Elizabeth Iorns on Biotech Companies in YC

So welcome to the podcast! How about we just start with your just quick background?

Sure! So I'm Elizabeth Lyons. I'm the founder and CEO of Science Exchange, and I'm a cancer biologist by training. I did my PhD at the Institute of Cancer Research in London and then did a postdoc at the University of Miami. I became an assistant professor there, and then I left in 2011 to create Science Exchange. That was part of the 2011 summer batch of Y Combinator, so that's really where I first got involved with Y Combinator. I have subsequently continued to grow Science Exchange, and two years ago, I joined as a part-time partner, which is now called the expert program here, to help out with the biotech companies that were starting to apply to Y Combinator.

Okay, and so let's just get this out of the way. A very common question is, like, "I'm a biotech company. I'm a founder. Why should I do...?"

Oh, I see. Yeah, we definitely get asked it a lot, and I think there are kind of two camps. One is that there are people who have genuinely not heard of Y Combinator previously, so they ask, "Oh, what is Y Combinator? Why should I do it?" more from a sense of "I'm not sure what this program is. What will the benefits be?" That's more a generic answer of, you know, YC is a great starting point for companies that, you know, want to kick off the launch of their company successfully. We provide a lot of expertise and resources around how to incorporate, how to really focus on building that first initial stage of creating product-market fit and getting a company off the ground.

So, there's that kind of avenue, and that's usually PhD students or postdocs who maybe haven't, you know, been exposed to entrepreneurship first, and I think that's a great program for them from that aspect. But then, we definitely have more established biotech people who are in the biotech world, and they sort of look at it like, "Well, why would I do that?" or "Why would any biotech company want to do that?" Our answer there is really around, you know, the tremendous access to capital and the expertise around fundraising. The opportunity to interact with really different sectors that you may not have been familiar with. So, we have a very diverse and cutting-edge group of companies that are part of every single batch, and so you actually get access to things like artificial intelligence, all of the technologies that are being developed in different verticals. You're exposed to those as you're part of the program, and that can really benefit the companies in interesting and unexpected ways as they participate.

But one of the unexpected ways is that there's been a lot of crossover funding that has occurred for the biotech companies. Many of our biotech companies have been able to raise significantly more capital than they would have been able to if they just sort of stuck with the traditional biotech venture world and not taken part in the program. I think an area where people didn't expect that there would be such an appetite, but there has actually been over 200 million dollars of capital raised for these companies already.

Mmm. And so how do you see, like, a batch play out for an average biotech company? Because one of the main questions I get during office hours is like, you know, "Why is it three months long? What do I actually do in three months?" You know, this is like a decade-long project. How does it work out normally?

Yes. So normally the companies, similarly actually to any, I think, enterprise company, right, so most enterprise companies are not going to be making really significant sort of fundamental advancements in a three-month program either. But what Y Combinator does is it provides that focus to really hone in on what is the company doing, what's the minimal viable product that's required to get, you know, to the next stage. For us, a lot of our biotech companies are genuinely able to figure out what their go-to-market strategy is very efficiently in the program and then execute against that. They may not make, you know, really significant advancement in terms of the experimental work, but they certainly will make a significant advancement in terms of understanding the market and understanding what steps are required to get to the market in a way that allows them to raise funding and capital that's required to take those steps.

That is a pretty big deal in terms of just figuring out what you need to do and minimizing the noise and increasing the focus on what you need to do.

Mmm. And so where do they put the money to work during YC?

So it's not a lot of money, so exactly. They really put the money to work through things like they may do some initial critical path experiments. Okay, so if they can outsource those experiments, then they'll often be able to get results quickly. We have had a lot of success with that with our biotech companies. We've actually worked with external companies that are already up and running to do, for example, proof-of-concept studies or efficacy studies that they can then submit to the regulated authorities to get approval for the next stage.

We've seen companies work with regulatory consultants to devise a go-to-market strategy and make, you know, significant advances in terms of their filing status. So definitely seen people be able to use that time to actually advance the company alongside with really planning and talking to initial customers and figuring out with their potential markets what would be required, you know, at each stage to get to the next level.

Hmm. And so you kind of talked about market and figuring out go-to-market and a lot of these business ideas, basically. Do you find that a lot of biotech companies are bringing on like a business co-founder, or are they just like picking it up while they're going through YC?

