---
title: 'Why We Can Only Slow Aging, Not Reverse It | Dr Peter Fedichev'
source: 'https://youtube.com/watch?v=NU8RAZhQhT4'
video_id: 'NU8RAZhQhT4'
date: 2026-08-04
duration_sec: 2704
---

# Why We Can Only Slow Aging, Not Reverse It | Dr Peter Fedichev

> Source: [Why We Can Only Slow Aging, Not Reverse It | Dr Peter Fedichev](https://youtube.com/watch?v=NU8RAZhQhT4)

## Summary

In this interview, Dr. Peter Fedichev, co-founder and CEO of Gero, discusses the physics-first approach to aging research, the recent $17 million funding round, and the concept of thermodynamic biological age. He explains why aging can be slowed but not reversed, drawing on the second law of thermodynamics, and outlines Gero's strategy to develop drugs that target aging itself, potentially extending human lifespan from the current average to the maximum of about 120 years.

### Key Points

- **Gero's $17M Funding and AI-Tech Convergence** [00:44] — Gero closed a $17 million financing round from AI and tech investors, reflecting a growing convergence between biology and tech. Investors are interested in aging-first platforms that use physics and machine learning to understand root causes of aging and chronic diseases.
- **GLP-1 as Proof of Concept** [02:01] — GLP-1 agonists, originally designed for diabetes and weight loss, show benefits beyond those indications, demonstrating that a single drug can reduce risks of multiple diseases. This class of drugs is now a $100 billion market, larger than AI revenue, and serves as proof that targeting aging biology can be commercially viable.
- **Physics of Aging: Emergent Damage** [05:21] — Aging is not just wear and tear but an emergent property of complex biological systems. Physics provides a language to understand how damage accumulates irreversibly, leading to a single 'master' biological age that can be measured but not reversed.
- **Single Biological Age** [08:41] — There is one fundamental biological age, akin to entropy, that drives aging. Other measures like epigenetic age are derived from this master variable. This master age is irreversible but its rate can be slowed.
- **Reversible vs Irreversible Changes** [10:17] — Age-related changes are classified into reversible (e.g., chronic diseases) and irreversible (entropic damage). Slowing the irreversible damage reduces the probability of reversible changes, thus extending healthspan and lifespan.
- **Lifespan Extension Levels** [12:13] — Treating individual diseases can add a few years to average lifespan. A new class of drugs targeting aging could raise average lifespan from ~80 to the maximum of ~120 years. Extending maximum lifespan beyond 120 would require novel technologies.
- **Against Epigenetic Rejuvenation** [13:46] — Epigenetic rejuvenation, which resets cells to embryonic state, is theoretically limited by the second law. It may rejuvenate tissues but risks tumorigenesis. Instead, Gero advocates slowing aging, as seen in naked mole rats, which is less challenging.
- **Thermodynamic Biological Age** [15:51] — Developed from dynamic organism state indicators, thermodynamic biological age is a single variable that controls resilience, functional decline, and maximum lifespan. It is measured from clinical data and can be used to assess drug efficacy.
- **Maximum Human Lifespan 120-150** [19:31] — The 2021 paper proposed a maximum human lifespan of 120-150 years. Current data shows no evidence that maximum lifespan is increasing, despite medical advances. Gero aims to bridge average lifespan to this maximum.
- **Measuring Biological Age** [23:38] — Thermodynamic biological age can be measured from various signals (proteomics, metabolomics, facial features). It is not yet standardized but is converging with other measures. Dogs are used as models because they age faster, enabling faster validation.
- **Irreversibility of Biological Age** [29:59] — Thermodynamic biological age is not affected by known interventions like caloric restriction or probiotics. It is a defining property that cannot be reversed, only slowed. This distinguishes aging from diseases, which are modifiable.
- **Gero's Pipeline and Strategy** [32:39] — Gero uses generative AI to model human health, identify drug targets for diseases (partnering with pharma) and for aging (developing first-in-class drugs). The funding will support collaborations and validation of anti-aging targets.
- **Future of Anti-Aging Drugs** [36:03] — GLP-1 success will drive pharma to develop anti-aging drugs within 3-5 years. The market for an anti-aging drug would be larger than GLP-1, making it an obvious next frontier. Gero aims to prove the concept with its models.
