Longevity science is moving quickly. New compounds, biological age tests, and artificial intelligence tools promise a more precise future. The useful question is not whether the field is exciting. It is whether the signal is strong enough to guide real decisions.
Where do you think this field is going? And you think the maturity of this is couple years away? Are we on on that trajectory? I I mean, I think it's going to be a process, right? I don't even know what mature means cuz I hope we continue to get better and better. There's too many biological clocks. What's your take on them? Do they work? Yeah. So, here's what I would say about the biological aging clocks. I think the idea that we can measure parameters that can predict your current health status is for sure. One of the things I struggle with is I think the way biological age is used right now is very confusing cuz what most people really mean is either your risk Welcome to Biopic Live brought to you by Longevity India where we speak and discuss all things health and longevity in the Indian context. I have with me my co-host Deepak. Hello everyone. I'm Deepak and I'm the co-host for the Biopic live podcast and today we have Matt Karin a veteran in aging and longevity community and Matt it's pleasure to host you here and it's thank you so much for coming
all the way to India and Bangalore where the air is so clean and nice I know I'm loving that you're here guys I don't know what Brian Johnson's problem now so let's come to what Matt's a little bit of introduction Matt is CEO of Optispan co-founder of Aura Biomedical and Eul and He is also an associate affiliate professor at University of Washington at Seattle. He's probably one of the best known persons in the aging and longevity community. He runs an extensive podcast channel where people have heard lot of ideas about longevity. Yeah. It's been one of my staple. Yes. And he's done a lot of interesting research areas and mtor diet restriction signaling. I think we're going to we're looking forward to very exciting session with you here today. Matt, welcome once more. And Pashant. Yeah. So Matt, first question from my side is has it been three decades? When when did you actually start using the word longevity for what you do? That's a good question. I'm not sure when I started using the word longevity. I started studying aging as a graduate student. So I I went to graduate school thinking I was going to do X-ray crystalallography or protein protein structure or something like that. And in my first year I heard a talk by Lenny Garenti who was a professor there. where he talked about how his lab just in the last few years had started studying aging and using genetics and molecular biology. And I had never thought about I mean I was in my you know I don't know late 20s
probably. So I never thought about aging really that much anyways. but certainly not that you could use it as a or that you could study it as a biological problem. And I just got fascinated by that idea. and joined Lenny's lab and started working on aging in yeast at that time. and I never looked back really. I think what evolved in my thinking was, you know, the recognition that a while aging is a really interesting biological problem, the impact if we can modulate aging for human health and and companion animal health. I'm I'm very interested in having an impact on the health and lifespan of dogs and cats and other companion animals as well. But the impact from targeting aging is so much greater than the impact from targeting individual diseases that that really has you know motivated me to continue in my career for yeah almost almost 30 years now. I was 1997 when I started graduate school. So a lot has changed in the field in that time. You're probably the first part of the first cohort, right? You know I mean Lennise I mean that lab seemed to be like kind of the fountain. Yeah. The way I think of it is I mean aging research goes back certainly to you know the the early 1900s if you think about the work of people like Clive McKay and others on caloric restriction. The way I think of it is
the time I came into the field was a really exciting period because it was really I feel the transition from sort of phenomenological observational work you know molecular biology, genetics, biochemistry and like you said mechanism focused. And so the people like Cynthia Kenyon and Gary Ruffkin and Lenny and Linda Partridge all around that same time started applying these tools in very mechanistic ways. And I think that it was exciting for lots of reasons, but it it shifted the view of the broader scientific community to start to view the biology of aging as a credible scientific subdiscipline. Even still, it was sort of looked at as a backwater. There were lots of questions about the quality of work. But because people started to use these mechanistic approaches, I think we gained credibility in the scientific community. It was also fun because that was a time when people started to do what I call large unbiased genetic screens for longevity. So you know using sea elegance and yeast primarily we could start to look at many many different genes and just ask the question which genes affect aging and so it felt like every week somebody was discovering a new gene that impacted aging and and so there so that led to many of the initial pathways being filled out the insulin signaling pathway and mTor and certuins and we can talk about certuins u if you want there's some
no I want to talk there's some stuff there. But it was a really exciting time and fun time in the field. and and then you know I've been around long enough that I've seen you know the the kind of next paradigm shift which I would characterize as the hallmarks of aging becoming sort of the dominant view of the field. And and I think that's also been good and bad in many ways and you know now I think we'll see are we going to have another shift with AI coming into the picture and you know really changing the way we think about the complexity of aging biology. Matt the question which I want to know from you you said you bumped into longevity by listening to probably Lenny. Yeah. And then it became a passion. Then your trajectory is interesting. You seem to moved away from academia and moved into setting up companies and trying to explore the corporate culture. Why you moved from academia? Ora we spun out of my lab when I was still at the University of Washington. That's Aura Biomed. and I'm I'm obviously still involved with Aura, but I'm not day-to - day involved with AR. So I kept my academic position. so I think there were many reasons I think as there always are for for you know especially for somebody who makes a a pretty significant career change after at that point 25 years you know being in academia. you know I think some of the things
