Ground Truths

Professor Doudna was awarded the 2020 Nobel Prize in Chemistry with Professor Emmanuelle Charpentier for their pioneering work in CRISPR genome editing. The first genome editing therapy (Casgevy) was just FDA approved, only a decade after the CRISPR-Cas9 editing system discovery. But It’s just the beginning of a much bigger impact story for medicine and life science.

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Eric Topol (00:06):

This is Eric Topol with Ground Truths, and I'm really excited today to have with me Professor Jennifer Doudna, who heads up the Innovative Genomics Institute (IGI) at UC Berkeley, along with other academic appointments, and as everybody knows, was the Nobel laureate for her extraordinary discovery efforts with CRISPR genome editing. So welcome, Jennifer.

Jennifer Doudna (00:31):

Hello, Eric. Great to be here.

Eric Topol (00:34):

Well, you know we hadn't met before, but I felt like I know you so well because this is one of my favorite books, The Code Breaker. And Walter Isaacson did such a wonderful job to tell your story. What did you think of the book?

My interview with Walter Isaacson on The Code Breaker, a book I highly recommend

Jennifer Doudna (00:48):

I thought Walter did a great job. He's a good storyteller, and as you know from probably from reading it or maybe talking to others about it, he wrote a page turner. He actually really dug into the science and all the different aspects of it that I think created a great tale.

Eric Topol (01:07):

Yeah, I recommended highly. It was my favorite book when it came out a couple years ago, and it is a page turner. In fact, I just want to read one, there's so many quotes out of it, but in the early part of the book, he says, “the invention of CRISPR and the plague of Covid will hasten our transition to the third great revolution of modern times. These revolutions arose from the discovery beginning just over a century ago, of the three fundamental kernels of our existence, the atom, the bit, and the gene.” That kind of tells a big story just in one sentence, but I thought I’d start with the IGI, the institute that you have set up at Berkeley and what its overall goals are.

Jennifer Doudna (01:58):

Right. Well, let's just go back a few years maybe to the origins of this institute and my thinking around it, because in the early days of CRISPR, it was clear that we were really at a moment that was quite unique in the sense that there was a transformative technology. It was going to intersect with lots of other discoveries and technologies. And I work at a public institution and my question to myself was, how can I make sure that this powerful tool is first of all used responsibly and secondly, that it's used in a way that benefits as many people as possible, and it's a tall order, but clearly we needed to have some kind of a structure that would allow people to work together towards those goals. And that was really the mission behind the IGI, which was started as a partnership between UC Berkeley and UCSF and now actually includes UC Davis as well.

The First FDA Approved Genome Editing

Eric Topol (02:57):

I didn't realize that. That's terrific. Well, this is a pretty big time because 10 years or so, I guess starting to be 11 when you got this thing going, now we're starting to see, well, hundreds of patients have been treated and in December the FDA approved the first CRISPR therapy for sickle cell disease, Casgevy. Is that the way you say it?

Jennifer Doudna (03:23):

Casgevy, yeah.

Eric Topol (03:24):

That must have felt pretty good to see if you go from the molecules to the bench all the way now to actually treating diseases and getting approval, which is no easy task.

Jennifer Doudna (03:39):

Well, Eric, for me, I'm a biochemist and somebody who has always worked on the fundamentals of biology, and so it's really been extraordinary to see the pace at which the CRISPR technology has been adopted, and not just for fundamental research, but also for real applications. And Casgevy is sort of the crowning example of that so far, is that it's really a technology that we can already see how it's being used to, I think it's fair to say, effectively cure a genetic disease for the first time. Really amazing.

Genome Editing is Not the Same as Gene Therapy

Eric Topol (04:17):

Yeah. Now I want to get back to that. I know there's going to be refinements about that. And of course, there's beta thalassemia, so we've got two already, and our mutual friend Fyodor Urnov would say two down 5,000 to go. But I think before I get to the actual repair of the sickle cell defect molecular defect, I think one of the questions I think that people listeners may not know is the differentiation of genome editing with gene therapy. I mean, as you know, there was recently a gene therapy approval for something like $4.25 million for metachromatic leukodystrophy. So maybe you could give us kind of skinny on how these two fundamental therapies are different.

Jennifer Doudna (05:07):

Right. Well, it's a great question because the terminology sounds kind of the same, and so it could be confusing. Gene therapy goes back decades, I can remember gene therapy being discussed as an exciting new at the time, direction back when I was a graduate student. That was little while ago. And it refers to the idea that we can use a genetic approach for disease treatment or even for a cure. However, it fundamentally requires some mechanism of integrating new information into a genome. And traditionally that's been done using viruses, which are great at doing that. It's just that they do it wherever they want to do it, not necessarily where we want that information to go. And this is where CRISPR comes in. It's a technology allows precision in that kind of genetic manipulation. So it allows the scientist or the clinician to decide where to make a genetic change. And that gives us tremendous opportunity to do things with a kind of accuracy that hasn't been possible before.

Eric Topol (06:12):

Yeah, no question. That's just a footnote. My thesis in college at University of Virginia, 1975, I'm an old dog, was prospects for gene therapy in man. So it took a while, didn't it? But it's a lot better now with what you've been working on, you and your colleagues now and for the last decade for sure. Now, what I was really surprised about is it's not just of course, these hemoglobin disorders, but now already in phase two trials, you've got hereditary angioedema, which is a life-threatening condition, amyloidosis, cancer ex vivo, and also chronic urinary tract infections. And of course, there's six more others like autoimmune diseases like lupus and type 1 diabetes. So this is really blossoming. It's really extraordinary.

Eric Topol (07:11):

I mean, wow. So one of the questions I had about phages, because this is kind of going back to this original work and discovery, antimicrobial resistance is really a big problem and it's a global health crisis, and there's only two routes there coming up with new drugs, which has been slow and not really supported by the life science industry. And the other promising area is with phages. And I wonder, since this is an area you know so well, why haven't we put more, we're starting to see more trials in phages. Why haven't we doubled down or tripled down on this to help the antimicrobial resistance problem?

Jennifer Doudna (08:00):

Well, it's a really interesting area, and as you said, it's kind of one of those areas of science where I think there was interest a while ago and some effort was made for reasons that are not entirely clear to me, at least it fizzled out as a real focused field for a long time. But then more recently, people have realized that there's an opportunity here to take advantage of some natural biology in which viruses can infect and destroy microbes. Why aren't we taking better advantage of that for…

Note: This podcast is a companion to the Ground Truths newsletter “A Big Week for GLP-1 Drugs”

Eric Topol (00:06):

It is Eric Topol with Ground Truths, and with me today is Dr. Daniel Drucker from the University of Toronto, who is one of the leading endocrinologists in the world, and he along with Joel Habener and Jens Juul Holst from the University of Copenhagen and Denmark, have been credited with numerous prizes of their discovery work of glucagon-like peptide-1 (GLP-1) as we get to know these family of drugs and he's a true pioneer. He's been working on this for decades. So welcome, Daniel.

Daniel Drucker (00:43):

Thank you.

Eric Topol (00:45):

Yeah, it's great to have you and to get the perspective, one of the true pioneers in this field, because to say it's blossom would be an understatement, don't you think?

Daniel Drucker (00:57):

Yeah, it's been a bit of a hectic three years. We had a good quiet 30 plus years of solid science and then it's just exploded over the last few years.

Eric Topol (01:06):

Yeah, back in 30 years ago, did you have any sense that this was coming?

Daniel Drucker (01:14):

Not what we're experiencing today, I think there was a vision for the diabetes story. The first experiments were demonstrating insulin secretion and patents were followed around the use for the treatment of GLP-1 for diabetes. The food intake story was much more gradual and the weight loss story was quite slow. And in fact, as you know, we've had a GLP-1 drug approved for people with obesity since 2014, so it's 10 years since liraglutide was approved, but it didn't really catch the public's attention. The weight loss was good, but it wasn't as spectacular as what we're seeing today. So this really has taken off just over the last three, four years.