- **Practical Advice** [41:45] — While waiting for drugs, Dr. Fedichev advises eating less, regular checkups, increasing physiological reserve through exercise, and maintaining social connections. Social status and inner peace have significant effects on lifespan.

### Conclusion

Dr. Fedichev argues that aging is an irreversible entropic process that can only be slowed, not reversed, and that targeting this process is the ultimate biotechnology. Gero's physics-based approach aims to develop drugs that slow aging, potentially extending average lifespan to the maximum of 120 years, with significant commercial and societal implications.

## Transcript

there is one GLP-1 drug that was found opportunistically, how more how much more drugs of that kind are there? Can we find them or better drugs? And what those some of those drugs not affect just a few diseases, which could be
itself because that would be essentially an ultimate biotechnology. the co-founder and CEO of Gero, a physics-first AI drug discovery company
who has pioneered the field of gerophysics, including publishing landmark research on the mathematical limits of human resilience in Nature Communications. So, Dr. Fedichev, thank you so much for
joining us on Modern Healthspan. >> Well, thank you for having me. >> So, Dr. Fedichev, uh congratulations on the announcement today that Gero has just closed a further 17 million financing round from uh AI and tech
investors. So, can you tell me what are the AI and tech investors looking for from an aging-first platform such as yours? >> Well, I think uh there is an increasing understanding um everywhere that uh
biology is the next frontier, maybe for tech uh and AI. Uh there are lots of startups being funded by ex-AI and IT people or range. I think there is a there is a great
convergence now. I you you you can look at AI companies doing folding and getting Nobel Prizes for that. So, obviously, the convergence is there and we represent a narrow niche uh within this project process uh that
is using uh physics and machine learning to understand aging, the root causes of aging and chronic diseases, and trying to get better drugs against this. So, in the new release you do kind of have a
analogy to GLP-1 and it and how do how do GLP-1 agonists but they're originally designed right to to reduce body fat but they not only from the benefits of the body fat but also just they seem to have
other benefits as well which are unrelated to losing the weight although they also may lose muscle it's worth keeping that in mind. So is that what you see you're trying to develop something that would be on a
similar line that would affect aging at a at a basic level? >> Well look for for years we knew that age is the most important risk factor of almost any disease and try to get us have a disease at 20 you would see what
I'm what I mean. That's why there has been always this idea that maybe there is a actionable biology that controls risks of multiple diseases at once and that's precisely what is gerontology
dream. So that's what people were promising and trying to demonstrate and that has to be that has been a dream and now we have a class of drugs that was developed outside of gerontology community by a pharma company
that for the first time shows that indeed you can have a modern biotech grade targeted drug and then with a very clear mechanism of action that is reasonably safe and reduces risks of multiple diseases.
Not necessarily related not immediately related to diabetes and body weight gain. So in a way this is now a proof of concept drug and by the way this drug is being sold and then this class of drugs is being sold for 100 billion dollars
right now. This is more than the revenue of AI by the way. I mean companies are big but their revenues is still in the same range. So that technically puts biology for the first
time on the scale of tech business in an interesting way. I mean pharma, I mean for for many people pharma looks big. But for tech companies pharma is just your grandfather's drugstore at the corner of your street.
So with GLP-1s for the first time I think tech industry sees pharma under this increasing interest from tech to bio and obviously from within bio to tech, right? Because uh
uh is getting into the risk to be disrupted by by biotech. So this is a very interesting time to live in and I think the GLP-1s is precisely the proof that uh everyone needed uh to to
to support uh the ideas like gerontology and uh everything else. What we're GLP-1 drug that was found opportunistically, how more how much more uh drugs of that kind are there? Can we find them or better drugs? And
would those some of those drugs not affect just a few diseases, which could be still a lot, but also affect aging itself because that would be essentially an ultimate biotechnology. >> I mean it does make sense because the
addressable market is very large. I mean it's like the biggest market you can have. But that kind of brings me to the just take a step back and you are a originally a physicist and you're using
physics to look at aging and to try and identify the underlying cause so that you can then drug it is my understanding of of what Gero is doing. So could you talk about how you look how
you're using physics to search for this this underlying reason for aging? >> Well, I think that uh if you go to almost uh, any uh, to people of any trades of life uh, other than maybe
what aging is, most of the people will tell you that this is kind of wear and tear, almost like any machine is getting old mostly by lots of microscopic insults that are accumulating over time, uh,
maybe different in in different place in different people. And it's almost, uh, as, uh, unavoidable and, uh, irreversible as just a lot of time, when they are used and we are using our body every day.
aging is more complex than any particular process in our body. It's actually, uh, a result. This is an unwanted result, a byproduct of, uh, metabolic processes that occur in our body. And they accumulate in ways
that, uh, has all features of, uh, all features of processes that we find irreversibility, is this kind of one-way, uh, I mean, people are not they are doing, people are getting older, unfortunately.