that contributed were as many people in academia feel frustrations around constantly having to fight for grant funding. feeling like your best ideas don't get funded yet you get funded for things that are sort of pedestrian and incremental. I think everybody in academia probably has felt this at one time or another. So that was a constant frustration source and and I I was very fortunate. I I was wellunded. I never went through a period when I was in academia where I really was concerned that I would run out of funding for my lab. But it felt like I spent so much of my time doing that that it wasn't being spent on things that were important. And then I think also just the sort of administrative creep that happens as you get more senior. So I went into science because I love the process of scientific discovery. I you know I'm very curious person. I love the feeling of discovering something nobody else has ever known before. I sort of lost that. And then, and I don't think I've ever told this story before, so I'm not going to use any names, but I think the moment I knew I needed to to look for something outside of the realm of academia, I was at a conference and, you know, like is often the case in in a relatively small field, which longevity was at that time and to some extent still is, but growing rapidly, is the same people giving the same talk, meeting after meeting. And so I've heard these talks, you know, a lot. And so I'm kind of just half daydreaming looking around the room and I look at like three or four of my
colleagues who are 20 years senior to me and I literally asked myself this question. I'm like, do I want to be them in 20 years?, and I don't mean that in a way like anything bad about them, but the fact that they were still struggling to write grants and still fighting the same battles. And the answer was immediately clear to me, no, I do not want to be doing this for 20 more years. So, I knew that I needed to be open to other possibilities. So, and so I didn't immediately like decide, oh, I've got to get out now, but I just became open to other ideas and and looked around. And the other thing I think that really weighed on me that helped crystallize that decision was there in my career, I feel like there have been a few situations where an idea that that I had I knew was the right thing to do. I knew it needed to be done. And one of those was the dog aging project. And that was back in 2014 or so., and I had to fight so hard for five years to get enough money. And it wasn't just me. I shouldn't I mean, Daniel Promise Locate Crevy, we were all doing this together, but we had to fight so hard for an idea that is so freaking obvious it should be done. I just didn't want to go through that again. And I felt the same thing was kind of happening with what we ended up doing at Aura with the worm button. Again, happy to talk about any of this in detail, but that frustration that in academia, big ideas that will actually move the
field are really, really hard. So all of those things just made me think, okay, I need to do something else. And so I did. No, I enjoy what you said, but it's giving me some scary thought also. Coming from a CIC background,, yes, I know where it goes. Yeah, look I that was the right decision for me. I'm not one of these people who comes out in bad. There's a lot of good stuff that happens in academia or I think the the best word which might captures is a frustration can actually drive you nuts if you're not really getting the right clicks. I mean for us I would actually say launch India was a crazy idea for us. Prashant happened it still stuck around. Yeah. Hey fantastic. I'm really impressed. So,, Matt,, you know, I think people can't talk about you without talking about, right? Sure. That's it's it's good and bad. So, yeah, but I've gotten to the point where I have to say like I have no financial interest in rapamy. Yes. Maybe you should have. I know, right? Yeah. I would be able to fund my own research one day. Yes. So, maybe you should have thought about the spin out earlier. Yeah. But anyway, I I think but but on the other hand, you know that of all the mechanisms, you know, that influence on the mtor pathway is still the central Asia influencer and nothing much has changed in the field. Yeah. So do you are sometimes do you feel that there has been now a phase where we are going through where there there has to be some
one more big fundamental unlock maybe AI will make that happen but you know like what the de did when it get to protein ping or does some such initiative concerted effort you know a lab that now kind of unlocks using AI the next big thing I I think that's likely the case I you know what we know for sure is that when we simply look at the our ability to to modulate the biological maybe for others just talk about mtor and just just that a little bit yeah sure happy to do that but I'm gonna start more broadly yes and because because I mTor is is clearly a key player in the the network that we know about that affects aging. But but more broadly speaking, if you just look at our ability to impact aging, and probably the best way to do that is to look at how much can we increase lifespan, right, in in any animal, but in particular in a mammal. So we're probably talking mice in the laboratory. to my knowledge the largest effect on lifespan taking out sort of changing genes and having animals go through development in a different genetic state is a experiment that was done 50 years ago by Wer and Waler a caloric restriction experiment. Yeah. And rapamy in an mTor inhibition are kind of second on the list but it's only about
half the size of the effective ecloric restriction experiment. So why aren't we continuing to get bigger and bigger effects? I think that tells us that there we're missing something, right? We should have done better than chloric restriction in the last 50 years. Now that's and again I I say that not because I want to diminish what has been learned because a lot has been we've learned a lot about the biology of aging, but we haven't fundamentally gotten past that barrier. So that makes me think there is a barrier that we don't really understand why we can't do better than that. probably it's at least in part because all of the the targets that we're going after right now mTor insulin signaling kinace are in the same that same part of the network and we need to figure out what don't we know that's fundamentally the way we characterize it I think we know what we know and we don't know what we don't know and we don't know whether what we don't know is 10% of aging or 95% of aging I suspect it's more like 95% of aging yeah so and I This is I mentioned a couple of minutes ago that I think the hallmarks of aging have been both good and bad for the field. I think one of the ways they've been harmful is that as soon as the hallmarks of aging were formalized, it became very difficult to study anything that didn't fit into things. And so people stopped looking. I mentioned also earlier that when I came into the field, people were doing lots of unbiased genetic screens. That all pretty much stopped about the time the hallmarks became formalized. Nobody's
looking outside of the hallmarks. I think we need to. I think there are a few approaches that should be taken. I think one is comparative biology of aging looking in animal models that haven't been studied yet. I think one is high throughput longevity intervention screening and that's where tools like the wormbot and aura can come in. And I think one are these, you know, very large data discovery science projects where the new AI tools can help us to understand that complexity and maybe like you said unlock something fundamental that we don't understand right now. and I and the nice thing is I think we're really starting to see momentum towards that that third approach, right? people taking large population-based multiomic sorts of approaches and then hoping and I expect this will happen that the AI tools will continue and help deconstruct that data in really meaningful ways. And I should say there's a lot of optimism around things like epigenetic reprogramming to be determined. Maybe that will be the unlock we're looking for. Again, I'm not particularly confident that it will be. but maybe that will be. But I do think we need I think there's something fundamental we don't understand yet about the biology of aging. And I'll just finish up by saying I was just just before I came here at a conference in Singapore. it was a
