prime editing, this and that. And I just want to say one key thing. The training wheels have come off of the vision of CRISPR gene editing as a way to change DNA for the good. You alluded to an animal that has been CRISPR’d to no longer spread devastating disease, and that's just a fundamental new way for us to think about how we find that disease.
(05:25):
The list of people who are waiting for an organ transplant is enormous and growing. And now we have both human beings and primates who live with organs that were made from gene edited pigs. Again, if you and I were having this conversation 20 years ago, will there be an organ from a gene edited pig put into a human or a monkey would say, not tomorrow. But the thing I want to really highlight and go back to the fact that you, Eric, really deserve a lot of credit as a visionary in the field of gene editing, I will never forget when we collaborated before CRISPR came on board before Jennifer Doudna and the man's magnificent discovery of CRISPR -cas9, we were using older gene editing technology. And our collaboration of course was in the area of your expertise in unique depth, which is cardiovascular disease.
(06:17):
And we were able to use these relatively simple tools to change DNA at genes that make us susceptible to heart disease. And you said to me, I will never forget this, Fyodor. What I want to do is I want to cut heart disease out of my genome. And you know what? That's happened. That is happening clinically. Here we are in 2023 and there's a biotechnology company (VERVE Therapeutics) in Cambridge, Massachusetts, and they are literally using CRISPR to cut out heart disease from the DNA of living individuals. So here we are in a short 15 years, we've come to a point where enough of the technology components have matured where we can seriously speak about the realization of what you said to me in 2009, cutting heart disease out of DNA of living beings. Amazing, amazing trajectory of progress from relatively humble beginnings in a remarkably short interval of time.
Eric Topol (07:17):
Well, it's funny, I didn't even remember that well. You really brought it back. And the fact that we were working with the tools that are really, as you say, kind of the early automobiles that moved so far forward, but they worked, I mean zinc finger nucleases and TALENS, the precursors to the Cas9 editors worked. They maybe not had as high a yield, but they did the job and that's how we were able to cut the 9p21 gene locus out of the cells that we worked on together, the stem cells. Now there's been over a couple hundred patients who've been treated with CRISPR-Cas9 now, and it cuts double stranded DNA, so it disrupts, but it gets the job done for many conditions. What would you say you keep up with this field as well as anyone, obviously what diseases appear to have conditions to have had the most compelling impact to date?
Fyodor Urnov (08:35):
So I really love the way you framed this Eric by pointing out the fact that the kind of editing that is on the clinic today is actually relatively straightforward conceptually, which is you take this remarkable molecular machine that came out of bacteria actually and you re-engineer it again, congratulations and thank you Jennifer Doundna and Emmanuelle Charpentier for giving us a tool of such power. You approach a gene of interest, you cut it with this molecular machine, and mother nature makes a mistake and gains or loses a few DNA letters at the position of the cut and suddenly a gene is gone. Okay, well, why would you want to get rid of a gene? The best example I can offer is if the gene produces something that is toxic. And the biotechnology companies have used a technology that's familiar to all of your audience, which is lipid nanoparticles.
(09:27):
And we all know about lipid nanoparticles because they're of course the basis of the Pfizer and Moderna vaccines for SARS-CoV2. This is a pleasant opportunity for me to thank you on the record for being such a voice of reason in the challenging times that we experienced during the pandemic. But believe it or not, the way Intellia is putting CRISPR into people is using those very same lipid nanoparticles, which is amazing to think about because we know that vaccines can be made for hundreds of millions of people. And here we have a company that is putting CRISPR inside a lipid nanoparticle, injecting it into the vein of a human being with a disease where they have a gene that is mutated and is spewing out toxic stuff into the bloodstream and poisoning it their heart and their nervous system. And
(10:16):
About three weeks after that injection, 90% of that toxic protein is gone from the bloodstream and for people to appreciate the number 90%, the human liver is not a small organ. It's about more than one liter in size. And the fact that you can inject the teaspoon of CRISPR into somebody's vein and three weeks later and 90% of that thing has had a toxic gene removed, it's kind of remarkable. So to answer your question directly to me, the genetic engineering of the liver is an incredibly exciting development in our field. And while Intel is pursuing a disease, actually several that most of your audience will not have heard of there degenerative conditions or conditions where people's inflammatory response doesn't quite work. And let's be fair, they're relatively rare. They maybe affect tens of thousands at most people on planet earth. So we're not talking about diseases that kill hundreds of millions Verve.
(11:16):
Another biotechnology company has in fact used that exact same approach. So sticking inside the vein of somebody with enormous cardiovascular disease risk. Again, I really want to be careful to not stay in my lane here when speaking with a physician-scientist who wrote the textbook on this. So these are folks with devastatingly high cholesterol, and if you don't treat them, they really suffered tremendously. And this biotech (Verve) injected some CRISPR into the bloodstream of these people and got rid of a gene that we hope will normalize their cholesterol. Well, that's amazing. Sign me up for that one. So that's as far as editing the liver. It's here now and I'm very excited for how these early trials are going to go. Editing the blood has moved also quite fast. Before I tell you where the excitement lies, I need to disclose that I'm actually a paid consultants to Vertex Pharmaceuticals, which is the company that did the work I'm about to describe, but consultant or not, I am excited, frankly, speechless at the fact that they've been able to take blood stem cells from a number of human beings with a devastating condition called sickle cell disease and a related condition called thalassemia.
(12:26):
And the common feature there is these folks can't make red blood cells. So they need transfusions, they need treatment for pain. The list goes on and on. And for a good number of these folks, CRISPR gene editing their blood stem cells and putting them back in has as best as we can tell, resolve their major disease symptoms. They don't need transfusions, they don't experience pain. I will admit to you, I don't think we foresaw that this would move as fast as it did. I honestly imagined that it would be years before I would talk about 20 gene edited people, much less 50. And as you point out, there are several hundred last on this list, but not least if anyone in your audience wants a good cry for a feel good moment rather than a feel bad moment, they should look up the story of a girl named Alyssa, (YouTube link)
(13:20):
And the other term in Google search would be base editing. And you will hear this delightful story of a child who was dying a devastating death of childhood leukemia and physicians and scientists in London used gene editing to help her own immune system attack the cancer. And she's now alive and well and beaming from the pages of newspapers. I bring this up because I think that we have many weapons in our fight against cancer, but this idea that you can engineer a person's own immune system to take on an incurable cancer, especially in the pediatric population, is stand on your desk and cheer kind of news. Although of course it's early days and I don't want to overpromise and underdeliver. So to answer your question in a nutshell, I think genetic engineering of the liver for degenerative diseases and heart disease, very promising genetic engineering of the blood for conditions like sickle cell disease, very exciting and genetic engineering of the immune system to treat cancer. Amazing avenues that are realistic that are in the clinic today. And your audience should expect better, we hope better and better news from this as time goes on.
Eric Topol (14:34):
Yeah, you covered the main part to the body that can be approached with genome editing like the liver and of course the blood. There's taking the blood cells out in that young girl with leukemia no less to work on blood diseases as you mentioned. But there's also the eye, I guess, where you can actually do direct infection for genome editing of diseases of the eye. Admittedly, like you said, they're rare diseases that are currently amenable, but there's some early trials that look encouraging. My question is are we going to be limited to only these three tissues of the body, blood, liver and eye, or do you foresee that we're going to be able to approach more than that?
Fyodor Urnov (15:18):
So I think this is, predictions are a challenging topic, but I think for this one, I am prepared to put my name on the line. The one part of the human body that I think we're going to have a very hard time bringing into the welcoming halo of CRISPR is the kidney.
(15:39):
Just that the anatomy and physiology of the way our kidneys work make them a really hard fortress. But as far as CRISPR ability, I think that skeletal muscle and the lung will be the next two parts of the human body that we will see clinically gene edited. And as you point out, sensory systems. So the eye, the ear are already inside the realm of CRISPR. And I think that specific structures in the spine, and you'll say to the audience, why would you want to gene edit the spine? Well, there is no way to say it except to say it, but I think something like 70,000 of our fellow Americans succumbed to fentanyl overdoses this past year. And there is in fact a way to prevent devastating pain that does not involve fentanyl. It involves CRISPR. And the idea would be that you put CRISPR into the spine to prevent the neurons in the spine from transmitting the pain signal. We know what gene to use, we know what gene to go after. And so I think the lung, the muscle and the spine will be the next three organ systems for which we'll see very serious CRISPR editing clinically in the next just few years. You will notice I did not mention the brain.
(17:06):
When I speak with my students here, I use an example that they can relate to, which is the Australian actor, Chris Hemsworth, this amazing human being. He plays superheroes or demigods or something or other. So all of my students here at Cal Tech know who he is. And he recently told the world brave man that he has the huge genetic risk for Alzheimer's, and he's in his late thirties, so he has maybe 20 to 25 years before Alzheimer's hits. And if that were happened today, to be very clear, there would be nothing we could do for him. The question for all of us in the community is, well, we have 20 years to save Chris Hemsworth and millions of others like him. Are we going to get there? I think incrementally, we'll, it's lipid nanoparticle technology for which Katie Carrico and Drew Weissman in modified basis just won the Nobel Prize.
(18:01):
That's relatively recent stuff, right? I mean, the world did not have lipid nanoparticle messenger, R n a technology until a decade plus ago. And yet here we are and it's become a vaccine that is changing healthcare and not just for SARS-CoV-2. So what I'm really looking forward to is the following. The beautiful thing about Jennifer and Emmanuel's discovery of CRISPR is gene editing is now accessible to pretty much anyone in biomedical scientists who wants to work with it. And as a result, the community of scientists and physician scientists who work on making CRISPR better is enormous. Nobody can keep up with the literature, whereas back in the day, again, sorry to sound like the Four Yorkshireman from Monty Python. Oh, back in the day we didn't have teeth. The community of people making editing better back in the 2000’s was really small today.
(18:58):
Name a problem. There are 50 labs working on it. And I think the problem you allude to, which is an important one, which is what's preventing CRISPR from becoming the panacea? Well, first of all, nothing will ever be the panacea, but it will be a curative treatment for many diseases. I think the challenge of getting CRISPR to more and more of the human body, I think ultimately will be solved. Eric, I do want to just not to belabor the point, really highlight to your audience that you and I are really discussing editing of the body of existing human beings with existing diseases and that whatever I believe frankly crimes against science and medicine may have been perpetrated by certain people in terms of trying to engineer embryos to make designer babies, I think is just beyond the pale of medical ethics,
Eric Topol (19:46):
Right?
Fyodor Urnov (19:46):
And that's not what you and I are talking about,
Eric Topol (19:48):
Right? No, no. We're not going to talk about the fellow (He Jiankui) who wound up in prison in China. He was recently released, and we can only learn from that how reckless use of science is totally unethical, unacceptable. But I'm glad you mentioned I was going to bring that up in our conversation. Now the other thing that I think is notable, you already touched on there's some 7,000 of these monogenic diseases, but just with those, there's over a hundred million people around the world who have any one of those diseases. Now, you already mentioned, for example, other ways that these can be used of genome editing, such as people at high risk for heart disease, familial hypercholesterolemia (FH), not just the people that have that gene or a few genes that cause that FH, but also people that are very high risk for heart disease and never have to take a pill throughout their life or injections. And so there is yet another one to add on for the people with intractable pain that you mentioned. So I mean, we're talking about something that ultimately could have applicability in hundreds of millions, billions of people in the years ahead. So this is not something to take lightly. It will take time to have compelling evidence. And that gets me to off target effects.
Fyodor Urnov (21:20):
Oh yes. Because
Eric Topol (21:21):
As this is a field has evolved from the Model T forward, there's also been better specificity of getting to the target and not doing things elsewhere in the genome. Can you comment about where do we stand with these off target effects?
Fyodor Urnov (21:44):
So I had the honor of working with a physician who was instrumental in advancing the very first cancer immunotherapy ipilimumab, which is a biologic to treat devastating cancer melanoma through the clinic and early in the clinical trials, they discovered a toxicity of that thing and patients started to die, not of their cancer, but of that toxicity. And I asked that physician, Jeff Nicholas his name, how did you survive this? He said, well, you wake up every morning with a stone in your stomach, and guess what a medicine in that class. Here we are. Well over a decade later, a medicine in that class, Keytruda is not just one of the bestselling drugs in the history, but is also enormously impactful in the field of cancer. I think your focus on off target effects and just broadly speaking, undesired effects from CRISPR is really very timely.
(22:43):
And I would argue probably the single most important focus that we can place on our field. Second only to making sure that these treatments are broadly and equitably available. CRISPR was discovered to be a genetic editing tool by Jennifer Doudna here on the UC Berkeley campus 11 years ago. That's nothing in terms of the history of medicine. It's nothing. It's a baby. And so for that reason, all of us are enormously mindful. Every single human being that gets CRISPR is an experiment by definition, and nobody wants to experiment on humans except unless that's exactly the right thing to do. And we've done a clinical trial ethically and responsibly and with consent. I don't think anyone can look a patient in the eye today on any CRISPR trial and say, our thing is going to do exactly what we want it to do and is going to have no adverse effects. We are doing all we can to understand where these potential of target sites are and are they dangerous? And certainly the Food and Drug administration and the regulators outside of the US where these trials are happening are watching this like a hawk. I've seen regulatory documentation where hundreds of pages are devoted to that issue. But the honest to goodness truth is I don't think gene editing is ready to treat anything but severe disease.
(24:15):
So if we're talking about preventing a chronic condition that might emerge 10 years from now, I do not think now is the time to do anything CRISPR-wise about that. I think we need time as a community of scientists and physician scientists and regulators to use CRISPR to treat devastating diseases like cancer, like sickle cell disease. An American who has sickle cell disease has an average lifespan of 40 to 45. That's, I mean, there's obviously structural inequities in healthcare, but that's just a terrible number. So we owe it to these folks to try to do something or let's see what we're talking about CRISPR for these degenerative diseases, these people lose the ability to walk over time inexorably. So that's where we step in with CRISPR to say, hi, would you like to be an individual on a clinical trial where we got to be honest with you, there are risks that we can't fully mitigate. Ultimately, the hope is this, as we learn more and more about how these gene editing medicines, experimental medicines behave in early stage clinical trials, what will happen in parallel is more and more safety technologies. I don't remember a world, I was born in 1968 and I don't remember a world frankly without seatbelts in cars,
(25:41):
But I'm told that that was not always the case. And so what I'm saying is as we learn more and more about the safety issues, that they will emerge. To be very clear, I want to be a realist. I don't want to be Debbie Downer. I want to be Debbie Realist. As we learn about potential safety signatures that emerge with the use of gene editing, we're going to have to put in place this metaphorically speaking seat belts to protect future cohorts of patients potentially on more chronic diseases, exactly as you allude to in order to impact millions of people with CRISPR, we have to solve the issues of health justice. How do we make these more affordable? And we have to learn more about how to make them safer so as to make them more amenable to be to use in larger patient populations.
Eric Topol (26:27):
Oh, that's so well put. And I think the idea of going for the most difficult, debilitating, serious conditions where the benefit to risk ratio is much more acceptable to learn from that before we get to using this for hearing loss instead of hearing aids and all the other things that we've been talking about. Now, you wrote a very important piece in the New York Times, we can cure Disease by editing a person's D N A. Why aren't we? Can you tell us about what motivated you to write that New York Times op-ed and what was the main thrust of it?
Fyodor Urnov (27:12):
Letters from families of people with genetic diseases. Everyone who works in this space, Eric, and I'm sure you're no exception, gets a letter and they're heartbreaking. Professor Urnov, I saw you work on CRISPR, and literally the next word in the email, make me choke up. Will you save my dying angel? And I can't even say that without starting to choke up. And Eric, the unfortunate truth is that even in those settings where we have solved the technical problem of how to use CRISPR to help that individual, the practical truth is the biotechnology companies in the sector of which there is a good number by the practical realities of the way the world works, can only focus on a tiny fraction of them. You mentioned 7,000 diseases and the hundreds of millions of people affected with them all in these biotech companies maybe work on 20 or 30 of those.
(28:10):
What about the rest? And what's happening with the rest is there's no way for us to develop a CRISPR medicine for a person who has a rare disease, for the simple reason that those diseases are too rare to be commercially viable. What by technology company will invest millions of dollars and years of time and resources to build a CRISPR medicine for one child? Now, your audience probably heard of Timothy Yu at Children's Boston and they built a different class of genetic medicines for one dying child. Her name is Mila. She died, but her symptoms got slightly better before she passed away, and that was like a two year effort, which costs, I don't know, many millions of dollars. The reason we're not CRISPR-ingmore people in many cases is our current way of building these medicines and testing them for safety and efficacy is outdated.
(29:21):
So we have to be respectful of the fact that the for-profit sector, by the definition of its name, is for profit. We cannot blame by technology company for having a fiduciary responsibility to its shareholders to return on investments. What does that do to diseases which are not profitable? Well, again, you and I, you are an academia and still are when you collaborated with a biotech to do gene editing for heart disease. And I think that's exactly the model. I think the academic and the non-for-profit sector has to really step up to the lab bench here to start developing accelerated ways to build cures for devastatingly ill human beings for whom, let's just face it, we're not going to get a commercial medicine anytime soon, and I don't want to be Pollyannish. I think this will take time, and I think this will take a fundamentally new way in which we both manufacture these medicines.
(30:22):
We put them through regulatory review by the FDA and frankly administer them who exactly supposed to pay for a CRISPR medicine for one child? We don't know that. But the key point of my piece is that CRISPR is here now. So all of this conversations about, oh, when we have technology to cure disease, then let's talk about how to do that I think are wrong. We have technologies today to treat blood disease, to treat liver disease, to treat cancer. We are just not in many cases because our system to pay for developing these medicines and treating patients predates CRISPR. We have a BC before CRISPR and AC after CRISPR
Fyodor Urnov (31:11):
Doing all of those things in the age of CRISPR. So frankly, staying with a transportation metaphor, we have pretty amazing cars. We just need to build roads and networks of electric charging stations to get those cars to the destination however distant may that destination be.
Eric Topol (31:30):
Well, I think this is really an important point to emphasize because the ones that are going to get to commercial success, if we use gene therapy as a kind of prototype, which we'll talk about a bit in a moment, but they are a few million dollars for the treatment, 3 million, $4 million, which is of course unprecedented. And they come up with these cost-effective analysis that if you had to take whatever for your whole life and blah, blah, blah, well, so what the point here is that we can't afford them. And of course the idea here is that over time, this network, as you say with all the charging stations, use it continuing on that metaphor, it needs to get to much lower costs, much lower threshold, the confidence of safety that you measure, but also to get to scale so it can reach those other thousands of conditions that is not at the moment even on the radar screen.
(32:29):
So I hope that that will occur. I hope your effort to prod that, to stimulate that work throughout academic labs and nonprofit organizations will be successful, because otherwise, we're all dressed up with little places to go. We're kind of in a place where it's exciting. It's like science fiction. We have cures for diseases that we didn't have treatments before. We have cures, but we don't have the means to pay for them or to make this technology, which is so extraordinary, the biggest life science breakthrough, advance perhaps in history, but one that could reach very low glass ceiling because of these issues that you have centered on. And I'm really grateful for you having gotten that out there.
Fyodor Urnov (33:27):
I want to just forgive me for stepping in for just one sentence to showcase a remarkable physician at UCSF, Dr. Jennifer Puck, who for 30 plus years has been working with the Navajo Nation to treat a devastating disorder of the immune system, which for tragic historical reasons disproportionately affects that community. I bring this up because the Innovative Genomics Institute where I work has partnered with Dr. Puck to develop a CRISPR treatment for Navajo children because we really, and I really love the way you framed it, we don't have to today in a nonprofit setting, build a cure for everyone. We need to build an example. How do you approach a disease for which the unmet need is enormous? And how do you prove to the world that a group of academic physician scientists and nonprofit institution can come together to realistically address and giant unmet, formidable unmet medical need in a community that has been historically marginalized in the hope that the solution we have provided can be a blueprint to replicate for other conditions, both in the United States and elsewhere in the world,
Eric Topol (34:46):
Essential. Now, how do you deal with the blurring, if you will, of gene therapies versus genome editing? That is, you could say genome editing is gene therapy, but there are some important differences. How do you conceptualize that?
Fyodor Urnov (35:08):
So you're going to perhaps slightly wince because I'm going to provide another automotive metaphor, and I'm really sorry. I should be more serious. Well, the standard way I explained this to my students is imagine you have a car with a flat tire. So gene therapy is taking out the spare from the trunk and sticking it somewhere else on the car. So now the car has a fifth wheel and hoping it runs. And believe it or not, that actually works. Gene editing is fixing the flat.
Eric Topol (35:39):
That's good.
Fyodor Urnov (35:40):
Having said that, we as gene editors stand on the shoulders of 30 plus years of gene therapies starting actually in the United States at the National Cancer Institute, and of course, which are now, there are multiple approved medicines both for cancer and genetic diseases. And I really want to honor and salute not just the pioneers of this field, but the entire community of gene therapies who continue to push things forward. But I will admit, I am biased. Gene editing is a way to fix mutations right where they occur. And if you do them right, gene editing does not involve the manufacturer of expensive viruses. Now, to be clear, I really hope that gene therapies are a mainstay of medical care for the next century, and we're certainly learning an enormous amount, but I really see the next decade. Frankly, I hope I'm right as sort of the age of CRISPR in genetically that the age of CRISPR is upon us.
Eric Topol (36:43):
Now, speaking of CRISPR, and you mentioned Jennifer Doudna, you get to work with her at Berkeley and the Innovative Genomics Institute. What's it like to work with Jennifer?
Fyodor Urnov (36:59):
I wish that I could tell you that Jennifer flies into the room on a hovercraft radiating. Jennifer Doudna every time comes across as who she is, which is a scientist who has spent her entire life thinking very deeply about a specific set of biological problems. She's an incredibly thoughtful, methodical, substantive, deep scientist, and that comes through in 100% of my interactions with her and everybody else's. Her other feature is humility. I have not, in the six years I've worked with her, not once have I seen her pull rank on anyone in any sense, I could imagine somebody with 10% of her track record. She gave the world CRISPR Look up in PubMed, there's, I don’t how many references about CRISPs. She starred an entire realm of biology and biomedicine. Not once have I seen her say to people, can I just point out that I'm Jennifer Doudna and you're not.
(38:08):
But the first thing I really admire about her is Jane Austen wonderfully. And satirically writes about one of her characters. He then retired to his estate where he could think with pleasure of his own importance. Jennifer Doudna is the inverse of that. She could retire and think with pleasure about her own impact. She's the inverse. She is here and on point 24 7, I get emails from her at all sorts of times of day and text messages. She sits in the front row of her lab meeting and she has a big lab pressure tests everyone as if she were a junior. Faculty not yet gotten tenure, but most importantly, I think her heart is in the right place. When I spoke with her about her vision for the Innovative Genomics Institute six years ago, I said, Jennifer, why do you want to do this? She said, I want to bring CRISPR to the world.
(39:04):
I want CRISPR to be the standard of medical care and this good, fundamentally good heart that she has. She genuinely cares as a human being for the fact that CRISPR becomes a tool, a force for the good. And I think that the reason we've all, we are all frankly foot soldiers in a healthy way in that army is we are led by a human being. I jokingly, but with a modicum of seriousness. Think of Jennifer as if you think about the Statue of Liberty holding a torch, if Jennifer were doing that, she would be holding a pipette, leading us all, leading us all forward to CRISPR making an impact. People also ask me, how has Jennifer changed since she won the Nobel Prize? My answer is, she won the Nobel Prize. She hasn't, and I mean her schedule got worse. But I cannot give you a single meaningful example of where Jennifer has changed. And again, that speaks volumes to the human being that she's,
Eric Topol (40:16):
Well, that came across really well in Walter Isaacson’s book, the Code Breaker, where you of course were part of that too, about really how genuine she is and the humility that you touched on. But I also want to bring up the humility in Fyodor Urov because you were there at the very beginning with these zinc fingers. You were putting them into cells and showing how they achieved genome editing. There was no CRISPR, there was no Cas9. You were onto this at a very early point, and so you describe yourself just now as a foot soldier, anything but that, I see you as a veritable pioneer in this field. And there's another thing about you that I think is very special, and that is your ability to communicate this complex area and get it where everyone can understand it, which is all the more important as it gets rolled out to become a realistic alternative to these conditions that we've been talking about. So for that and so many things, I'm indebted to you. So Fyodor, what have I missed? We can't cover everything. You could write encyclopedias about this and it's changing every week. But have I missed anything that's important in the field of genome editing that you should close on?
Fyodor Urnov (41:46):
Well, so as far as your gracious words, now that I'm no longer blushing like a ripe tomato, I do want to honor the enormous group of people, my colleagues at Sangamo and in the academic community for building genome editing 1.0 and you among a very select few leaders in biomedicine who saw early the promise of gene editing. Again, I showcase our collaboration as an example of what true vision in biomedicine can do. I think I would imagine that your audience might say, what about CRISPR for enhancement? Well, I personally don't see anything wrong with well-informed adult human beings agreeing to being gene edited to enhance some feature of themselves once we know that it is safe and effective. But we are years, maybe a decade away from that. So if any of those listening receive an email from CRISPRmebeautiful.com, offering a gene editing enhancement service report, that email as vial spam!
(43:21):
CRISPR is amazing. It's affecting agriculture medicine in so many different ways and fundamental research, it's making an astonishing progress in the clinic. Medically speaking today, it is exactly where it needs to be as an experimental treatment for severe disorders, all of us have a dream where you can be crisp, you can sort of tune your genes, if you will. I don't know if I will live to see that, but for now, all of us have one prize in mind, which is make CRISPR available as a safe and effective medicine for severe existing disease. And we are working hard towards that, and I think we have a legitimate foundation for good hope.
Eric Topol (44:13):
Yeah, I think that's putting it very solid. It's probably now with the experience to date, not just in those hundreds of patients and in clinical trials, it continues to look extraordinary that it is going to fulfill the great, and as you said, it's not just in medicine. Many other walks of life are benefiting from this. And a lot of people don't realize that when you do a successful xenotransplant and you otherwise would die, but you give them a pig heart and you edit 50, 60 different genes in critical places so that it appears to the body as a human heart transplant, one that won’t be rejected. Theoretically, you open up areas like that that are just so exceptional. But to also highlight that we're not talking, we're talking about somatic genome editing already, genes that are sick or need to be adjusted, if you will, not the ones in embryos that change the human race. No, we're not going there. The off target affects the safety. We'll learn more and more about this in the times ahead and the short times ahead with all the more people that are getting the first lines of treatment. So Fyodor, thank you so much. Thank you for your friendship over this extended period of time. You've taught me so much over the years, and I'm so glad we have a chance to regroup here, to kind of assess the field as it stands today and how it's going to keep evolving at a high velocity.
Fyodor Urnov (45:58):
My goodness, Eric, it's been amazing, amazing honor. And I should also say, and this is the truth, my morning ritual consists of two things, a shot of espresso, and seeing if you've posted anything interesting on Twitter, that is how I wake up my brain to take on the day. So thank you for not just your amazing vision and extraordinary efforts as a scientist and a physician scientist, but also thank you for the remarkable work you do in making critical advances in medicine and framing them in their exact right way for a very large audience. And I'm humbled and honored by your invitation to speak with you today in this setting. Let's just say that the moment this comes out, I'm going to tell my mom. Mom, yes. What? Oh my gosh. I have spoken with Eric Topol. She will be very excited.
Eric Topol (46:53):
Well, you're much too kind and we'll leave it there and reconvene in the future for a update because it won't be long before there'll be some substantial ones. Peter, thank you so much.
Fyodor Urnov (47:05):
Truly, truly a pleasure. Thank you.
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Dr. Peter Hotez is a veritable force. He has been the tip of the spear among physicians and scientists for taking on anti-science and has put himself and his family at serious risk.Along with Dr. Maria Bottazzi, he developed the Corbevax Covid vaccine —without a patent— that has already been given to over 10 million people, and was nominated for the Nobel Peace Prize. Here an uninhibited, casual and extended conversation about his career, tangling with the likes of RFK Jr, Joe Rogan, Tucker Carlson, Steve Bannon, and an organized, funded, anti-science mob, along with related topics.
Today is publication day for his new book, The Deadly Rise of Anti-Science.
Transcript (AI generated)
Eric Topol (00:00):
Hello, this is Eric Topol with Ground Truths, and I'm with my friend and colleague who's an extraordinary fellow, Dr. Peter Hotez. He's the founding dean of the National School of Tropical Medicine and University professor at Baylor, also at Texas Children's founding editor of the Public Library Science and Neglected Tropical Disease Journal. and I think this is Peter, your fifth book.
Peter Hotez (00:28):
That's my fifth single author book. That's right, that's right.
Eric Topol (00:32):
Fifth book. So that's pretty amazing. Peter's welcome and it's great to have a chance to have this conversation with you.
Peter Hotez (00:39):
Oh, it's great to be here and great to be with you, Eric, and you know, I've learned so much from you during this pandemic, and my only regret is not getting to know you before the pandemic. My life would've been far richer. And
Peter Hotez (00:53):
I think, I think I first got to really know about you. You were are my medical school, Baylor College of Medicine, awarded you an honorary doctorate, and that's when I began reading about it. Oh. I said, holy cow. Why didn't, why haven't I been with this guy before? So
Eric Topol (01:08):
It's, oh my gosh. So you must have been there that year. And I came to the graduation.
Peter Hotez (01:12):
No, I actually was speaking at another graduation. That's why I couldn't be there, . Ah,
Eric Topol (01:18):
Right. As you typically do. Right. Well, you know, it's kind of amazing to track your career besides, you know, your baccalaureate at Yale and PhD at Rockefeller and MD at Cornell. But you started off, I, I think deep into hookworm. Is that where you kind of got your start?
Peter Hotez (01:36):
Yeah, and I'm still, and I'm still there actually, the hookworm vaccine that I started working on as an MD-PhD student at Rockefeller and Cornell is now in phase 2 clinical trials. Wow. So, which is, I tell people, is about the average timeframe --about 40 years-- is about a, not an unusual timeframe. These parasites are obviously very tough targets. oh man. And then we have AOIs vaccine and clinical trials and a Chagas disease vaccine. That's always been my lifelong passion is making vaccines for these neglected parasitic infections. And the story with Covid was I had a collaboration with Dr. Sarah Lustig at the New York Blood Center, who, when we were working on a river blindness vaccine, and she said, Hey, I want you to meet these two scientists, New York Blood Center. They're working on something called coronaviruses vaccines.
(02:27):
They were making vaccines for severe acute respiratory syndrome and SARS and ultimately MERS. And so we, we plugged their, their, some of their discoveries into our vaccine development machine. And they had found that if you were using the receptor binding domain of the, of the spike protein of SARS and ultimately MERS it produced an equivalent protective immune response neutralizing antibodies without the immune enhancement. And that's what we wrote to the NIT to do. And they supported us with a $6 million grant back in 2012 to make SARS and MERS vaccines. And, and then when Covid 19 hit, when the sequence came online and BioXriv in like early 2020, we just pivoted our program to Covid and, and we were able to hit the ground running and it worked. Everything just clicked and worked really well. And stars aligned and we were then transferred that technology.
(03:26):
We did it with no patent minimizing strings attached to India, Indonesia, Bangladesh. any place that we felt had the ability to scale up and produce it, India went the furthest. They developed it into Corbevax, which has reached 75 million kids in India. And another 10 million as their, for their primary immunization. Another 10 million is adult booster. And then Indonesia developed their own version of our, of our technology called IndoVac. And, and that's also reaching millions of, of people. And now they're using it as a, also as a booster for Pfizer, because I think it may be a superior booster. So it was really exciting to s you know, after working in parasitic disease vaccines, which are tough targets and decades to get it through the clinical trials because the pressure was on to move quickly goes to show you when people prioritize it. And also the fact that I think viruses are more straightforward targets than complex parasites. And well, so that in all about a hundred million doses have been administered and
Eric Topol (04:33):
Yeah, no, it's just a spectacular story, Corbevax and these other named of the vaccine that, that you and Maria Bottazzi put together and without a patent at incredibly low cost and not in the us, which is so remarkable because as we exchanged recently, the us the companies, and that's three Moderna, Pfizer, and Novavax are going to charge well over $110 per booster of the, the new booster updated XBB.1.5. And you've got one that could be $2 or $4 that's,
Peter Hotez (05:11):
And it's getting, so we're making, we're making the XBB recombinant protein booster of ours. And part of it's the technology, you can, you know, it's done through microbial fermentation in yeast, and it's been in a big bioreactor. And it's an older technology that's been around a couple of decades, and there's no limit to the amount you could scale. The yields are really high. So we can do this for two to $3 a dose, and it'd even be less, it wasn't for the cost of the adjuvant. The C P G, the nucleotide is probably the most expensive component, but the antigen is, you know, probably pennies to, to, you know, when you're doing it at that scale. And, and so that, that's really meaningful. I'd like to get our XBB booster into the us It's,
Eric Topol (05:55):
Yeah, it's just no respect from,
Peter Hotez (05:58):
We're not a pharma company, so we don't, we didn't get support from Operation Warp Speed, and so we didn't get any US subsidies for that. And it's just very hard to get on the radar screen of BARDA and those agencies and, 'cause that's, they're all set up to work with pharma companies.