So, it looks like, uh, human bodies are complex machines and then these complex machines sometimes phenomena occur that, uh, cannot be derived on the level of And that's precisely what physics likes, right? When look when you look at stock
they start working together, yeah, they generate an index, right? And index is on our news. This is just a single number that characterizes our economics. systems and machines work and they generate damage that builds up and
generates a biological age, which we can read read from our faces. So, this particular organ. This is a whole organism property. It's emerged from the all all, uh, infinite number of processes that occur in our
body. And physics is precisely the science that, uh, actually actually excelled in understanding how this emergence occurs in complex systems, what levels of control are available for these emergent properties, uh
which of them can be reverted, which of them cannot, and can we stop them? And that gives you a language, actually, to talk about aging and chronic diseases. Uh and trying to focus your intentions attention on things that you can change
rather than those you cannot change. So, we predict that you cannot rejuvenate, but you can reduce the rate of aging. And that already would be a dramatic uh intervention, dramatic effect on human lifespan.
So, you people who use epigenetic age, like the ages, they find different epigenetic ages for different organs, right? And so, the question comes and and also you can use
Dr. Lutzes uh he uses uh glycation age, right? This is so but so but they all have different values. But what your system would imply is that
there is in fact a single biological age that would be able to define how old an >> Exactly. So, that's what we are try I mean, this is the contribution that we're building uh that we're providing uh back uh to
the to the academic field into the aging research. Because normally, if you have too many ways to measure something, this is a symptom of what you're measuring is not fundamental, right? I
are uh are built. If you have too many of them are not fundamental, right? And you're trying to build a better theory that has less parameters and uh less phenomena that uh that explain your your
experiments. Yes, we do believe that there is a master age in long-lived long-lived, by the way. We We are complaining, but we are long-lived So, in long-lived species, there is indeed one fundamental biological age,
which is unfortunately just entropy, just the damage, which is going just one That's really the clock. And yes, there are all sorts of processes that occur in by that age, but they are not fundamental.
fundamental clock that is going unfortunately on one way, but at the rate that we can eventually control. And if we can control it, if we can slow it down, that's the second law, right? You cannot
revert time, but you can slow uh the way the time goes. We can still get enormous life extension in our species, and that's what I think should be the goal of the industry. >> Right. Now, I would certainly like to
come back to that. But uh staying on the the basics, so you have come up with you have developed a theory of kind of three different layers um and change that
>> Uh maybe can go quicker um about that. So, yes, we believe that there is this irreversible entropic damage that defines age. And then uh this damage is on average pathological, and uh it essentially
deforms our bodies and uh the way all processes in our bodies occur uh in a way uh increasing the damage that manifests as chronic disease.
So, what we are telling to everyone is that uh all age-related changes can be roughly classified into reversible and irreversible. I mean, chronic diseases >> [laughter] >> And infectious diseases are more or less
reversible, and some of I mean, even diabetes type 2 is somewhat reversible if you start early, right? But if you wait long enough time, it would be with modern uh with the most modern drugs.