what do they call it? Geropysics conference physics and geroscience. And I think that was really interesting because there are some people who were taking very mathematical quantitative approaches very s simple in terms of the the math that was being used and being and already starting to tease out potentially interesting insights into for lack of a better way of saying it different dimensions to aging. And I think most of what we've been studying is only one dimension and there's at least two or three or four dimensions that are out there that we haven't yet been able to understand. So Matt, one of the more recent efforts and this is from the same Nemesis team is to build a virtual cell. Mhm. Yeah. And they believe that you know to really model a complex dynamic logistical system itself is a prerequisite before you can apply AI because you just can't apply on just a lot of brand update. Absolutely. Yeah. So anyway, so I think that's so this unlocks or something chaos and and I think in in the aging field with AI, you know, I mean there have been people who've been trying to use AI tools, you know, for for years in in the field. I think the challenge has been what you were alluding to, which is that our current sort of understanding of the system is relatively crude and you know it's a garbage in garbage out sort of situation., and when it comes to interventions, this is one of the
reasons why I've argued pretty strongly that we need to explore the intervention space for longevity much more deeply because you can't use the AI tools when you only really have three or four things you're confident robustly extend lifespan. You can't really use AI and predict what else is going to extend lifespan. That is very true. I think it's pretty fascinating what you said. There's a common denominator there. Denominator and topic of you you've got Optispan and Optispan we as far as we understand want to use AI data and combination thereof you said something very fascinating you know like that the viewpoint of hallmarks of aging actually narrowed our view so dramatically that we refused for a very long time to think beyond this conventional norm and I think this use of multiomic multiparametric and large datadriven approaches are definitely going to open up the next dimension of these facets of aging. Where do you think the new dimensions will come from? Because if you have ticked the boxes to saying information on stem cell exhaustion this wrong. Is it something which where do you think something beyond this picture will come from? So I think there's a couple of things that will happen for sure. I think one is we'll find more things that we could characterize as hallmarks. So the list could get longer. Okay, that's going to happen. I I expect where the AI tools will be
very powerful in the short term from these multiomic data sets is helping us understand why it is and how it is that the hallmarks are interconnected. In other words, what is the structure underlying network, right? That network thing is so so I think that that will happen and and I mean again I'm not saying anything super profound. I think but I think those are fairly obvious extensions of where we are now. What I wonder is whether or not we will start to get clues to you know sort of what in my own mind I think of as the dark matter of aging. Like again I am just more and more starting to believe there's something fundamental that we have missed that that's out there and I don't know I think that this this sort of physics approach is going to help us become more confident that that's there. I don't know that it's going to help us figure out what it is. And so maybe the marriage of those two things, the the AI large data approaches and these sort of modeling more basic or mathematical modeling will help us kind of figure out what that is. and then we can start to try to intervene in this these this second dimension, for lack of a better way of saying it, of of aging. So, I think that's that's an unknown, but I'm I think there's a chance that that could happen. I would also say this is why I don't think we need to we should put all of our, you know, eggs in in one basket,
so to speak, and just hope that if we measure enough stuff and use AI, we'll figure it out, right? So that's one approach to try to do that. The another approach is again to take a step back and just let the biology tell us what's going on, right? And this is where I think the largecale intervention screening should happen. So, again, this may be based on the fact that like I said, when I came into the field, that was kind of what revolutionized our understanding of aging. But I think what most people don't appreciate is we stopped early. So those large pale genetic screens even when you just look at the genetic landscape is a tiny fraction of the genetic landscape. These are mostly you know single knockdown or knockout mutations across the genome. So you can't query essential genes. There's nothing on increasing function. They were all knocking down function of genes. So nothing on increasing answering too. And if you think of it like a drug, those are all single dose experiments, right? We only we knock the gene down by a certain percent. So there's this huge landscape even genetically we haven't explored and then we look in the small molecule space we've done almost nothing. So another approach would be to just say if we could measure a million 5 million 10 million interventions and find out what which ones affect lifespan we're going to find new things. I am 100% confident we're going to find things that are better than caloric restriction and we can actually start to look at
combinations and I think that's an area where the field has really lagged. We haven't we have very little data on what happens if you combine two three four interventions. Sometimes you get interesting surprising effects and again may again maybe this is just my bias being a sort of curious science guy like I like to be surprised. I like it when you do an experiment you're like that is not at all what I expected. Right? So we will find things that are unexpected and those unexpected discoveries in my experience are the ones that fundamentally change your understanding. Better keep your heads open for those. So one quick questions this fantastic thing narrative you given about that we have not screened enough we have not tested enough. We have not really explored the dimension which is the one the big question coming from an academic perspective and also from industry. What's the best model system for aging to screen? Well, so yeah, I think that I'm glad you said that because I think what's the best model system for aging really depends on what your use case is, right? So,, and and for screening, it depends on the scale you're interested in and obviously resources. If resources were unconstrained, you know, in principle, you could do a million intervention screen in mice. Now, even there, you'd have to really ask yourself, is sacrificing 50 million mice worth it, right? I mean that's a different question but but it would also take you years decades really. so the way I thought about this was you know when we were thinking okay first of all one question is like what number of interventions would you need to test to