Eric Topol (01:58):

Yeah, no, it's actually, I've never seen a drug class like this in my life, Daniel. I mean, I've obviously witnessed the statins, but this one in terms of pleiotropy of having diverse effects, and I want to get to the brain here in just a minute because that seems to be quite a big factor. But one thing just before we get too deep into this, I think you have been great to recognize one of your colleagues who you work with at Harvard, Svetlana Mojsov. And the question I guess is over the years, as you said, there was a real kind of incremental path and I guess was in 1996 when you said, well, this drug likely will inhibit food intake, but then there were gaps of many years since then, as you mentioned about getting into the obesity side. Was that because there wasn't much weight loss in the people with diabetes or was it related to the dose of the drugs that were being tested?

Why Did It Take So Long to Get to Obesity?

Daniel Drucker (03:11):

Well, really both. So the initial doses we tested for type 2 diabetes did not produce a lot of weight loss, maybe 2-3%. And then when we got semaglutide for type 2 diabetes, maybe we were getting 4-5% mean weight loss. And so that was really good and that was much better than we achieved before with any glucose lowering drug. But a lot of credit goes to Novo Nordisk because they looked at the dose for liraglutide and diabetes, which was 1.8 milligrams once daily for people with type 2 diabetes. And they asked a simple question, what if we increase the dose for weight loss? And the answer was, we get better weight loss with 3 milligrams once a day. So they learn that. And when they introduced semaglutide for type 2 diabetes, the doses were 0.5 and 1 milligrams. But in the back of their minds was the same question, what if we increased the dose and they landed on 2.4 milligrams once a week. And that's when we really started to see that the unexpected spectacular weight loss that we're now quite familiar with.

Eric Topol (04:16):

Was there also something too that diabetics don't lose as much weight if you were to have match dose?

Daniel Drucker (04:22):

Yeah, that's a general phenomenon. If one goes from either diet to bariatric surgery, and certainly with weight loss medicines, we tend to see maybe two thirds to three quarters of the amount of weight loss in people with type 2 diabetes. We don't really understand it. The brain pathways are probably resistant to some of the pathways that are activated that lead to weight loss, and it's really an interesting observation that needs further study.

The Brain Effect

Eric Topol (04:50):

Yeah, it's fascinating really. And it might've at least in part, held up this progress that has been truly remarkable. Now, recently you published a paper among many, you're a very prolific scientist, of course, physician scientist, but back in December in Cell Metabolism was a very important paper that explored the brain gut axis, the ability to inhibit inflammation and the mechanism through Toll-like receptors that you were seeing that. So maybe you could summarize the fact that you saw this, you were quoted in this Atlantic piece by Sarah Zhang, the science behind Ozempic was wrong. The weight loss effects of GLP-1 drugs have little to do with the gut and basically claiming that it's related to the effects on the brain, which of course could be reduced inflammation, reduced or inhibiting centers of addiction craving, that sort of thing. So how do you interpret your recent results and ongoing studies regarding GLP-1's effect on the brain?

Daniel Drucker (06:02):

Sure, so to be clear, I don't think that was a quote. I never would've said the science behind Ozempic was wrong. I think that was a headline writer doing what they do best, which is catching people's attention. I think what I was trying to say is that where this field started with insulin secretion first and then weight loss second, those are clearly very important pharmacological attributes of GLP-1. But physiologically, if we take GLP-1 away or we take the receptor away, you don't really develop diabetes without GLP-1. You don't really gain a lot of weight without GLP-1. So physiologically it's not that important. Why do we have GLP-1 in the distal gut? I think physiologically it's there to defend against infection and reduce gut inflammation. But we noticed that GLP-1 reduces inflammation in many different places in the heart and blood vessels and in the liver and many organs where you don't see a lot of GLP-1 receptors and you don't see a lot of GLP-1 receptors on immune cells.

Daniel Drucker (07:04):

So that really led us to the question, well, how does it work and affect all these organs where we don't see a lot of the receptors? And that's where we landed on the brain. Obviously the nervous system can communicate with many different cell types in almost every organ. And we identified neurons that expressed the GLP-1 receptor, which when blocked abrogated or completely eliminated the ability of GLP-1 to reduce inflammation in the periphery in white cells or in lungs. So it's been known for some time that the brain can control the immune system. So this is just the latest piece in the puzzle of how GLP-1 might reduce inflammation.

Eric Topol (07:49):

And just to be clear, I was quoting the Atlantic headline, not you that you were quoted within that article, but this is something that's really interesting because obviously GLP-1 is made in the brain in certain parts of the brain, it's transient in terms of its half-life made from the gut. But when we give these drugs, these agonists, how does it get in the brain? Because isn't there a problem with the blood brain barrier?

Daniel Drucker (08:22):

So I don't think the drugs get into the brain very well. We have a lot of data on this, so people have done the classic experiments, they either make radioactive ligands or fluorescent ligands, and they look how much gets in it and not very much gets in beyond the blood-brain barrier. And we also have big drugs that are immunoglobulin based and they work really well, so they don't get into the brain very much at all. And so, the way I describe this is that GLP-1 talks to the brain, but it doesn't dire…

Siddhartha Mukherjee is a Professor at Columbia University, oncologist, and extraordinary author of Emperor of All Maladies (which was awarded a Pulitzer Prize), The Gene, and The Song of the Cell, along with outstanding pieces in the New Yorker. He is one of the top thought leaders in medicine of our era.

“I have begun to imagine, think about what it would be to be a digital human..”—Sid Mukherjee

Eric Topol (00:06):

Well, hello, this is Eric Topol with Ground Truths, and I am delighted to have my friend Sid Mukherjee, to have a conversation about all sorts of interesting things. Sid, his most recent book, SONG OF THE CELL is extraordinary. And I understand, Sid, you're working on another book that may be cell related. Is that right?

Sid Mukherjee  (00:30):

Eric, it's not cell related, I would say, but it's AI and death related, and it covers, broadly speaking, it covers AI, longevity and death and memory —topics that I think are universal, but also particularly medicine.

Eric Topol (00:57):

Well, good, and we'll get into that. I had somehow someone steered me that your next book was going to be something building on the last one, but that sounds even more interesting. You're going in another direction. You've covered cancer gene cells, so I think covering this new topic is of particularly interest. So let's get into the AI story and maybe we'll start off with your views on the healthcare side. Where do you think this is headed now?

A.I. and Drug Discovery

Sid Mukherjee  (01:29):

So I think Eric, there are two very broad ways of dividing where AI can enter healthcare, and there may be more, I'm just going to give you two, but there may be more. One is on what I would call the deep science aspect of it, and by that I mean AI-based drug discovery, AI-based antibody discovery, AI-based modeling. All of which use AI tools but are using tools that have to do with machine learning, but may have to do less directly with the kind of large language models. These tools have been in development for a long time. You and I are familiar with them. They are tools. Very simply put, you can imagine that the docking of a drug to a protein, so imagine every drug, every medicine as a small spaceship that docks onto a large spaceship, the large spaceship being the target.

(02:57):

So if you think of it that way, there are fundamental rules. If anyone's watched Star Wars or any of these sci-fi films, there are fundamental rules by which that govern the way that the small spaceship in this case, a molecule like aspirin fits into a pocket of its target, and those are principles that are determined entirely by chemistry and physics, but they can be taught, you can learn what kind of spaceship or molecule is likely to fit into what kind of pocket of the mothership, in this case, the target. And if they can be learned, they're amenable to AI-based discovery.