So, what we are what we are bringing in here is that uh some changes are reversible, and some changes are irreversible. And uh the more you let irreversible changes accumulate, the higher is the probability of getting
further reversible changes uh in your body. So, that's how chronic telling is that yes, [clears throat] this master biological age is the master If you can slow it down, you would not age. And that's kind of desired goal
at the end of the game. And there are other processes that uh produce organ-specific damage and they are chronic diseases. You can try to Actually, you can try to identify a novel class of drugs that will help our
bodies to accommodate aging, not to accumulate chronic diseases even in the face of aging. That would eventually increase our our lifespan from the current, let's
say, 80 to the maximum lifespan of about 120. So, these are the different levels of drugs. If you take chronic diseases one by one, you can increase lifespan by a few years. Right? I mean, early diabetes takes 6 years of life. A great
drug against diabetes will increase on average lifespan in the human population everyone has early diabetes. So, that's the upper limit of what you can get in terms of life extension by going after individual diseases one by
We believe that there is another level of drugs that can merge the mean the average lifespan, which is about 80 to 90 years old in different countries, to the maximum lifespan at about 120 years. And then there will be
another level of drugs that will eventually extend our maximum lifespan. maximum is something that we can achieve with the existing technologies, and that's the difference of almost 40 years. That's huge. And then there there
should be some novel technologies be developed, which I do not see precisely which, that will change human maximum lifespan, and that would be kind of a monumental change, which uh is still a little bit a little
bit of sci-fi-ish, I would say. >> Right. I mean, it does make sense. I I that um Max, I think your co-founder, pointed out that uh naked mole rats live for
a very long time and their rate of mortality does not increase. But they don't reset. It's not like they get old and then they get themselves first place.
And so that would be your aim is to rather than like cellular programming, which is to reset, you think that the answer is to stay young. at odds with the community right now because as
you may know, there is a huge hope right now in epigenetic rejuvenation technologies because it does appear that epigenetic rejuvenation can reset human age uh to by producing an embryonic cell of
essentially zero age. That's how human reproduction works. What we predict and what I think is supported by the experiments, if you try to do it with an adult organism, so it first of all have a little bit of rejuvenation and that's
how I think this technology will produce great drugs against particular diseases because it can rejuvenate particular tissues that has certain accumulated damage. But at the same time when you start gearing up this the the power of
regeneration in vivo, you start generating tumors. And by the way, the ultimate rejuvenation is translating is is transforming every cell of a human body into embryonic cell that's of that body. I'm sorry. So,
that uh yes, the technology has promised, but it's very hard to go against the second law. Yes, you can reset all the cells, but you will destroy the organism. So, that's how the second law works. We believe that
going after the second law is tough. I mean, adventurous individuals trying to do that, but instead we advocate advocating to try to stop. I mean, that's not against the second law. It should be a
lot less technologically challenging. Stopping human aging, I mean, naked mole rats have manifestations of aging, but that doesn't translate into acceleration of mortality. And that's precisely the class of drugs that we believe can be
if you start focusing on the things that are possible instead of chasing things that look cool, but uh not possible according to the theory. >> So, in in one of your papers you you developed um in your 2011 and 2021 paper
you you developed uh the dynamic organism state indicator, which is some organism state indicator, which is some way of measuring this level of damage. But since then you have developed that into the thermodynamic biological age,
which I guess it's Well, can you tell us what is the the thermodynamic biological >> Yeah, I I I I I can tell you. So, we were always fascinated with the idea where this maximum human lifespan comes, right? So, some I don't know
um fortunate or not individuals live up to 110 and some of them more years old. Uh we know that at that time uh their aging, so there's definitely something going on other than diseases because
these people are technically very healthy. They don't have many diseases survivors, so these diseases do not stick to them. So, these are exceptional individuals. It's very hard to live to 110 without having long-lived parents.
There's a lot of genetics there. But uh we definitely see degradation of important we have seen using these dynamic indices by observing trajectories health trajectories of these people, we were able to observe
that the older these people are, the longer it takes for them to recover. I people are getting older, it takes longer and longer for them to recover from any particular disruption, medical or whatever accident or things like
So, at the the of lifespan, this uh recovery time becomes infinite. That technically means that these people cannot survive even smaller even a small accident. So these people are becoming very fragile. So we were using these
dynamic dynamic factors measured from clinical data in order to measure the [clears throat] for the first time. And many people many people got this this paper as a
kind of negative and pessimistic view on things, which I do not share because my experience in engineering sciences is when something gets measured is getting modified eventually, right? So we gave people a way to measure indicators that
predict maximum human lifespan, meaning that uh you can still you can still about the biology that controls the maximum lifespan and eventually modify it. So by kind of if you would add up all the works that we are doing, we are
telling that this lack of resilience or degradation of resilience and functional decline like reduction in IQ and the maximum other measures of health is actually the same process that has a simple indicator the single indicator
which is this thermodynamic biological age. So in a way all of all our works converge to the idea that there is this kind of index index variable an emergent index variable, which is biological age. It
has very clear thermodynamic meaning and that variable controls the degradation of resilience, the degradation of functional state, and the maximum human lifespan. So those who would reduce
the thermodynamic biological age, the reduced rate of biological aging, will control at the same time the rate of functional decline, lifespan. And I think this is the most optimistic statement from from the
There is just one control variable, as hard as it is for control, but it's one, which is a gift of nature. And this single control variable controls essentially the quality of life uh diseases, which is a functional state
maximum lifespan. And this is the ultimate target for aging. >> So, you in in the 2021 paper you proposed that the maximum lifespan is 120 to 150.
