Welcome to the first episode of Bio Eats World, a podcast all about how biology is technology. Bio is breaking out of the lab and clinic and into our daily lives—on the verge of revolutionizing our world in ways we are only just beginning to imagine.
In this episode, we talk all about the science of aging. Once a fringe field, aging research is now entering a new phase with the first clinical trials of aging-related drugs. As the entire field shifts into this moment of translation, what have we learned? What are the basic approaches to developing aging-related drugs? How is studying aging helping us understand diseases like cancer and Alzheimer’s — and increasing the amount of time we are healthy — today?
In this conversation, Laura Deming, founder of The Longevity Fund; Kristen Fortney, co-founder of BioAge, a clinical-stage company focused on finding drugs to extend healthspan; Vijay Pande, general partner at a16z; and host Hanne Winarsky discuss the entire arc of aging science from one genetic tweak in a tiny worm to changing a whole paradigm of healthcare delivery.
- Overview of the research on aging [1:54] and the current state of the science [4:16]
- The three most common research approaches [6:19], why this field is expanding so rapidly [8:21], and possible applications for disease treatments [11:00]
- Discussion of pure aging research vs. treatments for disease [14:46]
- Getting this science into the healthcare system [18:42] and issues with research funding [22:16]
- The types of entrepreneurs needed to expand the field [25:23]
Hi, I’m Lauren.
Hanne: And I’m Hanne, and this is our first episode in the new podcast “Bio Eats World,” where we talk all about how biology is breaking out of the lab and clinic and into our daily lives — and really on the verge of revolutionizing our entire world in ways we’re only just beginning to imagine.
Lauren: So, Hanne, the title of this first episode is “The Biology of Aging.” What aspects of aging are we gonna be discussing today?
Hanne: Well, really we’ve been trying to dream up ways of slowing down aging for as long as we’ve been aging, right? But the field of studying aging as a science is pretty new. So in this episode, we look at the entire, kind of, biology of aging — what we’ve learned; what’s reality; and what is translating into actually increasing our health span, and potentially — one day — possibly [slowing] down aging.
Lauren: What’s healthspan, and how’s that different from lifespan?
Hanne: Your first thought when you think about studying aging might be how we might slow it down, but really the way a lot of people in the field think about it is increasing our healthspan — which is the amount of time that we live healthy. What’s really interesting about this episode is, it’s about not just increasing healthspan and age span, but what we’re learning about disease — and particularly chronic age-related diseases — that might help us be healthier today.
Joining me for this conversation is Laura Deming, founder and partner of The Longevity Fund; Kristen Fortney, founder of BioAge, a clinical-stage company focused on finding drugs that extend healthspan using machine learning; and Vijay Pande, a16z general partner on the bio fund.
Lauren: Were there any insights from this episode that changed the way you think about aging yourself?
Hanne: Yeah, well, I definitely enjoyed hearing about the drug already widely available that really might increase our lifespan. And I also loved hearing about what the difference between Benadryl and Unisom is. So we start with a little bit of a history of the field, talk about where it’s come [from], and where we are today.
History of research on aging
So, where actually are we in the biology of aging today? There’s been a big surge of talk, even over the past few years, about what the science of longevity is — how it’s developed — but where are we actually today?
Vijay: Mortality is, like, this thing that philosophers opined [about] for millennia, but yet the biology of aging seems new. <laughter>
Kristen: Right. New, insofar as it’s new that anything actually works, I guess, right? One of the earliest discoveries in aging research that goes back decades is that if you could severely restrict food intake in animals — calorie restriction — they would live substantially longer. But it’s only been fairly recently that we’ve been able to actually intervene, and actually impact how long a mammal can live. And one of the interventions that was first shown to work in mammals is parabiosis — exposing old mice to young blood. And that really was first discovered 50 years ago.
The major acceleration came during the 1990s, the 2000s — and it’s mostly attributable to the first finding, you know — Cynthia Kenyon, Gary Ruvkun, Tom Hughes — that you could delete a single gene in the worm C. elegans and double its lifespan. Everyone thought aging — so complicated. You know, how are we going to have a dramatic impact on aging when it’s really all of these different systems and processes that are going wrong simultaneously?