I think they pick it up. So I think there's, you know, I think one of the interesting differences between biotech companies and sort of software companies is that the actual market is not really that question. Like if you're a biotech company and you're going to cure Alzheimer's disease, there's no question of whether that's going to be, you know, a great market. That's obviously going to create enormous value. But the question is more the technological risk and how you do risk the actual innovation that's being developed.

For a lot of the time, we're really focused on that de-risking and figuring out for the biotech companies what they need to do to really significantly depress the technology as quickly as possible versus the software companies. I think there's questions around market. There are questions around how big could this really get, you know, a lot of questions around execution strategy to get to market as quickly as possible and have a competitive differentiation over others that are doing similar things. I mean, those are more fundamental questions. We are, you know, business partners and marketing and growth hackers and all those types of people coming into play, but I think with the science, you know, mostly the scientists are, I think, really scientists. People have this kind of illusion that they're like antisocial or like they don't understand business, and I just completely disagree with that. I think scientists have to be very articulate. They frequently present in front of large audiences. They write very complicated grant funding strategies. So many of these, you know, everyday skill sets are already present in scientific stuff, so it's I think actually like not really a need to have a business co-founder.

Okay, and by nature, like so many of these things are gigantic markets if you have... you know.

Absolutely! So you said something that I didn't fully understand. What does that actually mean over the course of the three months?

Yes! So de-risking is really around getting experimental data or actually looking at the go-to-market strategy for particular technologies. So mostly for de-risking technology is things like showing in an animal model that the therapeutic intervention is able to cure the disease or reduce the impact of the disease or in a cell model or something like that. So you're really looking for those proof points along the way.

So if you're starting out and your goal is to cure Alzheimer's disease, then your initial steps to get there will be to, one, come up with a biological mechanism that you think is plausible for the disease. You'll then need some sort of model system to be able to test how your intervention is going to impact that biological mechanism. Then you will basically develop a therapeutic, either an antibody or a medical device or a small molecule inhibitor, and you will add that into the model and see whether you actually improve the survival in that particular model. Then you'll have to do basic studies around toxicity studies that are required to submit for initial human studies.

So basically these steps happen before you're able to go into the clinic and test if my intervention really works to cure Alzheimer's disease.

Okay, gotcha! And so in your experience, you know, when people are applying to YC, how far have they gotten along that process? Because I imagine they're probably...

Yeah, a huge spectrum. And it's very interesting because actually, particularly in the earliest phase of discovery, that is an area where there’s significant underinvestment, just generally in the industry. So everybody wants these, you know, new molecules or new strategies that are at the clinical stage.

So if you have initial proof of concept data and a clinical phase, then you're probably already acquired, right? So there's like, "Oh, yeah!" So, I mean, people like the pharma companies are desperate for buying innovation, and so they're really looking for companies to get to that initial proof of concept in human studies, so that's really where a lot of their innovation is coming from as biotechs that have got, you know, new molecules through to that point.

The process and the investment in that earliest stage—that's really a lot of challenges. Like, definitely there's investment from the academic setting, but there's a huge gap between what's done in academia and then how do you get it into the clinic? That area of translation—it's called translational research—has increasing focus on it. But it's definitely an area where we see a lot of companies apply. They apply when they have, you know, some initial data that suggests that they have, you know, an interesting approach to studying curing a disease or to, you know, developing, for example, a new way to test for a particular disease. So they're sort of at that stage, and then it's really that going from the initial experimental data, the discovery, through to actually having some initial proof of concept and the clinic that it works. So that's the gap where we really focus.

So then, okay, I don't have a PhD like I do...

Yeah, it's okay. So walk me through, like, you know, say I'm doing a PhD program, which you said the average is seven years. The median is seven years, yeah, in the U.S. Yes, because yours was like three or four?

Yeah, okay, yeah. At what point in the process would I start thinking about, "Okay, maybe this is a company," versus, "I'm just gonna round out my seven years and complete it?" I don't like...

Yeah, I definitely want to complete it, of course, at that time. I think most people would want to complete it. But, yeah, so I think it’s when you're getting to the end of your PhD and you're thinking about, "What am I going to do next?" Of course, there's the excitement of you're finally finishing after all this time. It's time to go do something else, and traditionally that next thing was always the postdoc.

So if you were going to become a professor and be an academic investigator, you would go to a postdoc. That's a big shift that's happened recently, and this is a pretty well-known phenomenon called the postdoc ellipse that Ethan Pilsen came up with, which is this concept that there's so many postdocs now and no jobs for them, so there’s just nowhere for them to go in terms of staying as an assistant professor or a professor in academia.