Is your Does your current work kind of reinforce that that >> Yes, we we're not Yeah, we're now having better and better estimates current maximum lifespan and then attainable with the current technology.
If you look at the human demographic data for the last 300 years, you would possibly see that the disease-related mortality is going So, people are living longer and longer and actually the onset of diseases is
getting is getting older and then to to to to But at the same time there is no evidence whatsoever that the maximum human lifespan is increasing. So, the way people living older and
previous times I mean we have a lot more people right people A lot more people are trying to live longer, right? And that's why we But there are no people
there is no evidence that whatever we are doing medically, technologically, nutritionally is increasing the maximum human lifespan. So, what we are trying to do right now and we we try to develop a biotechnology that would bridge the
average lifespan to the maximum. Because that's what our theories believe up in our data and that's what we can test right now. And yes, I believe as I said there is another level of biotechnology that will
the maximum human lifespan, but as I said this to my opinion this is a bit said this to my opinion this is a bit This will show up a little bit later. >> Right. And hopefully I mean if if your work
pans out, then we will have more time to be able to develop that, which would be yes, still believe that reducing disease disease burden and increasing lifespan is a great idea, right? Because we are not There is still
a lot of people who are dying early still in the top of their functional and still in the top of their functional and professional state. So, if we can rescue at least this, I mean, that's lots of societal investment and also lots of
intellectual capital experience lost. I mean, aging is grabbing is is is robbing us from experience. Aging is killing the most experienced people right now, right? I mean, it takes 40 years to get on top of your profession
these days almost everywhere. I mean, think about even I mean, soldiers, restaurant owners, chefs. I mean, all days. It takes a lot to get on top of your profession. And that's exactly the
time where these unfortunate let's say health modifying events start to occur. So, I do believe and and look, I mean, that's what the theory is trying everything you can. And some of these things work. And
extend lifespan a little bit and there are unfortunately no known ways to extend lifespan by a lot. So, that tells you that there is a limit to what we can do to aging without understanding it. What we are doing with
first and with lots of machine learning on top of that to to feed actual human on top of that to to feed actual human data on our models is that we are trying to do other things and before trying to do anything, we ask ourselves
thing works. And we are just selecting for things that at least [clears throat] have a chance to work in big way. And I think that already I mean, this this mindset, not necessarily in our team. Aging is complex, right? So, more people
should should should move along with this mindset. But if you start asking question, what would be the maximum effect that you can achieve with that thing and how is that supported by the data, I think
eventually we'll get more focused on things that have chance to fly. >> How How measurable is thermodynamic biological age? I mean, if I want my epigenetic age, right, I can just go and get it done. I mean, it's
it's not not that expensive. So, is thermodynamic biological age the same kind of it doesn't have the same kind of measurability? And I mean, could you use it for clinical trials? Like to show that something has been effective.
>> Yeah, first of all, I I I need to say that the whole community is now there are many initiatives including biomarker consortium now internationally trying to establish measures of biological age. And there has been just a paper which
was on the front page of Nature paper by Vadim Gladyshev and his team from Harvard University where people established the two biological clocks different all mammals to to assess the drug effects of drugs.
What people see, and I think it's very important, is that they see that they have to find a few measures of biological age and one of them predicts chronological age better and the other predicts risks of death and diseases
better, right? So, there are different measures of biological age. What we believe is that the one that is closer to predicting your chronological age is actually closer to closer to thermodynamic biological age. And the
age. The other biological ages that people are developing uh uh kind of derived measures from that, right? So, the community, I think we that there are fewer and fewer different biological ages and I hope that
there is one master biological age and that the others are just following it. So, our thermodynamic biological age is close to one of those measures that people are increasingly using. And yes, okay, we are kind of sharpening
definitions and I think eventually this would biological Maybe somebody will come up with a better algorithm that we are using, by the way, right? I mean that's how science develops, but I think yes, I believe that in 5 years the whole
community will converge to the idea that there is one master biological age which is similar to one of the biological ages they are using right now, by the way. I mean they are like where as a community I using. So it's not yet
I mean the convergence is not yet there, but I believe I can see it's it's occurring. So now, the problem in using these things in actual clinical trials and actually the challenge with aging is precisely this. We are long-lived
meaning that it's very hard to measure true biological age is a very slow to grow. And also to respond to clinical trials. from Cornell University, Harvard University and recent preprint with
Roswell Park Cancer Center. We are pointing our attention to dogs now a lot because dogs are also long-lived mammal species. They age like us and but still like three to five times faster than us.