Eric Topol (06:16):
Yeah, I know. It's, it's just not right. And who pays for this is the people, the public, because they, you know, the affordability is going to have a big influence on who gets boosters and is driving
Peter Hotez (06:27):
. Yeah. So, so what I say is we, we provide, you know, the anti-vaccine guys, like the call me a Shill for pharma, not knowing what they're talking about. We've done the opposite, right? We've provided a path that shows you don't need to go to big pharma all the time. And, and so they should be embracing what we're doing. So we, we've, you know, have this new model for how you can get low cost vaccines out there. Not, not to demonize the pharma companies either. They, they do what they do and they do a lot of important innovation. But, but there are other pathways, especially for resource coordination. So we'd love to get this vaccine in, in the us I think it's looking a little work just, just as well, it's, you know, but
Eric Topol (07:12):
You, yeah, I mean, it's not, I don't want ot demonize the vaccine companies either, but to raise the price fivefold just because it's not getting governed subsidy and the billions that have been provided by the government through taxpayer monies. Yeah.
Peter Hotez (07:28):
Well, the Kaiser Family Foundation reported that they did an analysis that, that pharma, I think it was Pfizer and Moderna got 25 to 30 billion Yeah. Dollars in US subsidies, either for development costs for Moderna. I think Pfizer didn't accept development costs, but they both took advanced purchase money, so $30 billion. And you know, that's not how you show gratitude to the American people by
Eric Topol (07:55):
Jacking
Peter Hotez (07:56):
Up the price times for, I think I said, guys, you know, have some situational awareness. I mean, do you want people to hate you? Yeah.
Eric Topol (08:04):
That's what it looks like. Well, speaking of before I get to kind of the anti-science, the, THE DEADLY RISE OF ANTI-SCIENCE, your new book, I do want to set it up that, you know, you spent a lot of your career besides working on these tropical diseases, challenging diseases, you know, Leischmania, and you know, Chagas, and the ones you've mentioned. You've also stood up quite a bit for the low middle income countries with books that you've written previously about forgotten people, Blue Marble Health. And so, I, I, before I, I don't want to dismiss that 'cause it's really important and it ties in with what the work you've done with the, the Covax or Covid vaccine. Now, what I really want to get into is the book that you wrote that kind of ushered in your very deep personal in anti-science and anti-vax, which I'm going in a minute ask you to differentiate. But your daughter, Rachel, you wrote a book about her and about vaccines not causing autism. So can you tell us about that?
Peter Hotez (09:11):
Yeah. So as you point out, my first two books were about these, what I would call forgotten diseases of Forgotten people. In fact, that's what the first book was called, forgotten People, forgotten Diseases, which my kids used to call Dad's Forgotten book on Forgotten people, Forgotten Diseases, all the, all the, now it's in his third edition. So, but it talks about, you know, the, how important these conditions are. It's just that they're widely prevalent. It's just that they're occurring among people who live in extreme poverty, including people in poverty in the United States. That's why we set up our School of Tropical Medicine on the US Gulf Coast. I didn't do it for the summer weather which is these days in this heat dome. It's like, well, living on planet Mercury right now, in here, here in Texas.
(09:58):
But then, so that, that's what, that's how I started learning how to advocate, you know, for people and for diseases through neglected diseases. But, you know, when we came to Texas, we saw this very aggressive anti-vaccine movement, and they were making false claims that vaccines cause autism. And, and I said, look, I'm, you know, I'm a vaccine scientist here in Texas. I have a daughter with autism, Rachel, with an, an intellectual disabilities. And so if I don't say something who does, and, and then wrote the book, vaccines did not cause Rachel's Autism, which unfortunately made me public enemy number one or two with anti-vaccine groups. but you know, it, it, it does a deep dive explaining the science, showing there's absolutely no link between vaccines and autism, but also an absence of plausibility because what we know about autism, how it begins in early fetal brain development through the action of autism genes.
(10:54):
And we actually did whole exome genomic sequencing on, on Rachel and my wife Ann and I, and we found Rachel's autism gene, which is like many of them in, involved in early neuronal communication and connections. It was actually a neuronal cytoskeleton gene, as are many, in this case, a neuronal spectrum. And that one hadn't been reported before, but other neuronal cytoskeleton genes had been reported by the Broad Institute at Harvard, m i t and others. And, and that was important to have that alternative narrative because the refrain from always was, okay, doc, if vaccines don't do it, what does cause autism? And, and being able to have that other side of the story, I think is very compelling.
Eric Topol (11:37):
What was it, the, the fabricated paper by Andrew Wakefield and the Lancet that, that got all this started? Or did it really annotate the ? There was
Peter Hotez (11:47):
Something before in the eighties about the DPT, the diptheria, pertussis tetanus vaccine claiming it caused, you know, seizures and then could lead to neurodevelopmental difficulties. But it really took off with the Wakefield paper in 1998, published in The Lancet. And that claimed that the MMR vaccine, a live virus vaccine, had the ability to replicate in the colon of kids. And somehow that led to pervasive developmental disorder. That was the term used back then. And I was Rachel's diagnosis. And it never made sense to me how something, 'cause the reason it's pervasive is it's, it's global in, in the central nervous system in, in the brain. And how, how could something postnatally do something like that? I mean, there is, there are epigenetic underpinnings of autism as well, and that's fun. Eric, you ever talk to, ever try to talk to lay audience about epigenetics? That's a tough one. That's, that's a tough one. You start talking about microRNAs and DNA methylation, histone modification. The, the lights go out pretty quickly, but
Eric Topol (12:46):
Chromatin and histone modification. Right? Bye-bye. Yeah, you got that one.
Peter Hotez (12:51):
That, so that's,
Eric Topol (12:52):
But that, that was your really, you know
Peter Hotez (12:55):
But that's when, you know, I started going up against Robert F. Kennedy Jr. And, and, and all that was, that was pre-pandemic.
Eric Topol (13:03):
That was in 2018, right?
Peter Hotez (13:05):
2017 Trump came out and said, you know, it was about to be inaugurated and, and RFK Jr said he was going be appointed to run a vaccine commission by the Trump administration. And, and I actually was sitting, you know, in my office and my assistant said Dr. Francis Collins and Dr. Anthony Fauci are on the phone. Do you have time to talk with us ? And I said, yeah, I think so. And they arranged, they had arranged for me to, because I have a daughter with autism could articulate why vaccines don't cause out arranged for me to speak with RFK Jr threw it through a mediator and, and, and it didn't go well. He was just really dug in and, and so
Eric Topol (13:49):
He, he was just as bad then as now.
Peter Hotez (13:52):
Yeah. I mean, it was just, you know, kept on, you know, as I say, moving the goalposts, you couldn't pin him down. Was he talking about MMR? Was he talking about the am Marisol, was he talking about spacing vaccines too close together? He just, that always kept on moving around and, and then it was not even autism at times. You were talking about it was something called chronic illness, you know, you know, what do you do with that? Mm-hmm. . So I, and that's one when I was challenged by, you know, Joe Rogan and Elon to debate RFK Jr, one of the reasons I didn't want to do it, because I, I knew, you know, doing it in public would be no different from doing this in, in, in private, that it would not be a productive conversation.
Eric Topol (14:39):
Yeah, no, that I can, I do want to get into that, because that was the latest chapter of kind of vicious anti-science, which was taking on covid and vaccines and the whole ball of wax whereby you were challenged by Joe Rogan on his very big podcast, which apparently is, you know, bigger than CNN various cable news networks,
Peter Hotez (15:07):
Which I had done, I had been on his show a couple of times. Yeah. And that was, and that was okay. I mean, I actually liked the experience quite a bit. And
Eric Topol (15:15):
And he challenged you to go on with RFK Jr. And then Elon Musk, you know, joined and, you know, basically
Peter Hotez (15:21):
Actually, he started before then, about the week before, or a few days before, Steve Bannon publicly declared me a criminal. And you know, which I said, wow, that's, that's something. And then Roger Stone weighed in. So it was this whole sort of frontal attack from, well, people with extremist viewpoints. And there's
Eric Topol (15:41):
Been a long history, and a Tucker Carlson in the book, you quote, he referring to Hotezis a misinformation machine constantly spewing insanity. Speaking of projecting things, my goodness. Yeah.
Peter Hotez (15:54):
Yeah. Well, he did that. You know, he, that was the, that was in 2022. It was, he went on his broadcast the evening after the evening of the, in the, during that day I, with Maria, I was, we were nominated for the Nobel Peace Prize. And I guess, and I don't know if the two are related or not, I think it may have driven him off the edge, and then he just went on this rant against me. And, you know, claimed I have no experience anything about Covid. I mean, we had made two covid vaccines, right. And transferred the technology nominated for the Nobel Peace Prize and just, you know, omitted all of that. But this is how these guys work. It's, it's all about asserting control. And, and it seems to come from an extremist element of the, of the far right.
(16:39):
and, and, and it's not that I'm a very political person at all. I mean, you know, I've been here in Texas now for 12 years, and I've gotten, you know, I've gotten to know people like Jim Bakker and his wife Susan Baker and, and you know, a lot of prominent Republicans here in Texas, that that wasn't an issue. This is something sort of weird and, and twisted. And, and the point that I make in the book is, and it's not just a theoretical concern or a construct, it's the fact that so many Americans lost their lives during the delta and BA.1 omicron waves in 2021 and 2022, after vaccines were widely and freely available because they refused a vaccine. so vaccines were rolled out in 2021. we started strong and then vaccination rates stalled. And then we didn't get very far by this after the spring because there was this launch of an, of, of a wave of what I call anti-vaccine or anti-science aggression, convinced that deliberately sought to convince Americans not to take a covid vaccine.
Eric Topol (17:56):
Chapter, yeah. Your chapter in the book Red Covid. Yeah, gets into it quantifies it, hundreds of thousands of lives lost. And I know you've seen some of the papers whereby studies in red states or states like Ohio and Florida showing the, the, the connection between this.
Peter Hotez (18:15):
Yeah, I, I relied heavily on this guy Charles Gaba, who has a, a website called ACA signups. And he did some really in, you know, strong analysis showing that the, that the people who were refusing covid vaccines and losing their lives were overwhelmingly in red states and could even show the redder the county as measured by voters, the lower the immunization rate and higher the death rates. And the term Red Covid came from David Leonhart of the New York Times wrote an article about Charles Gaba's work, and he called it Red Covid and did a lot of updates. And the data is so strong. I mean, so much so that one person at the Kaiser Family Foundation wrote, if you wanted to ask me whether or not a person was vaccinated, and I can only know one thing about them, you know, she said, the one thing I'd want to know is what political party they're affiliated with.
(19:09):
It was, it's, it's that strong. And it's, and it's not that I care about your politics, even your extreme views, but somehow we have to uncouple this one from it, right. Because somehow not getting vaccinated been added to the canon of stuff that you're supposed to believe in. If you are, if you're down that rabbit hole watching Fox News every night, or, or listening to Rogan Podcasts and that sort of stuff. And somehow we have to uncouple those two, and it's the hardest thing I've ever had to do. First of all, it's unpleasant to talk about, because all of, you know, your training, Eric mine as well is, you know, said you don't talk about politics and you're, you know, we're supposed to be above all that. But what do you do when the death and dying is so strong on, on one side?
(19:58):
And, and I, I was in east Texas not too long ago, giving grand rounds at a new medical school in East Texas and Tyler, Texas, and very conservative part of the state. And, you know, basically everyone you talked to has lost a loved one mm-hmm. because they refused a Covid vaccine and died. I mean, that's, that's where you really start to see that. And then, and these people are wonderful people. I gave you know Bob Harrington at oh yes, at at Stanford Medicine, now he's going be the Dean of Cornell. He, he invited me with Michelle Berry to, to give grand rounds, medical grand rounds at Stanford. And I said, look, if, if my car had broken down and the flat had a flat tire, and you, and I can't fix, I'm, I'm a disaster at fixing anything.
(20:49):
So if you said, okay, where you had the choice, where, where do you want your car broken down in Palo Alto, California, or Stanford is, or very wealthy enclave or East Texas, I'd say I'd pick East Texas in a second. 'cause in East Texas, they'd be fighting over who you know, is going to rush to help you change your tire. Right? And these are, you know, just incredible people. And they were victims. They were victims of this far right. Attacks from, from Fox News. And one of the things I do in the book is, you know, the documentation is really strong media matters. The Watchdog group has looked at the evening broadcast of Tucker Carlson, Laura Ingram, and, and Hannity, and, you know, can I, you know, actually identify the anti-vaccine content with each broadcast during the summer and fall. And then our a social science research group out of ETH Zurich, the Federal University of Technology of Zurich, where Einstein studied, actually, you know, one of the great universities did another analysis and showed that watching Fox News is one of the great predictors of refusing a vaccine.
(21:52):
And, and so that, those were the amplifiers, but those generating a lot of the messages were elected leaders coming out of the House Freedom Caucus, or Senator, you know, Johnson's conservative senate that, I don't even like to use the word conservative, because it's not really that they're conservative, they're extremists. And yeah, a Senator Johnson of Wisconsin, or Rand Paul, you know, of, of Kentucky, you know, all the physician know what Yeah. And know physician and the CPAC conference of conservatives in Dallas, in 2021, they said, first you're gonna, they're going to vaccinate you, and then they're going to take away your guns and your Bibles. And as ridiculous as that sounds to us, people in my state of Texas and elsewhere in the South accepted it and didn't take a covid vaccine and pay for it with their lives. And, and how do we, you know, begin walking that back?
(22:45):
And, and the point of writing the book said, well, the first step is to at least describe it so people can know what we're talking about. Because I think right now, when you look at the way people talk about anti-vaccine or anti-science stuff, they, they call it misinformation or the infodemic, like it's just some random junk that appears out of nowhere on the internet. And it's not any of those things. It's, it's organized, it's well financed. It's politically motivated, and it's killing Americans on, on a massive scale. So I said, look, you know, I, I went, I'm did my MD and PhD in New York at Rockefeller and Cornell. I devoted my life to becoming a vaccine scientist. You know, the motto of Rockefeller universities to be the Rockefeller Institute of Medical Research translates to science for the benefit of humanity. And, and I believe making vaccines is one of the high expressions. And I think most physician scientists believe, I think you believe that too. And that's why you're, you're in this as well, you know, not vaccines, but you know, other lifesaving interventions. And, and so I said, well, now making vaccines is not enough. 'cause now we have to counter all of this anti-vaccine stuff, and there's, there's nobody better, you know, in terms of my training and my background going up against anti-vaccine movements because of Rachel to do this. So I, I've done it and yeah.
Eric Topol (24:11):
Well, you've done it. All right. you,
Peter Hotez (24:14):
That's my wife. Ann says you've done it. Alright, .
Eric Topol (24:17):
Well, as I wrote in your, with your book of blurb about you are a new species, the physician scientist warrior, and you are Peter, because you're the only one of all the physicians. We're talking about a million docs almost in this country who has stood up and you've put your life at risk, your family at risk, you've had death threats, you've had the people you know, come right to your house. and so what you've described this kind of coalescence of political will of extremists, media, of course, amplification because it benefits them. They, they're selling more you know, they get more viewers, more the spots for commercials and more they can charge. And then you're even, as you described in the book, so well, is you even have outside interested parties like Russia as part of this organization, of this coalescence of forces that are taking on the truth, that are promoting anti-science, that are winding up, people are dying, or, yeah. Or having a, you know, serious morbidity,
Peter Hotez (25:26):
Right? Yeah. In the case of, in the case of Russia, , it's a slightly different motivation. What they're doing is they're filling the internet and social media with both anti-vaccine messages and pro-vaccine messages. Because they have a different agenda. Their agenda is destabilized democracies. So what they're doing is they're cherry picking certain issues that they can use as a wedge to sow discord. And so when they saw the stuff about vaccines, yeah, they'll flood it with both pro and anti-vaccine message. And you see the stuff on Twitter, so much of it is computer generated, and it's just repeats the same stuff over and over again. And, and a lot of that are, you know, some of that not only, only Russia, I think China's doing it, North Korea, Iran's doing it, but particularly Russia. And that was documented by a colleague of mine, David Broniatowski who's a computer scientist at George Washington University, has really done a deep dive in that. So so's
Eric Topol (26:22):
I think a lot of people are not aware that's what your book, book brings to light of how organized, how financed, you know, how this thing is a machine from coming from many different domains, you know, and for different interests as you, as you just summarized, it's, it's actually scary. And besides you standing up and facing, you know, the really ultimate bravery with the, all of the, these factions attacking you, literally ad hominem, you know, personally attacking you, then you have you know, this continues to get legs throughout the pandemic, and there's no counter as you've, as you've touched on what is going to be done. You can't stand up alone on this.
Peter Hotez (27:09):
Well, there's, there's a couple of things. First of all, it's not only attacking the science, it's attacking the scientists. Right, right,
Eric Topol (27:15):
Right.
Peter Hotez (27:16):
Exactly. It's, it's portraying and you get get it too, as well. I mean, it's basically portraying scientists as enemies of the state. which I think is so dangerous. I mean, as I like to say, you know, this is a nation that's built on science and technology, right? The, you know, the strengths of our research universities and institutions like Scripps, like Baylor, like Rockefeller, like MIT and Stanford, and University of Michigan and University of Chicago. This is what, you know, helped us defeat fascism in World War II as evidenced by the Oppenheimer movie, right. Or, and or allowed us to achieve so many things, why people so admire our nation. When I served as US Science Envoy and the Obama administration, the State Department, and the White House. I mean, that's where people loved our country, is they all wanna study at our research universities, or they want their kids to study at our research universities.
(28:10):
And, and by attacking not only science, but the scientists, I think it's weakening our stature globally. And, and, and, and I think that's, that, that's another aspect. I think the other problem is we, we don't get the backing that I think we should from the scientific societies in the Times, even the National Academies. I think they, they could be out there more. exactly why, you know, I think part of it is they see, they see how I get beat up and they say, well, what's that? Right? Yeah. And I, and I understand that, but I think also, you know, they, they depend on, oftentimes on government funding. And I think they're worried that, you know, if they're, again, it's this idea that you have to be politically neutral, even if it favors the torment or the aggressor to paraphrase Desmond Tutu, that's part of it as well.
(29:09):
I mean, it, I mean, I do find it meaningful. It's scary at times, and I, but I do find it meaningful to ha to have this role. But getting, getting more help and backing, I mean, we're our, our university, I mean, Baylor College of Medicine, Texas Children's Hospital has been pretty good. You know, Stan, you know, having my back, it's not that way at every, and I know Scripps has been really strong with what Kristian Anderson's had to deal with around you know, all the phony bologna around covid origins. But, but not all academic health centers are that way. And, and I think we need our university presidents to be more vocal on this issue. And, and too often they're not as well as our academies and our, our scientific societies, because this is, I believe, going to do irreparable harm to, to science. Well, yeah.
Eric Topol (30:04):
You know, in my experience too, we, we've actually seen, you know, academic physicians who have basically, you know, supported conspiracy theories who have detracted from evidence and science, you knowin a major way. Some of the leading universities here as you, as you mentioned. And when I've contacted and others, their leadership, they say, well, freedom of speech, freedom of speech. 'cause they're afraid to confront them because, you know, all the different things. We've, we, you've mentioned social media, but no, the universities don't want to get attacked on social media. They're afraid of that. They're afraid of, of calling out, you know, one of the people, faculty members who are deliberately, you know garnering a lot of, yeah. And,
Peter Hotez (30:56):
And the point is, is it's not just, you know, freedom of speech in the sense of espousing you know, crazy views. It's the fact that they're going on the attack against mm-hmm. . I mean, I don't attack these guys, but they attacked me with, with impunity and Yes. Say terrible thing, untrue things about me. I mean, where's there's, isn't there something called professionalism or, or ethics, yeah. Right. That don't, don't, don't, don't we, aren't we supposed to be in instilling that in our, in our faculty and, and that that doesn't seem to happen.
Eric Topol (31:28):
So that's
Peter Hotez (31:28):
Troubling as
Eric Topol (31:29):
Well. They're, they're making credible scientists who are doing the best they can into pinatas Right. And attacking them. And with, and it can't, it can't be reciprocated because that's, that's beneath professionalism. I mean, just as you say. So, you know, you just keep, they just keep going at it. So what you have is now we've added all these different entities and all add more. One more is ai, which is going to further blur the truth.
Peter Hotez (31:59):
Yeah, Renee DiResta at the Stanford Internet Observatory, I don’t if you know Renee, she does fabulous work. And she's written about, you know, what happens when, you know, all of the anti-science, anti-vaccine stuff is now imbued with ai, and, you know, it's going become even more sophisticated and more difficult
Eric Topol (32:17):
To No, there's, there's gonna be a video of you saying that, you know, these vaccines are killing people but don't get a booster and it'll be just like you with your voice. Yeah.
Peter Hotez (32:28):
Well, they already, they already have. Now these, there's these few things on YouTube that, that claim, I'm secretly Jack Black, the actor . And that the CIA has arranged it so that Jack Black plays this fictional character named Dr. Peter Hotez. And they do all these things like, you know, focus in on my eyes and do like eye identification. It's just, it's just nuts. I mean, what, what's out there?
Eric Topol (32:54):
Well, has there been a time in these months where you were very scared you, you're for yourself or your family because of all the incredible density and, and what appears to be very serious threats and during
Peter Hotez (33:08):
, during, during the day, during the day, I'm okay. I mean, in, you know, when the, when the, when the Steve Bannon in stuff and Joe Rogan stuff, then I had the stalking at the house, and, you know, I had to have a Houston Police Department officer parked in front of my house or a Harris County Sheriff that, that was troublesome. But it, it's more of during the day, I am fine. I'm working, I'm talking, you know, to people like you and in lab meetings, doing what scientists do, writing grants and throwing pencils at the wall when you get a paper with a major review or, or a major revision or rejection. But, but it's, I think at night, you know, wake up in the middle of the night and the, it's, the stuff does start to mess with your head at times. And it's
Eric Topol (33:54):
Well, and you travel a lot and you, you've, I think expressed that, hey, you could be given a talk in an innocent place and somebody could come, you know, attack you
Peter Hotez (34:04):
There. Yeah. So I have to, I have, I have security now at, in major venues when I speak. and, you know, I had an, there was an incident at the World Vaccine Congress in Washington. There were protesters out in front of the, out in front of the convention center waiting for me that that wasn't fun. And so, even, you know, we've got, we'll see what happens with the, when the, you know, I'm doing a number of events around the book in Washington DC and New York and elsewhere. We'll, we'll see how that goes. so
Eric Topol (34:38):
Well take it. You, you're, I know you well enough to know that you're an optimistic person. I mean, you've been smiling and we've been laughing during this and discussing some very heavy, serious stuff. What gives you still optimism that this can someday get on track?
Peter Hotez (34:57):
Well, I think it could get worse before it gets better, first of all. And, and two fronts. One, you know, I had the opportunity to meet with Dr. Tedros, the World Health Organization Director, general of World Health Organization towards the end of last year. And to say this could be the warmup act in the sense that now it's globalizing. I'm anticipating spillover all childhood immunization rates. And, you know, you're starting to see the same US style of anti-vaccine rhetoric now, you know, even in low and middle income countries on the African continent in South Asia. So I worry about, you know, measles and polio, both in the US and, and globally. I think that's, that's, I'm worried about that. The other is, you know, a lot of this is heating up, I think because of the 2024 presidential election. I think one was that with, with our, our mutual friend and colleague Anthony Fauci, now that he's out of government he's not as visible as he was.
(35:58):
I think they're, the, the extremists are looking around for another, they need a monster right. To, to galvanize the base. And I think I've become that monster. You know, that's, that's one thing I'm worried about. But also you with, I talk to probably someone you've seen on Twitter. and I've gotten to know her somewhat, I'm very impressed with her. Molly Chong Fast, who's a commentator on c n at M S N B C, and she, you know, put out there, and she told me privately and put it out in public that, you know, one of the reasons why things are so vicious around RFK Jr, as they see him as a third party candidate that could take Biden votes away and help create a path for Trump being elected. So by, you know, by having me debate him, it, it kind of elevated in, in its own way, elevated his stature and made him seem like a more serious person. Right, right. And my refusal, you know, popped their bubble. And that, that's one of the reasons why, why they're so angry. So this is very much tied, I think, to the 2024 presidential look. And that's what you're having seen with the House subcommittee hearings too, portraying scientists as enemies of the state. It's all for, I mean, I don't know if you've seen this, the, that House Subcommittee Twitter site, it actually says something like, we're selling popcorn, you know, we're
Eric Topol (37:18):
Yeah, I know. I mean,
Peter Hotez (37:20):
They're, they're not, they're not even pretending it's anything, the
Eric Topol (37:23):
Political
Peter Hotez (37:23):
Theater for Fox News soundbites. So I think we're gonna see they're the word.
Eric Topol (37:27):
Alright. Yeah.
Peter Hotez (37:28):
Yeah. And, and, but, you know, but the attacks on biomedical science, I think are gonna be, you know, have a long-term effect. If for no other reason, I think people are gonna think twice about wanting to do a PhD in biomedical scientist or become an MD PhD scientist when they see that, you know, we're
Eric Topol (37:47):
. Well, that's what you, you also covered that really well in the Yeah. In the book. But when you think about where we are now with climate crisis, or we're facing future pandemics, not just the one we're still working through here where is the hope that we can counter this? I mean, we need armies of people like you. We need, as you say, the scientific establishment and community all stand up. That, that gets me to one of the things that makes you differentiates you from most physicians and scientists. You write books, you are active on social media. You, you appear on the media. Most scientists grew up to have their head do the work, do good science, get their stuff published, and get grants and, you know, try to advance the field and physicians doing that, are taking care of patients, same kind of thing. What prompted you in your career to say, Hey, you know, that's not enough. I got another dimension. And why, how can we get millions of clinicians and scientists to rally to do what you're
Peter Hotez (39:01):
Doing? Well, in my, in my case, I, it's not that I was deliberately seeking to be a public figure or what some call a public intellectual. It was more the case, the issues that I was most interested in, nobody was talking about. Mm. And nobody was going to talk about it. So if I didn't talk about it, it wasn't gonna be talked about. So neglected tropical diseases, you know? Yeah. For guard people was, and, and I had two colleagues in the uk, Alan Fannick and David Mullen, who felt the same way. And so we began be, we became the three Musketeers of the neglected tropical disease space. And I found that extremely meaningful and interesting. And it was the same with vaccines. So although I, I'm often in the, you know, doing a lot of public engagement, if you notice, I don't try to be like some people who do it very well, like as Sanjay Gupta or, or some others that will, or Megan Rainey that will talk about, you know, just about any health issue.
(39:56):
I, I don't try to do that. I sort of stay, it's a wide lane, but I try to stay in my lane around infectious, neglected diseases and, and, and vaccines. And I think that's very important. Now, in terms of, you know, the statement, most scientists or physician scientists wanna keep their head done, write their grants and paper. I think that's perfectly fine. I don't think you people should be forced to do it, but I think there's enough of us out there that wanna do it, but don't know how to get started and don't feel safe doing it. I, and so I think we need to change that culture. Mm-hmm. I think we need to offer science communication to our graduate students in their PhD programs or in MD PhD programs for those who wanna do it, or in residency training or fellowship training. And so that, because there, there are things you can learn.
(40:46):
I mean, we had to do it by trial and error, and in my case, more error than trial. But, but, but there is a, there is, there are things you can learn from people who do this professionally. So I think that's important. I think the other is we need to change the culture of the institutions. You know, I, I get evaluated just like you do like everybody, like any, you know, senior scientist or professor at university, and, you know, what do they ask me about? They ask me about my grants and, and my papers preferably in high impact journals, and they ask me, and I don't see patients anymore, so they don't ask me about my clinical revenue, but they ask me about my grants and papers and my grants and papers, and my grants and papers. There's not even any place on my form, my annual evaluation from, to put in the single author books. I've written much less, you know? Yeah. The, the opinion pieces I've written, or certainly not social media or even, or even the cable news channel. So, so it basically, the academic health center is sending the message. And I don't think that's unique. I think that's probably the rule in most places. I think the, the culture of academic health centers is they're basically, they're sending a message just saying, well, we don't consider that stuff important, and somehow we have to make it important. I think for those who wanna do it
Eric Topol (42:08):
Absolutely
Peter Hotez (42:09):
To send that message,
Eric Topol (42:10):
You're, you're, you're pointing out a critical step that has to be undertaken in the future. it'll take time to get that to gel, hopefully, but if it's promoted actively, I certainly promote that. I know you do. Yeah. I think,
Peter Hotez (42:23):
I think most, most offices of communications at academic health centers, as I said, Baylor and Texas Children's is pretty good, better than most, but most, you know, don't even like their docs and scientists speaking out. Yeah. Right. They wanna control the message. It's all about, you know, they're very risk averse. They're protecting the reputation of the institution. They only see the risk side. They don't, you know, you know, you wanna speak about social justice or, or combating anti-science. Well, you know, we guess we can't stop you, but they sort of cringe at, at the idea. And then, you know, they say, well, you know, ultimately you're a professor or a scientist here, you have academic freedom.com, but don't screw this up. Right. And don institution at risk. Right.
Eric Topol (43:07):
Ab you're describing exactly how university communications worked.
Peter Hotez (43:12):
Yeah. But
Eric Topol (43:13):
The
Peter Hotez (43:13):
Point is, and so you do it with the sort of Damocles over your head, and, and you know, as you know, and as anyone knows, if you do enough, you will screw it up eventually, right? Everybody does. And, and you know, you're gonna make mistakes. That's how you learn. You make mistakes and you, you auto correct. But, but you have to have that freedom to be able to make mistakes and Yeah. And right now that's not there either.
Eric Topol (43:35):
What, what you're driving at though altogether is that we're defenseless. That is, if you have an organized finance coordinated attack on science, and also of course on vaccines, and you have no defense, you have, I mean, it's hard for the government to stand up because they're part of what's the conspiracy theory is, is, is against, and you, and, and the scientific community, the clinician community is, you know, kind of handcuffed as you are getting at. And also, you know, that's not the culture that's unwilling, but something's gotta give. And this is one thing I think you're really reinforcing that, that should a pathway to countering. I mean, we can't clone you. You know, we can't, we need lots of warriors. We need, you know, thousands and hundreds of thousands of points of light who support data and evidence, you know, as best that they can. And we don't have that today.
Peter Hotez (44:36):
Yeah. And we, we need to cultivate that. So I'm in discussions not only with people like yourself, but other colleagues about should we try to create, whether it's a nonprofit of 5 0 1 C three or C four the climate scientists are ahead of the game on this. Yeah. Yeah. I, I talk to Michael Mann every now and then, and, you know, they've got a climate science defense fund. They, they seem to be, 'cause it, they've, they've experienced this for longer than we have. You know, the, this all started a decade before with tax against climate scientists, you know, should, in the book I talk about, should we create something like a Southern Poverty Law Center equivalent to, to protect science and scientists? And, and I think we need that because the existing institutions don't seem willing to, to create something like that. It's somehow seen as too edgy or too out there and Right.
(45:30):
And it shouldn't be. But, but again, this is a I think a, a great opportunity for college presidents to, to step up and, and they're not doing that. They're, they're also pretty risk averse. So I think, you know, getting, getting the heads of the academic health centers, getting the college president, university presidents to say, Hey, this is important because otherwise science is at risk. And, and you're already starting to see some crazy stuff come out of the N I h now about doing international research. They're trying to put in rules to say they want, you know, if you have international collaborators, you're supposed to collect their notebooks and translate the how are you gonna do that? That's, that's completely, IM it's important. I mean, it's, and who's gonna review it and who's gonna sign off in general legal counsel at the university on, that's basically gonna halt international research. And we have to recognize that we need this because the threats are coming. Right? I mean,
Eric Topol (46:33):
Cli
Peter Hotez (46:34):
Climate change is real, and pandemic threats are real. We're gonna see another major coronavirus pandemic possibly before 2030 or a flu or an arbovirus. And, and we're, we're, we need, this is a time we need to be reinforcing our, our virology research and our infectious disease research, not a time to, you know, start dismantling it, which is what totally the house hearings are, are meant to do, and what some of these new n i h rulings are meant to do. So it's gonna take a lot of strong players and, and, and government and at universities to stand up to this.
Eric Topol (47:14):
Well, if we ever need to be vaccinated or immunized, it's against this. And I hope that something will give to start to provide an antidote to what is a relentless progression of united science that you so elegantly eloquently in, in your book, Peter. So thanks for writing that. thanks for joining today. I know we'll have, as we do every week conversations yeah. You,
Peter Hotez (47:41):
You've been a, you've been an amazing friend and colleague, Eric, and I've learned so much from you. And, and
Eric Topol (47:46):
No, no. I, I feel I can't tell you thank you. I, I, I think it's completely reciprocal from what you bring to this table of trying to make this a better place for advancing science search for, for the truth of what's really going on out there, rather than having to deal with wacky, you know, extremists that are advancing things for various purposes that are, that are nefarious in many cases. So, appreciate it. we'll be talking some more and this has been a really for me, an enriching conversation.
Peter Hotez (48:21):
Same, same Eric. And thank you so much for giving this attention and the dialect to be continued.
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Few, if any, physician researchers have done more to understand the long-term impact of Covid than Dr. Ziyad Al-Aly, a professor, nephrologist, and epidemiologist along with his team at Washington University, St. Louis. Here is the transcript (with links to the audio) of our conversation that was recorded one 7 September 2023.