And then, you know, wow — wait a minute. This one tweak, and then suddenly this massive difference in lifespan. So a lot of invertebrate geneticists went into the fields and mapped out all these longevity genes that impact worms, flies, and yeast, which is awesome. But now, you know, which of those translate to humans? Those are the ones that matter for translation.
Laura: Going back to, kind of, the history of the field — you, kind of, have these really — you know, sort of, highly-advanced intellectuals, going to the field and then, kind of, losing a lot of their momentum forward, practically — Nobel laureates like Elie Metchnikoff, claiming that gut bacteria, kind of, control aging. And maybe that’s coming back around now in some areas of current biology, but back then, it’s not as well supported. It’s only recently that you started to have the traction in the field to make specific discoveries.
That period of time was just so critical to the field’s birth. Cynthia Kenyon, when she was making these first studies, was told, “You’ll fall off the face of the earth, literally, if you pursue this research and you do the study.” And if you look at her first paper, she was the lead author because no grad student was willing in her lab to do the work. That was such a controversial first step to take as a, you know, young principal investigator. That was how unexpected it was <Mmhmm.> that people really thought that it would be the end of your career to, kind of, go into this field — and then she, kind of, you know, started it anew.
Hanne: They didn’t even want to touch it.
Kristen: Yeah, exactly. Worse than unexpected — like, bad science.
Current state of the science
Hanne: So, can we talk about what that traction actually is looking like right now? What is the most promising traction?
Laura: I think one thing that we feel really strongly is — this is the critical decade. Patients are for the first time receiving drugs that were developed in the context of aging biology. And it’s fascinating to watch these first clinical trials occur, where companies are actually developing drugs.
And when that first patient gets an actual clinical benefit, we’re gonna see — you know, people actually affected by these, kind of, ideas that have percolated in the field for decades.
One of the, kind of, examples of this that’s most, sort of, prominent in the field was a trial testing a drug called Metformin in the elderly. And so it’s actually looking at all-cause mortality, not just a specific disease as an endpoint. Metformin itself, you know, is this drug which retrospectively has been shown to be somewhat correlated to a decreased mortality in, for example, diabetic patients.
Kristen: Well, it was discovered just by analyzing health records, right? So, so…
Vijay: Which itself is kind of fun.
Kristen: Which itself is, like, yeah — that’s a great way to find, sort of, repurposed drugs.
Vijay: Yeah, and who’s living longer.
Kristen: Exactly. Yeah. So it’s one of these drugs that millions of people have been taking for decades. You can actually go back in time and ask the question — you know, are people who are on Metformin living longer? And they are, and it’s kind of amazing. So that’s, sort of, where the whole hypothesis for this compound came from that’s now being tested in the clinic, which is so exciting.
Hanne: I gotta go get me a prescription right now! <laughter> Are there key approaches that we haven’t touched on yet that we should be describing as this new field kind of evolves?
Laura: There’s also resTORbio, which, you know, was testing a molecule that’s similar to rapamycin. And that was for respiratory tract infections in the elderly. That trial did not work when trying to get into Phase 3, but if that had replicated, that would have been one of the more — big, sort of, examples.
There are some, sort of, drugs in the clinical, sort of, landscape today that are for metabolic disease. So things like NASH, or diabetes, or obesity — which when you overexpress these proteins in mice, make the mice live longer. So there’s this key link between things that we already are using to treat metabolic disease in the clinic, and, kind of — what might actually impact lifespan.
Vijay: So, that’s the connection with Metformin?
Kristen: Metformin impacts cancer deaths, too. So again, it’s like a broader aging-related mechanism.
Vijay: Okay. That’s interesting.
Common research approaches
Laura: One way that we try to classify these companies is in three generations. One is focusing on traditional pathways — so things that might affect, for example, insulin signaling in the body. And those are, kind of, known targets that people are drugging with existing modalities.
The second would be trying to screen for novel targets using platforms that are high throughput, and, kind of — either novel model organisms, or, kind of, novel in vitro or in vivo screens.