technologies of measuring biological age and also proofs of drugs working against this true biological age may be assessed first, measured first, proved first in dogs, not in humans as unfortunate as it sounds.
But that that that that's that's life, right? So yes, we are looking for ways definition of the biological age to make sure that we converge with the rest of
the community or the community converges to us. And yes, we we just put up a preprint where certain drugs were tested using our and commonly used measures of uh see which reflect aging biology better. So, it's a
uh we fortunately we have dogs. And with dogs, I think we can prove And with dogs, I think we can prove these things a lot faster. What goes into the measure of the thermodynamic biological age? I mean, is
it a mixture? >> That's how Yeah, that's how uh I I I still like this analogy with uh stock exchange. If we were Martians, and they would see that we have different stock exchange indices. We have
S&P, we have Nasdaq. And uh as you may know, most of them correlate quite well. I mean, at least not at times of crisis, of the time there is quite strong correlation between them.
parts of the system. And if you know what to look at, you can derive more or less the same measure. Right? So, peo- people, I mean, even stock exchanges in different countries are correlated. So, if if we were Mar-
Martians uh trying to understand uh the the state of the Earth's economy, we could be having access to different signals. But, at the end of the day, extracting more or less the same quantity. So, we're trying to propagate
the same approach here. You can take proteomics, you can take metabolomics, what to do, you can take face features, by the way, from your iPhone. If you know what to do, I mean, we can derive our biological age by looking at
shape of our nose actually tells you about age. Quite a lot if not I mean, quite quite well if not better what you can extract from DNA methylation if you're not careful. So, what I'm trying to tell
master variable, you can read it. And this, by the way, emergence, right? So, different pieces of the system. Epigenetics is just one aspect. Metabolomics, proteomics, face features are different aspects.
important, right? So, for example, if you look at the recovery time in people, right? Recovery after gym session, for example, is also age dependent. So, actually, there are many many measures of biological age that could be
used in order to infer it, if there is one master biological age. So, for interested in resilience, because recovery time after stress is actually an exceptionally good metrics that could be used in clinical trials, by the way,
for the future anti-aging therapeutics. So, I wouldn't stick to any particular uh system to measure biological age. What we're trying to What we're What we're pitching to to the rest of the community is that let's look for common
features. Let's learn how to measure that master biological age, which is from different signals, and then by ways of learning that we will converge to the true definition of the biological age.
>> The You mentioned that the biological age, as you see it, is uh like it it's based on damage and irreversible damage, which would imply, you know, the
second law of thermodynamics, that it can't go backwards. Is that what you would see, that the but the the one biological age, that even with interventions, you could slow it, but you cannot remove reverse it?
>> I mean, that's what we are showing you, and that still has to become or not uh that uh the biological age that we define from our models that physics-grounded has this nasty feature,
and uh it's not affected by interventions. Neither caloric restriction, neither probiotics, neither some of the kind of proven anti-aging drugs. Yes, they affect uh features that change
with age. So, that's why they may look like drugs that affect aging, but they do not affect uh biological age, the terminal biological age. And uh yes, we believe that this is defining property, anyway, going one
and um mostly not being affected with most of known class of drugs. I mean, that's not the definition that we are putting into it, right? I mean, that's the outcome that we are judging the
properties of this biological age. >> Yes, and it kind of makes sense that you know, you know, yeah, I think you mentioned like your nose does not get intervention that will make that difference.