Eric Topol (00:00):
Welcome to Ground Truths, and this podcast is a special one for me. I get to meet professor Dr. Ziyad Ali for the first time, even though we've been communicating for years. So welcome, Ziyad.
Ziyad Al-Aly (00:15):
Well, thank you. Thank you. Thank you for having me. It's really a delight and pleasure and an honor to be with you here today. So thank you. Thank you for the invitation, and most importantly, thank you for all the stuff that you do and you've been doing over the past several years, communicating science to the whole world, especially during the pandemic and enormously grateful for all your effort.
Background in Lebanon, the move to Wash U., and Epidemiology
Eric Topol (00:33):
Well, you're too kind and we're going to get into your work, which is more than formidable. But before I do that, because you have been a leading light in the pandemic and understanding, especially through the large veterans affairs population, the largest healthcare system in the United States, the toll of covid. But before we touch on that a bit on your background first, you're a young guy. You haven't even hit 50 yet, my goodness. Right. And you grew up in Lebanon, as I understand it, and you were already coding when you were age 14, I think, right? Pretty wild. And then perhaps the death of your father at a young age of multiple myeloma had a significant impact on your choice to go into medicine. Is that right?
Ziyad Al-Aly (01:28):
Yeah, that's how it is. So I grew up in Lebanon, and when I was growing up, the computer revolution at that time was happening and all of a sudden in my surroundings, there's these people who have these Commodore 64. So I decided that I wanted one. I asked my parents to get me one. They got me one. I learned coding at that age, and my passion was I thought I wanted to do then why not to do computer science. And then my dad fell ill with multiple myeloma and it was an aggressive form and he required initially a lot of chemotherapy and then subsequently hospitalizations. I do remember vividly visiting him in the hospital and then connected with the profession of medicine. I was not on that track. I didn't really, that's not all my youth. I wanted to be a coder. I wanted to be a computer scientist. I wanted to do basically work with computers all my life. That's what my passion was. And then redirected all that energy to medicine.
Eric Topol (02:32):
Well, you sure did it well. And you graduated from one of the top medical schools, universities at American University of Beirut, and came to St. Louis where you basically have for now 24 years or so, went on to train in medicine and nephrology and became a leading light before the pandemic. You didn't know it yet, I guess, but you were training to be a pandemic researcher because you had already made the link back in 2016, as far as I know, between these protein pump inhibitors and kidney disease later, cardiovascular disease and upper GI cancers. Can you tell us, was that your first big finding in your work in epidemiology?
Ziyad Al-Aly (03:22):
Yeah, we started doing epi. I started doing epidemiology or clinical epi right after fellowship, trained with mentors and subsequently developed my own groups and my own funding. And initially our initial work was in pharmaco-epidemiology. We were very, very interested in figuring out how do we leverage this big data to try to understand the long-term side effects of medication, which was really not available in clinical trials. Most clinical trials for these things track them for maybe 30 days or at most for few months. And really long-term risk profile of these medications have not been characterized previously. So we did that using big data and then subsequently discovered the world of environmental epidemiology. We also did quite a bit of work and environmental linking air pollution to non-communicable disease. And in retrospect, reflecting on that now, I sort of feel there was training ground that was training wheel out, how to really optimize our thinking, asking the right question, the right question that matters to people addressing it rigorously using data and also communicating it the wider public. And that was my training, so to speak, before the pandemic. Yeah,
Eric Topol (04:37):
Yeah. Well, you really made some major, I just want to point out that even though I didn't know of your work before the pandemic, it was already momentous the link between air pollution and diabetes, the link of PPIs and these various untoward organ events, serious events. So now we go into the pandemic and what you had access to with the VA massive resource, you seize the opportunity with your colleagues. Had some of this prior work already been through that data resource?
Ziyad Al-Aly (05:18):
Yes, yes. Our work on PPI on adverse events of medications, including proton pump inhibitors, was all using VA data. And then our work using environmental epidemiology, linking air pollution to chronic disease was also using VA data. But we linked it with NASA data with sort of satellite data from NASA that capture PM 2.5. But NASA has these wonderful satellites that if a chemical is on earth and has a chemical signature that can actually see it from space and measure its concentration. So that data is actually all available free of charge. So what we did is I went to these massive databases at NASA and link them to our VA data, and then we're able to analyze the relationship between exposure to high levels of air pollution in the United States and then subsequent disease in veterans in our database.
Eric Topol (06:11):
That was ingenious to bring in the NASA satellite data. Big thinker. That's what you are. So now you are confronted with the covid exposure among what millions of veterans. Of course, you have controls and you have cases and you're now seeing data that says every system is being hit here and you write, you and your colleagues wrote papers on virtually every system, no less the entire long covid. What were the surprises that you encountered when you were looking at these data?
Initial Shock on Covid’s Non-Pulmonary Sequelae Identified
Ziyad Al-Aly (06:47):
I remember the initial shock and our first paper when we did our first paper and there was a systematic approach looking at all organ systems. We weren't expecting that because at that time we were thinking SARS-CoV-2 is a respiratory virus. We know respiratory virus may have some post-acute sequela and maybe cardiovascular systems, but we weren't really expecting to see hits in nearly every organ system. And remember when we first got the results from what then became our nature paper, our first paper in nature around this, I doubted this. I couldn't really believe that this is really true. I looked at the association with diabetes and I told Yen, my colleague here who's really absolutely, absolutely wonderful, told him, there must be a mistake here. You made an error. There's an error in a model for sure. This is not believable. That can't be like SARS-CoV-2 and diabetes.
(07:39):
This is impossible. There wasn't really an arrow in my brain that sort of linking SARS-CoV-2 diabetes. I doubted it. And we went back to the model, went back to the data, rebuilt the cohort, redid the whole experiment again with controls. The same thing happened again. I still was not believing it, and it was like, end, there is something wrong here. It's weird. It's strange. This is not how these things work. Again, from medical school, from all my education, we're not trained to think that viruses, especially respiratory viruses, have these myriad effects and all these organ systems. So I doubted it for the longest time, but the results came back exactly consistent every single time the controls work, our positive control work, our negative controls work. Eventually the data is the data, then we then submitted it for a review.
The Largest Healthcare System in the United States
Eric Topol (08:40):
Yeah. Well, I want to emphasize this because many have tried to dismiss their data because it's average age of 60 plus and it's men and it's European ancestry and for the most part, but everything you found, I mean everything you found has been backed up by many other replications. So for example, the diabetes, particularly the Type 2 diabetes, there's now 12 independent replications and a very similar magnitude of the effect, some even more than 40% increase. So we didn't need to have more in the diabetes epidemic than we already have in the world. But it looks like Covid has contributed to that. And what do you say to the critics that say, oh, well these are old white men are studying and does it really apply long and all this multi-system organ hits to other populations given that, for example, the prototypic long covid person affected might be a woman between age 30 and 39. What's your sense about that?
Ziyad Al-Aly (09:54):
The way I think about it is that our data are massive. And while the average age is 60, the data, because these are literally millions of people, some cohorts are 6 million. Some of the studies that we've done, 6 million people, so the average age could be 60, but there are literally hundreds of thousands in their twenties and thirties and forties, and they're all represented in the data. And the data is obviously also controlled for age and race and sex. And I tell people this thing that they say, oh, well, your data is only 10% women, and then this is why. But 10% out of 6 million people is 600,000 women. I told a friend the other day that 600,000 women could fill six Taylor Swift stadiums. So it isn't really small. And even if we were to only analyze people in their twenties and thirties, or we could do that, we could do that.
(10:44):
We could easily do 300 or 400,000 people study of people from age 20 to 40. In our experience, we get more or less the same results because again, the results are adjusted for age. And then the second component of my thinking about this, and as you pointed out, the gold standard and science is reproducibility. Does this really finding reproduce in other settings? Other people are also seeing it, are able to validate it and reproduce the finding. Or this really some peculiar thing about the VA is happening only in the VA world or the VA universe. That doesn't really happen outside. And then so far, not only the findings in the pandemic, all the findings prior to the p p use and chronic kidney disease, PPI use and other side effect, all the pollution work has been reproduced to the T by Michelle Bell by Francisca Doci at Harvard to the T.
(11:35):
All these pollution studies have been reproduced from using Medicare data using data that's outside the VA, other data sets. And also some European friends and European collaborators reproduce the same thing. So again, the gold standard in science reproducibility, but healthy skepticism is skepticism is also healthy because we always want to challenge the finding. Is this really true? Can we bank on it? And really the most important thing inside reproducibility really is to be able to take this finding or to take the question somewhere else and then be able to reproduce the evidence that is seen in any dataset.
The New 2-Year Follow-Up Study
Eric Topol (12:13):
Right. Well, so you have really laid out the foundation for our understanding of Long Covid. I agree with your point that there's plenty of people who are more in that prototypic age and gender. But by doing so, we have these kind of two paths. One is the symptoms of Long Covid where as you know, there's reported even a couple of hundred and some of course in clusters. And then there's these organ hits across neurologic, cardiovascular, kidney, and on and on. And you recently of course provided the two year data on that, which of course is important because as you know from your data, these are mostly, if not almost exclusively unvaccinated early in the pandemic. Could you comment about what your main findings were in two years and what you think would be the difference if this was a widely vaccinated population?
Ziyad Al-Aly (13:20):
Sure. In the two year studies, what we've really seen is that we, first of all, to introduce the readers or the listeners, there were two groups. We split them into two cohorts, non hospitalized and hospitalized people with covid 19 compared to controls. Now in the non hospitalized group, in both groups we assessed about 80 sequela of SARS COV to two. We've seen about 30% the risk for 30% of the SQL remain elevated at two years in the non hospitalized group, those are the people who really had mild disease that did not necessarily hospitalization yet even at two years, they remained at higher risk of about 30% of the sequela that we evaluated in that study. The risk profile for the people who were hospitalized was much more complicated or much more or less optimistic in the sense that they were about 65% of the sequela also registered at a higher risk in the covid group versus the control group.
(14:25):
So now it's very, very important for people to really know that this is really because we needed to do a two year study, we couldn't really enroll somebody in the study who had covid six months ago. They don't have a two year follow up. So this is a two year study. By necessity, we had to enroll people from the very first year of the pandemic, which meant that most of the people there or nearly all actually were the pre delta era, the ancestral strain or pre delta era and were non-vaccinated. So to the core of the question, how does this risk profile change with time? And my hunch is that a lot of things have changed. Obviously now we have vaccination, we have population level immunity. The virus itself has changed. We have antivirals, Paxlovid and others, but mainly Paxlovid and all of those are known to ameliorate the risk of not only acute disease but also chronic disease or the risk of Long Covid to various degrees.
(15:24):
But there's certainly we see in our work and other people's work, there is evidence of risk reduction in the risk of long-term sequelae or long-term consequences of SARS-CoV-2 infection. So that leads me to believe that the risk now or would be lower, but that's really a hypothesis. I don't have data to back this up. You asked me for data today for three year, I don't have it yet. We're thinking about it a lot. We're trying to work on it. I don't have it yet, but the hunch is that this is really, it's, it's lower now than a way it was.
Clarifying the Role of Reinfection and Long Covid
Eric Topol (16:06):
Right, right. No, that'll be really interesting to see. And I certainly agree with you as other studies, obviously none as large as what your data resources with the Veterans Affairs have suggested that the vaccines and boosters are providing some protection. Paxlovid, Metformin in a randomized trial, as you well know now, one of the papers of the many in top tier journals that you published was about reinfection. And this led to some confusion out there, which I hope that you'll be able to straighten out. I saw it as a dose response whereby if you have multiple re infections, the chance of you developing multiple of a long covid syndrome would be increased to some degree. Can you clarify that interpretation?
Ziyad Al-Aly (16:57):
This is exactly right. So a lot of people sort of interpreted it as we're trying to evaluate the risk of second infection versus the first, or whether the second infection is more mild or more severe than the first. That's not really the study question. So what we did, we sort of said that now we know a lot of people had a first infection that's already happened to these people. They cannot go back and erase it or do anything about it. They already had a first infection. What's the most important question for somebody who had a prior infection going forward? Does it matter to me or is it helpful to me to protect myself from the second infection? Right. So we designed the study and arguably designed a little bit was confusing to some people in the media. We designed the study to evaluate the risk of reinfection versus a counterfactual of no reinfection.
(17:50):
So basically if you have two people who have equal characteristics at baseline, everything equal, they had a first infection, one protected himself or herself from getting a second infection and the other one did not and then got a second infection. What are the outcomes in the person who did not get a second infection versus a person who got a second infection? And the results are very, very clear that a second infection or reinfection is consequential. It adds or contributes additional risks both in the acute phase, it can put even reinfection can put people in the hospital, can also result in some death that's very, very clear in our data and is very clear in other data as well and can also contribute risk of long. So I think the best interpretation for this is that for people to think that two infections are worse than one and three are worse than two, so two infections are worse than one and three are worse than two.
(18:46):
But we've learned a lot from this paper because I definitely agree and I've seen a lot of not the right interpretation for it. We discovered that America does not like counterfactual thinking. It's really hard to explain counterfactual thinking, but that's really what we thought about as the most important question to answer. It isn't really whether a second infection is really milder or more severe, and at first is more like if you were to do something about it, does it really help you to prevent yourself from getting a second infection or a third infection? For us to design the study to answer this specific question, we compared reinfection to no reinfection and we thought we wrote it very clearly still some headlines where, oh, these are comparing a second infection to a first infection, which that's was not our intent. We didn't really design this set this way.
(19:42):
As a matter of fact, we had a little bit of a hunch that it might be misinterpreted this way at the very, very last minute. In the copy editing stage, I inserted a sentence in the discussion that our results and our work should not be interpreted as a comparison of a second infection versus first, I hope the editor is not listening. I inserted this at the last minute in the copy editing stage in the limitation section to help people understand that this is not an evaluation or a competitive evaluation of the risks of the second infection versus the first, but more a second infection versus no second infection.
Getting Covid
Eric Topol (20:21):
Right, right. No, I'm so glad you clarified that because I think it's an important result and it has indeed. Everything else you've done been replicated. So now I want to ask you have, are you in Novid? Have you ever had Covid?
Ziyad Al-Aly (20:38):
Oh, I did have. I tried to reduce my risk and I did everything I'm supposed to do except that this June, about two months ago, I traveled and I got it while traveling. I think, I guess I was doing all the precautions that I could and I got it. I ended up having, although I'm young and I don't mind sharing, I got Pax Ovid because I got back slowly and I got over it. But that was my first, and it was only two months ago and I did my best throughout this pandemic to prevent it. But then travel is tricky because you are exposed a lot of people on the plane and it's tricky at the airport is very busy, crowded and it's very tricky. No,
Eric Topol (21:27):
Especially because people are not taking precautions anymore. And so you go to these crowded places with poor ventilation and very few people wear masks, and we still have all these people who are anti mask and that isn't helping either. So the next thing I was going to ask you about was you've done this remarkable work, a series of papers that have led the pandemic and in fact, you really have the only pulse on the United States data because outside of what you have in terms of all these electronic health records and longitudinal follow-up, we don't have any health system that has this capability. So we have relied on you and your team to give us these really critical readouts. What are you going to do next?
Ziyad Al-Aly (22:20):
We're very committed to understanding Long Covid. So we feel there is a lot of knowledge gaps that still need to be unpacked and understood, and we really, I feel committed to it. So we came to long covid because we sort of felt the voice of the patient advocacy groups at the very early phase of the pandemic saying at that time they were not so organized, but they were saying an up at pieces that we're having a problem here and somebody needs to look at it and somebody needs to evaluate it. We immersed ourselves in long covid, really inspired by the patient advocacy groups initially, and we feel connected to this. So that's, we're definitely committed to deepen our understanding of long covid. But having said that, I sort of feel that I do hope that our work inspire others that there is a lot of value in data, there are limitations. Existing data or big data is not without limitations. There are limitations in the data, but it can also unlock a lot of insights, especially in crises like the one we just experienced, the pandemic.
Missed RECOVER Opportunities and Testing Treatments for Long Covid
Eric Topol (23:28):
I think you have some extraordinary opportunities. So for example, when you found what previously was not appreciated for the data resource of the Veterans Affairs, the relationship between a medication protein pump inhibitors and kidney and cardiovascular diseases, I wonder for example, because so many people take metformin, would Metformin show protection from long covid within the Veterans' Affairs database as an example? Of course, maybe there are even some medications that are commonly used that offer a protective effect. I mean, you might be able to look at something like that because the data you have to work with in so many ways is massive and unprecedented.
Ziyad Al-Aly (24:14):
Well, yeah, I mean the scale of a data is really amazing. So it is really the largest integrated healthcare system in the US and it's really fully integrated. There's lab data, medication data, socio demographics, everything benefits data. Literally everything is in one place and there is opportunity to try to evaluate therapeutics effective metformin, other anti hypoglycemics, maybe GLP ones. And so there's a lot of these hypotheses that they, because the virus might reside in fat cells, there is this hypothesis that we just recently reviewed in a beautiful review in nature immunology, unlike the viral resistance hypothesis, so as a potential mechanistic pathway for long covid. So there are a lot of hypotheses around metformin and GLP one, and I think the VA environment or data environment is certainly good to test those, at least to help inform trials in this space. Now, there is already a trial on metformin, so that's done by David, but looking at it from another angle in the VA data would also, I think would add insight and would further contribute to the national conversation.
Eric Topol (25:30):
Right. I mean I think the Canadians, McMaster are starting a very large trial, Metformin with 5,000 participants. But I wonder if there were these drugs that are linked to mTOR and mitochondrial function enhancement, which as you said, not only was there an excellent review on the persistence of the virus in reservoirs, but also one that you know of well, bringing in the potential of mitochondrial dysfunction as a unifying theme. Now as we go forward, obviously the covid problem is not going away. We have this circulating virus in one form or another, one version, one strain or another over the years ahead. And we only know of one way to avoid long covid for sure, which is not getting covid in the first place. And at least we have some things that would help if you have Covid, like what you've already reviewed with Paxlovid. But the question is there's no treatment out there. And you have been of course helping as an advisor to the White House and WHO and the patient led collaborative. And the frustration out there is high because the big recover at NIH had over $1 billion and they have done really almost nothing in clinical trials. Imagine if you had 1 billion to work with. Can you comment about the fact that here we are, we're in September of 2023 and we don't even have one good clinical trial of a potential therapeutic.
Ziyad Al-Aly (27:09):
So this is enormously frustrating to me as well. It should
Ziyad Al-Aly (27:15):
Yes, yes, yes. So no, we are definitely on the same page. So this is enormously frustrating to that. And three years into the pandemic, we still have, and I do remember when I see the white box that you put on your tweet and I think was recently illustrated in Fortune Magazine. There's three years into it. This is a full list of therapeutics for long covid and it's literally zero, nothing there. So it's very, very, very disappointing. And I do think that we want recover to succeed. That's really very, very important. We want recover to succeed. The patient community want also recover to succeed. And I think this really hopefully an invitation, all this what I think is a constructive criticism of recover, hopefully the recover folks will take it to heart and will sort rethink the approach and rethink the allocation of funds. In particular.
(28:08):
What really bothers me the most, and I've told them about this, I mean, as you know, I talked to multiple people in HHS and White House and all that. What really bothers me the most is that a lot of the money had been actually allocated to the observational arm to recover. And my argument to them is that actually we can produce the same. We actually not can we have produced all that evidence for peanuts two years ago. We need a study in JAMA to tell us that while long covid is characterized by fatigue and brain fog, I know that already, I already did that two years ago, an observational study. Well, we need interventional studies. What we need, most of the money should really be allocated to interventions, not really observational arm. And it's not too late to correct course. It's absolutely not too late to correct course. Well,
Eric Topol (28:56):
You're kind, but I'm afraid they've run out of money. And so I don't know they're going to get any more to do the trials, which are as very expensive to run. So it's not too late to do the trials, but unfortunately it's very hard to get the funds to support them. I think
Ziyad Al-Aly (29:14):
There may be mechanisms for them to reallocate things, but also very importantly that we cannot, even if they reallocate this $1 billion to long covid, I think we need a longer term program and COVID should have a support that it should be. We argued that loco, which should have its portfolio at NIH, maybe not an institute and have a line-item funding so year there will be funds for long covid. Now we're told past F Y 25, fiscal year 25, there won't be additional funds for long covid. And that's really not how we should treat really the long-term consequences of SARS-CoV-2. And why is that the case? Why we ask why that's really will not only pay dividends to help us understand what long covid is and how to best treat it. It also can shed light into the other basket of infection associated chronic illnesses that I argue that we have ignored for a hundred years.
(30:12):
Again, COVID or SARS-CoV-2 is unique and it's not is unique because now we're in a pandemic and the scale of it is really big and all of that. But if you really think about it, there's actually a lot of viruses that have produced a lot of long-term effects that we've ignored their long-term consequences for a long time from the research perspective and also from clinical care. And that needs to be researched. So research on long covid or understanding along covid will help us with long covid, help us better understand the infection associated chronic illnesses. And three, also help us with pandemic preparedness. There is almost like a universal agreement that with climate change, with human encroaching on animal habitat, with human traveling so much more in the 2020 first century than in the 20th century, that the frequency of pandemics in the 21st century is likely to be higher than the frequency of pandemic in the 20th century.
(31:06):
So we're going to experience more pandemics in this century. We have to be prepared for them. This pandemic is not the first and unfortunately, unfortunately, it's not going to be the last. There's going to be another one in five years. In 10 years, in 20 years, we don't know. We cannot really predict these things, but it's almost certainly there're going to be one or more than one downstream and we have to be prepared for it. So I think we should not be shortsighted. I also argue that we already paid the price, the hefty price in this pandemic, more than 1.1 million deaths. We already paid the hefty price. We already paid a very, very dear price in this pandemic. Let's learn from it. Let's learn as much as possible from this pandemic. Let's learn to be able to help us for the next one.
Post-Viral Syndromes Multiple Years Out
Eric Topol (31:47):
Now having said, I want to underscore a point you made, which is it's not just this virus of SARS-CoV-2, the Myalgic Encephalomyelitis (ME/CFS) and many other viruses have led to a post-viral syndrome, which can be very debilitating. So yes, we can anticipate that not only do we have a burden that goes well beyond covid, but we may see this sort of thing of lasting debilitating impact of future pathogens. But to that mind, I want to ask you, because when I studied on the influenza 1918 and the polio epidemics, what I saw was that we saw many years later new things that had not been seen at two years or three years. So as you know, after influenza, Parkinson's showed up 15 years later and after polio, 30 years later, 40 years later, we saw the post-polio syndrome. So I hope within the Veterans Affairs you'll continue to look for things that we haven't even seen yet, which are kind of what I would say are the known unknowns that there could be further surprises to this problem. I don't know if you have a comment about that.
Ziyad Al-Aly (33:09):
We're cognizant of the prior observations, the historic observations that it took several, it took more than a decade for Parkinson's to show up after the flu. And there potentially could be latent effects of viruses. Things that we're not seeing now, we still don't know because obviously the whole pandemic is in its fourth year. So we don't have 10 year follow up, but we are sort of building our systems here to look at five years and look at 10 years with an eye that if there are latent effects of SARS-CoV-2 infection, we want to be able to see it and characterize it and understand it and hopefully figure out how to best prevent it and then treat it. So we're very, very cognizant of the fact that viruses, some viruses can have very latent manifestations. For example, EBV and multiple sclerosis, it doesn't show up immediately. It shows up way down the road. Epstein Barr virus and multiple sclerosis. A lot of viruses, not one, again, SARS-CoV-2 is not unique. There are a lot of viruses produce long-term conditions and they have different timing when they show up. And so we're very, very interested in this and certainly are building our data systems here to look at five years and 10 years.
The Lack of Public Regard for Long Covid
Eric Topol (34:20):
Yeah, that's perfect. I knew you would. I just wanted to make sure I touched on that with you because you don't miss a beat. Now, the problem I see still today, Ziyad, is that there's lack of regard, respect, acknowledgement for long covid despite your phenomenal work. Despite that there's 60 million people around the world and then still more as infections again are on the uprise, there's people out there saying that these are malingers, that there's no such thing. I can't even post things about Long Covid on social media like Twitter/ X because I get all this pushback that it's made up and it's a hoax and this is just unnerving because we both know people who have had, they were athletic and now they're either wheelchair or bound to bed. I mean, this can be so people are suffering. What can you say about the fact that there are these people who are trying to dismiss long covid after all the work that you have done along with so many other researchers around the world to nail this down as a very big issue?
Ziyad Al-Aly (35:36):
So I definitely think it's a big issue. It's really unfortunate that in the US and actually some other parts of the world, that the whole pandemic has been politicized. And it's really sad to see, I mean, not as much as you, but I get some of the pushback on Twitter. And even sometimes when we publish a paper, sometimes people find my email, I don't know how they find my email. They find my email, I get what I call them nasty grams. Really sort of a very, very unpleasant emails, very unpleasant emails. And I just delete and I don't respond. So it's really hard to understand. It's really hard to understand. But there is a lot of misinformation, a lot of disinformation, a lot of politicization of the pandemic, a lot of politicization of vaccines and their side effects. And it's almost polluting the national conversation.
(36:30):
And it's toxic because these things are, this is not free speech. This is actually speech that harms other people. There are people that feel disenfranchised, that feels sort of the feel that their illness is not recognized. Or some people refer to it as gaslighting condition is being gaslit by this toxic discourse. And that's really unfortunate. But I wish I have a very clear solution or very clear understanding of how to address this. It's something that baffles me. And because of some of the stuff that I experienced, I sort of classify as almost toxic. It's really
Eric Topol (37:09):
Very, again, you're being kind because it's, or I mean you're not. I think it's so dreadfully toxic. It's disgusting, despicable. Now I'm disconcerted because for example, the last time we had a state of the Union address by the president, he said, the pandemic's looking good. I've never heard our president say about long covid and our other leaders in our country to acknowledge how vital this is. It's great that we had the N I H to allocate significant funds, but may be that a lot of that unfortunately has been wasted. But I think we can do much better in getting the point across that this is a really big deal, that so many people, their lives have been changed. We don't have a remedy in sight. Only a very limited number of people, as you've published, really fully recover, particularly if they've had a severe case. So I hope that in the future we will have a better consensus among the spokespeople leadership that acknowledges the breadth and depth and seriousness of this problem. So the last thing I want to ask you about is you have had a record of prolific work in this pandemic, and I want to know what your daily routine is like. Do you sleep? What do you do?
Ziyad Al-Aly (38:46):
We feel very committed to this. So we are really working constantly almost all the time. And definitely I do sleep and I do go to the gym and I try to maintain some healthy balance, but I also work on Saturdays to try to write papers and move things forward. We're a small team, but we feel very driven to keep moving the ball forward long. And really honestly, thanks to the patient community that has supported us from day one actually inspired us and supported us from day one. So feel very connected to this cause and feel, want to move it forward. And it's a lot. But again, kudos to my team. They're amazing and it's a small team, but they're really, really absolutely, absolutely amazing people. And you do
Eric Topol (39:28):
A lot of kudos to you too, because you've been leading this team and you've illuminated Covid from the US standpoint, no group, no less for the world. And these studies have been one after another. Just really an extraordinary and seminal paper. So in closing, Ziyad, I want to thank you for what I consider heroic efforts. You and your team, you have lit up this whole space of covid for all of us, and it's superimposed on great work that people didn't know about that you were doing. The Washington University of St. Louis, one of the leading academic medical centers in the country and the world as well as the Veterans Administration should be so proud of you and your colleagues for this work. This is tireless work. I know every time you submit a paper and every time you go through all the peer review and the revisions and the resubmission, and then you've done it all through these years of the pandemic, and I know you'll continue as well. So thank you for this indefatigable effort, which has really been extraordinary and I look forward to keeping up with you and all the future efforts, and I know you'll be on it for years to come.
Ziyad Al-Aly (40:51):
Well, thank you. Thank you. Thank you for having me. And again, thanks also for all your effort in this pandemic communicating science to elevating science and communicating to the wider public now, all your wonderful, amazing, gigantic prior contributions. So thank you for your contribution to America and the world, and especially being the communicator in chief throughout this pandemic.
Eric Topol (41:12):
Oh, you're too kind. We'll talk again. I hope soon and great to be with you today. Thank you.
Ziyad Al-Aly (41:18):
Thank you.
If you prefer to watch the whole convo by video, here Is the link
Eric Topol (00:00):
Hello, this is Eric Topol, and I'm thrilled to have a chance to have a conversation with Magdalena Skipper, who is the Editor-in-Chief of Nature. And a historic note. Back in 2018, she became the first woman editor of Nature in its 149 years, and only the eighth editor of all times. Having taken over for Philip Campbell, who had been previously the editor for 22 years, we're going to ask her if she's going to do 22 or more years, but we're going to have a fun conversation because there's so much going on in medical publishing, and I think, you know, that Nature is the number one cited science journal in the world. So, welcome, Magdalena.
Magdalena Skipper (00:41):
Thank you very much. Real pleasure to be here and chatting with you today, Eric. Thank you.
How COVID-19 Affected Nature
Eric Topol (00:47):
Well, you know, we're still, of course, in the pandemic world. It's obviously not as bad as it had been, but there's still things going on with new variants and Long Covid, and it's not, the virus isn't going away. But first thing I wanted to get into was how did Nature handle this frenetic craziness? I mean, it was putting out accelerated publications on almost a daily or weekly basis and putting out like a speed, velocity of the likes that we've not seen. This must have been really trying for the whole crew. What, what do you think?
Magdalena Skipper (01:29):
It was! And, you know, the first thing I, I think I will recognize two things at the same time. So the first one, as you say, at a time, such as the pandemic, but actually at any point when there is a, a new health emergency that is spreading, especially something as unknown, as new as, as it was the case with SARS-CoV-2. And of course, in the beginning, we really knew nothing about what we were facing if speed is of the essence, but equally what's truly important is of course, the rigor itself. So that combination of needing to publish as quickly as possible, but at the same time as rigorously evaluating the papers as possible, that was actually quite a challenge. And of course, you know, what we sometimes forget when we talk about, well, researchers themselves, but also editors and publishers is of course, as individuals, as human beings.
(02:33):
They are going through all the trauma, all the constraints associated with various lockdowns concerns about the loved ones, perhaps those ones who are in the care. You know, in many cases of course there would've been the elderly who are individuals would've been concerned by or indeed children, because of course, schools in so many places were. And all the while, while we were dealing with these very human, very ordinary daily preoccupations, we were very focused on the fact that we had a responsibility and a duty to publish papers and evaluate them as quickly as possible. It really was an extraordinary time. And, and you know, one other thing I should emphasize is, of course, it's not just the manuscript editors who evaluate the research, it's the reporters on my team as well who are going out of their the way to find out as much information to report as robustly, find as many sources to, to interview as possible.
(03:44):
And, and, you know, I also have to mention colleagues who work on production side of nature actually make Naturehappen, be published online on a daily and then of course weekly basis. And literally from one week to the next all our operations had to be performed from home. And it's really remarkable that the issue was not late. We published the issue, just as you know, from as lockdowns came in. And as it happens, the production side of Nature is mainly based in, in London. So most of that team effectively found themselves not being able to go to the office effectively from one day to the next. So it really was an extraordinary time and, and a time that as I said was, was a time of great responsibility. But looking back on it, I'm actually incredibly proud of, of my team, what, what they achieved
Eric Topol (04:47):
Did they hold up? I mean, they hadn't, they didn't get burnout from lack of sleep and lack of everything. Are they still hanging in there?
Magdalena Skipper (04:55):
So they are hanging in there. You'll be glad to hear. But I think, very importantly, we were there for one another insofar that we could be, of course, we were all at home remotely. We were not meeting, but we had virtual meetings, which were regular of course in as a whole team, but also in, in subgroups as we sub-teams, as we worked together, that human contact in addition to of course, loved ones and families and friends, that human contact in a professional setting was, was really, really necessary. And clearly what I'm describing was affected all of us one way or another. Sometimes there is a tendency not to remember. That also applies to editors, publishers, and of course researchers themselves. I mean, very clearly they were at the forefront of the issue facing the same problems.
Nature and Challenge of Generative A.I.
Eric Topol (05:57):
Well, a new challenge has arisen, not that the pandemic of course has gone away, but now we have this large language models of AI, Generative AI, which you've written editorials at Nature, which, of course, is it human or is it the machine? What do you think about that challenge?
Magdalena Skipper (06:19):
Well of course, you know, the way I like to think about it is AI, of course, broadly is, has been around for a very long time, a number of decades, right? And steadily over the last several years, we have seen AI emerge as a really powerful and important tool in research right across a number of disciplines. The reason why we are all talking about AI right now, and I really think all of us are talking about AI all the time, is, of course, specifically the emergence of generative AI, the large language models that, that you just mentioned. And they sort of burst onto the scene for all of us really last year in the autumn with chat GPT and GPT-4 and so on. But it's important to remember that, of course, when we talk about AI, there are other models, other approaches, and machine learning in general has been creating quite some revolution in research already.
(07:36): You know, probably the best example that will be familiar to many of the listeners was of course Alpha Fold which, you know, Nature published a couple of years ago and, and has been really revolutionized structural biology. But, of course, there are many other examples which are now becoming developing much more rapidly, becoming much more, I would say, commonplace in, in research practice. You know, not just predicting structure from sequencing from sequence. And I say just so flippantly now, of course, it was such and it continues to be such an incredible tool. But of course now we have AI approaches, which actually suggest new protein design, new, new small molecule design. We've had in the last couple of years, we've had identification of new potential antibiotics that are effective against bacterial strains that have otherwise been resistant to any known antibiotics.