Instead, you have to look for what are the alternative paths. You've already invested all this time and energy into your research, and not all of it will be relevant to starting a company. But there's definitely people who've made some pretty significant advancements in that period of time, and they have something that might be commercially valuable. So for them, the question is, you know, do you go forward and just continue in academia? Probably if you do that, you'll work on something different because you usually switch labs and go and work on something different for your postdoc.

Or perhaps you can think about taking that idea and that discovery and thinking about an entrepreneurship opportunity to turn that into a company. There's more and more acceptance of that strategy, obviously. People have realized that, you know, leaving academia and going into industry is not so evil as it was once thought of because the reality is, you know, all innovations that come to market through industry.

You don’t see drugs on the market that came from academia—they were all many of them discovered. In the earliest phases, the basic research was done in academia, but then those were spun out into commercial biotech companies or pharmaceutical companies that licensed them and took them through to commercialization. So that is a long and very expensive path, and I think, you know, there's a big opportunity for these students to think about the discoveries they've made and say, "Well, maybe I want to create this into a company," and maybe there's a real opportunity for me to actually take the discovery I made and really use it in the real world as opposed to just in the academic setting, which is often distanced from the actual commercial application.

Of course, yeah. And are there, like, other IP concerns that are specific to biotech companies that someone is thinking about?

Absolutely! So, IP and biotech is like very, very critical, and this is something that's very challenging actually with academia. So, intellectual property is, you know, fundamentally the cornerstone of a biotech strategy because you need to own the intellectual property in order to invest all of that development time and money that's required to go through clinical studies to get it out into the market.

Then you need some window of exclusivity around your intellectual property to sell your compound before some generic manufacturer comes and sells it for $2, right? So that's really why IP is so important in the space. It's not because, you know, people are inherently trying to be greedy; it's just that there's such a lot of dollars invested in these molecules in the development of them that then you need some kind of time period at which you can recover your investment or else it's just simply unsustainable. There is no path to actually developing those drugs.

In academia, most intellectual property is owned by the actual university. So when you...

Yeah, so when you are working in a university, your IP belongs to the university. There is a path to actually licensure from the university, and so essentially you have to go through that process of licensing the intellectual property to develop the company. For most PhD students, there is an exception where they're not subject to it, so it really depends on the specific program. That's another, you know, area of complexity to have to research. But for professors and postdocs, definitely, the intellectual property generated there belongs to the university.

So how does that work for an average YC company? Do they license it prior to YC?

Yes, so they not always prior. They will have, you know, one of the things we do during the program is help them with strategies around licensing intellectual property, figuring out, you know, who can they work with to do that, like how does it work at the university? So there's a tech transfer office and a sponsored research office at each university, and so you have to interact with them and figure out, you know, a compelling business case for why, you know, they should license it to you. Usually, if you're the discoverer, then there is a compelling business case for why they should license it to you.

Okay, gotcha! And so then what about what happens after the three-month period? Right? Like, do we find, like, what's a fundraising process like for a biotech company?

Yes, really interesting actually because we don't have a tremendous amount of data on this so far because the program has only included biotechs for the last couple of years. But what has been really interesting is that there is a lot of appetite from early-stage technology investors for funding biotech and interesting science companies in general. I think we've seen, you know, many companies raise pretty large rounds straight out of YC from a seed funding perspective, so several million dollars or more.

Then it's that path of how do you get to that next stage for the institutional round. The Series A round. We've only just started to reach that point with several of the companies, and they are raising rounds, so we're definitely seeing success with them being able to continue to fund their companies even at the later stages, but we don't have, you know, a lot of historical data to go back, you know, three or four years. We're only at this two-year mark.

Hmm. Do they tend to raise from, you know, funds out here, or do they go, you know, are they raising money in Germany? Like, well, how does it, how's it going?

Oh, interesting! Mostly here! So definitely a lot of Silicon Valley funds are doing investments in this space. Even funds that people may not have thought about traditionally, so Khosla Ventures has done a lot, Jason Horowitz has done a lot, Data Collective, Founders Fund. So there's a lot of sort of funds like that, NEA, that are interested in, you know, science and interested in not just software applications but really interesting companies that are combining, you know, insights, particularly from the software world, into the actual biological world.

That's an area where there's been a lot of interest in investment, so it's actually much more of like a traditional fundraising path than I had thought.

Yeah! I don't know why I had figured that it was coming from other sources.