>> That's what I call the second law on your face, right? start understanding knowing out of training or dieting nose, right? I mean, people have to do
surgery to to look younger. They cannot do it without They cannot do it by I mean, traditional measures. So, this is very interesting feature of long-lived animals that obviously, if you control diseases, you
But you can do it without affecting aging, right? I mean, if tomorrow, for example, all cancers will be solved, well, we'll be living longer, but that wouldn't change aging in any way. So, I think by focusing on health span or life
span, by focusing on one feature, like the second date on the grave, people are matching together diseases and aging. And since diseases are more modifiable than aging as we know, right? I mean, if
you try hard, you can even regress metabolic disease, which is quite challenging. And it has clearly has lots of effect on life span, right? I mean, obesity you can get in shape and live longer. So,
there's no question that you can affect your life span by by certain actions. What is What is missing from this discourse is that the aging is not stopped. I mean, your nose still reveals
your age, and that's precisely the problem. this kind of brings me back to our original point. So, you have some more funding. So, what does your
What does your pipeline look like? What what are you working on in terms of developing interventions that may be able to address or slow down slow down the aging? >> Well, first of all, we we consume more
and more medical data and try to build better models of health, right? And all these theories are only only good if you can apply them in the real world. And we combine these theories with modern machine learning in order to analyze
So, in good old times, people were first computers to simulate these equations in order to model the data and that's how we have now airplanes that are flying after lots of
trying and building, right? And that's why engineering is going going so fast. We're trying to to build the same in biotech, right? Instead of doing lots of clinical trials, we try to better model human health first and hope that if you
are able to model human health, you should be able to identify targets for future drugs that hopefully will have better chances to end up successful in clinical trials. What happened over the last few years is that with the this
advent of generative AI and and I'm not talking about language models, I'm just telling you about generative AI as a mathematical tool. You can actually now outsource to AI actually writing those equations and then deriving those
both derive equations and model and simulate like that. That's the power of generative AI. So, first of all, we are building better models for human health understand the laws, the dynamic laws, how the human health evolves across the
lifespan. That allows you to do two things commercially. First, you can help people who develop drugs against specific diseases, right? Because if of biology with these models, you should provide better targets
That's precisely where interest from pharma is getting to us because these people now can develop a drug against any there are no undruggable targets anymore, right? So, if you come with any
biological idea, these guys can give you a drug that will work against this target and only problem with that if covering that target actually modifies the disease or not. So, why why bringing them targets with human evidence, right?
you. And the best thing is that these models under differentiate. So, these models can be used to estimate thermodynamic biological age. So, these models produce you both models of diseases and aging.
there is no difference in trying to find targets against diseases and against So, that's how we're trying to execute this dual strategy. We are phased to pharma with targets against common diseases.
models in order to get targets against aging and we're testing them in labs. So, we're trying to collaborate with pharma on diseases because pharma still diseases. Because there you have to do best in
class drugs, not first in class most of the time because well, people are doing drug discovery against diseases. And on the aging side, the situation is true drugs against aging and we're trying to be the first in class there.
That's the the way we operate. So, the funding will go more collaborations, and more validation of anti-aging targets on the inside. >> Right. And you're developing at at least
in preclinical models, your own >> Yes. So, our prediction is that look, with these GLP-1s, the situation that kind of to reflect back on the beginning develop a drug against aging. There's no regulatory path, lots of risk, and so
But now with GLP-1s, the situation is very interesting. So, those companies them. A lot of companies are trying to do drugs that will keep your muscle and then it's like ecosystem of iPhones.
Somebody is doing iPhones and the other are doing covers for iPhones. There will class of drugs. But for those companies that already have these drugs and they're already earning a lot and getting a trillion
dollars valuation with this only one kind of mega assets, I think the decision is pretty obvious. If you start developing almost any other And this is actually a a very normal and a very ridiculous situation.
10 years ago making a drug that would be selling for $5 billion per year would be that at least some companies, the largest companies, will be moved by the logic of progress to develop a drug against aging simply by the arithmetics.
larger than GLP-1 is a drug against aging. So, we believe that in 3 to 5 years any company that would have a good drug or prototype of a drug against aging with large effect will be will generate a lot of interest from pharma.