really move the needle 10, 000 100, 000 a million? and one way to answer that is to ask how many interventions do we know about? And that's hard to answer precisely, but you can look in the largest database that's out there. It's called drug age and there are about a thousand, right? So it's a pretty small number. So you know a 100, 000 would get you pretty far. So that's kind of where my thought process started. This was I don't know 10 years ago now., and then you have to say, okay, if I want to measure a 100, 000 interventions, I'm going to do this using small molecules rather than genetics just because, you know, it's easier and small molecules are interesting. Where could where where could we actually do that? Where is it feasible to test 100, 000 interventions? And so you're limited if you're not going to do it in mice to fruit flies, sea elegance or yeast. really those are the three model organisms that are have been used in the field you know with some frequency killifish starting to be used because small molecule delivery and killifish really tricky I I didn't actually know this I was talking to someone at the the meeting in Singapore they actually have to like go in with tweezers and stick it in the fish's mouth so that's probably not doable very tedious so thinking about this I really thought okay we have to automate it and so in what system could we automate and so that kind of takes fruit plies out of the system. This is getting a bit technical, but you have to change the vials and and all that. So, it'd be hard to automate. So, C elegance was the place we landed
and we were pretty confident we could build a robotic system that would mostly automate the lifespan experiments to take the human equation out as much as possible. We did that recognizing that sea elegance are not perfect, right? There's lots of reasons why stuff that works in sea elegance might not work in mice and people. As it turns out, I think it's pretty good going that direction. I think most things that work well in C elegance, and by well, I mean large effect size. That's the great thing about screening a million or 100 thousand interventions. You don't have to study the stuff that has tiny effects. You can study this stuff that has a big effect. Yeah., so I think it works pretty well going that direction. I think what you lose in C elegance, you miss a lot. And this again is the vagaries of segans biology, but they just don't respond well to some drugs. And so you're going to have a fairly high false negative rate. You just have to live with. So that's that's how we settled on segans. We built the wormbot. It's a robotic system coupled to AI that mostly automates the lifespan experiments. The million molecule challenge. Why would you hesitate to Yeah. So so the million molecule challenge was was my response to being well one of my responses to being frustrated by not being able to get this kind of a project funded in academia. So again, to me this was a no-brainer. Like if you could test a million interventions for a few million dollars, we waste millions of dollars. I shouldn't say that. We spend millions of dollars on projects
that are tiny incremental projects. But NIH reviewers have been trained to be very conservative and only fund incremental research. We we the scientists have trained ourselves to do this. It's a feature of highly hyperco competitive funding., you know, not wanting to fund a grant that that's going to fail. So, regardless, you can you could never get a project like that funded through NIH. And the first response you get is it's a fishing expedition, meaning you don't know what you're looking for. You're letting the biology, you're doing discovery science., and my response is, well, you're never going to catch any fish if you don't go fishing. But NIH reviewers don't don't buy that. So, I was frustrated by that and and so we tried to create well we spun Aura out of the lab as a for-profit company and at the same time trying to go out and raise investor funding for Aura wanted to create this community opportunity. It's like go to the community and say if you think this project is a good idea you can pick the intervention cost you $ 100. We'll test it. We'll put it in an open access database for anybody in the community to get access to. And believe me, that was my goal from the very beginning. like I wanted to create a large open access database with 100, 000 500, 000 a million interventions because I think that data set seeds a whole bunch of discovery science throughout the rest of the field including really smart people who know
how to use AI much more effectively than I do to query that data and then in a more targeted way start to tease out the patterns and tell us which interventions are likely to work. Then he can start to use AI to tell you, well, maybe if you combine these four things, it's going to give you a four-fold increase over one of them alone. So that was the idea behind the million molecule challenge was to create this opportunity where anybody in the world could sponsor interventions to contribute to this open access project. Is it still live? Is it It's still live. Yeah, it's been I mean like I you know I don't want to like I didn't have any preconceived notions as to how how much the project would raise. So, but it it's been a little bit disappointing to me that it hasn't hasn't u as much hasn't raised as much as you know I had hoped it would. Again, I sort of feel like I I did what I could like you presented to to the community if people are going to buy in they're going to buy in. If they're not going to buy in. So one interesting thing mama matis mentioned about middle molecule and you've been saying that if we need to find something which is better than caloric restriction and caloric restriction was a mixed set of physiological changes which is happening in your body. Yeah. So do you think one molecule can meet that kind of compost requirement and especially coming from a country like India we talk about a mixed formulation always tends to work better if you always talk about systemic drugs. Yeah. So like classic example is our curcumin the pure molecules just
doesn't work but the herbal extract of turmeric which is the source of curcumin does seem to have anti-inflammatory properties. So are we looking up the right way for a million molecules or we need to look into more mixed formulations. Well I want to I would first of all say we want to do both like I don't think even though it's called the million molecule challenge the intention was never to to make that as a million indiv individual molecules. Yeah, great. In fact, that's the beauty of this system is you're not constrained by the number of things you can measure., so you can test combinations. So yes, I I don't know whether a single molecule can be as effective as caloric restriction. My intuition is yes. I think we just haven't found the right molecule andor the right dose. certainly you're not going to have as many biological effects as caloric restriction with a single molecule. What we don't know is if you think of all of the different things caloric restriction does in a a living animal. how many and which ones are important for the lifespan extension. If it's 10, 000 then you're probably not going to get that with a single molecule. If it's a few, you might. And I think rapamy is a pretty good case example. We can come back to mTor and rapamy because we never really did did a deep dive on that. But rapamy is a drug that inhibits mTor. MTOR is a sort of