Eric Topol (03:57):

Right. Well, that's, isn't that what you'd call the fancy term structure-based discovery, where you're using such tools like what AlphaFold2 for proteins and then eventually for antibodies, small molecules, et cetera, that you can really rev up the whole discovery of new molecules, right?

Sid Mukherjee  (04:21):

That's correct, and that's one of the efforts that I'm very heavily involved in. We have created proprietary algorithms that allow us to enable this. Ultimately, of course, there has to be a method by which you start from these AI based methods, then move to physical real chemistry, then move to real biology, then move to obviously human biology and ultimately to human studies. It's a long process, but it's an incredibly fruitful process.

Eric Topol (04:57):

Well, yeah, as an example that recently we had Jim Collins on the podcast and he talked about the first new drug class of antibiotics in two decades that bind to staph aureus methicillin resistant, and now in clinical trials. So it’s happening. There’s 20 AI drugs in clinical trials out there.

Sid Mukherjee  (05:18):

It’s bound to happen. It is an unstoppable bound to happen systematology of drug discovery. This is just bound to happen. It is unstoppable. There are kinks in it in the road, but those will be ironed out, but it’s bound to happen.

(05:41):

So that’s on the very discovery oriented end, which is more related to learning algorithms that have to do with AI and less to do with what we see in day-to-day life, the ChatGPT kind of day-to-day life of the world. On the very other end of the spectrum, just to move along on the very other end of the spectrum are what I would call patient informatics. So by patient informatics, I mean questions like who responds to a particular drug? What genes do they have? What environment are they in? Have they had other drug interactions in the past? What is it about their medical record that will allow us to understand better why or why they're not responding to a medicine?

(06:51):

Those are also AI, can also be really powered by AI, but are much more dependent and much more sensitive to our understanding of these current models, the large language models. So just to give you an example, let's say you wanted to enroll a clinical trial for patients with diabetes to take a new drug. You could go into the electronic medical record, which right now is a text file, and ask the question, have they or have they not responded to the standard agents? And what has their response been? Should they be on glucose monitoring? How bad is their diabetes based on some laboratory parameters, et cetera, et cetera. So that's a very different information rich, electronic medical record rich mechanism to understand how to develop medicines. One lies, the first lies way in the discovery end of the spectrum. The second lies way in the clinical trials and human drug exposure end of the spectrum. And of course, there are things in the middle that I haven't iterated, but those are the two really broad categories where one can imagine AI making a difference and to be fair through various efforts, I'm working on both of those, the two end spectrum.

A.I. and Cancer

Eric Topol (08:34):

Well, let's drill down a bit more on the person individual informatics for a moment, since you're an oncologist, and the way we screen for cancer today is completely ridiculous by age only. But if you had a person's genome sequence, polygenic risk scores for cancers and all the other known data that, for example, the integrity of their immune system response, environmental exposures, which we'll talk about in a moment more, wouldn't we do far better for being able to identify high risk people and even preventing cancer in the future?

Sid Mukherjee  (09:21):

So I have no doubt whatsoever that more information that we can analyze using intelligent platforms. And I'm saying all of these words are relevant, more information analyzed through intelligent platforms. More information by itself is often useless. Intelligent platforms without information by themselves are often useless, but more information with intelligent platforms, that combination can be very useful. And so, one use case of that is just to give you one example, there are several patients, women who have a family history of breast cancer, but who have no mutations in the known single monogenic breast cancer risk genes, BRCA1, BRCA2, and a couple of others. Those patients can be at a high a risk of breast cancer as patients who have BRCA1 and BRCA2. It's just that their risk is spread out through not one gene but thousands of genes. And those patients, of course have to be monitored and their risk is high, and they need to understand what the risk is and how to manage it.

(10:57):

And that's where AI can, and first of all, informatics and then AI can play a big difference because we can understand how to manage those patients. They used to be called, this is kind of, I don't mean this lightly, but they used to be called BRCA3 because they didn't have BRCA1, they didn't have BRCA2, but they had a constellation of gene…

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 Truths

Video and audio tech support by Sinjun Balabanoff, Scripps Research

Eric Topol (00:00:05):

This is Eric Topol from Ground Truths, and I am delighted to have with me Holden Thorp, who is the Editor-in-Chief of the Science journals. We're going to talk about Science, not just the magazine journal, but also science in general. This is especially appropriate today because Holden was just recognized by STAT as one of the leaders for 2024 because of his extraordinary efforts to promote science integrity, so welcome Holden.

Holden Thorp (00:00:36):

Thanks Eric, and if I remember correctly, you were recognized by STAT in 2022, so it's an honor to join a group that you're in anytime, that's for sure, and great to be on here with you.

Eric Topol (00:00:47):

Well, that's really kind to you. Let's start off, I think with the journal, because I know that consumes a lot of your efforts and you have five journals within science.

Holden Thorp (00:01:02):

Oh, we have six.

Eric Topol (00:01:03):

Oh six, I'm sorry, six. There's Science, the original, and then five others. Can you tell us what it's like to oversee all these journals?

Overseeing the Science Journals

Holden Thorp (00:01:16):

Yeah, we're a relatively small family compared to our commercial competitors. I know you had Magdalena [Skipper]on and Nature has I think almost ninety journals, so six is pretty small. In addition to Science, which most people are familiar with, we have Science Advances, which also covers all areas of science and is larger and is a gold open access journal and also is overseen by academic editors, not professional editors. All of our other journals are overseen by professional editors. And then the other four are relatively small and specialized areas, and probably people who listen to you and follow you would know about Science Translational Medicine, Science Immunology, Science Signaling and then we also have a journal, Science Robotics which is something I knew nothing about and I learned a lot. I've learned a lot about robotics and the culture of people who work there interacting with them.

Holden Thorp (00:02:22):

So we have a relatively small family. There's only 160 people who work for me, which is manageable. I mean that sounds like a lot, but in my previous jobs I was a provost and a chancellor, and I had tens of thousands of people, so it's really fun for me to have a group where I at least have met everybody who works for me. We're an outstanding set of journals, so we attract an outstanding group of professionals who do all the things that are involved in all this, and it's really, really fun to work with them. At Science, we don't just do research papers, although that's a big, and probably for your listeners the biggest part of what we do. But we also have a news and commentary section and the news section is 30 full-time and many freelancers around the world really running the biggest general news operation for science that there is. And then in the commentary section, which you're a regular contributor for us in expert voices, we attempt to be the best place in the world for scientists to talk to each other. All three of those missions are just really, really fun for me. It's the best job I've ever had, and it's one I hope to do for many years into the future.

Eric Topol (00:03:55):

Well, it's extraordinary because in the four and a half years I think it's been since you took the helm, you've changed the face of Science in many ways. Of course, I think the other distinction from the Nature Journals is that it's a nonprofit entity, which shows it isn't like you're trying to proliferate to all sorts of added journals, but in addition, what you've done, at least the science advisor and the science news and all these things that come out on a daily basis is quite extraordinary as we saw throughout the pandemic. I mean, just reporting that was unparalleled from, as you say, all points around the world about really critically relevant topics. Obviously it extends well beyond the concerns of the pandemic. It has a lot of different functions, but what I think you have done two major things, Holden. One is you medicalized it to some extent.

Eric Topol (00:04:55):

A lot of people saw the journal, particularly Science per se, as a truly basic science journal. Not so much applied in a medical sphere, but these days there's more and more that would be particularly relevant to the practice of medicine, so that's one thing. And the other thing I wanted you to comment on is you're not afraid to speak out and as opposed to many other prior editors who I followed throughout my career at Science, there were pretty much the politically correct type and they weren't going to really express themselves, which you are particularly not afraid of. Maybe you could comment about if you do perceive this medicalization of science to some extent, and also your sense of being able to express yourself freely.