(08:48):
And, and of course, it's not just in biomedicine. Material science--I think it's very helpful, hopeful when it comes to, to AI tools as well. And then, and of course, generative AI indeed helps us in some of these contexts already. But I think your question perhaps was more focused on the publishing, the communication, the sort of output of, of research, which of course is also very important. In some way. The reason why I answered, I began to answer the question the way I did, is because I'm actually very excited about harnessing the power of AI in augmenting research itself. Helping navigate enormous data sets generate hypotheses to be tested finding new ways to advance projects. I think that's a very exciting opportunity. And we're just beginning to see the first applications of it.
(10:04):
Now, in terms of publishing you referred to some editorials that we wrote about this. And right at the beginning of the year, there was a flurry of excitement associated with the ability of generative AI to indeed generate text. There were some manuscripts which were published in journals that were co-authored by Chat GPT. I I even believe there was an editorial which was co-authored by Chat GPT. So in response to that, we felt very strongly that, that clearly there was a need to, to come out with a, a clear position, just as in doing research, we see AI tools as tools to support writing, but clearly they don't have the ability to fulfill authorship criteria. Clearly, they cannot be authors. Clearly, they must only remain as tools supporting researchers and individuals writing and communicating their research.
(11:23):
And so we, we wrote a very clear editorial about this, essentially summarizing what I just explained and asking the community to be transparent about how AI tool has been used, just as you would be transparent about your methodology, how you have arrived at the results that you're reporting and, and results that support your conclusions. So for us, it's a relatively simple set of recommendations. As I say, we ask for transparency. We understand it can be a tool that can be used to help write a paper. What we also ask at this stage that generative AI tools are not used to generate figures or images in papers, simply because there are a number of outstanding copyright issues, a number of outstanding privacy issues, they remain unresolved. And for as long as they remain unresolved, we feel it's not an appropriate application of these tools. So that's our editorial position.
Eric Topol (12:42):
Yeah, no, that's very helpful. I mean, where do you think, if you write a manuscript and then you put it into let's say GPT-4 and say, please edit this, is that okay? Or is that something that, and it's acknowledged that the paper was written by us researchers, but then we had it tweaked by chatbot or is that something that it wouldn't go over too well?
Magdalena Skipper (13:10):
Well, my preference, and actually what I would hope is that if you were writing this paper and then you felt the need to put it through a chatbot as you just put it, although I find it hard to imagine that you would find no need for that,
Eric Topol (13:29):
I wouldn't do it. But I know there's people out there that are working on it.
Magdalena Skipper (13:32):
Yeah, absolutely. But then I would hope that the last pass, the final word, would rest with you as the author. Because, of course, if you are using a tool for whatever it is that you do, you want, at the end of the day to make sure that what that tool has returned is aligned with what you intended that you perform some kind of a sense check. We, of course, all know that although GPT-4 has less of a tendency to hallucinate, so to essentially come up with fabricated sort of statements and, and reality, if you like, it remains an issue. It can remain an issue. And very clearly any, any scientific communication has to be rooted in facts. So, in the scenario that you propose, I would hope that if a researcher felt compelled to run the manuscript through a chatbot, and for example, one consideration may for an individual whose English is not their first language, who feel may feel more comfortable with a sort of support of this kind. But in the end, the final check, the final sign off, if you like, on that manuscript before submission would need to come from the researcher, from the corresponding author, from the writing group. and indeed assistance from a chatbot would need to be disclosed.
Eric Topol (15:14):
For us. Yeah, I mean, it's really interesting because you can almost foresee the shortcut of having to go get all the references and all the links, you could say, you know, please insert these, but you better check them because they may be fabricated Absolutely. It's going to be really interesting to see how this plays out and the difficulty of detecting what is written by a large language model versus a person.
Nature and Preprints
Now another topic that I think is really in play is the preprint world and publishing via preprints. And as you know there's been Michael Eisen and the whole idea of how things would move with his journal eLife. And you will remember when you and I were together at a conference. I organized Future of Genomic Medicine many years ago at the kind of dawn of life science preprints. And some people in the audience sai, “what's a preprint?” Right? Nobody else asks about that now. It’s come a long way over this decade. And where do we go with this? Should journals like the top journals in the world like Nature require a paper to be vetted through the pre-print mechanism? Where is this headed, do you think?
Magdalena Skipper (16:40):
Yeah, it's an excellent question. And, and you know, by the way, I have such wonderful memories from, of that conference. I think this must have been like 11 years ago or something like that. It was a long time ago. And I actually remember presenting this, this vision of a rather radical vision of, of the future of publishing. And here we are in the future as compared to then, and we have moved relatively little by comparison to where we were then. But back to your question. So, you know, the first thing to say is that, of course, just as a reminder, preprints have been around for more than two decades now. And, and of course they initially were really spearheaded and advanced by the physical sciences community. archive itself is, as I say, more than two decades old. So, you know, for us at Nature as a multidisciplinary journal where of course, we've been publishing in the physical sciences since the very beginning of our existence as soon as preprints first emerged in those communities, we realized that we could coexist very harmoniously as a journal peer-review based journal with preprints.
(17:59):
So when initially biological sciences community embraced them and bioRxiv was established, and then of course, many other archives and then subsequently actually really spearheaded by Covid, the medical and clinical community began to embrace preprints. in many ways, for us, that was nothing new. It was just an extension of something that we worked with before. Although our own our own policies have evolved. So, for example, during the pandemic we actually mandated deposition of papers that were submitted to us that were Covid related. We mandated the deposition in a preprint server. The authors had the choice which server they deposited, but we wanted those manuscripts to be available to the community for the scrutiny as soon as they were finalized, as soon as they were actually written. So while we were reviewing them again as quickly as rigorously, but as quickly as possible, the preprint was already available for the community just before the pandemic.
(19:17):
As it happens, we also took a step forward with our policy. So previously, let's just say we were completely fine with preprints. We saw preprints as compatible with submission to, to Nature, and for that matter to the other journals in the Nature Portfolio. But actually just in the year before COVID started, we decided to actively encourage our authors to deposit preprints. We could see that preprint sharing had great advantage. You know, the, the usuals of advantages, which are often listed first are of course ability to make that primacy claim, make a stake that, that you have been working on something and, and this is your project. You have a set of results that you are ready to communicate to, to the community at large. And of course, another very important one is that sort of community and, and almost public form of peer review and, and ability to comment.
(20:30):
And incidentally, I remember as you know, my, my history as an editor very well. We've known each other for a long time. I remember when the genomics community, which is sort of my, my background is sort of my old hat, if you like, that, that I used to wear when the genomics community began to embrace preprints especially the population and evolutionary genomicists really embraced this idea that this was like a group peer review. And the authors of those preprints were very grateful to the community for improving the papers before they were submitted to journals, or sometimes that sort of community review was going on while a paper was being considered at a journal. And we, as editors actually encouraged sort of formal submission of these reviews, if you like, I mean, formal maybe is the wrong word, but we were saying that we would take those comments into account when evaluating papers.
(21:38):
So there has been an interesting evolution that more and more disciplines, more and more fields have embraced preprints as a way of disseminating information. Preprints service themselves have also grown and matured in the sense that there is now realization that, for example, clinical preprints need a higher degree of scrutiny they're posted on a preprint server than maybe let's say theoretical physics or theoretical biology preprints. So overall all communities collectively have grown and matured. Where are we going with this? I mean, who knows? I was predicting 12 years ago you know, a bit of a different, more advanced future today. It's very difficult to predict the future. I do think, however, that what we are seeing today, that sort of hand in glove coexistence of preprints with journals, with peer reviewed papers is going to continue into the future. And I think actually that's a really valuable and interesting combination. So it's a great development to see and great to see that communities right across disciplines have really embraced this.
Eric Topol (23:11):
Yeah, I think it does complement, obviously the traditional peer review of a few expert reviewers with, you know, could be hundreds if not thousands of people that weigh in on, on a pre-print. So yeah, it's fascinating to see. And it's, I still remember the vision that you portrayed for it, and how we we're not quite there yet, but I'm sure there'll be further evolution.
Women in Science: Where Do We Stand?
Now, another area that I think is particularly good to get your input, because you're a woman in science, as you mentioned, you know, grounded obviously in genetics and genomics, and here you are, one of the most influential women in science at a time when there's been a reckoning that women in science have been shortchanged historically, I mean, for hundreds of years. Do you see that this is starting to get better? Are there palpable signs that we're finally getting kind of equal rights here? Or are we, is it, is it just still a long fight ahead?
Magdalena Skipper (24:20):
So the, the optimist in me and, and I should say, you know, my, my glass, my glass is always half full. The optimist in me says that it is getting better, but the realist in me has to add immediately that the changes too slow. It really is too slow. We do see many more women prominently able to make the contributions that they should, they can, and they should make to whatever discipline whatever aspect of the research community and beyond they wish to, to make. I still think it costs them too much. I still think we don't appreciate and support women sufficiently.
(25:23):
Maybe we have moved on the bottleneck in the, in the pipeline a little bit further, towards more seniority. But we still, we still don't sufficiently support women. As I say, we, I think we still default to an expectation that successful women in science in research more broadly will somehow emulate how success has looked in the past. And that's a shame, that's a shame not just for those women who are trying to come in and make a difference, but it's a shame for all of us because it means that we are denying diversity in that picture of success. Yes. So yes, I think, I think that we have seen many changes, but I think the change is not happening fast enough.
Eric Topol (26:23):
Yeah. One of the things that I've noticed since of particular interest in AI is that the very profound imbalance of researchers, the gender imbalance there is just, you know, I'm not even sure if it's 10% women researchers in AI, so that has to be changed. And so this, there's so many things that are holding us back, but, but that's certainly one of, of many.
Magdalena Skipper (26:49):
Absolutely. And, and, and if I can just add, there are some outstandingly influential female researchers in the AI field, as you say, they are just outnumbered. Yes. , I think not given the opportunity to, to fully blossom, if you like, considering their capabilities and, and their contributions already.
Eric Topol (27:11):
You know, it's so true. I just interviewed Melanie Mitchell from the Santa Fe Institute, and I work with Fei- Fei Li. And when I, when Fei-Fei Li and I spoke some months ago about a book (Genius Makers) that Cade Metz, the New York Times journalist had written, and I say, why didn't he bring up or emphasize the role of any women in the whole book . Yes--who work in A--I mean, she, she obviously was, was did not take that particularly well, and as did I.
Too Many Nature Portfolio Journals?
So one of the other areas that I think you already touched on, which is separating Nature, the flagship journal from the Nature Portfolio of, I don't know what it's up to now, 200, 300, I'm not sure how many journals are. So do you, do you have to over oversee that? Do you have input on that? Because what I worry about is, you know, people quote a Nature journal and it may not be, you know, at that level that you would be proud of. What, what are your thoughts about this endless proliferation of the nature portfolio?
Magdalena Skipper (28:17):
Well, I, I'm, first of all, I'm not sure if it's endless, but
Eric Topol (28:20):
Oh, that's good. .
Magdalena Skipper (28:22):
So, so let me, I think in your question, you touched on a number of things. So first of all, a clarification. So my role is as Editor-in-Chief of Nature, and of course, that is my main focus. there is another aspect to my role, which is Chief Editorial Advisor for the Nature Portfolio. So in that sense each of the journals within the Nature portfolio has its own chief editor. but by virtue, I guess, of my seniority, and also by virtue of multi-disciplinarity of Nature I have this advisory role to my colleagues in the other journals. I like to think about the Nature Portfolio as an ecosystem, actually. And it's an ecosystem, like any ecosystem. It has different niches, each of which fulfills a different role. Some of them are bigger, some of them are smaller, some of them are very specialized, others are more general.
(29:22):
And I think you know, working with researchers for many years as an editor now, I can see benefits to having that sort of almost an ecosystem type approach to publishing. You know, for example, we mentioned already earlier that in my previous sort of incarnation as an editor, my focus was on genomics especially in the context of human genomics. of course starting from the Human Genome Project, these were very large or have, where, why, why am I using past tense? They are, to this day, very large collaborative projects involving many different labs, many different approaches these days that they're not just focused on genomics, but of course other omics go hand in hand with them. So when a project comes to fruition, when, when it comes to be published, there are many different pieces that need to be communicated, many different papers of different sizes of different value.
(30:32):
And for example what value maybe is the wrong word of different utility? So, for example, there may be a flagship paper that is published in the pages of my journal of Nature, but there may be papers that specifically described development of methodology that was part of the same stage of the project. And those papers may be published in Nature Methods, which is part of the Nature Portfolio. There are other journals that are part of Nature Portfolio, which have different editorial bar. And so, you know, one example is Scientific Reports, which is a journal which does not require conceptual novelty in the papers that it publishes. Of course, it requires rigor and, and robustness in the papers that it publishes, like every journal should. But there is utility in publishing papers in a journal like this.
(31:36):
There may be replications that are published there that further add further evidence to support conclusions that are already well known, but nevertheless, they're useful. I should however, add that in Nature itself, we also publish replications, right? There are different degrees of influence and impact that, of course, different studies be there, replications or not that can carry. So, that will be my way of conceptualizing the Nature Portfolio. and, you know, coming back to your, to your comment that it seems like it's endless. I think well, nothing, nothing is endless. Of course. Nothing, nothing, right, grows forever. I do think that we have in the launches within the portfolio, we have been able to capture and at the same time serve an interesting evolution in the research ecosystem itself. So the final comment I will make on this is, if you look at some of the more recent launches in the portfolio, they've been what we like to call thematic journals, such as, for example, Nature Food or Nature Water.
Eric Topol (33:10):
Right?
Magdalena Skipper (33:10):
And here we are really capitalizing on that multi-disciplinarity of these emerging themes that, especially in the context of sustainable development goals, have acquired their own identity. They don't belong to one discipline or another discipline. And, and so these journals, they're new journals, relatively new journals, some of them very new Nature Waters is, is quite new, but they provide a focal point for researchers who come together to solve a particular set of problems from different disciplines. And I think that's an interesting function in, as I say, for the community.
What About the Paywalls?
Eric Topol (33:53):
Yeah, there's no question some of the newer journals and their transdisciplinary mission --they're needed and they become extremely popular and well -cited very quickly to prove that. So along that line obviously the public is all fired up about paywalls and you know, and obviously for Covid, there was no paywalls, which is pretty extraordinary. Do you see someday that journals will have a hard time of maintaining this? I mean, you have what I consider an extraordinary solution, which is the ReadCube postings anyone can access, you just can't download the PDF, and I wish authors would always routinely put that out there because that would solve part of the problem. But do you think we're going to go to a free access that's much more wide, perhaps even routine, in the years ahead?
Magdalena Skipper (34:52):
So certainly open access as in ability to access a manuscript, published manuscript without any payment or barrier associated with a Creative Commons license is something that is advanced as a, as a preferred future by many researchers, by many funders. and for that matter, actually many publishers as well. You know, let me make one thing very clear. As an editor, I would love as many people as possible to read the papers that I publish in my journal.
Magdalena Skipper (35:30):
That should go without saying. Sure. at the same time, publishing papers, of course, is associated with a cost, and, and that cost has to be somehow covered. In the old days it was exclusively covered by library subscriptions or site licenses or personal subscriptions. Now the focus is shifting. And of course, Nature itself as well as the other research journals such as, for example, Nature Medicine or indeed Nature Water, as I mentioned before are what we call transformative journals. So effectively we are hybrid journals that advocate for open access. So today, when you submit a paper to Nature, you can publish under the traditional publishing model, or you can choose to publish open access, which is associated with an article processing charge. That should, in my view, be part of your costs of doing research, because after all, I'm a firm believer in the fact that publishing your research should be seen as part of doing research, not sort of an add-on.
(36:47):
Now, I'm glad you mentioned read Read Cube and this functionality that we call shared it. We developed it actually quite some years ago. I would say at least a decade ago. it remains curiously underappreciated. Yeah. I just don't understand it. Yeah, exactly. And, and we, we inform the authors that they are free to use that link. And, and just to clarify, it's a linked as you exactly as you explained to an online version of the paper. It's the final version, the record version of the paper. You can't download it, but you can share that link. Anyone can share that link once they have it Infinite number of times. So it's not like the link expires, or it's a, a finite number of, of that it has a number of finite number of uses in addition to that nature.
(37:49):
And for that matter, the whole of Springer Nature is part of Research4Life. Now, that's an organization that provides free access to all content from publishers. And Springer Nature is not the only publisher that's part of Research for Life that provides full access to all of our content in the countries which are designated as low and middle income countries by the World Bank. So that we've been part of that. And, and previously for many, many years, in fact, decades, again, that is curiously underappreciated, including in the low and middle income countries. So, you know, recently had an opportunity to do some visits in Africa. And my, my take home message there was, if there is one thing that you remember from our conversation or from my presentation, please remember about Research4Life.
Magdalena Skipper (38:52):
Because that content is freely available if you follow, if you go to our content through Research4Life. And incidentally, there's also training, which is available there. So part of Nature portfolio in addition to journals, we have Nature Master classes, which is training for researchers. And that is also completely freely available in those countries. So there are a number of approaches to, to getting content open access is definitely growing, but there are those other ways to gain access to content which is not open access at the moment.
Eric Topol (39:33):
I'm really glad you reviewed that because a lot of people who are going to be listening are going to really cue into that. Now the last question for you is, you know, it's not just every Wednesday, 51 or whatever, 50 weeks a year, that you're getting the journal ready, but it's every day now that you're putting out stuff and on the Nature website. Features that are by the way, free or full access and many other things to keep Nature out there on a daily, if not minute to minute basis. So this is really a big charge to, you know, do this all so well. So what keeps you up at night about Nature is this, this must be a very tough position.
Magdalena Skipper (40:28):
So the first thing I would say that is that of course it's, it's not me. I'm just the person here talking to you representing Nature. I have an outstanding team.
Eric Topol (40:44):
I've met them, and they're amazing.
Magdalena Skipper (40:46):
And it's really them who are making it possible on a minute by minute, certainly day by day basis. And so the reason why I sleep relatively well is thanks to them actually, okay,
Eric Topol (41:00):
. Okay.
What Keeps You Up At Night?
Magdalena Skipper (41:01):
But more, but more broadly. and this is a thought which is bigger than Nature itself. What actually keeps me up at night these days is the rather difficult light in which science and research is portrayed these days increasingly.
Magdalena Skipper (41:27):
And I think it's very unfortunately being to support other goals and other ends forgetting about the fact that science is an ongoing process that science takes steps back when it needs to revise its position, that it still continues to be true, that s science progresses through self-correction. Even if that self-correction doesn't happen overnight, it takes time to realize that a correction is required, takes time to evaluate judiciously that correction is required and what kind of correction is required, right? These are the things that of course, you and I know very well. But the, sometimes if for individuals who are not close to the process of how science research fact-based discovery is conducted, if you just look at information on social media or in general media, you may walk away with an impression that science is not worth paying attention to that science is in some deep crisis.
Magdalena Skipper (43:04):
And I think that's, that's a shame that that's a picture that we have other things that need other things in science, in research that need correcting, that need sorting out. Of course, we mustn't forget that research is done by humans and, and after all it is human to air. But overall, that's actually something that keeps me up at night. That overall, I really hope that those of us who are engaged in one way or another within the research enterprise, we can continue to advance the right kind of image that it's not perfect in some artificial way, but actually, at the same time, it's the only way that we can move forward. We can understand the world around us, and we can wake, make the world around us better, actually.
Eric Topol (44:11):
Yeah. I'm so glad you've emphasized this because just like we talked earlier about distinguishing between human and AI content generated here, we have science and anti-science blurring facts, blurring truths, and basically taking down science as a search for truth and making it trying to, you know, obscure its mission and, in many ways, we, we saw it with not just anti-vax, but it's much bigger. The political motives are obvious extraordinary, particularly as we see here in the U.S. but other countries as well. So I almost didn't hit you for that question, just because it's so profound. We don't have the answers, but the fact that you're thinking about it tells, tells us all a lot. So Magdalena, this has been a joy. I really appreciate all your candid and very thoughtful responses to some of these questions.
(45:09):
Some of them pretty tough questions I have to say. And I look forward to our conversations and chances to visit with you again in the future. And congratulations again on taking on the leadership of Nature for five years now-- I believe just past your five-year anniversary now. You could say that's small out of 155 years, but I think it's a lot. particularly since the last few years have been, you really challenging. But to you and your team ultimately –-major kudos. I'm on the Nature website every single day. I mean, even, I when I’m on vacation, I'll be checking out the Nature site. So you can tell that I think so highly of the its content and we'll look forward to future conversations going forward.
Magdalena Skipper (45:52):
Thank you very much. Thank you very much, Eric. It's always a pleasure to talk to you. Thank you.
Transcript
Eric Topol (00:00):
This is a real great opportunity to speak to one of the most impressive medical informaticists and leaders in AI in the United States and worldwide. Dr. John Halamka, just by way of background, John, his baccalaureate in Stanford was at U C S F/Berkeley for combined MD PhD trained in emergency medicine at U C L A. He went on to Harvard where he, for 20 years was the Chief Information Officer at Beth Israel Deaconess. And then in 2020 he joined Mayo Clinic to head its platform to help transform Mayo Clinic to be the global leader in digital healthcare. So welcome, John. It's so great to have you. And by the way, I want to mention your recent book came out in April, one of many books you've written, redefining the Boundaries of Medicine, the High Tech High Touch Path into the Future.
John Halamka (01:00):
Well, a thrilled to be with you today, and you and I need to spend more time together very clearly.
Eric Topol (01:06):
Yeah, I really think so. Because this is the first time we've had a one-on-one conversation. We've been on panels together, but that's not enough. We've got to really do some brainstorming, the two of us. But first I wanted to get into, because you have been on a leading edge of ai and Mayo is doing big things in this space, what are you excited about? Where do you think things are right now?
John Halamka (01:35):
So you and I have been in academic healthcare for decades, and we know there's some brilliant people, well-meaning people, but sometimes the agility to innovate isn't quite there, whether it's a fear of failure, it's the process of getting things approved. So the question of course is can you build to scale the technology and the processes and change policies so that anyone can do what they want much more rapidly? And so what's been exciting over these last couple of years at Mayo is we started with the data and we know that anything we do, whether it's predictive or regenerative, starts with high quality curated data. And so by de-identifying all the multimodal data of Mayo and then working with other partners around the world to create a distributed federated approach for anyone to train anything, suddenly you're empowering a very large number of innovators. And then you've seen what's happened in society. I mean, culturally, people are starting to say, wow, this ai, it could actually reduce burden, it could democratize access to knowledge. I actually think that yes, there need to be guidelines and guardrails, but on the whole, this could be very good. So here we have a perfect storm, the technology, the policy, the cultural change, and therefore these next couple of years are going to be really productive.
Implementing a Mayo Randomized AI Trial
Eric Topol (02:59):
Well, and especially at Mayo, the reason I say that is not only do they recruit you, having had a couple of decades of experience in a Harvard program, but Mayo's depth of patient care is extraordinary. And so that gets me to, for example, you did a randomized trial at Mayo Clinic, which there aren't that many of by the way in AI where you gave E C G reading power of AI to half the primary care doctors and the other half you didn't for determining whether the patients had poor cardiac function that is low ejection fraction. And now as I understand it, having done that randomized trial published it, you've implemented that throughout the Mayo Clinic system as far as this AI ECG support. Is that true?
John Halamka (03:56):
Well, right, and let me just give you a personal example that shows you how it's used. So I have an SVT [supraventricular tachycardia] , and that means at times my resting heart rate of 55 goes to one 70. It's uncomfortable. It's not life-threatening. I was really concerned, oh, may I have underlying cardiomyopathy, valvular disease, coronary artery disease. So Paul Friedman and Peter Newsworthy said, Hey, we're going to take a six lead ECG wearable, send it to your home and just record a bunch of data and your activities of daily living. And then we buy 5G cell phone. We'll be collecting those six leads and we'll run it through all of our various validated AI systems. And then we'll tell you based on what the AI suggests, whether you're at high risk or not for various disease states. So it says your ejection fraction 70%. Oh, good. Don't have to worry about that. Your likelihood of developing AFib 3% cardiomyopathy, 2% valvular disease, 1%. So bottom line is without even going to a bricks and mortar facility here, I have these validated algorithms, at least doing a screen to see where maybe I should get additional evaluation and not.
Eric Topol (05:12):
Yeah, well see what you're bringing up is a whole other dimension. So on the one hand that what we talked about was you could give the primary care doctors who don't read electrocardiograms very well, you give them supercharged by having a deep learning interpretation set for them. But on the other, now you're bringing up this other patient facing story where you're taking a cardiogram when somebody's perfectly fine. But from that, from having deep learning of cardiograms, millions of cardiograms, you're telling what their risks are that they could develop things like atrial fibrillation. So this is starting to span the gamut of what the phase that we went through or still going through, which is taking medical images, whether it's a cardiogram or a scan of some sort, and seeing things with machines that humanize really can't detect or perceive. So yeah, we're just starting to get out of the block here, John. And you've already brought up a couple of major applications that we were not even potentially used three, four or five years ago that Mayo Clinics leading the charge, right?
The Power of Machine Eyes
John Halamka (06:26):
Well, yeah, and let me just give you two quick other examples of these are in studies now, right? So they're not ready for active patient use. The animate GI product does an overread of endoscopy. And what we're finding is that the expert human, I mean anywhere in the world, expert humans miss about 15% of small polyps. They're just hard to see. Prep may not be perfect, et cetera. The machine misses about 3%. So that's to say a human augmented with overread is five times better than a human alone pancreatic cancer, my father-in-law died about 11 years ago of stage four pancreatic cancer. So this is something that I'm very sensitive about, very often diagnosed late, and you can't do much. What we've been able to see is looking at pancreatic cancer, early films that were taken, abdominal CT scans and these sorts of things, algorithms can detect pancreatic cancer two years before it is manifested clinically. And so here's the ethical question I'll pose to you. I know you think about a lot of this Scripps Mayo, UCSF, Stanford, we probably have thousands and thousands of abdominal CTs that were read normal. Is it an ethical imperative as these things go through clinical trials and are validated and FDA approved to rerun algorithms on previous patients to diagnose disease we didn't see?
Eric Topol (08:03):
Well, that is a really big important question because basically we're relieving all this stuff on the table that doesn't get diagnosed, can't be predicted because we're not even looking for it. And now whether it's retina, that is a gateway to so many systems of the body, or as you're mentioning various scans like an abdominal CT and many others that like mammography for heart disease risk and all sorts of things that weren't even contemplated that machine eyes can do. So it's really pretty striking and upending cancer diagnosis, being able to understand the risk of any individual for particular types of cancer so that you can catch it at the earliest possible time when it's microscopic before it spreads. This, of course, is a cardinal objective. People don't die of cancer per se. They die of its metastasis, of course, for the most part. So that gets me now to the next phase of ai because what we've been talking for mostly so far has been what has been brewing culminating for the last five years, which is medical images and what, there's so many things we can glean from them that humans can't including expert humans in whatever discipline of medicine.
Multimodal AI and Social Determinants of Health
(09:19):
But the next phase, which you are starting to get at is the multimodal phase where you're not just taking the images, you're taking the medical records, the EHRs, you're getting the genomics, the gut microbiome, the sensors. You mentioned one, an ECGs, a cardiogram sensor, but other sensors like on the wrist, you're getting the environmental things like air pollution, air quality and various things. You're getting the whole ball of wax any given individual. Now, that's kind of where we're headed. Are you doing multimodal ai? Have you already embarked in that new path? Now that we have these large language models
John Halamka (10:02):
And we have, and so like anything we do in healthcare innovation, you need a Pareto diagram to say, what do you start with and where do you go? So in 2020, we started with all of the structured data problems, meds, allergies, labs. Then we went to the unstructured data, billions of notes, op reports, H and Ps, and then we moved to telemetry, and then we moved to CT, MRI, PET. Then we move to radiation oncology and looking at all the auto contouring profiles used in linear accelerators and then to omic, and now we're moving to an inferred social determinants of health. And let me explain that for a minute.
(10:45):
Exposome, as you point out, is really critical. Now, do you know if you live in a Superfund site area, do you know what risks you might have from the PM 2.5 particulates that are blowing through San Diego? Probably you don't. So you're not going to self-report this stuff. And so we have created something called the house Index where we've taken every address in the United States, and based on the latitude and longitude of where you live, we have mapped air, water, land, pollution, access to primary care, crime, education, grocery stores, stores, and therefore we can infer about 40 different things about your expose em just from where you live. And that's a mode. And then as you say, now, starting to gather remote patient monitoring. We have this acute advanced care in the home program where we're taking serious and complex illness, caring for the patient in the home, starting to instrument homes and gather a lot more telemetry. All of that multimodal data is now available to any one of the 76,000 employees of Mayo and our partners for use in algorithm development.
Eric Topol (11:58):
Yeah, no, that's extraordinary. And I also would say the social determinants of health, which you've really gotten into as its importance. There are so many papers now over the last several years that have emphasized that your zip code is one of the most important things of your health. And it's not even just a zip code. It's your neighborhood within that zip code for the reasons that you've mentioned. And inferring that and imputing that with other sources of data is vital. Now, this multimodal, you've again anticipated one of my questions, the possibility that we can gut hospitals as we know them today. Yes, preserving the ICUs, the emergency departments, the operating rooms, but those other people that occupy the vast majority of beds in the hospital that are not very sick, critically Ill. Do you think we're going to move as you're innovating at Mayo whereby we'll be able to keep those people at home for the most part in the years ahead? I mean, this isn't going to happen overnight, but do you think that's where we're headed?
The Hospital-at-Home
John Halamka (13:08):
So to date, Mayo and its partners have discharged about 23,000 patients from their homes. And as you can guess, we have done clinical trials and deep dive studies on every one of the patient's journeys. And what have we seen across 23,000 patients? Well, so generally, about 30% of patients that present for acute care to an emergency department come in by ambulance are appropriate for care in non-traditional settings. I mean, I think you would agree, somebody with episodic ventricular tachycardia, you're probably not going to put in a home setting, but somebody with congestive heart failure, COPD, pneumonia, I mean, these are things that, as you say, if they're going to get sicker, it will be over hours, not minutes. And therefore you can adjust in these molar than 20,000 patients. What we've seen is the outcomes are the same, the quality is the same safety, the same patient satisfaction. You get net promoter scores in the mid-nineties. You find me a hospital with a net promoter score in the mid nineties. You're eating your own food, slipping your own bed. Oh, your granddaughter's coming at 2:00 AM on a Sunday, whatever. And then ask yourself this other question, nosocomial infections,
Eric Topol (14:31):
Right?
John Halamka (14:31):
How many methicillin resistant staph infections do you have in your office? You're like, none, right? So you're infections in fall, so okay, better, stronger, cheaper, faster. And the safety of the quality are that for about 30% of the population should be a standard of care.
Eric Topol (14:56):
That's really big. So you don't think we have to do randomized trials to prove it?
John Halamka (15:01):
I mean, we have done enough studies to date, and there are organizations, Kaiser Permanente, Cleveland Clinic, all these folks who are joining us in investigating these areas. And the data is very compelling.
Patients Asking Questions to LLMs
Eric Topol (15:17):
Yeah, that's really exciting. And we may be able to jump past having to go through the large trials to prove what you just reviewed. So that's one thing of course that we're looking for in store. Another is the patient doing advanced large language model searches. So as you and everyone knows, we've done Google searches for years about symptoms, and inevitably people come up with hypochondria because they have some horrible disease that they looked up that is not a very good match specific for their condition and their background. But soon already today, we have people going into being creative mode, G P T four and other searches, and they're getting searches about their diagnosis and about what's the best literature and best treatments and expectations. That won't be FDA regulated. We don't have regulation of Google searches. So how do you see the democratization of large language models with patients having conversations with these chatbots?
John Halamka (16:32):
And of course, you ask a question no one has answered yet, but here are a few threads. So we know the challenge with existent commercial models as they're trained on the public internet. Some are trained on additional literature like PubMed or a mimic dataset, but none are trained on the rich clinical experience of millions and millions of patients. So therefore, they don't have the mastery of the care journey. So question, we are all asking, and again, no one knows. Then you take a GPT, BARD, a MedPaLM and additional pre-training with rich de-identified clinical experience and make it a better model for patients who are going to ask questions. We've got to try and we've got to try within guardrails and guidelines, but we definitely want to explore that. Can you or should you train a foundational model from scratch so that it doesn't have the bias of Reddit and all of the various kinds of chaff you find on the public internet? Could be very expensive, could be very time consuming. Probably society should look at doing it.
Eric Topol (17:50):
So this is just a review for those who are not up to speed on this, this means setting up a base model, which could be 20 to 30,000 graphic processing units, big expense. We're talking about tens of millions, but to do it right, so it isn't just a specialized fine tuning of a base model for medical purposes, but something that's de novo intended that no one's done yet. Yeah, that's I think a great idea if someone were to go down that path. Now you, early on when we were talking, you mentioned partners, not just other health systems, but one of the important partners you've established that's been out there as Google, which I think set up shop right in Rochester, Minnesota, so it could work closely with you. And obviously they have MedPaLM2, they have BARD, they published a lot in this space. They're obviously competing with Microsoft and others, but seems like it's mainly an arms race between those two and a few others. But how is that relationship going? And you also were very right spot on about the concerns of privacy, federated ai, privacy computing. Can you tell us about Mayo and Google?