key nutrient sensor in every ukareotic cell that we know of in the world. And one of the things caloric restriction does is it potently turns down mTor and rapamy is a drug that does the same thing. Now caloric restriction does about 10, 000 other things. were probably more like 100, 000. So rapamy is only hitting a subset of the bio changes that caloric restriction is hitting, but you get about half the lifespan effect, the the best lifespan effect we can get from caloric restriction. So, is there a dose of rapamy or a different way of inhibiting amitor that would get you closer to to caloric restriction? Probably. Would you get all the way there? I don't know. But but I think you could probably get pretty close. But it's always interesting that whenever you look at more small molecules used for aging, you keep hitting the insulin signaling pathway because we still trying to mimic calite restriction. But why don't we hit some other pathway? Is there are windows in narrow or this is the pathway? It's a good question. I don't know the answer. I'm pretty sure it's not the only pathway and I think there's a selection bias clearly that has gone into many of the studies over the last 15 years. Right? Again this is getting back to the hallmarks not only the hierarchs I mean scientists tend you know part of the reductionist mechanistic approach is you tend to dive deeper and deeper study the processes that you think are important.
for good reason. Like I'm not not saying that's the wrong approach, but I think you also want to take this this more unbiased approach from time to help you figure out what you don't know, what you don't understand. So that could also explain why we keep hitting the insulin signaling pathway. I will say if you look at the data from the the unbiased genetic screens, there are lots and lots of genes that came out of those screens that don't seem to map to the insulin signaling pathway. It's just that nobody has really studied them and figured out how they're working. I think the the closest was a project that we did in yeast that took 15 years from start to finish where this is Brian Kennedy and I who started this and it was the the sort of end of that project where we tested the lifespan of of like 5, 000 gene deletions in yeast was finished up by a a professor at New Mexico now named Mark McCormack and he did a really good job of kind of putting these things into buckets and and categories. And it turns out, I don't remember, there were like seven categories that we could put most of the genes into. Not all of them were related to nutrient sensing. So, I think there's a lot out there that just hasn't been followed up because it's not what everybody's been following. Yeah. And looking at you know, rapper bicep trial here in India. Mhm.
we just we've been kind of thinking about it. We just wanted to get your perspective on the learnings. have we done anything in tropical countries before? and is there any aspect of that that became our mind and second is just coming out of the pearl trial and learnings from there. I think maybe probably should take cognissance few things and set it up right. Yeah. Oh, I would definitely agree with that. yes so a tropical country piece is interesting. I can't think of any trials or data sets from off label use of rapamy that have been derived from tropical countries. the only one that that might be close is I I believe that Singapore is doing something. That's what I was going to say. I believe that I I don't know how many people have been treated or if that started that that there is something going on in Singapore. Obviously Brian would know about that. so the things I would think about so again you know the one of the questions is well one thing I would think about is if whe if and and whether mtor inhibitors have been used in organ transplant patients in India that's a literature that you could look at and just ask it has been extensively. So is there any difference in side effect profiles in that population? Yeah, not something which you look but we know the cases of infection does go up in those people but that because of
the dose differences and but the question would be does it go up more in people in India versus people in other parts of the world. that data is probably out there. Yes, it should be something which we can work looking into. So so that would be one place to just even because I think the question is would there be an increased risk of things like infection? is that's the and so if you don't see it in organ transplant patients then I think you probably don't have to worry about it because the do is quite high right exactly and and we know that those people are at higher risk for infections we don't really know in people using it off label and I'm being I'm I I'm using that term I guess I should be a little more precise most people using rapamy off label and all that means is prescribed by a physician for something other than the label in yeah so most people using rapamy off label use it differently than it's used in organ transplant patients. Most organ transplant patients are taking a few milligrams daily. Most people using rapomy off label are taking somewhere between 3 and 6 milligrams once a week. Much lower dose. Yeah. And there are some interesting differences in the pharmacocinetics that may lead to lower side effects. I think most people would say it's pretty sure bet now that that off label use both because of the lower total dose and the once weekly dosing has a lower side effect profile. So the question is in those people is there what do the side effects look like and we the honest answer is nobody's done the the randomized clinical trial. So we don't
know for sure but we did a study where we collected survey data from 300 some people who'd used rapomy off label andund and some people who'd never used rapomy to try to get a feel for you know what does the risk look like and so one of the ways we assessed side effects was we said in the last 3 months have you experienced X and X was a long list of potential side effects including bacterial infection viral infection whole bunch of stuff pain, and what we found was that there were, I think, seven things that were statistically different between the groups. The only one that was higher in the rapamy users was mouth sores. That's a known side effect in organ transplantations. In organ transplant patients, those are pretty severe ulcers. In the people using it off label, it was more like a typical canker sore. But that was I don't remember how much, seven or eight fold higher in the rapamy users. The other six things were all lower in the rapamy users. So interesting. Who knows what it means? infections, bacterial infections were trending a little bit higher in the rapamy users, but it did not reach statistical significance. So I don't know what the answer is. My guess is if there is an effect on bacterial infections with off label use of rapamy, I believe there probably is. It's pretty small. So that's interesting. So that's the information that I've got. And what's the scale that you would look at if we had to do something?