Capturing the Breakthroughs in Structural Biology

Holden Thorp (00:05:48):

Yeah, well, you're kind to say both of those things are certainly things we have worked at. I mean, I do come from a background, even though I'm trained as a chemist, most of what I did towards the latter end of my career, I mean, I did very basic biochemistry when I was a researcher, but the last part of my research career I worked in on development of a drug called Vivjoa, which is an alternative to the fluconazole family that doesn't have the same toxicity and is currently on the market for chronic yeast infection and hopefully some other things in the future when we can get some more clinical trials done.

Holden Thorp (00:06:35):

And I've hung around biotech startups and drug development, so it is part of the business that I knew. I think the pandemic really gave us an opening because Valda Vinson, who's now the Executive Editor and runs all of life sciences for us and policies for the journal, she was so well known in structural biology that most of the first important structures in Covid, including the spike protein, all came to us. I mean, I remember crystal clear February of 2020, she came in my office and she said, I got the structure of the spike protein. And I said, great, what's the spike protein? Turned out later became the most famous protein in the world, at least temporarily. Insulin may be back to being the most famous protein now, but spike protein was up there. And then that kind of cascaded into all the main protease and many of the structures that we got.

Holden Thorp (00:07:45):

And we seized on that for sure, to kind of broaden our focus. We had the Regeneron antibodies, we had the Paxlovid paper, and all of that kind of opened doors for us. And we've also, now we have two clinical editors at Science, Priscilla Kelly and Yevgeniya Nusinovich, and then the Insights section, somebody that you work with closely, Gemma Alderton, she is very fluent in clinical matters. And then of course we've had Science Translational Medicine and we seek continue to strengthen that. Science Immunology was very much boosted by Covid and actually Science Immunology is now, I think probably if you care about impact factors, the second highest specialized immunology journal after Immunity. I've put some emphasis on it for sure, but I think the pandemic also really helped us. As far as me speaking out, a lot of people maybe don't remember, but Don Kennedy, who was the editor in the early 2000s who had been the Stanford president, he was similarly outspoken.

Confronting Controversies

Holden Thorp (00:09:15):

It's funny, sometimes people who disagree with me say, well, Don Kennedy would never say anything like that. And then I can dig up something that Don Kennedy said that's just as aggressive as what I might've said. But you're r…

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 Panel

Daphne Koller (01:25):

I think we've been living on an exponential curve for multiple decades and the thing about exponential curves is they are very misleading things. In the early stages people basically take the line between whatever we were last year, and this year and they interpolate linearly, and they say, God, things are moving so slowly. Then as the exponential curve starts to pick up, it becomes more and more evident that things are moving faster, but it’s still people interpolate linearly and it's only when things really hit that inflection point that people realize that even with the linear interpolation where we'll be next year is just mind blowing. And if you realize that you're on that exponential curve where we will be next year is just totally unanticipatable. I think what we started to discuss in that panel was, are we in fact on an exponential curve? What are the rate limiting factors that may or may not enable that curve to continue specifically availability of data and what it would take to make that curve available in areas outside of the speech, whatever natural language, large language models that exist today and go far beyond that, which is what you would need to have these be applicable to areas such as biology and medicine.

Daphne Koller (02:47):

And so that was kind of the message to my mind from the panel.

Eric Topol (02:53):

And there was some differences in opinion, of course Yann can be a little strong and I think it was good to see that you're challenging on some things and how there is this “world view” of AI and how, I guess where we go from here. As you mentioned in the area of life science, there already had been before large language models hit stride, so much progress particularly in imaging cells, subcellular, I mean rare cells, I mean just stuff that was just without any labeling, without fluorescein, just amazing stuff. And then now it's gone into another level. So as we get into that, just before I do that, I want to ask you about this convergence story. Jensen Huang, I'm sure you heard his quote about biology as the opportunity to be engineering, not science. I'm sure if I understand, not science, but what about this convergence? Because it is quite extraordinary to see two fields coming together moving at such high velocity.

"Biology has the opportunity to be engineering not science. When something becomes engineering not science it becomes...exponentially improving, it can compound on the benefits of previous years." -Jensen Huang, NVIDIA.

Daphne Koller (04:08):

So, a quote that I will replace Jensen's or will propose a replacement for Jensen's quote, which is one that many people have articulated, is that math is to physics as machine learning is to biology. It is a mathematical foundation that allows you to take something that up until that point had been kind of mysterious and fuzzy and almost magical and create a formal foundation for it. Now physics, especially Newtonian physics, is simple enough that math is the right foundation to capture what goes on in a lot of physics. Biology as an evolved natural system is so complex that you can't articulate a mathematical model for that de novo. You need to actually let the data speak and then let machine learning find the patterns in those data and really help us create a predictability, if you will, for biological systems that you can start to ask what if questions, what would happen if we perturb the system in this way?

The Convergence

Daphne Koller (05:17):

How would it react? We're nowhere close to being able to answer those questions reliably today, but as you feed a machine learning system more and more data, hopefully it'll become capable of making those predictions. And in order to do that, and this is where it comes to this convergence of these two disciplines, the fodder, the foundation for all of machine learning is having enough data to feed the beast. The miracle of the convergence that we're seeing is that over the last 10, 15 years, maybe 20 years in biology, we've been on a similar, albeit somewhat slower exponential curve of data generation in biology where we are turning it into a quantitative discipline from something that is entirely observational qualitative, which is where it started, to something that becomes much more quantitative and broad based in how we measure biology. And so those measurements, the tools that life scientists and bioengineers have developed that allow us to measure biological systems is what produces that fodder, that energy that you can then feed into the machine learning models so that they can start making predictions.

Eric Topol (06:32):

Yeah, well I think the number of layers of data no less what's in these layers is quite extraordinary. So some years ago when all the single cell sequencing was started, I said, well, that's kind of academic interest and now the field of spatial omics has exploded. And I wonder how you see the feeding the beast here. It's at every level. It's not just the cell level subcellular and single cell nuclei sequencing single cell epigenomics, and then you go all the way to these other layers of data. I know you plug into the human patient side as well as it could be images, it could be past slides, it could be the outcomes and treatments and on and on and on. I mean, so when you think about multimodal AI, has anybody really done that yet?

Daphne Koller (07:30):

I think that there are certainly beginnings of multimodal AI and we have started to see some of the benefits of the convergence of say, imaging and omics. And I will give an example from some of the work that we've recently distributed on a preprint server work that we did at insitro, which took imaging data from standard histopathology slides, H&E slides and aligned them with simple bulk RNA-Seq taken from those same tumor samples. And what we find is that by training models that translate from one to the other, specifically from the imaging to the omics, you're able to, for a fairly large fraction of genes, make very accurate predictions of gene expression levels by looking at the histopath images alone. And in fact, because many of the predictions are made at the tile level, not at the entire slide level, even though the omics was captured in bulk, you're able to spatially resolve the signal and get kind of like a pseudo spatial biology just by making predictions from the H&E image into these omic modalities.

Multimodal A.I. and Life Science

Daphne Koller (08:44):

So there are I think beginnings of multimodality, but in order to get to multimodality, you really need to train on at least some data where the two modalities are simultaneously. And so at this point, I think the rate limiting factor is more a matter of data acquisition for training the models. It is for building the models th…

“A few years ago, I might have chuckled at the naiveté of this question, but now it's not so crazy to think that we will be able to take some sort of medicine to extend our healthy lifespans in the foreseeable future.”—Coleen Murphy

Transcript with external links

Eric Topol (00:06):

Hello, this is Eric Topol from Ground Truths, and I'm just so delighted to have with me Professor Coleen Murphy, who has written this exceptional book, How We Age: The Science of Longevity. It is a phenomenal book and I'm very eager to discuss it with you, Coleen.