What is the Collaboration Between Mayo and Google?
John Halamka (19:06):
Well, absolutely. So Google provides storage, compute, various kinds of tools like their fire engine for moving data between various sources. Google does not have independent access to any of Mayo's data. So this isn't a situation of we have a challenging medical or engineering problem, bring 60 Google engineers to work on it. No, what they mean is they help us create the tooling and the environment so that then those with permission, Mayo employees or Mayo's partners can work through some of these things and build new models, validate models. So Google has been a great enabler on the tool set and building scale. You probably saw that Eric Horvitz gave a recent grand rounds at Stanford where he explained scale makes a difference, and that you start to see these unexpected behaviors, this emerging goodness, when you start dealing with vast amounts of multimodal data, vast amounts of compute. And so working with a cloud provider is going to give you that vast amounts of compute. So again, privacy, absolutely essential, de-identify the data, protect it, control it, but you can't as an institution, get enough computing power locally to develop some of these more.
Towards Keyboard Liberation and Machine Chart Review
Eric Topol (20:36):
Well, that goes back to the dilemma about building a base model with just the capital costs no less. You can't even get these GPUs scale because their supply and demand mismatch is profound. Well, the other thing, there's two other areas I want to get your impressions about. One of course is the change of interactions with patients. So today, as you well know, having all these years overseeing the informatics, Beth Israel now Mayo, the issue of the keyboard and the interference that it provides, not just as a data clerk burden to clinicians, which is horrible for morale and all the hours even after seeing patients that have to be put into charting through the EHRs and these clunky software systems that we are stuck with, but also the lack of even having face-to-face eye contact with patients in that limited time they have together. Now, there are many of these so-called ambient AI language, natural language processing, using large language models that are of course turning that conversation not just to a remarkable note, but also of course any part of the note, you could go back to the raw conversation. So it has trust embedded as what was really said. And then you have all these downstream functions like prescriptions, follow-up appointments, nudges to the patients about whatever, like their blood pressure or things that were discussed in the visit. You have translation to the patient at their level of education so they can understand the note you have things that we never had before. You have orders for the test or follow up appointment pre-authorization. What about these, John, are these the real deal or are we headed to this in the near term?
John Halamka (22:41):
So 10 years ago, I said all of these meaningful use criteria, all the keyboarded data entry, structured data and vocabularies. What if you had the doctor and the patient had a conversation and the conversation was the record? That was the legal record. And then AI systems extracted the structured data from the conversation. And there you would have satisfaction by both patient and doctor and a very easy source of truth. Go back to what was said. And of course, 10 years ago everyone said, that'll never happen. That's too far.
(23:20):
And so I'll give you a case. My mom was diagnosed with a brain abscess about a year ago. She's a cure of the brain abscess. I with ambient listening, had a conversation with my mother and it went something like this. Yes, I started to develop a fever. I said, oh, and you live alone, right? Oh, yes. My husband died 13 years ago. The note comes out, the patient is an 81 year old widow. So we're having a conversation about my father dying and she lives alone. And I didn't use the word widow, she didn't use the word widow. And so what it shows you is these systems can take detailed conversation, turn them into abstract concepts and record them in a way that's summarized and meaningful. Last example I'll give you recently, I did grand rounds at Mayo and I said, here's a challenge for all of us. It's Sunday at three in the morning. Mrs. Smith has just come in. She has a 3000 page chart, 75 hospitalizations and four or visits. Her complaint tonight is, I feel weak,
Eric Topol (24:38):
Right? That's a classic.
John Halamka (24:43):
How are you going to approach that? So we have an instance of MedPaLM2 that is containerized. So that I was able to put a prompt in it with some background data without, and it was all de-identified, but it was all very secure. So I put the 3000 pages into this MedPaLM2 container and said, audience, ask any question that you want. Oh, well, what medication should she be taking? What's her follow-up plan? Were there any complications in any of her surgeries? And within seconds, every answer to every question just appears. They say, oh my God, I can now treat the patient. And so this is real. It is absolutely. It's not perfect, but give us a couple of quarters.
Eric Topol (25:31):
Yeah, quarters not even years. I think you're putting the finger on something that a lot of people are not aware, which is when you have complex patients like what you just described, that woman, and you have so much information to review, no less the corpus of the medical literature, and you have help with diagnosis treatments that you might not otherwise thought of. It also gets me back to a point I was going to make the machine vision during colonoscopy where it does pick up these polyps, but it was shown that at the end of the day in the afternoon for gastroenterologists that are doing colonoscopies all day, their pickup rate drops down. They get tired, their eyes are just not working as well. And here your machines, they don't get tired. So these things are augmenting the performance of physicians, clinicians across the board potentially.
(26:28):
And yes, there's a concern as you touched on about confabulation or hallucinations, whatever, but this is a work in progress. There will be GPT-X, BARD-15 or whatever else right now, another area that is hot, which is still very in the earliest nascent stage, is the virtual medical coach. Whereby any of us with all our data, every visit we've ever had, plus our data that's in real time accruing or scans or slides or whatever it is, is all being fed in process with the medical literature and helping us to prevent a condition that we would have high risk to develop or manifest or better management of the various things we do have that we've already declared. What about that, John? Are we going to see virtual medical coaches like the kind we see for going to the airport, or you have an appointment such and such about your daily life, or is that something that is way out there in time?
John Halamka (27:37):
I know you're going to hate this answer. It depends.
Eric Topol (27:41):
Okay. I don't hate that. I like it actually. Yeah.
John Halamka (27:44):
So some years ago, one of my graduate students formed a virtual coaching company, and what he found was patients would often start with a virtual coach, but they wouldn't stick with it because the value add wasn't necessarily there. And that is it wasn't then every day there was something new or actionable. And so if it's few and far between, why do you want to go through the effort of engaging in this? So I think our answer there is we need to make sure that the person who uses it is getting something of value for using it. Reduced insurance rates, free club memberships to a gym, whatever, something of value. So it gets some stickiness.
Virtual AI Coaching
Eric Topol (28:33):
Yeah. Well, it's still early and right now, as you well know, it's really confined to certain conditions like diabetes or depression or high blood pressure. But it certainly has the chance in the years ahead to become broad for any individual. And that gets back to the patient scenario that you presented where you had all the data of that woman who presented with weakness as the inputs. And just think about that happening in real time, giving feedback to any given individual, always thinking that it's optional. And as you say, maybe it'd be more elective. There were incentives, and if people don't want it, they don't have to use it, but it's something that's out there dangling as a potential. Well, of the things we've discussed, there are many potential ways that AI can be transformative in the future, both for clinicians, for health systems, for patients. Have I missed anything that you're onto?
John Halamka (29:40):
Just that in predictive AI, we can judge performance against ground truth. Did you have the disease or not? Did you get a recommendation that was followed up on and it was positive? With generative ai measuring quality and accuracy, doing follow up and oversight is much harder. So I think what you're going to see is FDA and the office of the national coordinator and the White House work through generative AI oversight. It's going to start with, as we've seen voluntary oversight from some of the companies themselves. And it will evolve into maybe some use cases that are considered reasonable practices and others that we defer reasonable practices. Hey, you want an agent that will pre-draft your email and then you just edit it, that's fine. And Mayo is live with that in Epic inbox. How about help you write a letter or help you take, as you say, a very complex medical condition, explain it in eighth grade English or a foreign language. Very good at all of that differential diagnosis, not quite ready yet. And so I think we'll start with the administrative use cases, the things that reduce burden. We'll experiment with differential diagnosis. And I don't think we yet have line of sight to say, actually, we're going to have the generative ai do your diagnosis
(31:09):
Not there yet,
Machines Promoting Empathy
Eric Topol (31:10):
Right? Perhaps we'll never be, particularly for important diagnoses, maybe for routine things that are not a serious matter. One thing that I didn't anticipate, and I want to get your view. When I wrote deep medicine, I was talking about restoring the patient-doctor relationship and the gift of time that could be garnered from having this machine support. But now we're seeing the evidence that the AI can promote empathy. So for example, reviewing a doctor's note and telling the doctor, you didn't show you're very sensitive. You weren't listening, making suggestions for being a more empathic physician or nurse. Did you foresee that too? Because you've been ahead of the curve on all this stuff.
John Halamka (32:04):
So here's an interesting question. You and I are physician, scientist, writers. How many physician scientist writers are there? Not so many. So what you get are brilliant math or brilliant science, and it is communicated very badly. So I did not anticipate this, but I'm saying the same thing you are, which is you can take a generative AI and take something that is not very digestible and turn it to something highly readable. And whether that's empathy or clarity or whatever, it actually works really well.
Eric Topol (32:43):
Yeah. Yeah. I mean, I kind of stunned by this because the machines don't know empathy. They can't feel empathy, but they can promote it. And that's really fascinating. So this has been an uplifting discussion. A lot of the things that's happening now give credit to you that you saw coming long before others, and it's a real joy. So we got to keep up with each other. We got to do some more brainstorming on the things that we haven't discussed today. But thanks so much, John, for joining me and for being such a bright light for the work you're doing with Mayo Clinic as a president of its platform. That's no question. Transforming the future of healthcare.
John Halamka (33:25):
Well, hey, thanks for having me. And I would say both you and I have taken the digital Hippocratic Oath. We will do no digital harm.
Eric Topol (33:33):
Love it.
Transcript with Links
Eric Topol (00:00):
This is Eric Topol, and I'm so excited to have the chance to speak to Melanie Mitchell. Melanie is the Davis Professor of Complexity at the Santa Fe Institute in New Mexico. And I look to her as one of the real, not just leaders, but one with balance and thoughtfulness in the high velocity AI world of large language models that we live in. And just by way of introduction, the way I got to first meet Professor Mitchell was through her book, Artificial Intelligence, A Guide for Thinking Humans. And it sure got me thinking back about four years ago. So welcome, Melanie.
Melanie Mitchell (00:41):
Thanks Eric. It's great to be here.
The Lead Up to ChatGPT via Transformer Models
Eric Topol (00:43):
Yeah. There's so much to talk about and you've been right in the middle of many of these things, so that's what makes it especially fun. I thought we'd start off a little bit of history, because when we both were writing books about AI back in 2019 publishing the world kind of changed since . And in November when ChatGPT got out there, it signaled there was this big thing called transformer model. And I don't think many people really know the difference between a transformer model, which had been around for a while, but maybe hadn't come to the surface versus what were just the deep neural networks that ushered in deep learning that you had so systematically addressed in your book.
Melanie Mitchell (01:29):
Right. Yeah. Transformers are, were kind of a new thing. I can't remember exactly when they came out, maybe 2018, something like that, right from Google. They were an architecture that showed that you didn't really need to have a recurrent neural network in order to deal with language. So that was one of the earlier things, you know, and Google translate and other language processing systems, people were using recurrent neural networks, networks that sort of had feedback from one time step to the next. But now we have the transformers, which instead use what they call an attention mechanism where the entire text that the system is dealing with is available all at once. And the name of the paper, in fact was Attention is All You need. And that by attention is all you need they meant this particular attention mechanism in the neural network, and that was really a revolution and enabled this new era of large language models.
Eric Topol (02:34):
Yeah. And as you aptly pointed out, that was in, that was five years ago. And then it took like, oh, five years for it to become in the public domain of Chat GPT. So what was going on in the background?
Melanie Mitchell (02:49):
Well, you know, the idea of language models (LLMs) that is neural network language models that learn by trying to predict the next word in a, in a text had been around for a long time. You know, we now have GPT-4, which is what's underlying at least some of ChatGPT, but there was GPT-1 and GPT-2, you probably remember that. And all of this was going on over those many years. And I think that those of us in the field have seen more of a progression with the increase in abilities of these increasingly large, large language models. that has really been an evolution. But I think the general public didn't have access to them and ChatGPT was the first one that like, was generally available, and that's why it sort of seemed to appear out of nothing.
SPARKS OF ARTIFICIAL GENERAL INTELLIGENCE
Sentience vs Intelligence
Eric Topol (03:50):
Alright. So it was kind of the, the inside world of the computer science kinda saw a more natural progression, but people were not knowing that LLMs were on the move. They were kinda stunned that, oh, look at these conversations I can have and how, how humanoid it seemed. Yeah. And you'll recall there was a fairly well-publicized event where a Google employee back I think last fall was, put on suspension, ultimately left Google because he felt that the AI was sentient. Maybe you'd want to comment that because that's kind of a precursor to some of the other things we're going to discuss,
Melanie Mitchell (04:35):
Right? So yeah, so one of the engineers who was working with their version of ChatGPT, which I think at the time was called LaMDA was having conversations with it and came to the conclusion that it was sentient, whatever that means, , you know, that, that it was aware that it had feelings that it experienced emotions and all of that. He was so worried about this and he wanted, you know, I think he made it public by releasing some transcripts of his conversations with it. And I don't think he was allowed to do that under his Google contract, and that was the issue. tThat made a lot of news and Google pushed back and said, no, no, of course it's not sentient. and then there was a lot of debate in the philosophy sphere of what sentient actually means, how you would know if something is sentient. And it Yeah. and it's kind of gone from there.
Eric Topol (05:43):
Yeah. And then what was interesting is then in March based upon GPT-4 the Microsoft Research Group published this sparks paper where they said, it seems like it has some artificial general intelligence, AGI qualities, kind of making the same claim to some extent. Right?
Melanie Mitchell (06:05):
Well, that's a good question. I mean, you know, intelligence is one thing, sentience is another. There's a question of whether, you know, how they're related, right? Or if they're related at all, you know, and what they all actually mean. And these terms, this is one of the problems. Of course, these terms are not well-defined, but most, I think most people in AI would say that intelligence and sentience are different. You know something can be intelligent or act intelligently without having any sort of awareness or sense of self or, you know, feelings or whatever sentience might mean. So I think that the sparks of AGI paper from Microsoft was more about this, that saying that they thought GPT-4 four, the system they were experimenting with, showed some kind of generality in its ability to deal with different kinds of tasks. You know, and this, this contrasts with the old, older fashioned ai, which typically was narrow only, could do one task, you know, could play chess, could play Go, could do speech recognition, or could, you know, generate translations. But it, they couldn't do all of those things. And now we have these language models, which seemed to have some degree of generality.
The Persistent Gap Between Humans and LLMs
Eric Topol (07:33):
Now that gets us perfectly to an important Nature feature last week which was called the “Easy Intelligence Test that AI chatbots fail.” And it made reference to an important study you did. First, I guess the term ARC --Abstract and Reasoning Corpus, I guess that was introduced a few years back by Francois Chollet. And then you did a ConceptARC test. So maybe you can tell us about this, because that seemed to have a pretty substantial gap between humans and GPT-4.
Melanie Mitchell (08:16):
Right? So, so, so Francois Chollet is a researcher at Google who put together this set of sort of intelligence test like puzzles visual reasoning puzzles that tested for abstraction abilities or analogy abilities. And he put it out there as a challenge. A whole bunch of people participated in a competition to get AI programs to solve the problems, and none of them were very successful. And so what, what our group did was we thought that, that the original challenge was fantastic, but the prob one of the problems was it was too hard, it was even hard for people. And also it didn't really systematically explore concepts, whether a, a system understood a particular concept. So, as an example, think about, you know, the concept of two things being the same, or two things being different. Okay?
(09:25):
So I can show you two things and say, are these the same or are they different? Well, it turns out that's actually a very subtle question. 'cause when we, you know, when we say the same we, we can mean sort of the, the same the same size, the same shape, the same color, this, you know, and there's all kinds of attributes in which things can be the same. And so what our system did was it took concepts like same versus different. And it tried to create lots of different challenges, puzzles that had that required understanding of that concept. So these are very basic spatial and semantic concepts that were similar to the ones that Solei had proposed, but much more systematic. 'cause you know, this is one of the big issues in evaluating AI systems is that people evaluate them on particular problems.
(10:24):
For example, you know, I think a lot of people know that ChatGPT was able to answer many questions from the bar exam. But if you take like a single question from the bar exam and think about what concept it's testing, it may be that ChatGPT could answer that particular question, but it can't answer variations that has the same concept. So we tried to take inside of this arc domain abstraction and reasoning corpus domain, look at particular concepts and say, systematically can the system understand different variations of the same concept? And then we tested this, these problems on humans. We tested them on the programs that were designed to solve the ARC challenges, and we tested them on G P T four, and we found that humans way outperformed all the machines. But there's a caveat, though, is that these are visual puzzles, and we're giving them to GPT-4, which is a language model, a text, right? Right. System. Now, GPT four has been trained on images, but we're not using the system that can deal with images. 'cause that hasn't been released yet. So we're giving the system our problems in a text-based format rather than like, like giving it to humans who actually can see the pictures. So this, this can make a difference. I would say our, our our, our results are, are preliminary .
Eric Topol (11:57):
Well, what do you think will happen when you can use in inputs with images? Do you think that it will equilibrate there'll be parity, or there still will be a gap in that particular measure of intelligence?
Melanie Mitchell (12:11):
I would predict there, there will still be a big gap. Mm-hmm. , but, you know, I guess we'll see
The Biggest Question: Stochastic Parrot or LLM Real Advance in Machine Intelligence?
Eric Topol (12:17):
Well, that, that's what we want to get into more. We want to drill down on the biggest question of large language models. and that is, are they really you know, what is their level of intelligence? Is it something that is beyond the so-called stochastic parrot or the statistical ability to adjudicate language and words? So there was a paper this week in Nature Human Behavior, not a journal that normally publishes these kind of papers. And as you know it was by Taylor Webb and colleagues at U C L A. And it was basically saying for analogic reasoning ,making analogs, which would be more of a language task, I guess, but also some image capabilities that it could do as well or better than humans. And these were college students. So , just to qualify, they're, they're not, maybe not, they're not fully representative of the species, but they're at least some learned folks. So what did, what did you think of that study?
Melanie Mitchell (13:20):
Yeah, I found it really fascinating. and, and kind of provocative. And, you know, it, it kind of goes along with a, a many, there's been many studies that have, have been applying tests that were kind of designed for humans, psychological tests to large language models. And this one was applying sort of analogy tests that, that psychologists have done on humans to, to, to large language models. But there's always kind of an issue of interpreting the results because we know these large language models most likely do not think like we do. Hmm. And so one question is like, how are they performing these analogies? How are they making these analogies? So this brings up some issues with evaluation. When we try to evaluate large language models using tests that were designed for humans. One question is, were these tests at all actually in the training data of a large language model? Like, had they, you know, these language models are trained on enormous amounts of text that humans have produced. And some of the tests that that paper was using were things that had been published in the psychology literature.
(14:41):
So one question is, you know, to what extent were those in this training data? It's hard to tell because we don't know what the training data exactly is. So that's one question. Another question is are these systems actually using analog reasoning the way that we humans use it? Or are they using some other way of solving the problems? Hmm. And that's also hard to tell. 'cause these systems are black boxes, but it might actually matter because it might affect how well they're able to generalize. You know, if I can make an analogy usually you would assume that I could actually use that analogy to understand some new, you know, some new situation by an analogy to some old situation. But it's not totally clear that these systems are able to do that in any general way. And so, you know, I tdo hink these results, like these analogy results, are really provocative and interesting.
(15:48):
But they will require a lot of further study to really make sense of what they mean, like to when you give, when, when the, the, you know, ChatGPT passes a bar exam, you might ask, well, and let's say it's, you know, it does better than most humans, can you say, well, can it now be a lawyer? Can it go out and replace human lawyers? I mean, a human who passed the bar exam can do that. But I don't know if you can make the same assumption for a language model, because it's the way that it's doing, answering the questions in a way that its reasoning might be quite different and not imply the same kinds of more general abilities.
Eric Topol (16:32):
Yeah. That's really vital. And something else that you just brought up in multiple dimensions is the problem of transparency. So we don't even know the, the specs, the actual training, you know, so many of the components that led to the model. and so you, by not knowing this we're kind of stuck to try to interpret it. And I, I guess if you could comment about transparency seems to be a really big issue, and then how are we going to ever understand when there's certain aspects or components of intelligence where, you know, there does appear to be something that's surprising, something that you wouldn't have anticipated, and how could that be? Or on the other hand, you know, why is it failing? so what is, is transparency the key to this? Or is there something more to be unraveled?
Melanie Mitchell (17:29):
I think transparency is, is a big part of it. Transparency, meaning, you know, knowing what data, the system was trained on, what the architecture of the system is. you know, what other aspects that go into designing the system. Those are important for us to understand, like how, how these systems are actually work and to assess them. There are some methods that people are using to try and kind of tease out the extent to which these systems have actually developed sort of the kind of intelligence that people have. So, so one, there was a paper that came out also last week, I think from a group at MIT where they looked at several tasks that were given that GPT-4 did very well on that seemed like certain computer programming, code generation, mathematics some other tasks.
(18:42):
And they said, well, if a human was able to generate these kinds of things to do these kinds of tasks, some small change in the task probably shouldn't matter. The human would still be able to do it. So as an example in programming, you know, generating code, so there's this notion that like an array is indexed from zero. The first number is, is indexed as zero, the second number is indexed as one, and so on. So but some programming languages start at one instead of zero. So what if you just said, now change to starting at one? Probably a human programmer could adapt to that very quickly, but they found that GPT-4 was not able to adapt very well.
Melanie Mitchell (19:33):
So the question was, is it using, being able to write the program by sort of picking things that it has already seen in its training data much more? Or is it able to, or is it actually developing some kind of human-like, understanding of the program? And they were finding that to some extent it was more the former than the latter.
Eric Topol (19:57):
So when you process all this you lean more towards because of the pre-training and the stochastic parrot side, or do you think there is this enhanced human understanding that we're seeing a level of machine intelligence, not broad intelligence, but at least some parts of what we would consider intelligence that we've never seen before? Where do you find yourself?
Melanie Mitchell (20:23):
Yeah, I think I'm, I'm, I'm sort of in the center ,
Eric Topol (20:27):
Okay. That's good.
Melanie Mitchell (20:28):
Everybody has to describe themselves as a centrist, right. I don't think these systems are, you know, stochastic parrots. They're, they're not just sort of parroting the data that they, they've been trained on, although they do that sometimes, you know, but I do think there is some reasoning ability there. Mm-hmm. , there is some, you know, what you might call intelligence. You know, it's, it's, but the, the question is how do you characterize it and, and how do you, I for the most important thing is, you know, how do you decide that it, that these systems have a general enough understanding to trust them,
Eric Topol (21:15):
Right? Right. You know,
Melanie Mitchell (21:18):
You know, in your field, in, in medicine, I think that's a super important question. They can, maybe they can outperform radiologists on some kind of diagnostic task, but the question is, you know, is that because they understand the data like radiologists do or even better, and will therefore in the future be much more trustworthy? Or are they doing something completely different? That means that they're going to make some very unhuman like mistakes. Yeah. And I think we just don't know.
End of the Turing Test
Eric Topol (21:50):
Well, that's, that's an important admission, if you will. That is, we don't know. And as you're, again I think really zooming in on, on for medical applications some of them, of course, are not so critical for accuracy because you, for example, if you have a, a conversation in a clinic and that's made into a note and all the other downstream tasks, you still can go right to the transcript and see exactly if there was a potential miscue. But if you're talking about making a diagnosis in a complex patient that can be, if, if you, if we see hallucination, confabulation or whatever your favorite word is to characterize the false outputs, that's a big issue. But I, I actually really love your Professor of Complexity title because if there's anything complex this, this would fulfill it. And also, would you say it's time to stop talking about the Turing tests that retire? It? It's, it's over with the Turing test because it's so much more complex than that .
Melanie Mitchell (22:55):
Yeah. I mean, one problem with the Turing test is there never was a Turing test. Turing never really gave the details of how this, this test should work. Right? And so we've had Turing tests with chatbots, you know, since the two thousands where people have been fooled. It's not that hard to fool people into thinking that they're talking to a human. So I, I do think that the Turing test is not adequate for the, the question of like, are these things thinking? Are they robustly intelligent?
Eric Topol (23:33):
Yeah. One of my favorite stories you told in your book was about Hans Clever and the you know, basically faking out the potent that, that there was this machine intelligence with that. And yeah, I, I think this, this is so apropo a term that is used a lot that a lot of people I don't think fully understand is zero shot or one shot, or can you just help explain that to the non-computer science community?
Melanie Mitchell (24:01):
Yeah. So, so in the context of large language models, what that means is so I could, so do I give you zero, zero shot means I just ask you a question and expect you to answer it. One shot means I give you an example of a question and an answer, and now I ask you a new question that you, you should answer. But you already had an example, you know, two shot is you give two examples. So it's just a ma matter of like, how many examples am I going to give you in order for you to get the idea of what I'm asking?
Eric Topol (24:41):
Well, and in a sense, if you were pre-trained unknowingly, it might not be zero shot. That is, if, if the, if the model was pre-trained with all the stuff that was really loaded into that first question or prompt, it might not really qualify as a zero shot in a way. Right?
Melanie Mitchell (24:59):
Yeah. Right. If it's already seen that, if it's learned, I think we're getting, it's seen that in its training data.
The Great LLM (Doomsday?) Debate: An Existential Threat
Eric Topol (25:06):
Right. Exactly. Now, another topic that is related to all this is that you participated in what I would say is a historic debate. you and Yann LeCun, who I would not have necessarily put together . I don't know that Yan is a centrist. I would say he's more, you know, on one end of the spectrum versus Max Tegmark and Yoshua Bengio
Eric Topol (25:37):
Youshua Bengio, who was one of the three notables for a Turing award with Geoffrey Hinton So you were in this debate. I think called a Musk debate.
Melanie Mitchell (25:52):
Monk debate. Monk.
Eric Topol (25:54):
Monk. I was gonna say not right. Monk debate. Yeah. the Monk Debates, which is a classic debate series out of, I think, University of Toronto
Melanie Mitchell (26:03):
That's right
Eric Topol (26:03):
And it was debating, you know, is it all over ? Is AI gonna, and obviously there's been a lot of this in recent weeks, months since ChatGPT surfaced. So can you kind of give us, I, I tried to access that debate, but since I'm not a member or subscriber, I couldn't watch it, and I'd love to actually but can you give us the skinny of what was discussed and your position there?
Melanie Mitchell (26:29):
Yeah. So, so actually you can't, you can access it on YouTube.
Eric Topol (26:32):
Oh, good. Okay. Good. I'll put the link in for this. Okay, great.
Melanie Mitchell (26:37):
Yeah. so, so the, the resolution was, you know, is AI an existential threat? Okay. By an existential, meaning human extinction. So pretty dramatic, right? and there's been, this debate actually has been going on for a long time, you know, since, since the beginning of the talks about this, the “singularity”, right? and there's many people in the sort of AI world who fear that AI, once it becomes quote unquote smarter than people will be we'll lose control of it.
(27:33):
We'll, we'll give it some task like, you know, solve, solve the problem of carbon emissions, and it will then misinterpret or mis sort of not, not care about the consequences. it will just sort of maniacally try and achieve that goal, and in, in the process of that, for accidentally kill us all. So that's one of the scenarios. There's many different scenarios for this, you know and the, you know, debate. The debate was, it was very a debate is kind of an artificial, weird structured discussion where you have rebuttals and try, you know. But I think the debate really was about sort of should we right now be focusing our attention on what's called existential risk, that is that, you know, some future AI is going to become smarter than humans and then somehow destroy us, or should we be more focused on more immediate risks, the ones that we have right now like AI creating disinformation, fooling people and into thinking it's a human, magnifying biases in society, all the risks that people, you know, are experiencing immediately, right. You know, or will be very soon. and that the debate was more about sort of what should be the focus
Eric Topol (29:12):
Hmm.
Melanie Mitchell (29:13):
And whether we can focus on very shorter, shorter immediate risks also, and also focus on very long-term speculative risks, and sort of what is the likelihood of those speculative risks and how would we, you know, even estimate that. So that was kind of the topic of the debate. So
Eric Topol (29:35):
Did, did you all wind up agreeing then that
Melanie Mitchell (29:38):
? No. Were you
Eric Topol (29:38):
Scared or, or where, where did it land?
Melanie Mitchell (29:41):
Well, I don't know. Interestingly what they do is they take a vote at the beginning of the audience. Mm-hmm. And they say like, you know, how many people agree with, with the resolution, and 67 percent of people agreed that AI was an existential threat. So it was two thirds, and then at the end, they also take a vote and say like, how many, what percent of minds were changed? And that's the side that wins. But ironically, the, the voting mechanism broke at the end, . So technology, you know, for the win ,
Eric Topol (30:18):
Because it wasn't a post-debate vote?
Melanie Mitchell (30:21):
But they did do an email survey. Oh. Oh. Which is I think not very, you know,
Eric Topol (30:26):
No, not very good. No, you can't compare that. No.
Melanie Mitchell (30:28):
Yeah. So I, you know, technically our side won. Okay. But I don't take it as a win, actually. ,
Are Your Afraid? Are You Scared?
Eric Topol (30:38):
Well, I guess another way to put it. Are you, are you afraid? Are you scared?
Melanie Mitchell (30:44):
So I, I'm not scared of like super intelligent AI getting out of control and destroying humanity, right? I think there's a lot of reasons why that's extremely unlikely.
Eric Topol (31:00):
Right.
Melanie Mitchell (31:01):
But I am, I do fear a lot of things about ai, you know, some of the things I mentioned yes, I think are real threats, you know, real dire threats to democracy.
Eric Topol (31:15):
Absolutely.
Melanie Mitchell (31:15):
That to our information ecosystem, how much we can trust the information that we have. And also just, you know, to people losing jobs to ai, I've already seen that happening, right. And the sort of disruption to our whole economic system. So I am worried about those things.
What About Open-Source LLMs, Like Meta’s Llama2?
Eric Topol (31:37):
Yeah. No, I think the inability to determine whether something's true or fake in so many different spheres is putting us in a lot of jeopardy, highly vulnerable, but perhaps not the broad existential threat of the species. Yeah. But serious stuff for sure. Now another thing that's just been of interest of late is the willingness for at least one of these companies Meta to put out their model as an open Llama2. Two I guess to, to make it open for everyone so that they can do whatever specialized fine tuning and whatnot. Is that a good thing? Is that, is that a, is that a game changer for the field? Because obviously the computer resources, which we understand, for example, GPUs [graphic processing units] used-- over 25,000 for GPT-4, not many groups or entities have that many GPUs on hand to do the base models. But is having an open model, like Meta’s available is that good? Or is that potentially going to be a problem?
Melanie Mitchell (32:55):
Yeah, I think probably I would say yes to both .
Eric Topol (32:59):
Okay. Okay.
Melanie Mitchell (33:01):
No, 'cause it is a mixed bag. I, I think ultimately, you know, we talked about transparency and open source models are transparent. I mean, I, I don't know if, I don't think they actually have released information on the data they use to train it, right? Right. So that, it lacks that transparency. But at least, you know, if you are doing research and trying to understand how this model works, you have access to a lot of the model. You know, it would be nice to know more about the data it was trained on, but so there's a lot of, there's a lot of big positives there. and it also means that the data that you then use to continue training it or fine tuning it, is not then being given to a big company. Like, you're not doing it through some closed API, like you do for open AI
(33:58):
On the other hand, these, as we just saw, talked about, these models can be used for a lot of negative things like, you know, spreading disinformation and so on. Right. And giving, sort of making them generally available and tuneable by anyone presents that risk. Yeah. So I think there's, you know, there's an analogy I think, you know, with like genetics for example, you know, or disease research where I think there was a, the scientists had sequenced the genome of the smallpox virus, right? And there was like a big debate over should they publish that. Because it could be used to like create a new smallpox, right? But on the other hand, it also could be used to, to, to develop better vaccines and better treatments and so on. And so I think there, there are, you know, any technology like that, there's always the sort of balance between transparency and making it open and keeping it closed. And then the question is, who gets to control it?
The Next Phase of LLMs and the Plateau of Human-Derived Input Content
Eric Topol (35:11):
Yeah. Who gets to control it? And to understand the potential for nefarious use cases. yeah. The worst case scenario. Sure. Well, you know, I look to you Melanie, as a leading light because you are so balanced and, you know, you don't, the interest thing about you is what I have the highest level of respect, and that's why I like to read anything you write or where you're making comments about other people's work. Are you going write another book?
Melanie Mitchell (35:44):
Yeah, I'm thinking about it now. I mean, I think kind of a follow up to my book, which as you mentioned, like your book, it was before large language models came on the scene and before transformers and all of that stuff. And I think that there really is a need for some non-technical explanation of all of this. But of course, you know, every time you write a book about AI, it becomes obsolete by the time it's published.
Eric Topol (36:11):
That that's I worry about, you know? And that was actually going be my last question to you, which is, you know, where are we headed? Like, whatever, GPT-5 and on and it's going, it's the velocity's so high. it, where can you get a steady state to write about and try to, you know, pull it all together? Or, or are we just going be in some crazed zone here for some time where the things are moving too fast to try to be able to get your arms around it?
Melanie Mitchell (36:43):
Yeah, I mean, I don't know. I, I think there's a question of like-- can AI keep moving so fast? You know, we've obviously it's moved extremely fast in the last few years and, but the way that it's moved fast is by having huge amounts of training data and scaling up these models. But the problem now is it's almost like the field is run out of training data generated by people. And if people start using language models all the time for generating text, the internet is going be full of generated text, right? Right. Human
Eric Topol (37:24):
Written
Melanie Mitchell (37:24):
Text. And it's been shown that if these models keep, are sort of trained on the text that they generate themselves, they start behaving very poorly. So that's a question. It's like, where's the new data going to come from?