You mean how big would the clinical trial need to be? How much does it have to be powerful? Well, that really depends on what your endpoint is, right? So you know what I can tell you is there are a few clinical trials happening now for different indications. So one is a clinical trial for periodonal disease. So so we had shown in mice that raphamy can reverse perodonal disease. Jonathan on who was the graduate student of my lab who did that work is now doing a clinical trial in people. so for that I think they're looking at I don't know 40 or 60 people. So it's not a huge trial. the news it was that the one in use it and yeah Brad Stanfield's got got his clinical trial there that is muscle function as measured by I think a sit stand test and and maybe grip strength yeah and and that's also relatively small I will also say though you know this is pretty typically the case in these kinds of trials they're often not really solid ly powered for detecting efficacy. They're often branded as a safety trial. And again, this is one of my frustrations with the academic system in general. People do this because reviewers will fund these small quote unquote safety trials. Yes. And the investigators will will do this because then then they think that if it doesn't cause any problems, they'll be able to get another grant in
another two years to do, you know, a little bit larger trials. So it's an incentive structure that leads to underpowered clinical trials many years to ever get to an answer. So I dodged your question because I don't know the answer but it it does depend on the it does depend on what the end point is. You know my guess is if you really wanted to have a rockolid clinical trial you need a few hundred people just ballparking it. So staying to the topic of clinical trial, let's say you're trying something raped and you're trying or rap metformin combination or rapamy metform and urolithin combo which could be very easy and tempting thing to try. What were the markers you would look for? What would be your bet on say five 10 markers I would look for to see if there's some kind of even incremental gain in quality of life if given choice. Right. So the things I think about, well, first of all, I I would say I'm I I don't think I'd include metform, but that's just my personal bias. If I was going to do something to try to hit glucose regulatory control, I'd probably use an SGLT. But that's a different discussion., and I know different people have different opinions on metformin. So,, I think the things I would think about obviously, you know, you'd want to look at the the kind of standard bloodbased biomarkers. I'd want to do a deeper dive on inflammation because we have good reason to believe that that particularly rapamy is gonna going to gonna knock down chronic sterile inflammation. You know, one of the things we've been doing at Optispan and
and it's a little bit of a discovery process is looking at some of these autoimmune these blood tests that are specifically for autoimmune disease and asking whether within even the reference range we might be able to detect early indications of chronic inflammation. obviously CRP and things like that as well., so I' I'd look there., I'd look at body composition for sure. So, you mentioned the Pearl trial. That trial ended up having a lower dose than they wanted to deliver because they used encapsulated compounded rapamy., but even then there was a hint of improvements in body composition in women. This is an anecdotal thing that I've seen over and over. just from people telling me that it rapamy may facilitate weight loss and preservation of lean mass. Actually thinking of combinations, I'm super interested in a trial of a GLP-1 agonist and at the same time. So I'd look at body composition as as one of the things that I would monitor. infection obviously because we talked about it. One of the things there I would be interested in is looking at both bacterial infection and viral infection. So my intuition is like I said maybe a slight uptick in risk of bacterial infection and probably a larger decrease not in risk of viral
infection but risk of downstream severity of the the viral outcome. And one of the reasons I say that is another thing that came out of that study I mentioned where we looked at rapamy users was the use period was this was right after co that we started the survey. So many of the people who took the survey were using rapamy during the pandemic. And so one of the questions we asked was you know did you have a diagnosed co 19 infection and if so we had them rate the severity according to specific criteria and then also asked them if they had prolonged symptoms something that might be longco. There was no difference in the risk of having had a co 19 infection between the users and the non-users. But when we looked at severity, the users were significantly less likely to have a a moderate or severe outcome and also less likely to have these symptoms that might be long COVID. So this fits with a growing body of literature that rapamy can boost antiviral gene expression and probably also the many of the prolonged negative consequences of viral infections not just co 19 but other viruses as well are driven by chronic inflammation and that's exactly what rapid me is really good at knocking down. So that's another thing I would think about looking at. so those would be the things and you know if you could do it vaccine response would be really
interesting too because Joan Manak had these early studies with a derivative of rapomy showing boosted flu vac. But the reality is you know we could sit down and in an hour probably come up with 15 or 20 end points that would be interesting to think about looking at and then you really have to kind of focus in and which ones to you. Yeah. Another one, this is something I recently learned. there's a couple of papers that have come out. I'd be interested in in doing brain MRI. They're they're So the studies that have come out, they're small. They're not neuroinflammation suppression. Yeah. So there's a couple things that are probably going on. And these studies were were both in APOE3 E4 carriers, right? So hetereroygos, they're at higher risk of dementia. They often show early declines in brain volume particularly in the hippocampus and the codate nucleus. And in in one of the studies they did MRIs before and after actually saw within I think it was four or six weeks increases in brain volume in both of those regions as well as increases in cerebrovascular blood flow. and the other study replicated I wasn't replicated because they weren't trying to replicate but they also saw increases in in cerebral vascular blood flow so it would be interesting to think about whether rapamy could have an effect there and now I'm going to speculate wildly just because I can't help myself so I also had a chance to talk to Greg Fehee