The third would be to actually target damage directly — where you’re not saying there’s an evolved pathway that we’re knocking up or down. You’re, rather, saying there’s a set of damage accumulated, and that’s what we’re, kind of, going after in a more engineered fashion. So, you know — for example, targeting what are called senescent cells — so, cells that get, kind of, old and decrepit with age.
Hanne: Mmhmm. The idea of zombie cells, right?
Laura: There’s damage that builds up in the lysosome of each cell, called lipofuscin. And that is an aging-related type of damage which, when targeted, you know, may be relevant to these neuro disorders that people are, kind of, starting to work on. So there’s three different, you know — just small examples of clinical, sort of, work being done, but for age-related diseases.
Hanne: That’s like three different frameworks, basically.
Kristen: Well, the question, right — for the first generation of companies — is what’s the low-hanging fruit? If something is very well conserved through invertebrates up to mammals, probably it’s gonna do something in humans too. So mTOR is a very interesting target. That said, the genes that are the most important for invertebrates are probably not the most important ones for humans, right? So I think a lot of those new pathways have yet to be discovered, and will have much higher impact on longevity — phenotypes as well.
And damage, I guess, also is sort of going directly to the major causes of disease. So I think those all make sense as approaches. I mean, it’s so unexplored now therapeutically, right? Even those drugs that have a very mild impact on longevity are, I think, going to be incredibly meaningful. I think that’s a really important consideration as well.
Vijay: And what do you call mild? Like, 10% increase in…
Kristen: Yeah, like a few percent increase in lifespan.
Laura: Rapamycin is probably the most well-validated drug for extending mouse lifespan, right? But, you know, the amount of compounds that were tested to that level of scientific rigor — it’s about 30 compounds. They put 30 drugs into mice, you know — did 30 random experiments. Right? <laughter> And one of them, you know, boosted lifespan by 14%. So, I think there’s going to be tons of things that have [a] much higher effect than rapamycin.
Vijay: Getting back to thinking about just the biology of it, it’s — is there any other trend for the “why now”? Is it just finally people like Cynthia Kenyon being brave enough to, sort of, help create the field? Are there any other, sort of, confluence of things coming in here?
Kristen: Mapping out every single molecule in a blood sample, in a human blood sample — proteins, metabolites, whatever we can get our hands on — and seeing which of those predict living a long, healthy lifespan — and going after those that are causal. Even five years ago, really, the technologies that we’re using didn’t exist.
Laura: Kristen, you really, kind of, changed my thinking here. When we first met, you were talking about biomarkers for longevity, and how important those were — and to be able to test our hypotheses in humans, and that’s where it all counts. And so, kind of, when you had pointed out to this was the key problem, I think that was such a big watershed for the field of — if we just make a fast, easy, cheap, reliable biomarker for aging, that’s really gonna change the whole field in a way that is more than just, kind of, getting one pathway to work it.
Vijay: The biomarker thing is actually very interesting, because — let’s make an analogy. We have cholesterol as a biomarker for heart disease. And because there’s such a causal relationship between cholesterol and heart disease, you don’t have to run a trial waiting for people to die of heart disease. And that’s huge.
And especially, also, you can measure it. You can see small changes go up and down. You have something that’s not binary — dead or alive. You have something that has a lot of nuance to it. And so, having biomarkers is both really useful, but — I actually think somewhat reflects just the maturation of the space, too.
Hanne: Is there another approach where we’re all aging differently, and we need to understand things on an individual level in terms of what our aging type is? That different systems age in different ways?
Kristen: It’s the same as with any biomarker, right? <Yeah.> Or with cancer. <Yeah.> You can, like, personalize the hell out of it, and say you’ve got these weird mutations — and therefore you’re part of this special subtype, right? And I kind of think that personalized medicine is where you go after you’ve, sort of, exhausted the things that are going to work for a broader population.
I mean, as we discussed earlier, there are already mechanisms of aging conserved across species — you know, from yeast to us. So certainly there are also really potent mechanisms of aging that are conserved across humans. We’re focused on targeting those first, looking at the commonalities first — but certainly, you know, for certain individuals, there will be particularities to how they age that you could also, you know, treat differently in different people.
Vijay: When we’re talking about changing paradigms, it’s not just a scientific paradigm, or even a clinical paradigm — but as a healthcare delivery paradigm as well.