Coleen Murphy (00:25):

Thanks for having me on.

Eric Topol (00:27):

Oh yeah. Well, just so everyone who doesn't know Professor Murphy, she's at Princeton. She's the Richard Fisher Preceptor in Integrative Genomics, the Lewis-Sigler Institute for Integrative Genomics at Princeton, and director of the Paul Glenn Laboratories for Aging Research. Well, obviously you've been in this field for decades now, even though you're still very young. The classic paper that I can go back to would be in Nature 2003 with the DAF-16 and doubling the lifespan of C. elegans or better known as a roundworm. Would that be the first major entry you had?

Coleen Murphy (01:17):

Yeah, that was my postdoctoral work with Cynthia Kenyon.

Eric Topol (01:20):

Right, and you haven't stopped since you've been on a tear and you’ve put together a book which has a hundred pages of references in a small font. I don't know what the total number is, but it must be a thousand or something.

Coleen Murphy (01:35):

Actually, it's just under a thousand. That's right.

Eric Topol (01:37):

That's a good guess.

Coleen Murphy (01:38):

Good guess. Yeah.

Eric Topol (01:39):

So, because I too have a great interest in this area, I found just the resource that you've put together as extraordinary in terms of the science and all the work you've put together. What I was hoping to do today is to kind of take us through some of the real exciting pathways because there's a sentence in your book, which I thought was really kind of nailed it, and it actually is aligned with my sense. Obviously don't have the expertise by any means that you do here but it says, “A few years ago, I might have chuckled at the naivety of this question, but now it's not so crazy to think that we will be able to take some sort of medicine to extend our healthy lifespans in the foreseeable future.” That's a pretty strong statement for a person who's deep into the science. First I thought we'd explore healthy aging health span versus lifespan. Can you differentiate that as to your expectations?

Coleen Murphy (02:54):

So, I think most people would agree that they don't want to live necessary super long. What they really want to do is live a healthy life as long as they can. I think that a lot of people also have this fear that when we talk about extending lifespan, that we're ignoring that part. And I do want to assure everyone that the people in the researchers in the aging field are very much aware of this issue and have, especially in the past decade, I think put a real emphasis on this idea of quality of life and health span. What's reassuring is actually that many of the mechanisms that extend lifespan in all these model organisms also extend health span as well and so I don't think we're going to, they're not diametrically opposed, like we'll get to a healthier quality of life, I think in these efforts to extend lifespan as well.

Eric Topol (03:50):

Yeah, I think that's important that you're bringing that up, which is there's this overlap, like a Venn diagram where things that do help with longevity should help with health span, and we don't necessarily have to follow as you call them the immoralists, as far as living to 190 or whatever year. Now, one of the pathways that's been of course a big one for years and studied in multiple species has been caloric restriction. I wonder if you could talk to that and obviously there's now mimetics that could simulate that so you wouldn't have to go through some major dietary starvation, if you will. What are your thoughts on that pathway?

Coleen Murphy (04:41):

Yeah, actually I'm really glad you brought up mimetics because often the conversation starts and ends with you should eat less. I think that is a really hard thing for a lot of people to do. So just for the background, so dietary restriction or caloric restriction, the idea is that you would have to take in up to 30% less than your normal intake in order to start seeing results. When we've done this with laboratory animals of all kinds, this works from yeast all the way up through mice, actually primates, in fact, it does extend lifespan and in most metrics of health span the quality of life, it does improve that as well. On the other hand, I think psychologically it's really tough to not eat enough and I think that's a part that we kind of blindly ignore when we talk about this pathway.

Coleen Murphy (05:30):

And of course, if we gave any of those animals the choice of whether they want to start eating more, they would. So, it's like that's not the experiment we ever hear about. And so, the idea for studying this pathway isn't just to say, okay, this works and now we know how it works, but as you pointed out, mimetics, so can we target the molecules in the pathway so that we can help people achieve the benefits of caloric restriction without necessarily having to do the kind of awful part of restriction? I think that's really cool, and especially it might be very good for people who are undergoing certain, have certain diseases or have certain impairments that it might make it difficult ever to do dietary restrictions, so I think that's a really great thing that the field is kind of getting towards now.

Eric Topol (06:15):

And I think in fact, just today, it's every day there's something published now. Just today there was a University of Southern California study, a randomized study report comparing plant-based fasting-mimicking diet versus controlled diet, and showed that many metabolic features were improved quite substantially and projected that if you stayed on that diet, you'd gain two and a half years of healthy aging or that you would have, that's a bit of an extrapolation, but quite a bit of benefit. Now, what candidates would simulate caloric restriction? I mean, what kind of molecules would help us do that? And by the way, in the book you mentioned that the price to pay is that the brain slows down with caloric restrictions.

Coleen Murphy (07:10):

There's at least one study that shows that.

Coleen Murphy (07:13):

Yeah, so it's good to keep in mind. One of the big things that is being looked at as rapamycin, looking at that TOR pathway. So that's being explored as one of these really good mimetics. And of course, you have things that are analogs of that, so rapalogs, and so people are trying to develop drugs that mimic that, do the same kind of thing without probably some of the side effects that you might see with rapamycin. Metformin is another one, although it's interesting when you talk to people about metformin who work on it, it's argued about what is exactly the target of metformin. There's thought maybe also acts in the TOR pathway could affect complex one of mitochondria. Some of the things we know that they work, and we don't necessarily know how they work. And then of course there's new drugs all the time where people are trying to develop to other target, other molecules. So, we'll see, but I think that the idea of mimetics is actually really good, and that part of the field is moving forward pretty quickly. This diet that you did just mention, it is really encouraging that they don't have to take a drug if you don't want to. If you eat the right kind of diet, it could be very beneficial.

Eric Topol (08:20):

Yeah, no, it was interesting. I was looking at the methods in that USC paper and they sent them a box of stuff that they would eat for three cycles, multiple weeks per cycle. It was a very interestin…

Transcript with audio and relevant external links, recorded on 6 Feb 2024

Eric Topol (00:05):

Hello, this is Eric Topol with Ground Truths, and I have a remarkable guest with me today, Professor Michelle Monje, who is from Stanford, a physician-scientist there and is really a leader in neuro-oncology, the big field of cancer neuroscience, neuroinflammation, and she has just been rocking it recently with major papers on these fields, no less her work that's been on a particular cancer, brain cancer in kids that we'll talk about. I just want to give you a bit of background about Michelle. She is a National Academy of Medicine member, no less actually a National Academy of Medicine awardee with the French Academy for the Richard Lounsbery Award, which is incredibly prestigious. She received a Genius grant from the MacArthur Foundation and is a Howard Hughes Medical Institute (HHMI) scholar, so she is just an amazing person who I'm meeting for the first time. Michelle, welcome.

Michelle Monje (01:16):

Thank you. So nice to join you.

Long Covid and the Brain

Eric Topol (01:18):

Well, I just am blown away by the work that you and your colleagues have been doing and it transcends many different areas that are of utmost importance. Maybe we can start with Long Covid because that's obviously such a big area. Not only have you done work on that, but you published an amazing review with Akiko Iwasaki, a friend of mine, that really went through all the features of Long Covid. Can you summarize your thoughts about that?

Michelle Monje (01:49):

Yeah, and specifically we focused on the neurobiology of Long Covid focusing on the really common syndrome of cognitive impairment so-called brain fog after Covid even after relatively mild Covid. There has been this, I think really important and exciting, really explosion of work in the last few years internationally trying to understand this in ways that I am hopeful will be beneficial to many other diseases of cognition that occur in the context of other kinds of infections and other kinds of immune challenges. But what is emerging from our work and from others is that inflammation, even if it doesn't directly initially involve the nervous system, can very profoundly affect the nervous system and the mechanisms by which that can happen are diverse. One common mechanism appears to be immune challenge induced reactivity of an innate immune cell in the nervous system called microglia. These microglia, they populate the nervous system very early in embryonic development.