recently and I don't know if you guys know who Greg is but he developed this trim protocol the thymic rejuvenation protocol combination of yeah growth hormone metformin and DHEA And I'm intrigued by the idea of what effect does rapamy have on the thymus. My intuition is while you're taking it is probably not going to do much but when you come off you might see something similar to what Drake is sort of. I have absolutely no data to back that up but but I would be interested you if you're going to do MRI you might as well measure the thymus and exact exactly that's true. So one last question and I think we can probably jump to the the quick fire. Okay. So Optispan in some sense has the vision to be kind of a new age medical conscious or a new age 4. 0. What's where do you think this field is going and you think the maturity of this is couple years away? Are we on on that trajectory? I process, right? I don't know I don't even know what mature means cuz I hope we continue to get better and better. But what I would say is I think right now there are real opportunities given the tools we have available when applied in a rigorous and and medically
sound way to have major impacts on the health of of most people. You know, again, I I have some data to back it up, but again, it's a little bit of speculation, but I I really think in the United States,, most people can get 15 years of quality life back by using a mix of, you know, early diagnosis,, preventative,, health care, lifestyle modification. I don't think you really even need to go to rapamy and things like that. maybe jail fee ones for people where it's the right thing. I think most people can get 15 years back that way., and then the opportunity. So then the question is, can we kind of deploy that in an effective way at a large scale? That's that's part of what Optispan is about is what is the best way to use the toolkit that we have now and to hopefully standardize a little bit across the space. what works, what doesn't work, where's the gray area. so I think there are real opportunities now and then I think going forward the question is how do we determine within the gray area and I put rapamy in the gray area even though I I've used rapomy I'm pretty bullish the real answer is we don't know with the 100% certainty right who's going to benefit from it who's not what the benefits are going to be so start to figure out how do we get sufficient data within the gray area and I'm not suggesting we're going to be able to say
with 100% certainty you know rapamy yes urly a no I'm just picking on euroly and a I actually sort of bullish on euroly and a but really put them into solid buckets but it's all prob probability right but if we can actually get good data so that the docs in this space are empowered instead of just guessing and honestly most of them are just guessing and most of them are guessing from a position of ignorance they hear some influencer on the internet talk about x and they start prescribing it to people right it it I think we need more rigorous approaches and we've got to collect data from lots of sources and we don't really have a way to do that right now. So I think that'll that'll be kind of the next step. I'll kick it off with a relative few one of our quick three supplements that you would really stand by and three that you think are kind of be on the overhyped side. no vitamin D, no omega. Oh, well that just took two of my three. I mean, the reality is I I am very I think the ones we can stand by 100% are the things that we we know are strongly associated if not me mechanistically causal for disease, right? So, and we can measure, right? That's the other thing. If we can't measure it, it's really hard. So, vitamin D and omega-3 I put in the solid bucket because we can measure it. We're we're really confident there. Is there a next level which is I mean I you know I don't have a lot in
that level honestly. So so personally what I would use you know would be things like magnesium. I know that kind of many people would put that in the same bucket. The issue there is we can't really measure it. So creatine I'd put in there. I think the data is pretty solid and and you know personally I've I've I feel like I've benefited from using creatine. So you're povertying. Yeah. So I so the pre-clinical literature is good. I believe that they have pretty significant impact. The human data is missing mostly. There's a little bit on uroly a little bit on epidemiology and spermadine. I think the question there is not so much I I believe that they can have positive impacts in people. It's more around dosing and which people. So, I don't, you know, I don't see a huge downside other than, you know, what it costs you, which for some people is significant, right? But,, but I also am not confident that in the way that these supplements are being,,, developed, and sold that they're they've got sufficient bioavailability and we know if there's enough of a difference that that make Yeah. And I mean, I would put a lot of stuff in that category. I would put, alpha ketoglutarate. Again, data is pretty good, solid. even the NAD precursors. I think I believe there are many people who have deficiencies in NAD that could benefit from the NAD precursors. I don't again when I say
many I don't know if it's 10% of the population or 60% of the population. I'm pretty sure it's not 100% of people. I think part of the challenge with the NAD precursors is they have been so overhyped hyped. Yeah. that you know if there's there I I have sort of this visceral push back against that and so I tend to come off as a little bit more negative about them than I What about the three bad ones? Well, I mean the one that I have studied that always you know sticks up number one in my mind is resveratrol. It's not that it's bad. It's just that it was it it was you know presented as a you know game-changing longevity drug and that has been completely debunked. like it is the most debunked longevity. I've said that before. Yes. So again, doesn't mean it can't have some health benefits for some people, but there's just no evidence that it targets the biology of aging or has positive impact on on longevity. There's not a lot that I feel strongly like that visceral negative reaction about. I I just think there's a lot of things that there's no real evidence to support and that's like a super long list. I'd put resveratrol in that that list too. I just talk about it because it was, you know, it got such a attention. Yeah. And I think it contributed to a lot of people thinking that that drinking red wine is good for you. the other red wine moderately is good for you or not. I think it's a different question, but it certainly isn't because resveratrol. That's true. So it's a standard thing is