Well, this is in the biotech world generally, there's kind of two ways that people raise money. So one is if they are sort of unknown and they're young and they haven’t got any history of doing successful biotech companies before, then that would be, you know, one of the paths they do is to come and do something like Y Combinator, get some area of growth, and de-risk their strategy, right?

Then they can raise money. But what we've seen, you know, historically in the biotech sector is very much like the old-school days of raising funding as a software company where, you know, a lot of the funding was going to repeat entrepreneurs. People who basically, these funds will create—they'll basically license in a discovery and then they'll create a team internally to develop that to a student stage, spin it out, and put in a professional team of like a professional CEO and everything to run the company.

That's a much more common sort of path that the biotech funds would take to developing companies, so it's almost like an internal incubation strategy that then spins out companies, and that's been effective. That's very effective! I mean, that's like Third Rock, Atlas, Polaris, Flagship—like, all of those big funds take that strategy, and it's very successful. They have created a lot of, you know, the most well-funded and recognized companies that you probably have heard of in the biotech space.

But there's also the opportunity to look at the landscape, you know, in a more broad setting, which is what else is there? Like, if you just find—well, if you just fund like the same people over and over again, then yes, they'll be like really efficient. They’ll understand what it takes to do this, but you’ll also miss out on all of the other innovative different thinkers who are out there who actually made the discoveries, like the PhD students and postdocs—the ones who've actually made the discoveries.

If you can provide a path for them to also participate as founders and as entrepreneurs, they potentially have, you know, a lot of inside knowledge about the discoveries that they made that can help this company succeed. What we're kind of missing is the funding and the mentorship because it is a steep learning curve to navigate how you take a drug to market through all of the regulatory hurdles.

So if we can build, you know, a path that supports those people more effectively, that's going to be, I think, you know, the killer sort of application. It's going to be how do you get a lot more of these shots on goal and actually get a lot more people involved in the biotech innovation ecosystem, developing companies and bringing drugs to market, and not just staying in academia.

So how do you, how do you advise folks when that, you know, maybe you do office hours or maybe do a YC event or someone just emails you: "Say, my PhD student, what's your advice to me?" and "I'm working on something that I'm excited about but like the whole thing is completely foreign to me," like we were talking about before.

Yeah, you thought—like, or rather you met with PhD students and they thought the money that they raised was a loan, wasn't an investment?

Yeah, like they would have to pay it back?

Yeah! So I think that it's like just an education in the ecosystem thing. I think if you're a software developer, you kind of know about entrepreneurship. It's become such a central part of the ecosystem that people just inherently understand: How does entrepreneurship work? Like, how do you start a company? What are the basics? But that's not the case for a lot of PhD students and postdocs. They may not be exposed to that world at all.

So I found it very interesting when I was talking to them as we, you know, when I basically gave presentations for alternative careers—like there's this whole alternative careers focus in the industry because for PhD students and postdocs in academia, they're like I mentioned, there's not often a job path that exists for them to become professors. So there is actually a focus on saying, "What other alternative careers are out there?" One of those alternative careers is entrepreneurship.

I've talked to some of these groups, and I really did get questions like, "Okay, well, you know, what if my company fails? Like, will I have to pay back that money? Will I be personally liable?" I mean, so just removing those misconceptions—like I can't even imagine, you know, all of the questions around starting a company. Like, you're worried about like job path and what if it doesn't work out, and like, will I be able to get a job if it doesn't work out?

Like, all those questions are still there and scary, but like you also have, you know, so much misconception around even things like, you know, I'd have to pay back money that I raised. Like that's, that is to me a big red flag that like this particular sector just doesn't even know what steps are required and like doesn't understand the path to being able to start a company and doesn't realize that there's like real, there's real opportunity to do this in a way that isn't as scary as it looks.

Right! So if you take part in programs like Y Combinator, it really does sort of provide you with the framework and the confidence to know that you're incorporating your company properly. You are not, you know, personally at risk when you do this. Obviously, if the company fails, you have to think about what other job you're going to do, but I mean, at the same time, there's like, there's a huge degree of risk of staying in academia when you're at that stage in your career because there's no job certainty.

So you’re definitely like, "What’s going to be my next job? I don’t know." I could be a postdoc forever, which is really difficult.

So is that what happened with your—because you weren't—you were in Miami, right? When you started Science Exchange?

Yeah! What's happening with the people you're working with? How have their careers progressed alongside yours?