(02:58):

And their job is to protect the nervous system from infection, but also to respond to other kinds of toxic and infectious and immune challenges. They also play in healthy conditions, really important roles in neurodevelopment and in neuroplasticity and so they're multifaceted cells and this is some population of those cells, particularly in the white matter in the axon tracks that are exquisitely sensitive it seems to various kinds of immune challenges. So even if there's not a direct nervous system insult, they can react and when they react, they stop doing their normal helpful jobs and can dysregulate really important interactions between other kinds of cells in the brain like neurons and support cells for those neurons like oligodendrocytes and astrocytes. One common emerging principle is that microglial reactivity triggered by even relatively mild Covid occurring in the respiratory system, not directly infecting the brain or other kinds of immune challenges can trigger this reactivity of microglia and consequently dysregulate the normal interactions between cells and the brain.

(04:13):

So important for well-tuned and optimal nervous system function. The end product of that is dysfunction and cognition and kind of a brain fog impairment, attention, memory, ability to multitask, impaired speed of information processing, but there are other ways that Covid can influence the nervous system. Of course there can be direct infection. We don't think that that happens in every case. It may not happen even commonly, but it certainly can happen. There is a clear dysregulation of the vasculature, the immune response, and the reaction to the spike protein of Covid in particular can have very important effects on the vessels in the nervous system and that can trigger a cascade of effects that can cause nervous system dysregulation and may feed directly into that reactivity of the microglia. There also can be reactivation of other infections previous, for example, herpes virus infections. EBV for example, can be reactivated and trigger a new immune challenge in the context of the immune dysregulation that Covid can induce.

(05:21):

There also can be autoimmunity. There are many, we're learning all the different ways Covid can affect the nervous system, but autoimmunity, there can be mimicry of some of the antigens that Covid presents and unfortunate autoimmunity against nervous system targets. Then finally in severe Covid where there is cardiopulmonary compromise, where there is hypoxia and multi-organ damage, there can be multifaceted effects on the nervous system in severe disease. So many different ways, and probably that is not a comprehensive list. It is certainly not a mutually exclusive list. Many of these interactions can happen at the same time in the same individual and in different combinations but we're beginning to wrap our arms around all the different ways that Covid can influence the nervous system and cause this fairly consistent syndrome of impaired attention, memory, multitasking, and executive functions.

Homology with Chemo Brain

Eric Topol (06:23):

Yeah, well there's a lot there that you just summarized and particularly you highlighted the type of glia, the microglia that appear to be potentially central at least a part of the story. You also made analogy to what you've seen with chemotherapy, chemo brain. Maybe you could elaborate on that.

Michelle Monje (06:42):

Yeah, absolutely. So I've been studying the cognitive impairment that can happen after cancer therapies including chemotherapy, but also radiation and immunotherapy. Each time we develop a new model and dig in to understand what's going on and how these cancer therapies influence the nervous system, microglia emerge as sort of the unifying principle, microglial reactivity, and the consequences of that reactivity on other cell types within the nervous system. And so, understanding that microglia and their reactive state to toxic or immune challenges was central to chemotherapy induced cognitive impairment, at least in preclinical models in the laboratory and confirm by human tissue studies. I worried at the very beginning of the pandemic that we might begin to see something that looks a lot like chemotherapy induced cognitive impairment, this syndrome that is characterized by impaired attention, memory, executive function, speed of information processing and multitasking. When just a few months into the pandemic, people began to flood neurologists’ office complaining of exactly this syndrome. I felt that we needed to study it and so that was the beginning of what has become a really wonderful collaboration with Akiko Iwasaki. I reached out to her, kind of cold called her in the midst of the deep Covid shutdown and in 2020 and said, hey, I have this idea, would you like to work with me? She's as you know, just a thought leader in Covid biology and she's been an incredibly wonderful and valuable collaborator along the way in this.

Eric Topol (08:19):

Well, the two of you pairing up is kind of, wow, that's a powerful combination, no question. Now, I guess the other thing I wanted to get at is there've been many other studies that have been looking at Long Covid, how it affects the brain. The one that's frequently cited of course is the UK Biobank where they had CT or MRI scans before in people fortunately, and then once they had Covid or didn't get Covid and it had a lot of worrisome findings includin…

Jim Collins is one of the leading biomedical engineers in the world. He’s been elected to all 3 National Academies (Engineering, Science, and Medicine) and is one of the founders of the field of synthetic biology. In this conversation, we reviewed the seminal discoveries that he and his colleagues are making at the Antibiotics-AI Project at MIT.

Recorded 5 February 2024, transcript below with audio links and external links to recent publications

Eric Topol (00:05):

Hello, it's Eric Topol with Ground Truths, and I have got an extraordinary guest with me today, Jim Collins, who's the Termeer Professor of Medical Engineering at MIT. He also holds appointments at the Wyss Institute and the Broad Institute. He is a biomedical engineer who's been making exceptional contributions and has been on a tear lately, especially in the work of discovery of very promising, exciting developments in antibiotics. So welcome, Jim.

Jim Collins (00:42):

Eric, thanks for having me on the podcast.

Eric Topol (00:44):

Well, this was a shock when I saw your paper in Nature in December about a new structure class of antibiotics, the one from 1962 to 2000. It took 38 years, and then there was another one that took 24 years yours, the structural antibiotics. Before I get to that though, I want to go back just a few years to the work you did published in Cell with halicin, and can you tell us about this? Because when I started to realize what you've been doing, what you've been chipping away here, this was a drug you found, halicin, as I can try to understand, it works against tuberculosis, c. difficile, enterobacter that are resistant, acinetobacter that are resistant. I mean, this is, and this is of course in mice models. Can you tell us how did you make that discovery before we get into I guess what's called the Audacious Project?

Jim Collins (01:48):

Yeah, sure. It's actually a fun story, so it is origins go broadly to institute wide event at MIT, so MIT in 2018 launched a major campus-wide effort focused on artificial intelligence. The institute, which had played a major role in the first wave of AI in the 1950s, 1960s, and a major wave in the second wave in the 1980s found itself kind of at the wheel in this third wave involving big data and deep learning and looked to correct that and to correct it the institute had a symposium and I had the opportunity to sit next to Regina Barzilay, one of our faculty here at MIT who specializes in AI and particularly AI applied to biomedicine and we really hit it off and realized we had interest in applying AI to drug discovery. My lab had focused on antibiotics to then close to 15 years, but primarily we're using machine learning and network biology to understand the mechanism of action of antibiotics and how resistance arise with the goal of boosting what we already had, with Regina we saw there was an opportunity to see if we could use deep learning to get after discovery.

(02:55):

And notably, as you kind of alluded in your introduction, there's really been a discovery void and the golden age of discovery antibiotics was in the forties, fifties and sixties before I was born and before you had the genomic revolution, the biotech revolution, AI revolution. Anyways, we got together with our two groups, and it was an unfunded project and we kind of cobbled together very small training set of 2,500 compounds that included 1,700 FDA approved drugs and 800 natural compounds. In 2018, 2019, when you started this, if you asked any AI expert should you initiate that study, they would say absolutely not, there's going to be two big data. The idea of these models are very data hungry. You need a million pictures of a dog, a million pictures of a cat to train a model to differentiate between the cat and the dog, but we ignored the naysayers and said, okay, let's see what we can do.