Now there is this opportunity to say, “Given that knowledge, what can we do against existing therapeutic areas — existing disease?” We don’t have to talk about “fountain of youth” — we’re talking about learning new biology. Learning new targets that can directly go into a clinical trial for a new disease. And I suspect that could be a very interesting, sort of, initial area — initial application.
Hanne: So it’s, like, what can learning about aging actually do to make you healthier right now? In the age you’re actually in.
Vijay: Or it can actually help you cure a disease that you have.
Applications for treating disease
Hanne: How — what is that connection? Can we just spell that out?
Vijay: Yeah, well — and there’s a couple of variants of this. One variant would be an aging-related disease, like Werner’s disease — these diseases where you age rapidly. That’s kind of an obvious one, but maybe what’s less obvious is other diseases, like — could we be talking cancer? Could we be talking Alzheimer’s? What are the possibilities?
Kristen: It’s all of those, right? I mean, age is the single biggest risk factor for those diseases. Like, 20-year-olds do not get Alzheimer’s — and we cannot cure Alzheimer’s today, and therapeutically it’s been a disaster. Everything has failed in the clinic thus far, and part of that is probably because we’re studying it in the wrong way. I mean, when we’re testing drugs in animal models, mice don’t get Alzheimer’s, and young animals do not get Alzheimer’s at all.
Laura: Alzheimer’s disease, cancer, heart disease, and stroke — we have to study these diseases in the context of aging. And that, I think, is a new perspective.
Vijay: If you think about just the biology of Alzheimer’s, it’s not even clear what’s going on. Like, even which protein is it? A-beta? Is it tau? Is Alzheimer’s an A-beta aggregation problem? Is it a fibril problem? Is it a tauopathy? Like, even the field can’t even agree on the biology. Even targeting a fibril, or targeting tauopathies — it’s not a traditional pocket that you get a small molecule to go into.
If you have something where the current drug design methods don’t work, it seems like applying the current drug design methods is not the right thing to do. This feels like the type of radical shift that could have an impact, and still keep us in small molecule land. When we think about this, then actually the translation part is pretty straightforward — because I think the beauty of what we’re talking about here is, the current healthcare system won’t have to change.
Vijay: That basically we have indications — and, as Kristen mentioned — like, not just any indications, but the biggest killers that we have to deal with.
Hanne: Huge amount of need.
Vijay: Huge amount of need.
Vijay: And Alzheimer’s, where there’s at least, to date, no drug at all. I’m curious, like — you could have a patient with the early signs of Alzheimer’s — like, you know, with MCI, mild cognitive inhibition. Could you reverse a phenotype, or could you just delay a phenotype?
Kristen: I think that is the whole promise and the practical approach as well. That really, if you have a drug in hand that treats aging fundamentally, it should treat several different diseases. And yes, we can work within that — the existing medical system. With the one caveat — I don’t think an aging drug is going to be a great drug for metastatic cancer.
Vijay: Yes. So stage four is probably too far.
Kristen: Yeah. And, sort of, how far is too far? And really, these targets will probably have their most potential when they’re used in a preventative fashion. And, of course, that’s not something that the existing system can deal with. But I do think that early disease, like MCI — you can at least halt progression, which would be massive, you know. And potentially reverse it with some of these mechanisms.
Vijay: Well, and the reversals would — I think, gets everyone excited.
Vijay: But even if you could just slow down — in Alzheimer’s, slowing down could still be very, very valuable.
Kristen: Yeah. It would still be disease-modifying. Yeah.
Vijay: And you could have <inaudible> point against that.
Hanne: So, it’s interesting — you’re saying almost that, like, the biggest hurdle is getting the biology of aging in its — approach of its own. And then once you can get the right targets, then you can, sort of, slot into the existing system and keep moving.
Vijay: I think there’s so much about the science — the biology of aging — that has been validated, that now has opened the door to now treating these as targets. And actually, you know, the <inaudible> is you could, like, just identify that target, toss it over the fence to your favorite pharma — and it would slot into the same type of programs that they would be running right now. It doesn’t require a radical, sort of, reenvisioning of pharma to make this happen.