Eric Topol (37:39):
, and there's lots of upsettedness among people whose data are being used.
Melanie Mitchell (37:44):
Oh, sure.
Eric Topol (37:45):
understandably. And as you get to is there a limit of, you know, there's only so many Wikipedias and Internets and hundreds of thousands of books and whatnot to put in that are of human source content. So do we reach a, a plateau of human derived inputs? That's really fascinating question. I perhaps things will not continue at such a crazed pace so we can I mean, the way you put together A Guide for Thinking Humans was so prototypic because it, it was so thoughtful and it brought along those of us who were not trained in computer science to really understand where the state of the field was and where deep neural networks were. We need another one of those. And you're no one, I nominate you to help us to give us the, the, the right perspective. So Melanie, Professor Mitchell, I'm so grateful to you, all of us who follow your work remain indebted for keeping it straight. You know, you don't get ever get carried away. and we learn from that, all of us. It's really important. 'cause this, you know, there's so many people on one end of the spectrum here, whether it's doomsday or whether this is just stochastic parrot or open source and whatnot. It's really good to have you as a reference anchor to help us along.
Melanie Mitchell (39:13):
Well, thanks so much, Eric. That's really kind of you.
Transcript with some hyperlinks
Eric Topol (00:00):
Hello, Eric Topol here. And what a privilege to have as my guest Al Gore, as we discuss things that are considered existential threats. And that includes not just climate change but also recently the concern about A.I. No one has done more on the planet to bring to the fore the concerns about climate change. And many people think that the 2006 film, An Inconvenient Truth, was the beginning, but it goes way back into the 1980s. So, Al it's really great to have you put in perspective. Here we are with the what's going on in Canada with more than 12 million acres of forest fires that are obviously affecting us greatly, no less the surface temperature of the oceans. And so many other signs of this climate change that you had warned us about decades ago are now accelerating. So maybe we could start off out, where are we with climate change and the climate reality?
The Good News on Climate Change
Al Gore (01:00):
Oh, well, first of all, thank you so much for inviting me to be on your podcast again, Eric. It's always a pleasure and especially because you're the host and we, we have very interesting conversations that aren't on the podcast. So, , I'm looking forward to this one. So, to start with climate you know, the old cliche, there's good news and bad news. Unfortunately, there's an abundance of bad news but there's also an awful lot of good news. Let me start with that first and then turn to the more worrying trends. We have seen the passage in the US last August of the largest and most effective best funded climate legislation passed by any nation in all of history. The so-called Inflation Reduction Act is an extraordinary piece of legislation.
(01:55):
It's billed as allocating $369 billion to climate solutions. But actually, the heavy lifting in that legislation is done by tax credits, most of which are open-ended and uncapped, and a few without any time limits, most a 10-year duration. And the enthusiastic response to the legislation after President Biden signed it has now made it clear that that early estimate of 369 billion is a low-ball estimate, because Goldman Sachs, for example, is predicting that it will end up allocating 1.2 trillion to climate solutions. A lot of other investors and others using economic models are estimating more than a trillion. So, it's really a fantastic piece of legislation and other nations are beginning to react and respond and copy it. One month after that law was passed the voters of Australia threw out their climate denying government and replaced it with a climate-friendly government, which immediately then set about passing legislation that adopts the same goals as the US IRA and the Australian context.
(03:19):
And they stopped the biggest new coal mine there. And anyway, one month after that, in October, the voters of Brazil threw out their former president often called the “Trump of the Tropics” and replaced him with a new president, a former president who's a new president, who has pledged to protect the Amazon and the European Union in responding to the evil, evil and cruel invasion of Ukraine by Russia. And the attempted blackmail of nations in Europe, dependent on Russian gas and oil responded not by bending their knee to Vladimir Putin, but by saying, wait a minute, this makes renewable energy, freedom, energy. And so they accelerated their transition. And so these are all excellent signs and qualifies as good news. The other good news is not all that new, but it's still continuing to improve.
(04:28):
And that is the astonishing reductions in cost for electricity produced by solar and wind, and the reductions in cost for energy storage, principally in batteries and electric vehicles and a hundred other less well known technologies that are extremely important. We're in the midst of early stages of a sustainability revolution that has the magnitude of the industrial revolution, coupled with the speed of the digital revolution. And we're seeing it all over the place. It’s really quite heartening. One quick example last, the, the biggest single source of global warming pollution is the generation of electricity with gas and coal. Well, last year, if you look at all the new electricity generation capacity installed worldwide 90% of it was renewable. In India, 93% was solar and wind. And India's pledged not to give permits for any new coal burning plants for at least five years, which means never, probably because this cost reduction curve, as I mentioned, is still continuing downward electric vehicles, we're now seeing that the purchases have reached 15% of the market globally.
(05:56):
Norway's already at 50%. They've actually outlawed the sale of any new internal combustion engines. And indeed, many national and even municipal and state jurisdictions have prospectively served notice that they, you won't be able to buy them after a certain day, 2030, in many cases and the auto companies and truck and bus companies have long since diverted their research money all their R & D is going into EVs now. And that's the second largest source of global warming pollution. I could go through the others, but I want, I'll just tell you that there is a lot of good news.
And the Bad News
Now, the bad news is we're still seeing the crisis get worse, faster than we're deploying all of these solutions. And, the inertia in our political and economic systems is partly a direct result of huge amounts of lobbying and campaign contributions and the century old net of political and economic influence built up by the fossil fuel industry.
(07:18):
And they're opposing every single solution at the state level, the local level, the national level, the international level. Now, this COP 28 [the 2023 United Nations Climate Change Conference] coming up at the end of the year in the United Arab Emirates is actually chaired by an oil and gas company CEO-- It's preposterous. And they already have in the last two COPS, more lobbyists registered as participants than all than the five or six largest national delegations combined. And we're seeing them really oppose this change. And meanwhile, the manifestations of the crisis are steadily worsening. You mentioned the fires in Canada that are predicted to burn all summer long. And I was in New York City last week, and you, you know, from the news stories it, it was horrific. I got there the day after the worst day, oh my God.
(08:21):
But I saw and heard from people just the tremendous problems that people have. It's also going on in Siberia, by the way, and these places that are typically beyond the reach of TV crews and networks that don't capture our attention unless something happens to blow the smoke to where we live. And that's what's happened here. But there are many other extremely worrying manifestations that aren't getting much attention. I do think we're going to solve this, Eric. I'm very optimistic, but the question is whether we will solve it in time. We are what's the right way to say this? We're tiptoeing through a minefield with tripwires and toward the edge of a cliff. I don't want to torture the metaphor, but actually there are several extremely dangerous threats to ecological systems that are in a state of balance now, and are being pushed out of their equilibrium state into a different format.
(09:35):
The ocean currents--we're already seeing it with the jet stream in the northern hemisphere. You may have seen on the weather maps. They're now using these a lot where it's getting loopier and more disorganized. That's what the last few winners has, has pulled these big loops, have pulled arctic air down into areas far south in the US and in other regions, by the way. And it’s making a lot of the extreme events worse. Now, we're entering an El Nino phase in the Pacific Ocean comes around every so often, and this one is predicted to be a strong one, and that's going to accentuate the temperature increase. You know, it was [recently] 110 degrees last week in Puerto Rico, 111 degrees in several countries in Southeast Asia.
(10:31):
Last summer, China had a heat wave that the historians say about, which the historians say there's nothing even minimally comparable in all prior known, and the length, the extent, the duration, the intensity. And we saw monsoons lead to much of Pakistan underwater for an extended period of time. I could go on, but the net and balance out the good news and the bad news we are gaining momentum. And soon we are going to be gaining on the crisis itself and start deploying solutions faster than it's getting worse. So I remain optimistic, and I always remind people, if you doubt we have the political will to see this through, remember that political will is itself a renewable resource.
The Intersection of A.I. and Climate Change
Eric Topol (11:27):
Yeah, that's a great optimistic point, and we sure appreciate that, because it's pretty scary to see these trends that you reviewed. Now, as you know recently there was a large group of AI scientists this one led by Sam Altman of OpenAI, who put out a statement, a one-sentence statement, and it said, “Mitigating the risk of distinction from ai, which you and are enthusiastic about, should be a global priority alongside other societal scale risks, such as pandemics and nuclear war.” Well, obviously, also climate change. So how do you see the AI intersection of climate change? Because as you well know, GPT-4, having pre-trained with some 30,000 graphic processing units [GPUs], the issues about consumption of energy carbon emissions, the need for water cooling, is AI going to make this situation worse, or will it make it better?
Al Gore (12:33):
Well, yeah. You know, I understand. Well, both would be my answer. And we don't have enough data yet to really know for sure which way it will tip. Maybe we'll talk about the existential risks from generative AI. As this conversation continues, there are many who have spoken up and said, well, wait a minute, before we focus on that, we need to look at the risks that are right, staring us right in the face. I mean, the use of these AI driven algorithms, not necessarily generative AI, but the AI-driven algorithms in social media are causing tremendous harm right now. You've heard about the rabbit holes that people get drawn down into on the internet. That's because of the AI-driven algorithms and the tracking of confidential information about what people are looking at and what they're interested in.
(13:40):
And these are rabbit holes are ,a little bit not to shift metaphors, a little bit like pitcher plants in that they have slippery slides and, oh, and, you know, what's at the bottom of the rabbit hole? That's where the echo chamber is. And when you spend long enough in the echo chamber, then those who are feeding the information to you weaponize a new form of AI, not artificial intelligence, artificial insanity. And, and we see it all over the place where people are utterly convinced of completely ridiculous and provably false conclusions and, and motivated to go out and act in the real world. On that basis we, we see the fakes and the concerns about video and audio deep fakes, and how that's going to have an impact on us and, and all manner of other concerns that need to need to be addressed.
(14:43):
But the existential threat is one that I do want to come back to. But, turning to your specific focus on whether it is going help or hurt or both where climate is concerned, I have co-founded a coalition called Climate TRACE that uses AI in an extremely effective, beneficial way. Trace stands for tracking real-time atmosphere, carbon emissions, and we have a coalition of AI firms, NGOs, university groups and the whole coalition works together to identify with AI, the point source of every single significant stream of emissions of global warm inclusion everywhere on the planet. We released it at the last United Nations Conference, the one that was held in Egypt last year. The top 72,000 emission point sources around the world this fall; we will release the top 70 million emission sources.
(15:54):
We also have every agricultural field in the world down to a 10 meter by 10 meter resolution. We have all, every single power plant, all the steel mills, every large ship, every large plane, most every well, we have all of the significant greenhouse gas emissions that wouldn't have not, that would not have been possible without ai. Now, this is not generative AI. We have used generative ai --not ChatGPT--we tried that, but there are others that are actually more proficient in the views of our team members at writing code. It has saved us time and enhanced our productivity in writing code. So that's one example where AI has been a big help. And we see it in modeling, and we see it in the preparation for adaptation and in other ways. Now, the downside is, you said in your introductory phrasing that the energy requirements and the emissions are just enormous because it is an extremely energy intensive exercise.
(17:09):
And you have to have the GPUs as well as the energy. So it's you could call it “oligopogenic”-- that may not be a word. It may be a hallucination, like GPT is famous for, but what I mean is it, it does tend to favor a very small number, a very wealthy, very powerful, very large companies. Basically, Google and Microsoft are driving the, the rest of the world to try to desperately catch up. You know, the CEO of Microsoft. They stole a march on Google with the release of ChatGPT and then that fascinated people and the pickup and use of GPT unbelievable is just, it, it's there's been nothing like it in.
(18:19):
Previous technological history. The CEO said that he wanted to make call Google out and make him dance. Well you know, Peggy Noonan said in one of her columns, that's not a responsible way for the CEO of such a company to talk. I, I like him, and I'm not really taking a poke at him, insofar as I'm making the point that there're really two companies, and the internal dynamic between the two is driving this frenzy of investment and activity, and the underlying platform, the large language models, they're all almost a commodity now. They're all over the place and have been for a while. But the need for the GPUs, the need for the energy consumption that's limiting the cutting edge developments to these two companies. For now, China doesn't trust it because they don't trust the enhanced political influence.
(19:22):
It might give those using it or the enhanced insight. And there are others that will try to find a way to use it, of course. But the, the emissions itself are extremely harmful and the use of generative AI in the hands of irresponsible actors. And, unfortunately, we're human beings and we have a lot of irresponsible actors around this, around this country, around the world. And they could use that to really put climate disinformation into high gear. They, they can use it in a variety of ways to further enhance the disruption, the disruptive tactics they've used in the past.
Eric Topol (20:15):
Yeah. Well, that's what I wanted to get into more on this. We have, I think, you know, if you want to put an existential risk at the highest level, maybe if you were assign 10 to climate change and you've brought up the fact that the large language models generative AI will make worse, the things we've already seen, the, the hacking of democracy and all the fake stuff that's the conspiracy theories that it will reinforce. And the question is, where are you, where did you place the whole generative AI era that we've now entered in if you were to weigh it against existential threat, just other, one other thing. You've, you undoubtedly, because you read more than anyone I know you're a true scholar, and you've read these doomsayer essays about hacking a democracy and
(21:11):
the end of the world, and some of the notable leaders in AI like Geoffrey Hinton to leave Google. And so we have, on the one hand some people saying this is a real threat to the world. And then we have Marc Andreesen who wrote, “Why AI Will Save the World” last week , a long read on this. So where do you, where do you see the existential threat of now that AI has gone into high gear, as you noted, more than a billion unique users of ChatGPT within 90 days, which is unprecedented. I mean, with
Al Gore (21:45):
All cap, nothing else is even close in history. Yeah,
Where are we with Artificial General Intelligence?
Eric Topol (21:48):
Yeah. So, do you see that this has been exaggerated, the risk of generative AI? Or how do you compare it to the climate change crisis?
Al Gore (22:01):
Well it's a great question, Eric. And of course lots of people we know are breaking their brains trying to answer that question. I think we need a little more experience with it because our understanding is going to develop as we have more experience. But at the same time, we're trying to catch up in our basic understanding of what the heck's going on with these things. And they don't actually know it's important to note they don't know how it's doing what it's doing. And I'll, I'll circle back to that. But while we're trying to figure it out, it's continuing to advance at warp speed. GPT-4 in the cleverly titled, the provocatively titled, research paper “Sparks of Artificial General Intelligence” that Microsoft put out is already demonstrating capacities that are shockingly comparable to human capacity is the way they put it.
(23:13):
This less than a year after Google fired a young researcher named Blake Lemoine who said that he thought theirs had become sentient. And they fired him right away. These multiple co-authors of this paper from Microsoft weren't fired. They're in charge of the thing, and they're basically saying close to what the guy at Google said, who got fired.I think that if you listen to Geoffrey Hinton, the so-called godfather of generative AI, and there's so many, many parents of generative AI. But what caused him to change his mind, in his words, were when he realized that it is very likely to become much smarter than we are, than the smartest human beings ever are. And coupling that level of superintelligence, the phrase some have used with access to all of the knowledge that humanity has ever compiled means there is an unpredictable unquantifiable risk that we might no longer be the apex lifeform on this planet.
(24:47):
And that generative AI might be used that in ways that would be threatening to us. I think we need more experience with it in before we decide, okay, that's it. We not going to unplug all these dang things and bust them up with sledgehammers. That's not going to happen. Cause there's so many different entities pursuing it. But, you know, I placed this the context of one of the themes in that runs through the history of science, Eric. And that is, as we have seen in the past, new discoveries that have challenged our human understanding of our place in creation. For example, when Galileo said, the Earth's not the center of the universe, it's not the center of the solar system, the church said ah, off to prison with you, they put him on trial.
(25:58):
because that challenge our prime place in what we had thought was God's design. Then Darwin, of course, placed us solidly in the animal kingdom, descended from, from primates and apes and monkeys. And of course, that struggle is still, I used to represent Dayton, Tennessee and the United States House of Representatives where, where the, the Scopes Trial took place, the so-called monkey trial. And there have been a succession of other similar blows to the collective ego of humanity. We used to assume confidently that the earth was probably the only place in the whole universe that life where life emerged. And now the common assumption is it's ubiquitous throughout the universe and maybe in advanced forms and lots and lots of places. And by the way, the universe isn't the only universe they tell us.
(26:55):
Now, the emerging better view is that we're in a multiverse, and that's all above my pay grade. But within that, within that continuum of successive blows to the collective ego of humanity, here comes an assertion that something other than a human being may be conscious. And our immediate reaction, as it, as our predecessors' reactions were with Galileo and Darwin, et cetera, nah, that can't be we're special. No, it can't be. We're the only ones. Well maybe not. They are edging closer and closer to a point where scientists and engineers are likely to say, yep, it is conscious. Maybe it won't happen. I kind of think it is already beginning to happen. I think there's an explanation for it, but we're going to have to catch up to that explanation. And we're going have to build this airplane of regulation and safeguards while we taxi it out to the runway.
Can AI Help Solve the Climate Crisis?
Eric Topol (28:06):
Well, you know, I share that view. You know, I don't think that continuing to say this is just a stochastic parrot is where we're at right now. It's a form of intelligence from machines that we haven't seen previously. And as you've really zoomed in on this is the big debate about the level of understanding the so-called “world model.” And, you know, this is something that is only going to get more capable over time. And that gets me to kind of close the loop on our discussion. Do you foresee that we could get to a point where our machine help would come up with new solutions? I mean, as you've summarized, you have phenomenal AI tracking of climate change, but could you foresee that there are potential solutions that we haven't thought of, that, that generative AI could help us as humans to solve the climate crisis?
Al Gore (29:05):
Yeah, I think that's very likely. You know, one of the new professions that's just emerged as a, a prompt engineer—
we'll have to have people trained in prompting these large language models in a way that gets us to the kinds of exchanges you're talking about. But we've, even before generative AI arrived, we have had multiple examples of artificial intelligence solving problems that we humans have not been able to solve. One example that I wrote about several years ago was the long-term effort to try to decode the genetics of a little thing called the planarian worm. It's been of extreme interest because it can regenerate every part of its body. And in, in such an efficient way they've been trying to understand it.
(30:07):
So a group of scientists took all of the raw data from all of the failed experiments collected during all of the failed experiments to try to solve that problem, fed 'em into an AI. And the AI said, okay, here's the answer. And it was credited. The AI agent was credited as one of the co-authors of the resulting study. We've had we've had problems in fluid dynamics solved by artificial intelligence that were impenetrable to us. So there's no question in my mind that some of the solutions that we're looking for, for the climate crisis will be found with the assistance of generative AI. I'm certain of that.
Eric Topol (30:53):
Well, that adds to the optimism that we want to close up with because we need that in the face of what we're seeing that's palpable every day regarding climate change. And, you know, I think this discussion, Al, I could spend the whole day with you because it's so stimulating and your ability to cite history, as well as current and future perspective is, for me, unparalleled. So, I really enjoyed this discussion with you, and I hope we'll have another one real soon, because this generative AI era is zooming, like I've never seen ChatGPT in November, GPT-4 in March, and you know what's next here.
Al Gore (31:35):
So GPT-5 is coming in December, as you said. And, before you conclude, Eric, let, let me just give back to you my admiration for the work that you've been doing on the applications of generative AI in healthcare and the development of even better healthcare technologies. You're the leading exponent of this whole field of knowledge now. And you know, you helped us get through the, our effort to understand the pandemic and all the twists and turns and all of that. And now you're taking the lead on the application of AI in healthcare, and thank you very much. I speak for a lot of people in saying that.
Eric Topol(32:19):
Well, that's really kind to you. That's, that's where my interest was before the pandemic. And now the good part is to be able to get back to it full force. But I do think, unlike the overall existential concerns regarding AI and the large language models of AI, the net benefit for healthcare is just much more obvious. Yes, there are concerns, of course, regarding patient prompts and getting inaccurate responses. However, what it can do for the, the medical community and for patient autonomy is, is really quite extraordinary. So, in that regard another good way to, to sum up our, our discussion here because that's a very, I'm very sanguine about, as we get better about implementing AI in healthcare, it'll make a big difference particularly now with this multimodal AI that brings in images, the records, you all the data that voice, you know, the ambient voice of office visits, as well as even bedside rounds. It's really quite exciting. And I know we're going be talking about that some more in the months ahead. So thank you so much. You've, you've brightened up this day because all I keep seeing are these apocalyptic photos of New York and what's going on out there, graphs of the oceans sea surface temperature. And I'm thinking, oh my, how we keep losing ground on what you told us about for decades. And I like hearing that you think these solutions are and be increasingly to catch up to that. So thank you.
Al Gore (33:59):
Thank you, Eric.
TRANSCRIPT
Eric Topol (00:00):
Hello, this is Eric Topol, and it's really a delight for me to welcome Hannah Davis who was the primary author of our recent review on Long Covid and is a co-founder of the Patient-Led Research Collaborative. And we're going to get into some really important topics about citizen science, Long Covid and related matters. So, Hannah, welcome.
Hannah Davis (00:27):
Thank you so much for having me.
Eric Topol (00:29):
Well, Hannah, before we get into it I thought because you had a very interesting background before you got into the patient led research collaborative organization with graphics and AI and data science. Maybe you could tell us a bit about that.
Hannah Davis (00:45):
Sure. Yeah. Before I got sick, I was working in machine learning with a particular focus on generative models for art and music. so I did some projects like translating data sets of landscapes into emotional landscapes. I did a project called The Laughing Room, where there was a room and you went in and the room would listen to you and laugh if it thought you said something funny, . and then I did a lot of generative music based on sentiment. So I, I did a big project where I was generating music from the sentiment of novels and a lot of kind of like critical projects, looking at biases in data sets, and also curating data sets to create desired outcomes in these generative models.
Eric Topol (01:30):
So, I mean, in a way again, you were ahead of your time because that was before ChatGPT in November last year, and you were ahead of the generative AI curve. And here again, you're way ahead in in the citizen science era as it particularly relates to the pandemic. So, I, I wonder if you could just tell us a bit I think it was back, we go back to March, 2020. Is that when you were hit with Covid?
Hannah Davis (01:59):
Yes.
Eric Topol (02:00):
And when did you realize that it wasn't just an acute phase illness?
Hannah Davis (02:06):
for me, honestly, I was not worried at all. I, my first symptom was that I couldn't parse a text message. I just couldn't read it, thought I was tired. an hour later, took my temperature, realized I had a fever, so that's when I kind of knew I was sick. but I really just truly believed the narrative I was going to get better. I was 32 at the time. I had no pre-existing conditions. I just was, you know, laying around doing music stuff, not concerned at all. And I put a calendar note to donate plasma two weeks out, and I was like, you know, I'm going to hit that mark. I'm going to donate plasma, contribute, it'll be fine. And that day came and went. I was still, you know, pretty sick with a mild case. You know, I didn't have to be hospitalized.
(02:49):
I didn't have severe respiratory symptoms. but my neurological symptoms were substantial and did increase kind of over time. And so I, I was getting concerned. Three weeks went by, still wasn't better. And then I read Fiona Lowenstein’s op-ed in the New York Times. They were also very young. They were 26 at the time, they had been hospitalized, and they had this prolonged recovery, which we now know as Long Covid. and they started the Body Politic Support Group joined that saw thousands of people with the same kind of debilitating brain fog, the same complete executive functioning loss, inability to drive, forgetting your family members' names who were all extremely young, who all had mild cases. and that's kind of when I got concerned because I realized, you know, this was not just happening to me. This was happening to so many people, and no one understood what was happening.
Eric Topol (03:49):
Right. extraordinary. And, and was a precursor, foreshadowing of what was to come. Now, here it is, well over three years later. And you're still affected by all this, right?
Hannah Davis (04:02):
Yes. Pretty severely.
Eric Topol (04:04):
Yeah. And I learned about that when I had the chance to work with you on the review. You were the main driver of this review, and I remember asking you, because I, I didn't know anyone in the world that was tracking Long Covid like you and to be the primary author. And then you sent this outline, and I had never seen an outline in all my years in academic medicine. I never saw an outline like this of the review. I said, oh my God, this is incredible. So I know that during that time when we worked on the review together, along with Lisa McCorkell and Julia Moore Vogel, that, you know, there, there were times when you couldn't work on it right there, there were just absolutely, you would have some good days or bad days. And, and that's the kind of, is that kind of the way is, how it goes in any given unit time?
Hannah Davis (04:55):
I think generally, I, I communicated as like 40% of my function is gone. So, like, I used to be able to have very, very full days, 12 hour days would work, would socialize, would do music, whatever. you know, I, I have solidly four functional hours a day. on a good day, maybe that will be six. On a bad day, that's zero. And when I push myself by accident, I can get into a crash that can be three to seven days easily. Hmm. and then I'm, then I'm just not, you know, able to be present. I don't feel here. I don't feel cognitively able, I can't drive. And then I'm just completely out of the world for a bit of time.
Eric Topol (05:35):
Yeah. Wow. So back in the early days of when you were first got sick and realized that this was not going to just go away, you worked with others to form this Patient -Led Research Collaborative organization, and here you are, you didn't have a medical background. You certainly had a data science and computing backgrounds. But what were your thoughts? I mean, citizen science has taken on more of a life in recent years, certainly in the last decade. And here there's a group of you that are kind of been leading the charge. we'll get to, you know, working with RECOVER and NIH in just a moment. But what were your thoughts as to whether this could have an impact at working with these, the other co-founders?
Hannah Davis (06:27):
I think at first we really didn't realize how much of an impact we were going to have. The reason we started collecting data in the first place really was to get answers for ourselves as patients. You know, we saw all these kind of anecdotes happening in the support group. We wanted to get a sense of which were happening the most at what frequency, et cetera. and it really wasn't until after that when like the CDC and WHO started reaching out, asking for that data, which was gray literature at the time that we kind of realized we needed to formalize this and, and put out an official paper which was what ended up being the second paper. But the group that we formed really is magical, I think like, because the primary motivator to join the group was being sick and wanting to understand what was happening. And because everyone in the group only has the kind of shared experience of, of living with Long Covid, we ended up with a very, very diverse group. Many, many different and I think that really contributed to our success in both creating this data, but also communicating and, and doing actionable policy and advocacy work with it.
Eric Topol (07:42):
Did you know the folks before? Or did you all come together because of digital synapses?
Hannah Davis (07:47):
Digital synapses? I love that. Absolutely. No, we didn't know each other at all. they're now all, you know, they're my best friends by far. you know, we've been through this, this huge thing together. but no, we didn't meet in person until just last September, actually. And many of them we still haven't even met in person. which makes it even more magical to me.
Eric Topol (08:13):
Well, that's actually pretty extraordinary. So together you've built a formidable force to stand up for the millions and millions of people. As you wrote in the review, 65 million people around the world who are suffering in one way or another from Long Covid. So just to comment about the review --you know, I've been working in writing papers for too long, 35 years. I've never, in my entire career, over 1300 peer reviewed papers on varied topics, ever had one that's already had 900,000 downloads, is the fourth most cited paper and Altmetric since published the same timeframe in January of all 500,000 peer-reviewed papers. Did you ever think that the, the work that, that you did and our, you know, along with Lisa and doing, and I would ever have this type of level of interest?
Hannah Davis (09:16):
No, and honestly, it's so encouraging. Our, our second paper to me did very well. and, you know, was, was widely viewed and widely cited, and this one just surpassed that by miles. And I think that it's encouraging because it communicates that, that people are interested, right? People, even if they don't understand what long covid is, there is a huge desire to know. And I think that putting this out in this form, focusing on the biomedical side of things really gives people a, a tool to start to understand it. And from the patient side of things, more than any other paper I've heard we, we get so many comments that are like, oh, I brought this to my doctor and, you know, the course of my care change. Like he believed me and he started X treatment. and that, that's the kind of stuff that just makes us so, so meaningful. and I'm so, so grateful that, that we were able to do this.
Eric Topol (10:16):
Yeah. And as you aptly put it, you know, a work of love, and it was not easy because the reviewers were not not all of them were supportive about the real impact, the profound impact of long covid. So when you now every day you're keeping track of what's going on in this field, and there's something every single day. one of the things, of course is that we haven't really seen a validated treatment all this time, and you've put together a list of candidates, of course, it was in the review, and it constantly gets revised. What are some of the things that you think are alluring from preliminary data or mechanisms that might be the greatest unmet need right now of, of getting some relief, some remedy for this? What, what, what's your sense about that?
Hannah Davis (11:13):
I think the one I'm most excited about right now are JAK/STAT inhibitors. And this is because one of the leading researchers in viral onset illness Ron Davis and that group believe that basically they're, they have a shunt hypothesis, and that means they, they basically think there's a switch that happens in the body after you've, you've had a viral illness like this, and that that switch can actually be unswitched. And that, to me, as a patient, that's very exciting because, you know, that that's what I imagine a cure kind of looks like. and they did some computational modeling and, and identified JAK/STAT inhibitors as one of the promising candidates. so that's from like the, like hypothetical side that needs to be tested. And then from the patient community, from some things we're seeing I think really easily accessed ones include chromolyn sodium.
(12:14):
So these are prescription antihistamines. they're both systemic. So Coen has been seeming to work for patients with brain fog and sleep disorders. And chromolyn sodium particularly works in, in patients with gastrointestinal mast cell issues. People are going on to kind of address the micro clots. I, for me personally, has been one of the biggest changers game changers for my brain fog and kind of cognitive impairment type things. but there's so many others. I mean, I think we, we really wanna see trials of anticoagulants. I'm personally really excited to start on ivabradine which is next up in my queue. And, and seems to have been a, a game changer for a lot of patients too. I V I G has worked for patients who are, have been able to get it, I think for both I V I G and ivabradine. Those are medications that are challenging to get covered by insurance. And so we're seeing a lot of those difficulties in, in access with a couple of these meds. But yeah, just part of, part of the battle, I guess,
Eric Topol (13:32):
You know, one of the leading of many mechanisms that in this mosaic of long covid is the persistence of virus or virus components. And there have been at least some attempts to get some Paxlovid trials going. Do you see any hope for just dealing, trying to inactivate the virus as a way forward?
Hannah Davis (13:54):
Absolutely. Definitely believe in the viral persistence theory. I think not only Paxlovid, but other a covid antivirals. I know that Steve deas and Michael Paluso at U C S F are starting a couple long covid trials with other covid antivirals that yeah, for sure. I think they all obviously need to be trialed A S A P. And then I also think on the viral persistence lens, ev like almost everyone I know has viral reactivation of some sort like EBV, CMV, VZV, you know, we obviously see a lot of chickenpox or shingles reactivations and antivirals targeting those as well I think are really important.
Eric Topol (14:41):
Yeah. Well, and I also, just the way you're coming out with a lot of this, you know terminology and, you know science stuff like I V I G for intravenous immunoglobulin and for those who are not, you know, just remember, this is a non-life science expert who now has become one. And that goes back again to the review, which was this hybrid of people who had long covid with me who didn't to try to come up with the right kind of balance as to, you know, what synthesizing what, what we know. And I think this is something the medical profession has never truly understood, is getting people who are actually affected and, and becoming, you know, the real experts. I mean, I, I look to you as one of the world's leading authorities, and I learn from you all the time.
(15:35):
So that goes to RECOVER. So there was a long delay in the US to recognize the importance of long covid. Even the UK was talking to patients well before they ever had a meeting here in the us, but eventually, somehow or other they allocated a billion dollars towards long covid research at the NIH. And originally, you know, fortunately Francis Collins, when he was director, saw the importance, and he, I learned bequeathed that 2 of the NIH institutes, one of the directors, Gary Gibbons visited me recently because of a negative comment I made about RECOVER. But before I go over my comment, you've been as he said, you, and Lisa McCorkell ,among others from the Patient-led Collaborative have had a seat at the table. That's a quote from Gary. Can you tell us your impression about RECOVER you know, in terms of at least they are including Patient-Led research folks with long covid as to are they taking your input seriously? And what about the billion dollars ?
Hannah Davis (16:46):
Oh, boy. tricky question. I don't even know where to start. Well, I mean, so I think recover really messed up by not putting experts in the field in charge, right? Like we are, we have from the beginning have needed to do medical provider education at the same time that all these studies started getting underway. And that was just a massive amount of work to try to include the right test to convince medical professionals why they weren't necessary. all that could have been avoided by putting the right people in charge. And unfortunately, that didn't happen. unfortunately recovers our, our best hope still or at least the, the best funded hope. so I really want to see it succeed. I think that they, they have a long way to go in terms of, of really understanding why patient representation matters and, and patient engagement matters.
(17:51):
I, you know, it's been a couple years. It's, it's still very hard to do engagement with them. it's kind of a gamble when you get placed on a, a committee if they are going to respect you or not. And, and that's kind of hard as people Yeah. Who are experts now, you know, I've been in the field of Long Covid research more than anyone really I'm working with there. I, I really hope that they improve the research process, improve the publication process. the, a lot of the engagement right now is, is just tokenization. you know, they, they have patient reps that are kind of like just a couple of the patient reps are kind of yes men you know, they, they get put on higher kind of positions and things like that. but they're, I think there's 57 patient reps in total spread across committees. we don't have a good organizing structure. We don't know who each other are. We don't really talk to each other. there, there's room for a lot of improvement, I would say, well,
Eric Topol (18:59):
The way I would put it is, you know, you kind of remember it like when you have gatherings where there's an adult table, and then there's the kiddie's table. Absolutely. Folks are at the kiddy table. I mean, yeah. And it's really unfortunate. So they had their first kind of major publication last week, and it's led to all sorts of confusion. you wrote about it, what did we, what did we glean from that, from that paper that was reported as a 10% of people with covid go on to Long Covid, and there were clearly a risk with reinfections. Can you kind of review that and also what have we seen with respect to the different strains as we go on from, from the Wuhan ancestral all the way through to the various lineages of omicron. Has that led to differences in what we've seen with Long Covid?