(03:41):

And we apply these to E. coli, so a model pathogen that's used in labs but is also underlies urinary tract infections. So it’s a look to see which of the molecules inhibited growth of the bacteria as evidence for antibacterial activity and we could have measured and we quantified each of their effects, but because we had so few compounds, we just discretized instead, if you inhibited at least 80% of the growth you were antibacterial, and if you didn't achieve that, you weren't antibacterial zero in ones. We then took the structure of each molecule and trained a deep learning model, specifically a graphical neural net that could look at those structures, bond by bond, substructure by substructure associated with whatever features you look to train with. In our case, making for good antibiotic, not for good antibiotic. We then took the train model and applied it to a drug repurposing hub as part of the Broad Institute that consists of 6,100 molecules in various stages of development as a new drug.

(04:40):

And we asked the model to identify molecules that can make for a good antibiotic but didn't look like existing antibiotics. So part of the discovery void has been linked to this rediscovery issue we have where we just keep discovering quinolones like Cipro or beta-lactams like penicillin. Well, anyways, from those criteria as well as a small tox model, only one molecule came out of that, and that was this molecule we called halicin, which was named after HAL, the killing AI computer system from 2001 Space Odyssey. In this case, we don't want it to kill humans, we want it to kill bacteria and as you alluded, it turned out to be a remarkably potent novel antibiotic that killed off multi-drug resistant extensively drugs, a pan-resistant bacteria went after to infections. It was affected against TB, it was affected against C. diff and acinetobacter baumannii and acted to a completely new mechanism of action.

(05:33):

And so we were very excited to see how AI could open up possibilities and enable one to explore chemical spaces in new and different ways. We took them model, then applied it to a very large chemical library of 1.5 billion molecules, looked at a subset of about 110 million that would be impossible for any grad student, any lab really to look at that experimentally but we looked at it in a model computer system and in three days could screen those 110 million molecules and identified several new additional candidates, one which we call salicin, which is the cousin of halicin that similes broad spectrum and acts to a novel mechanism of action.

Eric Topol (06:07):

So before we go further with this initial burst of discovery, for those who are not used to deep neural networks, I think most now are used to the convolutional neural network for images, but what you use specifically here as you alluded to, were graph neural networks that you could actually study the binding properties. Can you just elaborate a little bit more about these GNN so that people know this is one of the tools that you used?

Jim Collins (06:40):

Yeah, so in this case, the underlying structure of the model can actually represent and capture a graphical structure of a molecule or it might be of a network so that the underlying structure itself of the model will also look at things like a carbon atom connects to an oxygen atom. The oxygen atom connects to a nitrogen atom and so when you think back to the chemical structures we learned in high school, maybe we learned in college, if we took chemistry class in college, it was actually a model that can capture the chemical structure representation and begin to look at sub aspects of it, associating different properties of it. In this case, again, ours was antibacterial, but it could be toxic, whether it's toxic against a human cell and the model, the train model, the graph neural model can now look at new structures that you input them and then make calculations on those bonds so a bond would be a connection between two atoms or substructures, be multiple bonds, interconnecting multiple atoms and assign it a score. Does it make, for example, in our case, f…

“The history of science, it turns out, is filled with stories of very smart people laughing at good ideas.”—Katalin Karikó

Ground Truths podcasts are now available on Apple and Spotify!

The list of obstacles that Kati Karikó faced to become a scientist, to make any meaningful discovery, to prevail over certain scientists and administrators who oppressed her, unable to obtain grants, her seminal paper rejected by all of the top-tier journals, demoted and dismissed, but ultimately to be awarded the 2023 Nobel Prize with Drew Weissman, is a story for the ages. We covered them in this conversation, which for me will be unforgettable, and hopefully for you an inspiration.

Recorded 30 January 2023, unedited transcript below

Eric Topol (00:06):

Well, hello, this is Eric Topol with Ground Truths, and I am really thrilled to have with me Kati Kariko, who I think everyone knows won the Nobel Prize with the Drew Weissman in 2023 and she has written a sensational book, it's called Breaking Through. I love that title because it's a play on words, a breakthrough and breaking through, and we have a lot to talk about Kati, so welcome.

Katalin Kariko (00:34):

Thank you very much for inviting me.

Eric Topol (00:36):

Yes, well I'd like to start off, as you did in the book with your background in Hungary where of course you started with a tough background in a one room house without running water and you never had exposures to scientists and somehow or other you became interested in science and you attributed some of these things like your biology teacher, Mr. Tóth and the book Stress of Life [by Hans Selye] Could you tell us a little bit more what stimulated you in a career of science?

Katalin Kariko (01:18):

I have to say that every child is interested in understanding the nature around them and so I was surrounded with nature because we had big garden, we had animals around and it was an exciting thing. The children ask questions and if they try to find an answer and teachers or parents might give the answer, but definitely the school, even elementary school was very stimulating. Teachers, chemistry teacher, figure out how we can make crystals and I was so excited to have my own crystals and things like that and in high school the teachers were so engaging and not like they tried to put all of the information into your brain, but they encourage you to think yourself, so that's all contributed. I think that most of the child in the first, I don't know, six, seven years of their life that’s how they can see their parents behaving, their friends, the school, classmates, and they shaped what kind of people they will be at the end and the rest of it is refining.

Eric Topol (02:41):

Right, right. Well one of the things I loved that you brought up in the book was how much you liked the TV show Columbo. That's one of my favorite TV shows of all time and one more thing, one more thing. Can you talk a little bit about Columbo? Because in some ways you were like the Peter Falk of mRNA in terms of one more thing.

Katalin Kariko (03:09):

Yes, so I realized that we as researchers, we are not called searchers, we researchers, so we are repeating things. Of course everybody knows who committed the crime in Columbo because this is how it starts and you don't have to figure out, but it seems always that things in a different direction you would lead but all the little clues and some of my colleagues said that they as a physician, they have this tunnel vision. So the patient comes and they can figure out probably from some clues that this is the disease and they get back the lab results and others. Then they realize that one or two things is not fitting, but they always so strongly believe their first instinct. What I taught them to focus on those which will not fit because that will lead to the real perpetrator in case of Columbo.

(04:23):

And so I like the simplicity. I know that what we are doing this research is very over complicated, but we can break down in very simple question, yes or no and then repeating things and many experiments. When I did one was the experiments really the question and the nine of them was like just controls always. I have to have a control for that, control for that and since I work most of the time with my own hands myself, so I had to make sure that I think through that what will be the experimental outcome and then think about that. Do I have a control for that? So that many times in my brain before I performed the experiment in my brain, I predicted that what will be the outcome, of course you never get the outcome what you expect, but at least you have the control that you can exclude a couple of things and so this is how I function usually in the end of the 20th century, 21st century people did not work like I did alone most of the time.

Eric Topol (05:35):

No, I see how you described it in the book was just so extraordinary and it really was in keeping with this relentless interrogation and that's what I want to get into is particularly the time when you came to the United States in 1985 and the labs that you worked in predominantly in Philadelphia through that period before leaving Penn to go on to BioNTech. So, you first kind of beached in at Temple University with a monster at least as you portray him in the book. I mean it was nice that he picked you up at the airport, you and your family. How do you say his name? Suhadolnik.

Eric Topol (06:31):

But not only was the lab kind of infested with cockroaches, but also after working there for a number of years, a few years, you then had gotten an offer to go to Johns Hopkins and when you informed him about that he threatened and did everything he could to ruin your career and get you deported. I mean this was just awful. How did you get through that?

Katalin Kariko (06:58):

As I mentioned later on, I went back and gave a lecture there and I have to say that I always put positivity in forefront, so I learned a lot from him, and he invited me to America. I was always very grateful, and he was kind, and we did very well, and we did a lot of publication. In one issue of biochemistry, we had three papers and two of them I was the first author, so I worked very hard and so he liked that, and he wanted me to stay there. I just learned that from this Selye book that this is what is given and then what I can do, I cannot change him. I cannot change the situation, how I can get out from it and that's what I focused on, so I am not bitter about him. I liked him and the same for other people. When I get an award, I usually thanks to all of these people who try to make my life miserable. They made me work harder.