Moreover, I think — you know, if you look at the history of pharma, it goes through waves of new technologies. And maybe it’s an interesting question when or if longevity becomes that hot new trend. And I suspect that in order for that to happen, you have to have one or two clinical trials that have shown this works, and then it probably just catches fire.
Aging treatments vs. disease treatments
I want to amplify one thing Kristen said that I think went by relatively quickly that is very, very important — is that these compounds, if they are truly going after the biology of aging, will be useful in multiple indications. At first, that sounds magical, but there are actually precedents for existing compounds. So that alone is interesting that they’re already precedents.
Hanne: Can you compare an example there?
Vijay: I mean, some of my favorite stupid one is actually Benadryl and Unisom. So actually it’s the exact same drug. You go to the pharmacy. Often they just happen to be on opposite sides of the aisle — and actually, when sold as a sleeping pill, it costs a lot more than as a… <laughter>
Hanne: Oh, I’ve never noticed that. Is that true?
Vijay: It’s the exact same compound, exact same dose. And if you ever take Benadryl for allergies, you get very sleepy. So that’s a simple example. There are better examples in other diseases.
Kristen: Humira, for example, is one of the ones.
Vijay: That’s a great example. Humira has like what, five or six indications?
Kristen: That’s right. I think even more — and, like, the world’s most valuable drug as well, right? So…
Vijay: But this is a little different. I think in that one you just happen to like…
Hanne: They’re similar. They’re similar diseases — they’re more similar.
Vijay: The Humira case, it’s similar diseases. In the Benadryl case, it happens to make you sleepy. <Right, right.> And it’s almost like taking advantage of the side effect. This is something fundamentally different. This is something where actually the, sort of, way to save all these diseases is to slow down aging — and that’s why it has such a broad impact.
Hanne: So, is it oversimplifying it to say aging — as a kind of root cause of all these diseases or is that…
Vijay: Or an amplifier of the diseases.
Kristen: Or a causal driver.
Vijay: Or a causal driver.
Kristen: Well, look at immune aging, right? I mean, your immune system declines horribly with age. You don’t respond as well to vaccines. You’re more likely to get incredibly sick when you do get the flu or a cold, and that affects everything in your whole body — that makes everything worse.
Vijay: From a pure basing point of view, it is a causal driver.
Kristen: There you go.
Vijay: From just a mathematical-statistical point of view.
Kristen: By definition.
Vijay: And then that makes it a very natural, philosophical way to think about it.
Laura: One of the hypotheses about why we have genetic pathways that control aging is that we’ve evolved those for a reason. That there’s a benefit to living longer enough to have kids in a different environment. And it really wouldn’t do you well to live longer and be sick, right? You want to have ways to impact all your health that pushes back all diseases. Otherwise, kind of, you just get — you know, dead [from] a different thing earlier. And so that’s, kind of, perhaps why it’d be plausible to believe that there’d be, sort of, all-disease sort of efficacy for these kinds of anti-aging therapeutics.
Vijay: Actually, what is the evolutionary selection for aging or lack of aging? Because you could see that once you’ve given birth to children, or maybe gotten them to grandchildren — then you have no purpose, right? <laughter> I mean, you’re done from an evolutionary point of view, and you’ve — let’s say, diminished purpose from a purely, sort of, cold, evolutionary point of view — but you’re still taking up resources.
Laura: If you have a certain fixed mortality rate year over year — if that’s actually much higher than it is today in our developed society, your probability of being dead at any one point in time in your life is actually — it gets pretty high, even independent of aging over time.
And so, if there are any things that benefit you when you’re young that might be harmful to you older — or just, kind of, maybe things that accumulate randomly past the point at which you’re likely to be dead from other non-aging causes — they might just accumulate. And so, now that we have actually the ability to live long enough to potentially have benefited from the number of years, there’s been no selective pressure, potentially — to, kind of, live longer in that, sort of, period of life.
Vijay: One of the things that I’m always just curious about is — what don’t we know now that we need to know? Because the problem with biology is that it’s just so complicated. Longevity and aging biology seems to be amongst the most complicated. That’s the thing that I’m always wondering about, is — what is going to be the big surprise or the big curveball, and what can we learn from it?