that resrol is going to keep you young because you die young. Yeah, that's right. Yeah. So, last quick fire question. Okay. There's so many biological clocks. Oh, yeah. idea that we can measure parameters, I'm just going to say it that way because they could be molecular, but they don't have to be that can predict your current health status is for sure. I one of the things I struggle with is I think the way biological age is used right now is very confusing because what most people really mean is either your risk of dying or your health status. So these things that's what they actually were trained on. They're correlated to either your risk of dying, your risk of getting a certain disease or your overall health status. All of those things are correlated to biological age but they aren't identical to biological age. So none of the clocks are measuring biological age in my view. I'm not even sure what biological age is. I said really good concept. I'm not sure what it is. You know papers. Yeah. Yes. It's in the titles of many papers. I'm actually going to say this in my talk tomorrow. it's probably even talked about in in this meeting 18 different ways like 18 different things people called biological age. So what I would say is I think there are tools that we can use to predict somebody's risk of dying or somebody's
current health status or future health status and there are a variety of flavors. The one that gets talked about the most are epigenetic age clocks. And I have two things to say about that. one is as research tools they are extremely powerful and I don't want to diminish the potential of these epigenetic patterns to be used to tell us fundamental things about aging to predict interventions to predict personal trajectories I think that there are real opportunities there and I'm I'm excited to see where that goes but they have been rushed to market by companies who are selling these these tests to consumers and there's really no quality control there and they just don't work that the noise is bigger than the signal and so that has created I think a lot of you know uncertainties and I think a growing distrust while at the same time you're seeing more and more medical providers offer yes sometimes recommend these tests to their clients and like they don't know how the tests work. There's nothing actionable from these things. And so it's it's a challenging time I think in the the industry side. And and I point to the epigenetic test cuz that that that's the place where I actually have data. I suspect this is true for all of the different consumerf facing biological age clocks that are out there. Yeah.
Fantastic speed to we covered a lot of things. Yes. Hope to Bangalore again and hope you like the air. Yep. Yeah, I can't wait. I've I've had a great time and looking forward to coming. Thanks for being there on this episode of Bopit Live. I look forward to
Transcript auto-generated by YouTube. Verbatim — duplicates intentionally preserved.
In this conversation with Professor Matt Kaeberlein, the tone is deliberately measured. Rapamycin matters. Biological clocks are interesting. Discovery science may change what we know about aging. But healthspan is not served by hype, and a number on a test is not the same as a resilient life.
Rapamycin draws attention because it acts on mTOR, a cellular pathway that helps the body sense nutrients, growth signals, and energy availability. In aging biology, mTOR matters because growth and repair are always in conversation. The body must decide when to build, when to conserve, and when to clean house.
In plain language, rapamycin is interesting because it touches one of the body's central switches for resource allocation. That does not make it a universal answer. It makes it a serious research tool, with questions that still need careful human evidence.
Kaeberlein returns to a practical limitation in longevity medicine: we still need better ways to know whether an intervention is working. Lifespan takes too long to measure directly. Disease outcomes are important, but they can be slow and noisy. Biomarkers help only when they connect to something meaningful.
The felt experience is straightforward. A useful longevity practice should preserve capacity. Better strength. Better metabolic health. Better recovery. Better function over time. A marker has value when it helps illuminate those outcomes, not when it replaces them.
Biological age is often used as if it were a destination. It is better understood as an imperfect signal. — Contrast Collective
Epigenetic and biological age clocks are among the most visible tools in modern longevity. Kaeberlein's caution is not that measurement is useless. It is that the phrase biological age can create more certainty than the science currently supports.
Many clocks predict present health status or mortality risk. That can be valuable. But if a test moves after a supplement, protocol, or medication, the deeper question remains: did health actually improve, or did the measurement change without a durable benefit?
The conversation also makes space for unbiased discovery. Aging biology has frameworks, including the hallmarks of aging, but frameworks can become too tidy. They help organize thinking. They should not close the field too early.
Large-scale screening, model organisms, better analytics, and AI-assisted pattern recognition may reveal interventions that do not fit current expectations. This is where the future feels most promising: not in louder claims, but in better questions.
Preventative longevity medicine asks for a different rhythm. It is less dramatic than treating a crisis, and more dependent on long-term evidence, risk awareness, and restraint. The goal is to extend the years of clear function, not simply to chase youth as an aesthetic.
That orientation belongs naturally beside contrast therapy, recovery, strength, sleep, and metabolic health. Each is a practice in preserving capacity before decline demands attention.
The future of longevity will be built on better signals, not louder promises.
Treat biological age tests as imperfect signals, not verdicts.
Separate promising mechanisms from proven human outcomes, especially with compounds such as rapamycin.
Measure longevity by preserved function: strength, metabolism, recovery, cognition, and resilience over time.