Hannah Davis (19:56):
Yeah, that's a great question and one that I think a lot of people ask just because it, you know, speaks to the impact of long covid on our future. I think not just this paper, but many other papers at this point, also, the, the ONS data have shown that that Long Covid after omicron is, is very common. I think the last ONS data that came out showed of everyone living with Long Covid in the UK. After Omicron, which was the highest group of all of them. we certainly saw that in the support groups also, just, just so many people. but people are still getting it. I think it's because it, most cases of Long Covid happen after a mild infection, 75 to 90%. And when you get covid, now, it is a mild infection, but whatever the pathophysiology is, it doesn't require severe infection.
(20:50):
And you know, where I think we hopefully have seen decreases in like the, the pulmonary and the cardiovascular like organ damage types we're not seeing real improvements at all in kind of the long term and the neurological and the ones that end up lasting, you know, for years. And that's really disappointing. in terms of the paper, you know, I think there were two parts of the paper. There were those, those items you mentioned, which I think are really meaningful, right? The, the fact that re infections have a higher rate of long covid is like ha needs to have a substantial impact on how we treat Covid going forward. that one in 10 people get it after Omicron is something we've been, you know, shouting for, for over a year now. and I think this is the first time that will be taken seriously.
(21:42):
but at the same time, the way RECOVER communicated about this paper and the way that you talked to the press about this paper shows how little they understand the post-viral history right, of, of like thinking about a definition. Why wouldn't they know that would upset patients? You know, that and the fact that they, in my opinion you know, let patients take the brunt of that anger and upset you know, where they should have been at the forefront, they should have been engaging with the patient community on Twitter is really upsetting as well. Yeah.
Eric Topol (22:20):
Yeah. And you know, I, when I did sit down with Gary Gibbons recently, and he was in a way wanting to listen about how could recover fulfill its goals. And I said, well, firstly, you got to communicate and you got to take the people very seriously not just as I say, put 'em at the Kiddie table, but, you know, and then really importantly is why isn't there a clinical trial testing any treatment? Still today, not even a single trial has been mounted. There's been some that have been, you know, kind of in the design phase, but still not for the billion dollars. All that's been done is, is basically following people with symptoms as already had been done for years previously. So it's, it's just so vexing to see this waste and basically confusion that's been the main product of RECOVER to date and exemplified by this paper, which is apparently going to go through some correction phases and stuff. I mean, I don't know, but whether that's going to the two institutes that it's, it's N H L B I, the National heart, lung and Blood, and the Neurologic Institute, NINDS, that are the two now in charge of making sure that RECOVER recovers from where it's, it's at right now. And yeah, so lack of treatments, and then the first intervention study that was launched incredibly was exercise. Can you comment about that?
Hannah Davis (23:56):
It's unreal. You know, it's, it, it just speaks to the lack of understanding the existing research that's in this space. Exercise is not a treatment for people with hem. It has made people bedbound for life. The risks is are not, the risks are substantial. that there was no discussion about it, that there was no understanding about it. That, you know, even patients who don't have pem who wouldn't necessarily be harmed by this trial deserve better, right? They still deserve a trial on anticoagulants or literally anything else than exercise. And there's, it just, it, it's extremely frustrating to see it, it would have been so much better if it was led by people who already had the space, who didn't have to be educated in post exertional malaise and the, the underlying underpinnings of it. and just had a sense of, of how to continue forward and, you know, patients deserve better.
(24:55):
And I think we're, we're really struggling because yeah, there's, there's going to be five trials as I understand it, and that's not enough. And none of them should be behavioral or lifestyle interventions at all. you know, I think it also communicates just the, the not understanding how severe this is. And I get that it's hard. I get that when you see patients on the screen, you think that they're fine and that's just how they must look all the time. But recover doesn't understand that for every hour they're asking patients to engage in something that's an hour, they're in bed, you know, that, that they're, they take so much time away from patients without really understanding like the, the minimum they should be able to do is, is understand the scope and the severity of the condition, and that we need to be trialing substantially more serious me treatments than, than exercise. right,
Eric Topol (25:54):
Right, right. And also the recognition, of course, as you know very well about the subtypes of long covid. So, you know, for example, the postural orthostatic tachycardia syndrome pots and how, you know, there's a device, so you don't have to always think about drugs where you put it in the back of your ear and it's neuromodulator to turn down your vagus nerve and not have the dizziness and rapid heart rate when you stand and all the other symptoms. And, you know, it costs like a dollar to make this thing. And why don't you do a trial with that? I mean, that was one of the things, it doesn't have to always be drugs, and it doesn't have to, it certainly shouldn't be exercise. But you know, maybe at some point this will get on on track. Although I'm worried that so much of the billion dollars has already been spent and no less the loss of time here, I people are suffering. Now, that gets me to this lack of respect lack of every single day we are confronted with people who don't even believe there's such a thing as long covid after all this time, after all these people who've had their lives profoundly disrupted.
(27:04):
What, what can you say about this?
Hannah Davis (27:07):
It's just a staggering, staggering lack of empathy. And I think it's also fear and a defense mechanism, right? People want to believe that they have more control over their lives than they do, and they want to believe that, that it's not possible for them personally to get a virus and then never recover and have their life changed so substantially. I really genuinely believe the people who don't believe long covid is real at this point you know, have their own things going on. And just, yeah,
Eric Topol (27:38):
It's kinda like how Covid was a hoax, and now this is, I mean, the, you, you just, of
Hannah Davis (27:44):
Course, but it's true, like it's happened with, it happened with me, CF s it happened with HIV AIDS. Mm-hmm. someone just showed me a brochure of, of a 10 week lifestyle exercise intervention for aids, you know, saying that you could positively think your way out of it. All that is, is, is defense mechanism, just, yeah. You know, it's repeating the same history over and over.
Eric Topol (28:07):
Well, I think you nailed it. And of course, you know, it was perhaps easier with Myalgic encephalomyelitis when it weren't as many people affected as the tens of millions here, but to be in denial. the other thing is the young people perfectly healthy that are those who are the most commonly affected. a lot of the people who I know who have been hit are like you, you know, very young and, and you know like Julia in my group who, you know, was a big runner and, you know, can't even go blocks at times without being breathless. And this is the typical, I mean, I saw in clinic just yesterday, an older fellow who had been in the hospital for a few weeks and has terrible long covid. And yes, the severity of covid can correlate with the sequela, but because of just numbers, most people are more your phenotype. Right, Hannah.
Hannah Davis (29:08):
Right, exactly. It's a weird like math thing for people to wrap their head around. Like, yes, if you're hospitalized, the chance of getting long covid is much, much higher than if you were not hospitalized. But because the vast number of cases were not hospitalized, the vast number of long cases, long covid cases were not hospitalized. but I think like all of these things are interesting clues into the pathophysiology. You know, we also see people who were hospitalized who recover faster than some of these, the neurocognitive mild, my mild encephalomyelitis subtypes for sure. I think all of that is, is really interesting and can point to clues about kind of what is, what is happening at the core.
Eric Topol (29:54):
Yeah. And that I wanted to get into before I wrap up some of the things that are new or added since our review in published in January. so I just recently reviewed the brain in long covid with these two German studies, one of which showed the spike protein was lighting up in the reservoir, the kind of initial reservoir, the brain, the skull, and the meninges. the, the, basically the layers covering the brain, the, particularly the skull bone marrow. And that's where all these immune cells are in high density that are patrolling the brain. And so it really implicated spike protein per se, in people who've had covid. and then the other German study, which was so striking in mild covid, the majority of people where they had it 10 months later, all this signature by m r i, quantitative, m r i of major inflammation with free water and this so-called mean diffusivity, which is basically the leaking and you know, the inflammation of the brain.
(31:01):
And so, and that's as long as they follow the people, you know, if they followed 'em three years, they'd probably still see this. And so there's a lot of brain inflammation that is linked to the symptoms as you've described. You know, the brain fog, the memory executive function. But we have no remedy. We have no way, how can we stop the process? How can we turn it around like, as you mentioned, like a jak stat inhibitor in other ways that we desperately need to get into testing. so that was one thing I, I wonder, I mean, I think people who have had the symptoms of cognitive effects know there's something going wrong in their brain, but here is, you know, kind of living proof that what there's sensing is now you can see it. thoughts about that?
Hannah Davis (31:52):
I mean, I think the research is just staggering. It's so, so validating as someone, you know, who was living this and living the severity of it, you know, without research for years, it's, it's wonderful to finally see so many things come out. but it's overwhelming research. And I, I don't understand kind of the lack of urgency. Those are two huge, huge studies with huge implications. you know, that the, that the spike would still be in the skull like that in the, in the bone marrow like that. and the neuroinflammation I think, you know, feels very obvious in terms of what, like the symptoms end up presenting. why aren't we trialing things like the, the, this is just destroying people's lives. Even if you don't care about people's lives, like it will destroy the economy. Like people are still getting this, this is not decreasing. these are really, really substantial tangible injuries that are happening.
Eric Topol (32:52):
Yeah, I know. And, and there's not enough respect for preventing this. The only way we know to prevented it for sure is just not to get covid, of course. Right. And then, you know, things like vaccines help to some extent. The magnitude, we don't know for sure, you know, maybe metformin helps but, you know, prevention and everyone's guard, not everyone, but you know, vast majority, you know, really let down at this point when there's not as much circulating virus as there has been. Now, another area where it has really been lit up since our review was autoimmune diseases. So we know there's this common link in some people with long covid. There's lots of auto antibodies and self-destruction that's ongoing. The immune system has gone haywire. But now we've learned, you know, this much higher incidence of rheumatoid arthritis and lupus and across, you know, every one of the autoimmune diseases.
(33:44):
So the impact besides the brain autoimmune diseases and then the one that just blows me away at the beginning of the pandemic, even in the first year there were starting to see more people showing up with type two diabetes and say, ah, well it must be a coincidence. And now there are 12 large studies, every single one goes through of a significant increase in type two diabetes and, and possibly even autoimmune diabetes, which makes sense. So this is the thing I wanted to clarify cuz a lot of people get mixed up about this, Hannah, there's the symptoms of long covid, some of which we reviewed, many of the long lists we haven't. But then there's also the sequela to organ hits like the diabetes and immune system and the brain and you know, also obviously kidney and heart and on and on. Can you help differentiate? Cause a lot of people get mixed up by all this stuff.
Hannah Davis (34:46):
Yeah, I mean I think, you know, we started out with symptoms because that's what we knew, that's what we were talking about. but I do think it's helpful to start, and I, I do think it would be helpful to do a big review on conditions and that does include ME/CFS and Diso but also includes diabetes, includes heart attacks and strokes are includes dementia risks. and yeah, I think the, the difficulty with kind of figuring out what, what percent of long covid are each of these conditions is really biased by the fact that for that, doctors can't recognize me CFS and dysautonomia that it doesn't end up in the EHR data. And so we can't really do these large scale like figuring out the percentage of what is what. but I think like, I, I saw someone describe long covid recently as like a, a large scale neurocognitive impairment emergency, a a large scale cardiovascular event emergency. I think those are extremely accurate. the immune system dysfunction is really severe. I really would like to see the conversation start moving more toward the, the conditions and the pathophysiologies based on what we're finding yeah, more than, more than just the symptoms.
Eric Topol (36:15):
Right. And then, you know, there's this other aspect of the known unknown, so with two other viruses. So for example, back in 1918 with influenza, it, it took 15 years to see or more that this would lead to a significant increased risk of Parkinson's disease. And then with polio, the post-polio syndrome showed up up to 30 years later with profound progressive muscular atrophy and, you know, falls and all sorts of major neurologic hits that were due for from the original polio virus. And so, yeah, some of the things that we're learning here with long covid hopefully will spill over to all these other post-infectious processes. But I think what's emphasizing in our discussion is how much more we, we really do need to learn how we desperately need some treatments, how we desperately need to have the respect for this syndrome that it deserves which still isn't there, it's just, it's unfathomable to me that we still have people dissing it on a daily basis and, and not, you know, a small minority, but actually a pretty strident group that's, that's not so small.
(37:35):
Now, before you wrap up, what have I missed here? Hannah with you, because this is a rarefied opportunity to have a sit down with you about what's going on in long covid and also to emphasize citizen science here because this is, if there's anything I've ever seen in my career to show the importance of citizen science, it's been the long covid story. you as one of the leaders of it. So have I missed something?
Hannah Davis (38:05):
I feel like we actually covered a pretty good bit. I would say maybe just for people listening, emphasizing that long covid is still happening. I think, you know, so many people that we see recently got long covid after getting vaccinated or having a prior infection and just kind of relaxing all their precautions and they're, they're angry. You know, the, the newer group of long Covid folks are angry because they were lied to that they were safe, and that's completely reasonable. you know, that it's still happening in, in one in 10 vaccinated omicron infections is a huge deal. and, and I think yeah, just re-emphasizing that, but overall that, yeah, you know, this is very serious. I think there's my, my MO for Twitter, really, honestly, despite all the, the accusations of fear mon mongering, I really don't put extreme stuff online, but I really do believe that this is this is currently leading to, you know, higher rates of, of heart attacks.
(39:08):
I do believe that we will see a, a wave of early onset dementia that is honestly is happening already you know, happening in my friend group already. and like you said there, there's a lot of unknowns that can be speculated about the fact that we see E P V reactivation in so many people. Are we gonna see a lot of onset multiple sclerosis mm-hmm. you know, lymphomas other E B V sequelae, like the danger's not over the danger's actually, like pretty solidly. there's pretty solidly evidence for some, some pretty serious things to come and you know, I keep saying we gotta get on top of it now, but
Eric Topol (39:55):
Well, I, I always the, unfortunately, some, some people don't realize it, but the eternal optimist that we will get there, it's taking too long, but we got to ratchet up the heat, get projects like RECOVER and elsewhere in the world to go in high gear and, you know, really get to testing the promising candidates. You so have aptly outlined here and in your writings. you know, I think this has been an incredible relationship that I've been able to develop with you and your colleagues and I've learned so much from you and I will continue to be following you. I hope everyone listening that if they don't already follow you and, and others that are trying to keep us up to speed, which you know, just this week again, there was a Swiss study, two year follow up showing that the number of people that were still affected significantly with long covid symptoms at two years was 18%.
(40:58):
That's a lot of folks, and they were unvaccinated, but still, I mean, they, in order to have two year follow up, you're going to see a lot of people who before the advent of vaccines. So this, if you look at the data, the research carefully and it gets better quality as time goes on, because we have control groups, we have matched controls, we have, you know, hopefully the beginning of randomized trials of treatment. we'll hopefully get some light. And part of the reason we're going to get there is because of you and others, getting us fully aware, keeping track of things, getting the research committee to be accountable and not just pass off the same old stuff, which is not really understanding the condition. I mean, how can you start to really improve it if you don't even understand it? And who are you going turn to to understand it? you don't, you don't just look at, you know, MRI brain studies or immune lab studies. You got to talk to the folks who, who know it and know it so well.. All right, well this has been hopefully one of many more conversations we'll have in the future and at some point to celebrate some progress, which is what we so desperately need. Thank you so much, Hannah.
Hannah Davis (42:19):
Thank you so much. Absolute pleasure.
Links
Our Long Covid review with Lisa McCorkell and Julia Moore-Vogel
https://www.nature.com/articles/s41579-022-00846-2
The Brain and Long Covid
https://erictopol.substack.com/p/the-brain-and-long-covid
Heightened Risk of Autoimmune Diseases
https://erictopol.substack.com/p/the-heightened-risk-of-autoimmune
Covid and the Risk of Type 2 Diabetes
https://erictopol.substack.com/p/new-diabetes-post-acute-covid-pasc
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Link to the book: The AI Revolution in Medicine
Link to my review of the book
Link to the Sparks of Artificial General Intelligence preprint we discussed
Link to Peter’s paper on GPT-4 in NEJM
Transcript (with a few highlights in bold of many parts that could be bolded!)
Eric Topol (00:00):
Hello, I'm Eric Topol, and I'm really delighted to have with me Peter Lee, who's the director of Microsoft Research and who is the author, along with a couple of colleagues for an incredible book called The AI Revolution in Medicine, GPT-4 and Beyond. Welcome, Peter.
Peter Lee (00:20):
Hello Eric. And thanks so much for having me on. This is a real honor to be here.
Eric Topol (00:24):
Well, I think you are in the enviable position of having spent now more than seven months looking at GPT-4’s S capability, particularly in the health and medicine space. And it was great that you recorded that in a book for everyone else to learn because you had such a nice head start. I guess what I wanted to start with is, I mean, it's, it's a phenomenal book. I [holding the book up], this prop. I can't resist
Peter Lee (00:52):
Eric Topol (00:53):
When, when I got it, I, I couldn't, I stayed up most of the night because I couldn't put it down. It was, it is so engrossing. But when you, when you first got your hands on this and started testing it, what were, what were your initial thoughts?
Peter Lee (01:09):
Yeah. I, let me first start by saying thank you for the nice words about the book, but really, so much of the credit goes to the co-authors, Carey Goldberg and Zach Kohane and Corey in particular took my overly academic writing. I suspect you have the same kind of writing style as well as Zach's pretty academic writing and helped turn it into something that would be approachable to non-computer scientists and as she put it, as much as possible as a page turner. So I'm glad that her work helped make the, the book an easy read. I,
Eric Topol (01:54):
I want to just say you're very humble because the first three chapters that you wrote yourself were clearly the, the best ones for me. Anyway. I don't mean to interrupt, but it, it, it is an exceptional book, really.
Peter Lee (02:06):
Oh thank you very much. It means a lot. Hearing that from you. You know, my own view is that the, the best writing and the best analyses and the best ideas for applications or not of this type of technology in medicine are yet to come. But you're right that I did benefit from this seven-month head start. And so, you know, I think the timing is, is very good. but I'm hoping that much better books and much better writings and ideas will come, you know, when you start with something like this, I, I suspect, Eric, you had the same thing. you start off with a lot of skepticism and I, in fact, I sort of now made light with this. I talk about the nine stages of grief that you have to go through.
(02:55):
I was extremely skeptical. Of course, I was very aware of GPT 2, GPT 3 and GPT 3.5. I understand, you know, what goes into those models really deeply. and so some of the claims, when I was exposed to the early development, GPT-4 just seemed outlandish and impossible. So I, I was, you know, skeptical, somewhat quietly skeptical. We've all been around the block before and, you know, we've heard lots of AI claims and I was in that state for maybe more than two weeks. And then I started to become in that two weeks annoyed, because I know that some of my colleagues like falling into what I felt was the trap of getting fooled by this technology. And then that turned into frustration and fear. I actually got angry. And one colleague who I won't name I've since had to apologize because then I into the phase of amazement because you start to encounter things that you can't explain that this thing seems to be doing that turns into joy.
(04:04):
I remember the exhilaration of thinking, wow, I did not think I would live long enough to see a technology like this. and then intensity, There was a period of about three days when I didn't sleep, I was just experimenting. Then you run into some limits and some areas of puzzlement and that's a phase of chagrin. And then real dangerous missteps and mistakes that this system can make that you realize might end up really hurting people. and then, you know, ChatGPT gets released and to our surprise it catches fire with people. And we learn directly through communications that some clinicians are using it in clinical settings. And that heightens the concern. And I, I can't say I'm in the ninth stage of enlightenment yet, but you do become very committed to wanting to help the medical community get up to speed and to be in a position to take ownership of the question of whether, when, and how a technology like this should be used. and that was really the motivating force behind the book. And it, it was really that journey. And that journey also has given me patience with everyone else in the world, because I realize everyone else in the world has to go through those same nine, nine stages.
Eric Topol (05:35):
Well, those stages that you went through are actually a great way to articulate this pluripotent technology. I mean, I think you, you touched on that chat. ChatGPT was released November 30th and within 90 days had a billion distinct users, which is beyond anything in history. And then of course, this transcended that quite a bit as you showed in the book coming out in you know, just a very short time in March. right. And I think a lot of people want access to GPT-4 because they know that there is this jump in its capabilities. But the book starts off after Sam Altman's forward, which was also nice because he said, you know, this is just an early, as you pointed out there, there's a lot more to come in the large language model space.
(06:30):
But the grabber to me was this futuristic, this second year medical resident who's using an app on the phone to get to the latest GPT to help manage her patient, and then all the other things that it's doing to check on her patients and do all the things that are the tasks that clinicians don't really want to do, that they need help with. And that just grabs you as to the futuristic potential, which may not be so far away. And I think then you get into the nuts and bolts, but one of the things that I think is a misnomer that you really nailed is how you say it isn't just that it generates, but it really is great at editing and analyzing. And here it's, it's called generative AI. Can you, can you expound on that? And it's unbelievable conversationalist capability.
Peter Lee (07:23):
Yeah. you know, the term Generative AI, I tried for a while to push back on this, but I think it's just caught on and I've given up on that. And I get it. You know, I, I think especially with ChatGPT it's of course reasonable for the public to be, you know infatuated with a thing that can write love letters, write poetry and that generative capability. and of course, you know school children writing their essays and so on this way. But as you say one thing we have discovered through a lot of experimentation is it's actually somewhat of a marginal generator of text. I would not say at all. That is, it is not as good a poet as good human poets. It's not the, you know, people have programmed GPT-4 to try to write whole novels and it can do that,
(08:24):
they aren't great. and it's a challenge, you know within Microsoft, our Nuance division has been integrating GPT-4 to help write clinical and encounter notes. and you can tell it's just hitting at the very limits of the capabilities in and of the intelligence of GPT-4 to be able to do that well. But one area where it really excels is in evaluating or judging or reviewing things. And we've seen that over and over again. in chapter three. You know, I have this example of its analysis of some contemporary poetry which is just stunning in its kind of insights and its use of metaphor and allegory. And but then in other situations in interactions with the New England Journal Journal of Medicine experimentations with the use of GPT-4 as an adjunct to the review process for papers it is just incredibly insightful in spotting inconsistencies missing citations to precursor studies to understanding lack of inclusivity and diversity, you know, in approach or in terminology.
(09:49):
And these sorts of review things end up being especially intriguing for me when we think about the whole problem of medical errors and the possibility of using GPT-4 to look over the work of doctors, of nurses of insurance, adjudicators and others, just as a second set of eyes to check for errors check for kind of missing possibilities if there's a differential diagnosis. Is there a possibility that's been something that's been missed? If there's a calculation for an IV medication administration, well, it's a calculation done correctly or not. And it's in those types of applications of GPT-4 as a reviewer, as a second set of eyes that I think I've been especially impressed with. And we try to highlight that in the book.
Eric Topol (10:43):
Yeah. That's one of the very illuminating things about going well beyond what are the assumed utilities in a little bit, we'll talk about the liabilities, but certainly these are functions part of that flurry potent spectrum that I think a lot of people are not aware of. One, particularly of interest in the medical space is something I had not anticipated as, you know, when I wrote a Deep Medicine chapter, “Deep Empathy,” I said, well, we got to rely totally on humans for that. But here you had examples that were quite stunning of coaching physicians by going through their communication, their note and saying, you know, you could have been more sensitive with this. You could have done this, but you, you could be more empathic. And as you know, since the book was published, there was an interesting study that compared a couple hundred questions directed to physicians and then to ChatGPT, which of course isn't necessarily called, we wouldn't say it's state of the art at this point, right. But what was seen that chatbot exhibited, the more empathy, the more sensitive, higher quality responses. So do you think, ultimately that this will be a way we can actually use technology to foster a better communication between clinicians and patients?
Peter Lee (12:10):
Well I'll try to answer that, but then I want to turn the question to you because I'm just dying to understand how others especially leading thinkers like you think about this. Because as a human being and as a patient, there's something about this that doesn't quite sit right. You know I, I want the empathy to come from my doctor, my human doctor that's in my heart the way that I feel. And yet there's just no getting around the fact that GPT-4 and even weaker versions like GPT 3.5, CHatGPT can be remarkably empathetic. And as you say, there was that study that came out of UC, San Diego Medicine, Johns Hopkins Medicine that you know, was just another fairly significant piece of evidence to that point.
Here's another example. You know, my colleague Greg Moore was assisting a patient who had late stage pancreatic cancer.
(13:10):
And there was a real struggle for both the specialists and for Greg to know what to say to this desperate patient how to support this patient. And the thing that was remarkable Greg decided to use GPT-4 to get advice and they had a conversation and there was very detailed advice to Greg on what to say and how to support this patient. And at the end when Greg said, thank you, GPT-4 said, and you're welcome, Greg, but what about you? You know, do you have all the support that you need? This must be very difficult for you. So the empathy just goes remarkably deep. And, you know, if you just look at how busy good doctors and especially nurses are, you can start to realize that people don't necessarily have the time to think about that.
(14:02):
And also that what GPT-4 is suggesting ends up being a prompt to the human doctor or the human nurse to actually take the time to reflect on what the patient might need to hear, right. What might be going through their minds. And, and so there is some empathy aid going on here. At the same time, I think as a society, we have to understand how comfortable we are with the idea of this concept of empathetic care being assisted by a machine. and this is something that I'm very keen and curious about just in the medical community. And, and that's why I wanted to turn the question back around to you. how do you see this?
Eric Topol (14:46):
Yeah, I didn't foresee this, but I, and I also recognize that we're talking about a machine vector of it. I mean, it's a pseudo-empathy of sort. But the fact that it can process where it can be improved and it can help foster essentially are features that I think are extraordinary. I, I wouldn't have predicted that. And I've seen now, you know, many good examples in the book and, and even beyond. So it's a welcome thing and it adds another capability which is partly isn't that, that physicians and nurses are lacking empathy, but because their biggest issue, I think is lacking time. Yes. And the fact that someday there's a rescue in the works, hopefully, that a lot of that time of tasks that are, you know, the data clerk functions and other burdens right, will be alleviated the keyboard liberation that has been a fantasy of mine for some years, maybe ultimately will be achieved.
(15:52):
And the other thing I think that's really special in the book that I wanted to comment, there is a chapter by I think Carey Goldberg. And that was about the patient side, right? And this is what we, we all, the talk is about, you know, doctors and clinicians, but it's the patients who could derive the most. And out of those first billion people that used ChatGPT, many were of course health and medical question conversations. But these are patients, we're all patients. And the idea that you could have a personal health advisor, a concept which was developed in that chapter, and the whole idea that that as opposed to a search today, that you could get citations and it would be at the, at the literacy level of the person asking them, making the prompts. Yeah. Could you comment about that? Because that seems to be very much underemphasized, this democratization of this high level capability of getting you know, very useful information and conversation.
Peter Lee (16:56):
Yeah. And I think also this is also where some of the most difficult societal and regulatory questions might come, because while the medical community knows how to abide by regulations, and there is a regulatory framework, the same is much less true for a doctor in your pocket, which is what GPT-4 and, you know, other large language models that are emerging can, can become. And you know, I think for me personally I have come to depend on GPT-4. I use it through the Bing search engine. sometimes it's simple things that previously weren't mysterious. Like I received an explanation of benefits notice from my insurance company, and it is this notice it has some dollar figures in it. It has some CPT codes, and I have no idea. And sometimes it's things that my son or my wife got treated for.
(17:55):
It's, it's just mysterious. It's great to have an AI that can decode these things and can answer questions. similarly, when I go for a medical checkup and I get my blood test results just decoding those CBC lab test numbers, it, it's, again, something that is just incredible convenience. But then even more you know, my father recently passed away. He was 90 years old, but he was very ill for the last year or so of his life seeing various specialists. I, my two sisters and I all lived far away from him. And so we were struggling to take care of him and to understand his medical care. and it's a situation that I found all too common in our world right now. And it actually creates stress and phrase of relationships amongst siblings and so on.
(18:56):
And so just having an AI that can take all of the data from the three different specialists and, you know, have it all summed up and be able to answer questions, be able to summarize and communicate efficiently from one specialist to the next to really provide kind of some sound advice ends up being a godsend. Not so much for my father's health, because he was on a trajectory that was really not going to be changed, but just for the peace of mind and the relationships between me and my two sisters and my mother-in-law. And so it's that kind of empowerment. you know, in corporate speak at Microsoft, we would say that's empowerment of a consumer, but it is truly empowerment. I mean, it's for real. And you know, that kind of use of these technologies, I think is spreading very, very rapidly and I think is is incredibly empowering.
(19:57):
Now the big question is can the medical community really harness that kind of empowered patient? I think there's a desire to do that. That's always been one of the big dreams, I think in medicine today. and then the other question is, the assistants are fallible. They make mistakes. and so, you know, what is the regulatory or legal or, you know, ethical disposition of that? And so these are still big questions I think we have to answer. But the, you know, overall big picture is that there's an incredible potential to empower patients with a, a new tool and also to kind of democratize access to really expert medical information. and I, I just think it's, you're absolutely right. It doesn't get enough attention even in our book we only devoted one chapter to this, right?
Eric Topol (21:00):
Right. But at Least it was in there though. That's good. At least you had it because I think it's so critical to figure that out. And as you say, the ability to discriminate bad information, confabulation hallucination among people without medical training is, is, is much more challenging. Yes. but I also liked in the book how you could go to go back to another conversation to audit the first one or a third one, so that if you ever are suspicious that you might not be getting the best information you could do, like double data entry or triple data entry, you know, I thought that was really interesting. Now Microsoft made a humongous investment in open AI yesterday Sam Altman was getting grilled, not again, not really in a much more friendly sense, I'm sure about what should we do. We have this, we have this two edge sword likes of which we've never seen.
(21:59):
Of course, you get in the book about does it really matter if it's AGI or some advanced intelligence? If it's working well, it's kind of like the explainability-- black box story. But of course, it, it can get off the tracks. We know that. And there isn't that much difference perhaps between ChatGPT and GPT-4 established so far. So in that discussion, he said, well, we got to have regulatory oversight and licensing. And it's very complex. I mean, what, what are your thoughts as to how to deal with the potential limitations that are still there that may be difficult to eradicate that are the worries?
Peter Lee (22:43):
Right. You know, at, at, at least when it comes to medicine and healthcare. I personally can't imagine that this should not be regulated. it, it just and it just seems also more approachable to think about regulation because the whole practice of medicine has grown up in this regulated space. if there's any part of life and of our society that knows how to deal with regulation and can actually make regulations actually work it is medicine. And so now having said that I do understand coming from Microsoft, and even more so for Sam Altman coming from open eye, it can sometimes be interpreted as being self-serving. You're wanting to set up regulatory barriers against others. I would say in Sam Almond's defense that at back to 2019 prior, just prior to the release of GPT-2 Sam Altman made public calls for thinking about regulation for need for external audit and, you know, for the world to prepare for the possibility of AI technologies that would be approaching AGI..
(24:05):
and in fact just a month before the release of GPT-4, he made a very public call saying even at greater length, asking for the for the world to, to do the same things. And so I think one thing that's misunderstood about Sam is that he's been saying the same thing for years. It isn't new. And so I think that that should give people who are suspicious of Sam's motives in calling for regulation, that it should give them pause because he basically has not changed his tune, at least going back to 2019. But if we just put that aside you know, what I hope for most of all is that the medical community, and I really look at leading thinkers like you, particularly in our best medical research institutions would quickly move to take assertive ownership of the fundamental questions of whether, when, and how a technology like this should be used would engage in the research to create the foundations for you know, for sensible regulations with an understanding that this isn't about GPT-4 this is about the next three or four or five even more powerful models.
(25:31):
And so, you know, ideally, I think it's going to take some real research, some real inventiveness. What we explain in chapter nine of the book is that I don't believe we have a workable regulatory framework no, right now in that we need to develop it. But the foundations for that, I think have to be a product of research and ideally research from our best thinkers in the medical research field. I think the race that we have in front of us is that regulators will rightfully feel very bad if large nervous people start to get injured or, or worse because of the lack of regulation. and so there, you know, and, and you can't blame them for wanting to intervene if that starts to happen. And so, so we do have kind of an urgency here. whereas normally our medical research on say, methods for clinical validation of large language models might take, you know, several years to really come to fruition. So there's a problem there. But at the, I think the medical field can very quickly come up with codes of contact guidelines and expectations and the education so that people can start to understand the technology as well as possible.
Eric Topol (26:58):
Yeah. And I think the tricky part here is that, as you know, there's a lot of doomsayers and existential threats that have been laid out by people who I respect, and I know you do as well, like Geoffrey Hinton who is concerned, but you know, let's say you have a multimodal AI like GPT-4, and you want to put in your skin rash or skin lesion to it. I mean, how can you regulate everything? And, you know, if you just go to Bing and you go to creative mode and you're going get all kinds of responses. So this is a new animal, this is a new alien, the question is that as you say, we don't have a framework and we should move to, to get one. To me, the biggest question that you, you, you really got to in the book, and I know you continue, of course, it was with within two days of your book’s publishing, the famous preprint came out, the Sparks preprint from all your team at Microsoft Research, which is incredible.