It just will take a few years for them to realize that after GLP-1s, I mean, aging is the next obvious frontier. It's up to us now to show that there is the the only thing that is missing in this narrative. And that's what we will,
you know, burn every dollar of our funding proving. >> So, you're also I saw you were also working with Chugai, which is a >> Yes, exactly. >> And but they are they're not looking at
small molecules. They're looking at antibodies or monoclonal antibodies. So, you're helping how you're working with them? >> Well, that's the the same idea to the extent we can talk about that. So,
Chugai obviously is one of the leading drug developers in the world. So, in my against any target, if they wanted. So, these people have exactly everything that you need to develop best-in-class
drugs against any disease and what they want now more biological ideas. To to to to to to use as a seed for further future drug discovery
problems. So, I think the same happens to to other pharma companies right now. deals with Pharma buyer, for example, Eli Lilly with Insilico Medicine. So, lots more and more biggest drug discovery companies are going to smaller
biotechs and licensing novel biology from them. That's I think another manifestation of the logics of the of the progress. These any target. The only thing that they are still
missing is what are those novel targets. And we hope that with these better health, we can actually help them because humans are, as I said, very long-lived guys. It's It's impossible to model human
disease in labs, right? It's like it's it's it's very close to this situation in an airspace. You cannot I mean, mice are dead in 2 years. How you can ever model diabetes in humans and mice? You can literally kill a mouse in 8 weeks on
high-fat diet like on a McDonald's diet. And humans can go to McDonald's for 50 variation, but these guys will still be alive, which means that there is a limit to what you can model in the lab. And what we are proposing instead of
modeling, actually look up the answer in actual human data. By providing biology with human real-world evidence, we hope to accelerate drug discovery, to help our partners to to build better drugs with better probabilities of
And as I said, on the inside, we will be used the same models in order get on to aging because I believe in 5 years they >> Do you have any idea what kind of timeline you would be
looking at before these would be the some of your solutions that because you you mentioned that it was current technology. So, do you would have something that could be
in the clinic? >> Well, of course, it depends uh it depends on many variables because what we're trying to do what has not yet been done. Obviously, when Chugai takes your target and tries to develop it,
we're talking about the short timescales. So, if the biology is really good, they can be just in very few years they could be in clinical trials. So, that's actually the reason why working with big pharma is actually huge force
multiplier in our situation. If we're talking about aging, we are now looking for exceptional biology, not necessarily at the quick development time because we believe that exceptional biology is precisely what is missing
right now. So, once this exceptional biology is year or two times in the lab, we will be looking for a in the lab, we will be looking for a specific regulatory pathway to translate
it into a drug. I believe that the problem of the longevity field is not exactly the regulatory path right now, but really having good proof-of-concept demonstrators of strong anti-aging solutions. And that's still in the
>> In the in the meantime, while we're waiting for these solutions, is there is there any advice that we can learn from kind of the the theory and then what
you're seeing from the data? >> Unfortunately, I'm sorry, I'm not known for very optimistic advice, right? I mean, you all know that eating less works quite well.
That knowing your good doctor after a certain age helps a lot more than probably that. Like regular checkups and somebody who actually cares about your health. But uh if you would ask me about something on
top of that that uh uh Remember I told you about functional decline. And functional decline is uh 1% a year of anything that you cherish from IQ to your body mass, from testosterone level in boys and uh everything else.
Um it goes. What really matters is your physiological reserve. So, it's almost not never too late to increase your physiological reserve
by physical training and active uh life, right? Your body mass is your protector against almost everything. Uh VO2 max is your protector against Uh your brain uh also has reserve. So, you know,
making your brain busy is another good idea. There are many ways to do that. Uh the last maybe thing that uh somehow is underreported in uh longevity community. And by the way, I'm not a doctor, so I should not be
advice. Nevertheless, uh people underestimate how things like that manifest themselves in uh biobanks and medical statistics as social status is important. Of course, I don't believe
that social status like the amount of money uh is uh really the medical But, we are social animals. So, self-appreciation, self-respect, feeling of success, quality of your social connections, being
uh needed, being uh respected, these are things that if are not there, can kill I mean, you know, pretty quickly. >> So, don't don't underestimate the quality of your whatever social life
and uh you know, inner peace. That these things are totally underappreciated, but have social animals like us have enormous effect on our lifespan. >> Okay. So, thank you, Dr. Fedichev, so
want to follow your work and they want to know more about Gero, where can they >> Oh, by my name on X, formerly known as Twitter. That that that that's one way. And uh
yes, by name and by also Gero name. Uh we have some social media presence and we're trying to talk about our science because well, aging is Solving aging is the best biotechnology you can do in this century. So, that's
the best scientific problem you can get your hands on. Okay, Dr. Fedichev, thank you so much on the funding. >> I thank you very much for your
questions. >> Thank you.