Kristen: That’s a really good point, right? Because I think we’re all waiting for the first clinical trial to be successful, and that’s going to be so important for the field. So for pharma companies that traditionally don’t work in this area to really get confidence and excitement around it. But, yeah, there’s so much risk associated with bringing these first mechanisms forward and figuring out the indication path.
I mean, you can even have a good mechanism but have, you know — defining these indications for the first time. Of course, we’re gonna get it wrong the first few times. There’s so much to figure out because it’s really such a new field.
The current healthcare system
Hanne: Okay. So, we’ve talked about the explosion of the field — of the study of the science, the biology of aging. And then we’ve talked a little bit about what that brings us actually right now, in terms of understanding biology and disease — but where do we meet resistance again, where we try to get this into the health system that exists today, as a kind of preventative medicine? What does that look like in terms of the end goal being a healthier life, a longer life, a longer healthspan?
Kristen: I think that’s a great question, because you’ve got this therapy in hand — you think it’s actually slowing down aging, and, yes, you can work with the existing healthcare system and layer on indications one at a time. But really you’re not getting to the whole aging population as quickly as you can, right? And what could that path look like in the future?
So, biomarkers is one route. I mean, maybe people are still pre-disease, but they’re frail. There’s sort of functional and molecular biomarkers that predict they’re going to be sick soon.
Vijay: Like statins.
Kristen: Like statins. Exactly like statins.
Vijay: And satins will, you know — sort of, does handle a biomarker <Yep.> with the hope — when it’s done prophylactically — to avoid disease. People often say that people don’t want to pay for prevention, but we do pay for statins. There’s this old joke that plumbers have saved more lives than doctors. And that’s this point about sanitation — has just been this fundamental, sort of, floor just for human health.
And then I think the next level up, in my mind, is getting rid of the Fritos — and no disrespect to Frito-Lay or Pepsico, <laughter> or minimizing the Fritos, you know — as much as I do like them. That’s what comes to mind.
I mean, basically, no one should have type 2 diabetes. I mean, that’s another version of sanitation. And so now the question is — could you imagine, like, with longevity biology in hand, where you have these biomarkers, no one should have these aging-related diseases — or maybe nobody should have disease before the age of blank? And that blank goes from, like, 60 to 70 to 80 to 90 and onward?
Hanne: That’s right.
Vijay: Perhaps what we really just need is something to have this rock-solid biomarker that the clinicians are convinced is an issue — and then you have therapeutics that can help you manage to that biomarker. At least there’s a paradigm for that.
Hanne: Well, well, exactly — but any therapy that really delayed aging — that really delayed the onset of disease — would save a tremendous amount of money, you know. And you can put a number on that, and you can justify a certain cost. It shouldn’t be that hard.
Kristen: I think that’s where it comes back to “this is the decade” — because this is the first time that we’re going to see trials actually looking at all-cause mortality with therapies that are already on market today, and we’re going to see the impact of those readouts. That’s never been something that’s ever been done before. That’s truly different from any other time in history.
Laura: And that’s the proof we need to get the system to really start recognizing it that way.
Kristen: One would hope. <laughter> If that doesn’t move hearts and minds, what will?
Vijay: So, that’s a great point. I’m wondering, like, what would be the analogy? Like, are we at, like — first Lipitor, kind of thing. We’re looking — because then there’s been, what? Four generations of statins since then? Before then, actually, that model didn’t even exist.
Laura: It means you kind of form it responding to, like, the first watermark trial of the shift in paradigm — and that kind of occurring potentially as a result of these, but yeah.
Kristen: For the field too, right? I mean, we’re now at the point where several of these hypotheses are being tested clinically. We’re going to have to wait while we really get the human proof of concept for the idea, and then once that data comes in, I think that’s going to be huge.
Osteoporosis is a really good example, too, right? It didn’t used to be considered a disease, but there are, sort of, markers of — you know, your bones get weaker as you age, and that predisposes you to really severe outcomes and events. And now it’s recognized as one, and now there are drugs, and there’s a way forward, and payers were convinced, right? So there are case studies, I think, that we can follow
Hanne: Where it’s kind of flipped. The understanding has flipped.