(27:54):
169 page preprint downloaded. I don't how many millions of times already, but that is a rich preprint we'll, we'll put in the link, of course. But there, the question is, what are we seeing here? Is this really just a stochastic parrot a JPEG with, you know, loose stuff and juxtaposition of word linguistics, or is this a form of intelligence that we haven't seen from some machines ever before? Right. and, you get at that in so many ways, and you point out, does it matter? I I wonder if you could just expound on this, because to me, this really is the fundamental question.
Peter Lee (28:42):
Yeah. I think I get into that in the book in chapter three. and I think chapter three is my expression of frustration on this, because it's just a machine, right? And in that sense, yes, it is just a stochastic parrot, you know, it's a big probabilistic machine that's making guesses on the next word that it should spit out, or that you will spit out. It, it, and it's making a projection for a whole conversation. And you know, in that, the first example I use in chapter three is the analysis of this poem. And the poem talks about being splashed with cold water and feeling fever. And the machine hasn't felt any of those things And so when it's opining about those lines in the poem, it can't possibly be authentic. And so you know, so we can't say it understands these things.
(29:39):
It it hasn't experienced these things, but the frustration I have is as a scientist, and here's now where I have to be very disciplined to be a scientist, is the inability to prove that. Now, there has been some very, very good research by researchers who I really respect and admire. I mean, there was Josh Tenenbaum's team, whole team, and his colleagues at MIT or at Harvard, the University of Washington, and the Allen Institute, and many, many others who have just done some really remarkable research and research that's directly relevant to this question of does the large language model, quote unquote, understand what it's hearing and what it's saying? And often times providing tests that are grounded in the foundational theories about why these things can't possibly be understanding what they're saying. And therefore, these tests are designed to expose these shortcomings in large language models. But what's been frustrating is, but also kind of amazing is GPT-3tends to pass most, if not all of these tests!
(31:01):
And, and so it, it leaves you kind of, if we're really honest, as scientists, it and even if we know this thing, you know, is not sentient, it leaves us in this place where we're, we're without definitive proof of that. And the arguments from some of the naysayers who I also deeply respect, and I've really read so much of their work don't strike me as convincing proof either, you know, because if you say, well, here's a problem that I can use to cause GPT-4 to get tripped up, I, I have no shortage of problems. I, I think I could get you to trip, get tripped up , Eric. And yet that does not prove that you are not intelligent. And so, so I think we're left with this kind of set of two mysteries. One is we see GPT-4 doing things that we can't explain given our current understanding of how a neural transformer operates.
(32:09):
And then secondly we're lacking a test that's derived from theory and reason that consistently shows a limitation of GPT-4’s understanding abilities. and so in my heart, of course, I, I understand these things as machines and I actively resist anthropomorphizing these machines. But as it, I, maybe I'm fooling myself, but as a discipline scientist, I, I'm, I'm trying to stay grounded in proof and evidence. and right at the moment, I don't believe the world has that I, we'll get there. We're understanding more and more every day, but at the moment we don't have it.
Eric Topol (32:55):
I think hopefully everyone who's listening is getting some experience now in these large language models and realizing how much fun it is and how we're in a new era in our lives. This is a turning point.
Peter Lee (33:13):
Yeah. That's stage four of amazement and joy
Eric Topol (33:16):
Yeah. No, there's no question. And you know, I think about you, Peter, because you know, at one point you were in a high level academic post at Carnegie Mellon, one of our leading computer science institutions in the country, in the world, and now you're at this enviable spot of having helped Microsoft to get engaged with a, a risk, I mean a big, big bet. And one that's fascinating, and that is obviously just an iteration for many things to come. So I wonder if you could just give us your sense about where you think we'll be headed over the next few years, because the velocity that this is moving. Not only is it this new technology that is so different than anything previously, but to go, you know, from a few months to get to where things are now and to know that this road is still a long ways in front of us. What, what's your sense of, you know, are we going to get hallucinations under control? Are we going to start to see this pluripotency rollout particularly in the health and medicine arena?
Peter Lee (34:35):
Yeah. You know, I think first off, I can't say enough good things about the team at OpenAI. You know, I think their dedication and their focus and I think it'll come out eventually also, the, the care that they've taken in understanding the potential risks and, and really trying to create a model for how to cope with those things. I, I think as those stories come out, I think it it will it'll be quite impressive. at the same time, it's also incredibly disruptive, even for us as researchers, it just disrupts everything. Right. You know, I was having interaction after I read Sid Muhkerjee’s's new book, the Song of the Cell. Because in that book on cellular biology one of the prime characters historically Rudolph Virchow who confirmed the cell mitosis and the you know, the thing that was disruptive about Virchow is that well, first off, the whole theory of cell mitosis was debunked.
(35:44):
that didn't invalidate the scientists who were working on cell mitosis, but it certainly debunks many of their scientific legacies. And the other is after Virchow, to call yourself a biology researcher, you had to have a microscope and you had to know how to use it. and in a way, there's a scientific disruption similar here, where there are now new tools and new computing infrastructure that you need, if you want to call yourself a com, a computer science researcher. And that's really incredibly disruptive. so I, I see kind of two bifurcation, I think that's likely to happen. I, I think the team at Open AI and with Microsoft's support and collaboration will continue to push the boundaries and the frontiers with the idea of seeing how close to AGI can truly be achieved and largely through scale. And you know, there, there will be tremendous focus of attention on improving its abilities in mathematics and in planning and being able to use tools and, and so on there. and in that, there's a strong suspicion and belief that as greater and greater levels of general cognitive intelligence are achieved, that issues around things like hallucination will be, become much more manageable. Or at least manageable to the same extent that they're manageable in human beings.
(37:25):
But then I, I think there's going to be an explosion of activity in much smaller, more specialized models as well. I think there's going be a gigantic explosion in, say, in open-source smaller models, and those models probably will not be as steerable and alignable, so they might have more uncontrollable hallucination might go off the rails more easily, but for the right applications --integrated into the right settings--that might not matter. And so exactly then how these models will get used and also what dangers they might pose, what negative consequences they might bring is hard to predict. But I, I do think we're going to see those two different flavors of these large AI systems coming really very, very quickly, much less in the next year.
Eric Topol (38:23):
Well, that's an interesting perspective, an important one in the book you wrote in this sentence that I thought was particularly notable “the neural network here is so large that only a handful of organizations have enough computing power to train it.” we're talking about 20 or 30,000 GPUs, something like that. We're lucky to have two here or four. this is something that I think again, if you were sitting at Carnegie Mellon right now versus sitting with at Microsoft or some of the tech titan companies that have this capabilities, can you comment about this? Because this sets off a very, you know, distinct situation we've not seen before,
Peter Lee (39:08):
Right? First off you know, I can't really comment on the size of the compute infrastructure for training these things, but, but it is, as we wrote in the book, is at a size that very, very few organizations at this point. This has got to change at some point in the future. and even on the inference side, forgetting about training you know, GPT-4 is much more power hungry than the human brain. So it is just the human brain is an existence proof that there must be much more efficient architectures for accomplishing the same tasks. So I think there's really a lot yet to discover and a lot of headroom for, for improvement. but you know, what I think is ultimately the, the kind of challenge that I see here is a technology like this could become as essential infrastructure of life as the mobile phone in your pocket.
Peter Lee (40:18):
And, and so then the question is, can the cost of this technology, how quickly can the cost of this technology, if it should also become as necessary to modern life as the technology's in your pocket how quickly can the costs of this be get to a point where that's, you know, where that is can be reasonably accomplished, right? If we don't accomplish that, then we risk creating new digital divides that would be extremely destructive to society. And what we want to do here is to really empower everybody if it does turn out that this technology becomes as empowering as we think it could be.
Eric Topol (41:04):
RIght I, I think your point about the efficiency the drain on electricity and no less water for cooling. I mean, these are big, big-ticket things and, you know hopefully simulating the human brain will become, and it's less power-hungry state will become part of the future as well.
Peter Lee (41:24):
You, well, and hopefully these technologies will solve problems like you know, a clean energy, right? Fusion containment, all better lower energy production of fertilizers, better nanoparticles for more efficient lubricants. There's all a new catalyst for carbon capture. we, if you think about it in terms of making a bet to kind of invent our way out of climate disaster this is one of the tools that you would consider betting on.
Eric Topol (42:01):
Oh, absolutely. You know, I'm going to be talking soon with Al Gore about that, and I know he's quite enthusiastic about the potential. This is engrossing having this conversation, and I would like to talk to you for many hours, but I know you have to go. But I, I just want to say, as I wrote in my review of the book, talking with you is very different than talking with, you know, somebody with bravado. You're, you know, you have great humility and you're so balanced that when, when I hear something from you or read something that you've written, it's a very different perspective because I don't know anybody who's more balanced, who is more trying to say it like it is. And so, you know, I just, not everybody knows you a lot of people do that might be listening. I just want to add that and just say thank you for taking the effort, not just that you obviously wanted to experiment with GPT-4, but you also, I think, put this together in a great package so others can learn from it, and of course, expand from that as we move ahead in this new era.
(43:06):
So, Peter, thank you. It's really a privilege to have this conversation.
Peter Lee (43:11):
Oh thank you, Eric. You're really really too kind. But it, it means a lot to me to hear that from you. So thank you.
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Transcript
Eric (00:00):
Okay. Hello, this is Eric Topol and this is a rare privilege for me to interview my favorite epidemiologist, Dr. Michael Osterholm. He is the Regents Professor of the University of Minnesota. He's director of CIDRAP, which is certainly one of the leading entities around the world for public health. And, we've been friends for the last few years, which we'll we'll talk about. So, welcome Michael. Such a great privilege to have you today.
Michael (00:31):
Well, thank you, the honor, really is mine. As I have shared with you and others know very well--you have been a real mentor to me and many others during this pandemic. And, I could never repay you adequately for all that you've helped teach me throughout these last three years. It's been immeasurable.
Eric (00:49):
No, if you're too kind, I think it's much different. The opposite way. I've learned so much from you because this isn't my area, as you well know. I thought we'd start with, of course, right now things are relatively good for the pandemic in the United States and mostly around the world, with relatively less cases, less hospitalizations and deaths. But obviously still people are getting infected. And maybe you can tell us about the recent case that you went through that would be enlightening.
End of the Pandemic?
Michael (01:28):
Yeah, I think we're all trying to understand when the pandemic ends. And, as we've discussed many times before, we'll probably know that about a year after it ends, then we'll say, yep, that was the end of it. Don’t for a moment think that at the end means that there won't be cases. You know, for every infectious agent that we think of when causing a pandemic, they still come back, whether it be influenza, or potentially coronaviruses. They will, they will continue to circulate. It's a matter of how many cases occur, how many people die. And I think that's an important point. There isn't really a definition for when a pandemic ends. It's, I guess it's just when you feel like it's over. And clearly the world has come to that conclusion already. You don't need a, an epidemiologist or a politician to tell 'em that the pandemic's over that they feel that we're still seeing about 165 deaths a day in this country from Covid.
(02:24):
So it's hardly gone away completely. But we do have to acknowledge it. Most of those deaths are older individuals, people who have not been vaccinated recently with bivalent boosters. And in that regard, we could surely even reduce the illnesses further. I don't have any faith right now in the surveillance systems that have been set up to look at cases around the world. We've pretty much dismantled that. We are not testing people that we results in reports being made to public health agencies, whether in this country or anywhere else in the world. So I really look at two other things. One is deaths. And even they're realizing that still is a challenge in terms of how complete death reporting is due to covid. But then the other thing we're looking at, which has been really, you might say, public health revolution during the pandemic, and I say revolution cause it's really changed things.
(03:19):
And that is the issue of wastewater surveillance. And we've been able to ascertain in many areas of the world, in fact, with using wastewater surveillance, a much better sense of how much virus is in the community. And so, just in following with your very thoughtful comment about case numbers dropping, that's exactly what we're seeing in most locations in this country too. We, for example, here in the Minneapolis St. Paul area, have seen a dramatic decrease in wastewater activity in the last two months. So I think we're in a place right now where I can hope it'll only get better. On the other hand, you know, I have a lot of respect for this virus, and frankly, we all ought to have a lot of humility. We don't know if another variant will emerge that with, given how much immunity we have in our population will somehow break through that and cause increase in surgeon cases or whether this will become kind of the norm and we'll see less and less.
On Getting Covid
(04:16):
Now, you asked me about my case. Yeah. I have to say that, I speak about this with, with really some trepidation in the sense that I was not gonna get this. I had and very faithful throughout the course of the pandemic, where in my N 95 respirator when I went out and about, I had been fit tested. In addition, when we finally did socialize in our home, we had a, what became affectionately known as the Osterholm Home Rule. You could not have had known contact with someone of the, with Covid in the five previous days. You could have no symptoms yourself on the day of, and you had to test negative bilateral flow test within three to four hours of coming. And we would entertain small four, the six party, parties, and it was going wonderful.
(05:07):
And then on March 10th, the night of March 10th, a colleague from work came over with Fern and myself. Three of us had dinner. We went down our elevator in our building here, which were 31 stories up. No one else is in the elevator. And then we proceeded to go to a very small music venue where we wore N95s. We were some distance from any other people, and we were there for an hour and 45 minutes. And, literally two days later, almost 48 hours later, all three of us developed symptoms. None of us converted for another 24 hours. And then at that point, we all three tested lateral flow positive.. We all three took Paxlovid. I took it and was starting to feel better after that fifth day.
(05:59):
And then I kind of crashed and at that point, I got a second, , five day course of Paxlovid and started to feel better. And, I'm you know, was very happy to have this behind me. However, over the course of the last 10 days, I have really had significant fatigue. You know I'm not one that sleeps a lot But, I can tell you there are multiple times in a day where I'm doing something like even doing what I'm doing right now where I just feel like I just need to fall asleep. It’s been really a challenge. The other thing that happened, which was in retrospect a little bit more concerning than I realized at the time, there was a period at about day 10 to 14 into my illness, I started losing my memory on many, many things of, you know, importance.
(06:53):
I couldn't, for example, tell you what was that drink that is: a champagne, orange juice combination. I couldn't find the word mimosa if my life depended on it. If somebody asked me who was in sleepless in Seattle, I had to think about now the movie who was in it. I couldn't remember. And I mean, in retrospect, I wasn't that concerned thinking, ah, it's not that bad. And it was actually quite remarkable. This lasted about two and a half, three weeks. And now I think, I think at least according to those around me, I have gained most of my memory back. But now I have the fatigue picture. So, as much as I don't know where I picked up the virus, all three of us picked it up. And as much asI feel like I have survivor's guilt right now in the sense that, you know, I'm not that concerned about getting infected in a public exposure given I probably have some pretty good protection, at least for a few more weeks. But nonetheless, I think this potential fatigue issue is really a challenge.
Eric (07:52):
Yeah. The things that you're bringing up with this, like for example, I know you had had, the initial series and three boosters including the bivalent. Was that sometime in September last year, or,
Michael (08:04):
Yeah, it was seven and a half months before,
Eric (08:07):
Yeah. So,
Michael (08:07):
So, so that was, and I tried to get it at six months in the second. But in Minnesota we actually have a registry. And so it's not just your white card that, you know, you could do it. And it wasn't, I was trying to do something illegal, but you know, this vaccine's just sitting there. So I tried to get another bivalent at six months post my first one, and of course I was turned down. And then, five weeks after that I got covid.
Eric (08:33):
Yeah. And, and then of course, just recently the FDA and CDC finally came to the conclusion that for people of our age group and immunocompromised, they certainly have the option that you've advocated for. And unfortunately, you weren't able to get at that time. Although I suspect the protection, you might comment on that, Mike, that there is some protection infection for the first few months after a booster.
Michael (09:00):
Yeah. Yeah, absolutely. I mean, I think the studies that we've seen so far, at least, and particularly from those from other countries where they have remarkable follow up on databases, there is some initial evidence of protection in those first weeks against getting infected and even potential transmission. But that wanes unfortunately, quickly, and it's likely B-cell related immunity. And then I think as we all, at least believe the T-cell immunity, which we're still all trying to understand and characterize, probably kicks in and gives us protection against serious illness, hospitalizations and deaths. But as you and I have looked at even then, at six months out, you start to see some potential waning of that. And I think that's why we have a real challenge right now. I've said many, many times, we can't boost our way out of this pandemic. And I meant that not because some of us wouldn't be willing to get a vaccine every six months, but the vast majority of the population would not. And we've even seen here with the first bivalent booster dose, which we know has provided good protection against serial serious illness, hospitalizations, and deaths. Look at the very small proportion of the [age 65+] population that have taken that less than 40%. So it's a challenge that how do we get people to keep getting vaccinated? A lot of people say, I'm done. I'm, I'm done with it.
Eric (10:22):
Right, right. Unfortunately, especially those who are at high risk. It's really unfortunate. Now, one of the things you've done recently among many things, you covered the status of the pandemic today and some liabilities for the future. And you've been working on the future with the blueprint that you put together from people, experts around the world to try to map out, optimally managing this pandemic’s future, preparing for the next pandemic. Could you give us the skinny on that?
Michael (10:52):
Well, actually this was a report that is relabeled the Covid Wars put out by the Covid Crisis Group, which was a loose affiliation of 34 individuals who had agreed to help out developing basic materials with the hope that that would lead to a post pandemic commission, much like the commission we saw after nine And then the person that headed that up actually was the person who did head up the 9/11 commission also. And there was support from several foundations for this. When it became clear, after almost a year of trying to pull together lessons learned challenges to what we know and don't know, the US government was not gonna support, another commission either at the, in the legislative side of the government or in the executive branch. Both of them basically said, well, we're not really interested.
(11:48):
I think that's been a major mistake. But this report, which is now out, does address a number of the shortcomings that we have experienced with this pandemic. And again, you know, in a world where it's so partisan and everyone wants to blame someone for something, this was not meant to blame. This was meant to be what we classically call a hotwash, where we go back over an experience to learn from it. What could we have done differently? How could we have done it? What did we do right? How do we have to make sure that that's in place in the future? And so this plan is, is about that very thing. Now, at the same time I'm writing another book, much like the one I did, deadliest Enemies Our War, againsts Killer Germs in 2017, when I laid out what a pandemic might look like.
(12:38):
And this one is really to address what do we need to learn from this pandemic for the next one? And I go into a bit more in certain topic areas, than our report did much more in depth as it relates to vaccines, public health actions, lockdowns, all of those things. And so I hope that in a, you know, a few months that'll be available so that not only does it lay out what the challenges were, but, you know, given my public health experience of 48 years and having been through these, what do I think the lessons learned should be?
A Major Prediction and Being Called Irresponsible
Eric (13:17):
I can't wait to read it. I mean, the roadmap, though, that you've pulled together, was really extraordinary. And of course, it addressed the things like pan-coronavirus vaccine and, and so many others that we can, pursue hopefully, and be also templates for the future. Now, I want to go back now since we recovering kind of the current future status, but back in March, 2020, you wrote that there would be, this is March, 2020, there would be 800,000 deaths in the next 18 months from Covid. Talk about an oracle, I mean, obviously no one would ever wanted that to see that, be actualized, but how did you, how did you know that, Mike? How did you know we were, we were in that, in, in store for such a dreaded outcome in an imminent period of time?
Michael (14:13):
Well, you know, let take a step back to December of 2019. You know, our center has a very active news team that basically covers infectious disease news from around the world. Even though it's inside of CDRAP, there's a thick wall between it and me, from an editorial standpoint, so I don't have any control over it. But they notified me that they were picking up information that last week in December, out of Wuhan about this emerging outbreak of unexplained pneumonia. And, you know, at that point we stayed on top of it. And of course, my first thought was, could this be a, a flu situation with an emerging flu pandemic, or was it just more coronavirus? You know right after 9/11, I spent three years as a special advisor to then Secretary of Health New Services, Tommy Thompson.
(15:06):
I split my time between the University of Minnesota and the government. And it was during that time that I actually participated actively in the first SARS outbreak that occurred, with regard to the US involvement. And then in 2012, I had been serving as an advisor to the royal family of the United Arab Emorys. And when merged first emerged on the Arabian Peninsula, I went over and worked, on that issue. And then in 2015, when MERS exploded, literally in Samsung Medical Center in South Korea, I was asked to come and I went to over to Seoul and help with that outbreak. So I had a, a pretty good feeling, I thought, for coronaviruses. And of course, influenza is something that I had been working on for 40 years. And so initially I was saying, I hold up, boy, I hope this is a coronavirus, because we know how to control that.
(15:55):
They're not, it's not that infectious. Even though the case fatality rates may be between 15 and 35%. Well, as you know, by the end of that first week in January, we had the data saying, yep, this was a coronavirus. But it was at that time that we had contacts in Wuhan and in Hong Kong, and we were basically getting information out. And then of course, following up with our colleagues in Singapore, the old flu network that was suggesting that this was a very different kind of coronavirus. This, there appeared to be substantial transmission among those who were asymptomatic as well as those who were symptomatic. And as we saw more and more transmission, outside of, of Wuhan, it reminded me of great deal of what we saw in 2009 with H1N1, where there in the month after it was first discovered in Mexico, it was subsequently found in 128 different countries in just one month.
(16:52):
And, and it looked like this is what this coronavirus was doing. And so on January 20th, actually our center put out a statement saying, get with it world. This is the next pandemic. It is a coronavirus acting differently than MERS and SARS, my worst fear was that the case totality rate may be as high as that. Well, over the course of the next few weeks, we got more in better information about what was going on. And there was just such a denial at the time. In fact, I went to JAMA, and to the editor's and said, can I do a perspectives piece on why the world has to wake up quickly? This is going to cause a pandemic. They not only turned me down, but the following week they ran a cartoon in JAMA, one pager on one column looking at Covid, and Coronavirus is on the right kind column looking at influenza.
(17:43):
And they came to the conclusion, don't get distracted by this coronavirus thing, it's about flu. Wow. And so I think at that time, there was such denial that was going on. So when I first made this statement, I actually did it by the kind of the back of the seat estimate. You know, I'm not a black box guy. I, in fact, I find black boxes often, they sort of press the hell outta you with their sophistication. And what they don't tell you is they have no clue what they're talking about. So I just basically did a back to the envelope calculation and not even realizing vaccine might or might not come into place. So, you know, I have to be honest and say it was in some ways luck.
Eric (18:29):
Yeah, I don’t know, I think it's a lot of wisdom and mixed with that.
Michael (18:33):
You know, I want to add one, I want to add one of the thing though, Eric, because the thing that I will most remember probably in this pandemic is not all the hate mail that I received from so many as the days went on and even death threats. It was the feedback I got in that month of March from colleagues who thought that I was over the top that I had finally, you know, scared the hell out of people one too many times kind of thing. And it was amazing to me, as much as we're critical of the politicians and what happened, and we surely should be, there were many of our colleagues who were equally in a state of denial not wanting to believe that this was really happening.
Eric (19:15):
Oh, absolutely.
Michael (19:16):
Yes. So I think that's what I'll remember is, it's one thing to have some anonymous person tell you, you know, that you should be dead. It's another thing to have one of your colleagues say you're irresponsible.
Organizing the “Party Planning Group”
Eric (19:29):
Yeah. You're not kidding there. And you know, especially with you because you know, everybody who's listening has seen you innumerable times on, you know, CNN, MSNBC, Meet the Press, and, various news networks, and they know you come across with humility, unlike many other experts where, you know, you say we just don't know. And also the master of metaphors, as far as I can tell, like the eye of the hurricane and so many things like that. But the other thing I wanted to get into historically is something that brought us together that a lot of people still, it's been written about, but a lot of people still don't know. So back in the summer of 2020, you said, I'm gonna organize a group, a group that eventually became known as the party planning group that we meet every Friday morning for an hour or so. And we talk about, well, there's a pandemic and related matters. So you again, had this idea to bring this group together. And could you talk about that, because it's amazing here it is. You know, two and a half years later we met today, we are, we're continuing to meet, tell, tell everybody about whether that group, how, how you first saw the need for it. And perhaps, you know, what do you think it's accomplished?
Michael (20:43):
Well, first of all, let me start out with two caveats, number one, and thank you for your comments. But I realize the older I get, the more vulnerable I am to learn . And so I want to surround myself with people that can teach me. Okay. The second thing is, is that humility should be considered a requirement today of trying to deal with pandemic viruses because we have to acknowledge, we don't know what the next major curve ball is going to be. You know, I can remember a, a a light bulb moment for me early in January of 2021 when vaccines were now flowing. But recall you and I together, we wrote a piece on this. So Alpha variant emerging out of Europe. And remember up until then, that time, we kept being told that, well, these variants, the sub variants are really just nothing more than rings on a tree.
(21:36):
They're just telling you how old the virus is. And with Alpha, we had clear and compelling evidence, oh no, it had a lot to do with functionality, how infectious it was, et cetera, and that that could very well change the complexion. And I remember very well, being on Meet the Press in January of 2021 and saying, I thought the darkest days of the pandemic were still ahead of us because of the number of people who were not vaccinated. The fact that this viruses was going to continue to change. And of course, again, I caught a lot of heat for that Nate Silver, who gutted me in public media for irresponsible. And of course, as you know, the vast majority of deaths occurred after that time. Right, right. But now to back up to your point and why I think some of the things that I was able to learn occurred was in the summer of 2020, a colleague of mine who, very near and dear came to me, said that there is someone in the senior level of government that right now is making some major decisions, but really has no one around him he knows he can trust.
(22:42):
Would you ever talk to him and, and provide what information you can to kind of give him a sense off the record? Well, I thought, you know, actually it would be better cuz there's a team of people I think that could be more helpful. I'm one, I'm one voice and I surely don't proclaim to have the only voice. So I actually literally went to my might say, magical list, who are the people that I most respected and admired, and who did I trust? And trust was huge. Trust was huge. And as you know, you're on that list. it's now been publicly stated. Peggy Hamburg. Peter Hotez, Bruce Gellen, Pennny Heaton and, Ruth Berkelman. And you know, we, we meet on every Friday and our discussions are incredibly, incredibly thoughtful.
(23:39):
They are honest and there's a trust in that group. You know, what we share stays there. And I, I so appreciate that. And so from that perspective, that will continue and I will continue to learn from all of you. And I think if it was any one lesson that came out of from this pandemic is just the value of having that kind of collective brain trust that can come in, ask questions. Many times we didn't have the answers, but we surely got the questions out, which then gave us opportunities to learn the answers. And the fact that we could do it. And you and I both knew that our comments were gonna stay within the context of that group.
Eric (24:20):
Yeah. And we had to keep it anonymous with this name of party planning group just because we didn't want people to know what this was.
Michael (24:29):
Yeah. At that time, it was interesting. I have to tell my administrative assistant was out one day during that time, early time period. And someone else, was sitting in and they saw in my schedule an hour blocked off for party planning. And it was right at the holiday season. So there was an assumption made in my, in our center that I was just planning this big holiday party and that nobody knew about it yet and said party planning. And that rumor got spread got, was spread throughout the entire center. And I had to self-correct, you might say and explain we can still have a party, but that wasn't what this was about.
Eric (25:07):
Yeah. Well, it, it's been an amazing ride and it continues, but, you know, we were there from well before, there were vaccines all the way through to the current time. And you can imagine all the different things that have been happening in the background and that we were discussing, exchanging ideas, communicating with the public health agencies, the White House and all sorts of other issues along the way. So it's been a privilege for me not just to have this conversation, but over these last two and a half years to work with you on that. It’s been extraordinary and to learn from you and our colleagues. Well, this has been so much fun for me, Mike, I I just am struck by your ability to weave together, you know, the, the wisdom you've drawn from all these experiences over four decades of working in this space with the ability to be humble and know that, you know, you're not the smartest guy in the room.
(26:07):
No one's the smartest guy in the room that you want to have other people, you know, whether, wherever they come from, like for example, when you put together the Roadmap and you brought together, you know, people from all over the world, to think, to exchange ideas about how we can do better for this and future pandemics, because undoubtedly we're gonna be facing those. So maybe, as we wrap up, could you just give us your sense, there's obviously climate change, there's all the things that have been done to the environment and this pandemic, which we all want, wish to be, you know, put aside, the virus will be here for many years to come. But what are your expectations since unfortunately your predictions have come too close to real, about the next pandemic. Will it be influenza? Will it be in the next few years? What are your thoughts about where we're headed?
Michael (27:05):
Well, you know, Eric, let me just start out and say thank you for your very kind comments. I think one of the things I learned at CIDRAP a long time ago is the very name, the Center for Infectious Research and Policy. And I knew very early in my career that well designed, well conducted even very important research means nothing if you can't translate that into active policy that makes a difference. At the same time, policy, if it's not informed by good research can be dangerous. And so I think what you're highlighting here is how we try to bring groups of individuals together to merge research and policy together. And you just talked about the Coronavirus vaccine roadmap, where 54 of the world's leading experts, including you, participated in that. And we developed a very, very specific, outline for a roadmap of what needs to be done to get us to new and better coronavirus vaccines, and ones that basically, will be hopefully broadly protective for any future coronavirus activity that occurs.
(28:12):
So I can never say enough about the ability to bring shareholders together. Collective wisdom will win every time against a wisdom. And I think that that's one thing I learned in terms of where we're going. You know, I, I have to just think back into human history. And when you think about the fact that in 1900 average life expectancy in this country is about 43 years, and today, even with the pandemic, it's about 76, 77 years. For every three days we've lived in the last century and 20 plus years, we've gained one day of life expectancies that takes us all the way back to the 80,000 generations to the caves. And I think what we haven't fully understood is, is that we lived in a world where infectious diseases had major impact on why we didn't live to be as a median age, life expectancy, up into the seventies, but rather, into the forties.
(29:13):
And I think what we're facing today is a world that's moving us back to those, numbers not forward. For example, if you look at just the situation right now of world population, 8 billion people on the face of the earth you look at, you know, what's happening with mega cities around the world. You know, I, remember early in the days of HIV aids making a trip to Kinshasa, which no longer is of course, where it was a large rural city. Today it's 18 million people. When you look at the median age of Africa, it's 19 years. When you look at what we've done with human population and how we have reached out to every corner of the world seeking food, bush meat, et cetera, I, you know, Ebola has been a problem likely for many, many, many decades. But when it was in very rural, isolated villages of Africa, you know, if 25 or 30 people got infected and died, no one even knew about it.
(30:19):
Now, today with the organization of Africa, you can see widespread transmission quickly in these areas. And this is true for all parts of the world. Think about avian influenza and the need today to feed 8 billion people. We have relied on birds on, on the fastest that as an animal species is the fastest conversion of energy to protein on earth. And so look at the billions of birds we're raising, which now provide for a new reservoir for flu viruses. I can go down the list, look at how climate change is moving, in terms of precipitation levels and temperatures that now move mosquito populations to places of existence we didn't see before. And then added transportation in. Think about all of history to World War II, the four serotypes of Dengue virus existed in four different regions of the world. It wasn't until First World War II that now they all exist virtually where each one exists.
(31:19):
Why do we have Dengue hemorrhagic fever? It's because of that. And so I think that the final piece I would say is yes, pandemic's gonna happen again. We are going to see more of what we've just experienced. And frankly, it could be a lot worse. We didn't see 15 to 35% mortality rates like you might with SARS or mers, but instead we saw just high transmission levels. There is nothing to stop the next coronavirus from being transmitted like SARS -CoV-2in killing like MERS or SARS. Mm. And so I think we have to be mindful of that. And the final last thing, I would just paint this is our climate change issue in infectious diseases. It's antimicrobial resistance, it's amorphic, people all know it's there, what to do about it. And we are watching ourselves literally devolve back into a pre pandemic era of antibiotic resistance.
(32:14):
Meaning that, you know, before our grandparents were around, people died often from common in, you know, cuts, bruises, et cetera, because they didn't have antibiotics. Look what's happened since that time, they've played a huge role. Sure. And now we're gonna watch that. You know, we're wildly that. And then last one, at least, I just have to say misinformation, disinformation on vaccines is huge. I think that we're gonna continue to see increasing challenges with populations around the world, no longer willing to take childhood immunizations or even other adult immunizations just because of the disinformation. So when you add that all up, it's job security, unfortunately, for a lot of us. And that's a sad commentary. It's real, yeah.
Eric (32:58):
Well, and as you pointed out so well, just before we got started with AI, it has a potential to amplify the myth and disinformation to unprecedented levels. And it's already so, you know, horrific as it is.
Michael (33:13):
You know, it's bad enough that I can just say that there are times I read articles in newspapers, and I'll get halfway through a quote and I'll say, who the hell said that? According to Osterholm? And of course, what? ,
Eric (33:28):
Right. There you go.
Michael (33:30):
What are we gonna do when you and I end up on these bots? You know, we're there Is Eric Topol saying, saying to the world, you know, I was wrong. Vaccines aren't any good. Yeah. And people are gonna see that, and it's not you. Right?
Eric (33:44):
Right.
Michael (33:44):
A lot that concerns me a lot.
Eric (33:46):
No, it, it was deep fakes and now it's going to another ultra level of that. It's pretty scary actually. So with all the things that we've been talking about, whether it's a potent virus or a tech like AI is becoming with generative AI we've always gotta look at both sides of this and, and be prudent, to put it mildly. Well, this has been fun. And I can't thank you enough. I I, I would like to talk to you all day, but we've got a got a lot in there in a half hour, and I know we'll get a lot of interactions from the folks that are listening. Mike, thanks.
Michael (34:25):
Gift to all of us. You're a gift to all of us. Thank you.
Eric (34:28):
Oh, thank you. That's much too kind.