Eric Topol (08:05):

Well, but you were very kind like you said when you went back to Temple many years later to give the lecture because what he did to you, I mean he was so vindictive about you potentially leaving his lab, which he demanded that he be called the boss and he was going to basically, he ruined the Johns Hopkins job. He called them and you were so nice and kind when you went back to give the lecture without saying a negative word about him, so I give you credit, when somebody goes low, you went high, which is nice.

Katalin Kariko (08:40):

It is important, which I learned from the Selye book, that you don't carry any grudge against anybody because it'll poison you and as Selye also said that when you are very frustrated and very upset, the quickest way you can think about how you can release the stress is revenge. He said, don't do that. It escalate. It hit you back. You have to think about how you can be grateful for the same person you were just ready to take some revenge and that's what you have to practice. Sometimes it is difficult to feel that, but I don't have any bad feeling against my chairman who put my stuff on the hallway.

Eric Topol (09:24):

Oh yeah, I was going to get to that. So then after a short stint at the Uniformed University of Health Science where you had to drive three …

Jonathan Howard is a neurologist and psychiatrist who practices at NYU-Bellevue and posts frequently on Science Based Medicine.

Transcript, unedited, with links to audio

Eric Topol (00:05):

Well, hello, Eric Topol with Ground Truths and I'm really pleased to have the chance to talk with Jonathan Howard today, who is a neurologist and psychiatrist at NYU at Bellevue and has written quite an amazing book published a few months months ago called We Want Them Infected, so welcome Jonathan.

Jonathan Howard (00:27):

Hey, thanks so much for having me. I really appreciate it.

Eric Topol (00:30):

Yeah, I mean, there's so much to talk about because we're still in the throes of the pandemic with this current wave at least by wastewater levels and no reason to think it isn't by infections at least the second largest in the pandemic course. I guess I want to start off first with you being into the neuropsychiatric world. How did you become, obviously caring for patients with Covid, but how did you decide to become a Covidologist?

Jonathan Howard (00:59):

Well, I developed a strong interest in the anti-vaccine movement of all things about a decade ago when a doctor who I trained with here at NYU in Bellevue morphed into one of the country's biggest anti-vaccine doctors a woman by the name of Dr. Kelly Brogan. I knew her well and we were friends; She was smart and after she left NYU in Bellevue, she became one of the country's most outspoken anti-vaccine doctors and really started leaving off the wall things that germ theory didn't exist, that HIV doesn't cause AIDS. When Covid struck, she felt that SARS-CoV-2 was not killing people because she doesn't believe any virus kills people and so I became very fascinated about how smart people can believe strange, incorrect things and I dedicated myself to learning everything that I could about the anti-vaccine movement. In 2018, I wrote a book chapter on the anti-vaccine movement with law professor Dorit Reiss.

(02:01):

And so when the pandemic came around, I was really prepared for all of their arguments, but I got two very important things wrong. I thought the anti-vaccine movement would shrink. I was wrong about that and I was also really caught off guard by the fact that a lot of mainstream physicians started to parrot pandemic anti-vaccine talking points. So all of the stuff that I'd heard about measles and the HPV vaccine, these are benign viruses, the vaccines weren't tested, blah, blah, blah. I started hearing from professors at Stanford, Harvard, UCSF, Johns Hopkins, all about Covid and the Covid vaccine.

Eric Topol (02:40):

Yeah, we're going to get to some of the leading institutions and individuals within them and how they were part of this, and surprisingly too, of course. Before we do that in the title of your book, We Want Them Infected, it seems to bring in particularly the Great Barrington Declaration that is just protect the vulnerable elderly and don't worry about the rest. Can you restate that declaration and whether that's a core part of what you were writing about?

Jonathan Howard (03:21):

Yeah, the title of the book is to be taken literally. It comes from a quote by Dr. Paul Alexander, who was an epidemiologist in the Trump administration and he said in July 4th, 2020, before anyone had been vaccinated, infants, kids, teens, young people, young adults, middle age with no conditions, et cetera, have zero to little risk so we want to use them to develop herd, we want them infected. This was formalized in the Great Barrington Declaration, which was written by three doctors, our epidemiologist, none of whom cared for Covid patients, Jay Bhattacharya at Stanford, Martin Kulldorf who at the time was at Harvard, and Sunetra Gupta who is at Oxford. If I could state their plan in the most generous terms, it would be the following that Covid is much more dangerous for certain people, but we can relatively easily identify older people and people with underlying conditions.

(04:19):

It's much more benign for a healthy 10-year-old, for example and their idea was that you could separate these two groups, the vulnerable and the not vulnerable. If the not vulnerable people were allowed to catch the virus develop natural immunity that would create herd immunity. They said that this would occur in three to six months and in that time, once herd immunity had been achieved, the vulnerable people who have been in theory sheltering at home are in otherwise safe places could reenter society. So it was really the best of both worlds because lives would be saved and schools would be open, the economy would be open. It sounded very good on paper, kind of like my idea of stopping crime by locking up all the bad guys. What could go wrong? It was a very short document. It took about maybe an hour to write.

(05:17):

I imagine there were some nefarious forces behind it. One of the main instigators of it was a man by the name of Jeffrey Tucker, who sounds like a cartoon villain and he worked at the, I forget, is the American Enterprise Research Institute. It was some right-wing think tank and he is a literally pro child labor. He wrote an article in 2016 called Let the Kids Work, which suggested that children drop out of school to work at Walmart and Chick-fil-A I'm not making that up and he's overtly pro child smoking. He feels that children, teenagers should smoke because it's cool and then they can quit in their twenties before there are any bad harms from it. Needless to say, the Great Barrington's premises that one infection led to permanent immunity didn't really work out so well, but they were very influential. They had already met with President Trump in August of 2020 and the day after their Great Barrington Declaration was signed, they were invited to the White House. This was October 5th, 2020 to meet with Secretary Human Health and Secretary Services, Alex Azar, and they are advisors to Ron DeSantis. They became mini celebrities over the course of the pandemic and it was a very pro infectious movement. As I said, the title of the book, We Want Them Infected, and they did.

Eric Topol (06:42):

Right. In fact, I debated Martin Kulldorf, one of the three principals of the Great Barrington Declaration. It was interesting because if you go back to that debate we brought out, at least I tried to highlight the many flaws in this. You've mentioned at least one major flaw, which was to this virus. There's not a long-term immunity built by infections. It's just, as we say with vaccines the immunity for neutralizing antibody production and protection from infections and severe Covid is limited duration for four to six months, and at least for the antibodies and maybe the T-cell immunity is longer, but that doesn't necessarily kick in and quickly. So that was one major flaw, but there are many others, so maybe you could just take that apart further. For example, I like your analogy to lock up all the bad guys, but compartmentalizing people is not so easy in life and I think this is a significant concern of the idea that is, while you indicated there may be some merits if things went as planned, but what else was a flaw of that argument or proposition?

Jonathan Howard (08:11):

So yeah, this could be a 10-hour conversation and I think importantly, we don't have to speak hypothetically here. A lot of defenders of the Great Barrington Declaration will say, oh, we never tried it, but they promised that herd immunity would arrive in three to six months after lockdowns ended. So we don't have to speak theoretically about what would've happened had we done it. Lockdowns ended a while ago and we don't have herd immunity. They were very clear on this. Dr. Kulldorf tweeted in December 2020 that if we use focus protection, the pandemic will be over in three to six months. So, what could have gone wrong if about 250 million unvaccinated Americans contracted Covid simultaneously in October and November of 2020? A lot of things, as we said, they dichotomized people …