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Sid Mukherjee: On A.I., Longevity and Being A Digital Human Mar 29, 2024

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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 MukherjeeEric 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 DiscoverySid 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 CancerEric 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 LongevityEric 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 ve…

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Holden Thorp: Straight Talk from the Editor-in-Chief of the Science family of journals Mar 17, 2024

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There was so much to talk about—this is the longest Ground Truths podcast yet. Hope you’ll find it as thought-provoking as I did!Transcript, with audio and external links, edited by Jessica Nguyen, Producer for Ground TruthsVideo and audio tech support by Sinjun Balabanoff, Scripps ResearchEric 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 JournalsHolden 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 BiologyHolden 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 ControversiesHolden 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 ResignationEric 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…

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Daphne Koller: The Convergence of A.I. and Digital Biology Mar 10, 2024

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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?Video link for the WEF PanelDaphne 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 ConvergenceDaphne 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 ScienceDaphne 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 DiscoveryEric 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 BucketsDaphne 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 t…

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Coleen Murphy: The Science of Aging and Longevity Mar 03, 2024

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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 MurphyTranscript with external linksEric 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 re…

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Michelle Monje: The Brain in Long Covid and Cancer Feb 25, 2024

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Transcript with audio and relevant external links, recorded on 6 Feb 2024Eric 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 BrainEric 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 BrainEric 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 SystemEric 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 CancerMichelle 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…

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Jim Collins: Discovery of the First New Structural Class of Antibiotics in Decades, Using A.I. Feb 13, 2024

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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 publicationsEric 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 piec…

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Katalin Karikó: The unimaginable, obstacle-laden, multi-decade journey to discover the mRNA platform and win the 2023 Nobel Prize Feb 02, 2024

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“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 belowEric 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, beca…

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Jonathan Howard, author of We Want Them Infected Jan 25, 2024

Show notes

Jonathan Howard is a neurologist and psychiatrist who practices at NYU-Bellevue and posts frequently on Science Based Medicine.Transcript, unedited, with links to audioEric 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 Apri…

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An exhilarating conversation with Azeem Azhar on medical A.I., science of aging, genome editing and the GLP-1 drugs Jan 18, 2024

Show notes

Azeem Azharis 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. Exponential View by Azeem Azharis 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.

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Liv Boeree: On Competition, Moloch Traps, and the A.I. Arms Race Jan 13, 2024

Show notes

A snippet of our conversation belowTranscript of our conversation 8 January 2023, edited for accuracy, with external linksEric TopolIt’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 BoereeThanks for having me, Eric.Eric TopolYou 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 PokerLiv BoereeAh, 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 TopolWell, 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 BoereeThe 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 TopolThat 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 BoereeOh, 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 TopolRight, right.Liv BoereeYet 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 TopolWell, 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 BoereeRight.Game Theory and Moloch TrapsEric TopolThat 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 BoereeYeah, 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 TopolNo, 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 GoogleThe A.I. Arms RaceLiv BoereeIf 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 TopolYou're right on it, I think the issue is how to get the right balance of progress versus guardrails.Liv BoereeYou 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 trivial…

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