Vijay: The mentality towards it has flipped.
Issues around funding further research
Hanne: Where are we in the hype cycle, would you say?
Kristen: Yeah, aging and biotech generally, like — it’s shifted in the last few years to be a lot more accessible with, I would say, like, low upfront capital, right?
So, first of all, the data sets that my company relies on — you know, we were for the first couple of years a data company — you know, like, for people with laptops, vastly cheaper than biology. Even if we were doing biology, now there are incubator spaces. Now there are CROs like WuXi that can do all your chemistry outsourced.
So, I think the barrier to entry for biotech has gotten a lot lower, and really enabled a lot of these new and exciting ways to work on targets and therapies.
Laura: And in 2011, 2013 — like, there were so few companies that, like, just having enough money to finance those companies in the space was the limiting thing. Now I think there is actually enough money, just even from the past couple of years, to fund the good ideas and the good people.
And so when an entrepreneur comes to us and says, “Hey, I want to make,” this is a common thing, “make a lot of money and then put it back into biotech,” it’s like, no, no, no, if you’re actually a good entrepreneur, please start a company. That’s what we need more of — start a company if you want to impact the space. We lack people.
Kristen, one thing I’m just fascinated by — as you know, you were one of the first to really go out there and do a couple of things. One will say we need biomarkers for aging, but also just build an aging company at all. I mean, there were very, very few new companies when you started. What have been the, sort of, easier and harder things that you’ve encountered as a result of that focus?
Kristen: I mean, it’s new, right? So everyone, I think, understands that it’s riskier, I guess, than if you have, you know, another company for NASH, another company for cancer — where everybody knows exactly how that’s going to go, from discovery through validation, through your clinical trial design, through your reimbursement.
There’s a lot of uncertainties because the space is so new, but related to that, there’s also so much opportunity. I would say that there’s more awareness now that these drugs are in trials. That there’s more — I would say — also appetite for novel mechanisms now <Yeah.> that the usual approaches are not working. So I think the landscape has changed a lot — not just, you know, at the startup level, but in terms of, like, big biotech as well.
Vijay: Well, there’s — one, sort of, just common question for any founder serving the biopharma side — when you can do many things, what do you do first? How do you pick a therapeutic area? That’s probably one of the hardest questions an entrepreneur has to deal with.
Kristen: Yeah, so there’s no, sort of, clear, well-trodden path — but that means that we also have the opportunity to really optimize and build something new. We’re trying to design our first clinical trials. So should it be for an age-related disease? Should it be for something closer to aging? Again, uncertainty plus opportunity, right? And trading those two things off, and making a bet.
Laura: We’re really focused right now on just getting more people to be longevity founders. Early 2010s, it was lack of capital. Like, there was just no money in the space. Right now the big bottleneck is founders. And we’ve seen many amazing companies built by both grad students directly out of their, kind of, Ph.D — but also people coming from software engineering, managerial positions.
And a lot of these people self-select out of the population. They say, “I can’t start a longevity company because I don’t fit the profile of a brilliant scientist founder — or a, kind of, traditional, say, investment banker type.” But, you know, they make incredible founders, and there’s just a huge population of folks out there who, I think, should be starting companies. So just to double down the idea that, like, if you want to really impact longevity, start a company. That is, like, exactly what we need right now.
Hanne: What are the other types of founders that you tend to see coming into the field — you know, in this new field?
Vijay: The founders in this space typically combine a couple of things. They either are biologists who have embraced, you know, machine learning or other areas — or even people that are coming from the tech side that are learning the biology. It’s a really unusual time where you can actually learn both.
And maybe you’ve learned both from the beginning — but actually it almost feels like it’s never too late, because you can pick up both sides. But that if you can capture both sides, I think you’ll have a huge advantage.
A nontraditional founder for us would be someone that is coming, maybe, from the pure pharma side. And we haven’t seen that yet, but I suspect they’re coming on — and Kristen’s nodding her head. And I suspect they’re coming either to be founders or as, you know, CSOs — and that they may become some of the key employees for these companies.
Hanne: So the culture and the talent landscape [is] changing too, evolving and changing. Interesting.
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