Yeah, that's a good question. I mean, I think a lot of them have done some pretty interesting things actually. So, one of the students that was in NL Labs, he actually did start a biotech company with my mentor, so they started up—I see it coming together! It's pretty cool!

Yeah, so I definitely think that there's, you know, more and more people doing this, and particularly if they see other people doing it, then it gives them, you know, one person to ask: "How did you do that? How did you like figure out those nice tips? Can you introduce me to people that can help me?"

Just having more colleagues that have done similar things allows you to explore it as an opportunity for yourself.

So, and does that—are you still reading journals? What's the cool stuff coming up? I mean, I guess you read applications for YC, so maybe there's like a particular focus. What are the things that are like right on the edge you feel like, you know, that with like 10 years realistically, so like reaching market that you're excited about?

Well, there are a lot of things that are really exciting at the moment, and you know, I think science in general is like a huge board area, so I try to stay on top of the areas where I have like the deepest understanding. Cancer biology is obviously for me an area of intense interest, and so we actually see a lot of those new technologies and new advancements through Science Exchange, actually, because we set between pharmaceutical companies that are outsourcing their R&D through us, and we also work with lots of early-stage biotech companies. Then on the other end, we have all of these actual service providers that are running the experiments for them, and so we sort of see, "Well, that's interesting! People are starting to do this differently. You're—this time to think about the next stage," or "They're looking at this new therapeutic area."

I think they're very cutting edge. That's mostly still coming from academia, so you're seeing those in journals like Science, Nature, Cell, and there's some really interesting work that's happening. But for me, the applied area that's probably one of the more interesting areas is looking at things like the application of artificial intelligence in the biological sector.

So there's some pretty interesting work that's occurring around how to be better at predicting efficacy or toxicity in the preclinical stage, so you don't have clinical failures. There's a lot of innovation in that space. There's actually some interesting innovation even in the design of clinical studies, so how to more quickly get initial data in humans that will tell you whether or not your application is likely to work.

Historically, people would do, you know, phase one, which is basically just like a dose escalation study where you're just looking for toxicity, and then you would do a much larger study afterwards where you were looking for efficacy, and so that's really time-consuming and expensive.

People are starting to say, "Let's look for like endpoints that we can use more quickly in those initial first-in-human studies that can give us a sense for whether our targets are going to work." So I think there's a lot of innovation in that space, which is pretty exciting.

And, and I'm sure you've seen like everyone's talking about, you know, editing of human embryos with CRISPR, so...

Okay, so we're going to call in, like, are you—you get very excited about CRISPR?

I mean, CRISPR is like game-changing! It allows us to do things that in the past we only dreamed about. I actually, for my PhD, I worked on RNAi screens, and RNAi screens were the first technology where you could basically inhibit gene expression that you couldn't knock it out completely, okay?

So you would be, you know, systematically knocking down the expression of a gene maybe 50% or 60%, but over generations—no, no. It's just in real time!

But then a couple of basically RNAi works within like 48 hours, and it's only transient unless you create at least stable cell lines. But so you can knock it down transiently and then see the effect, but it was so era-prone because you couldn't really control it that well, and you would—you would knock down a gene's mission like fifty, but seeing, you would be like, "What does it really mean?" versus if you knock out the gene and it's no longer there, or you create a mutation that truncates the gene and it's no longer expressed, then you're good, right?

Like, it's gone! So you know for sure what is the functional effect of doing so. So we had, you know, we had a ton of issues with artifacts and, you know, challenges in that space with RNAi but it did lay a lot of the frameworks for some of the really interesting applications with CRISPR now, not in the therapeutic space, but doing like high-throughput screens where you basically knock out every single gene, and for the first time, you can do that at scale and understand what is the function of every single gene, right?

So it's incredibly powerful! So where do you think CRISPR, like, assuming everything, you know, it gets tested, it goes all the way through, where will it start to see traction first?

So, it—I mean, it's already, I think there's some really interesting applications that people are looking at. Obviously, the most obvious application is to edit genetic defects, so if there's like a lethal genetic defect, being able to create that.

So definitely, there's a lot of innovation in that space. Actually, there was just a recent application of it for RNA-like editing, so that's the expression of the genes. So rather than editing the gene itself, editing the expression of the gene kind of transiently, okay?

Which is pretty interesting, so I think there you'll see less of a regulation barrier because you're not editing the actual genetic code—you’re just editing the end product, so...

Huh! Alright! Well then, I guess I have one more question. So people in Silicon Valley seem to continually be obsessed with life extension. Yeah, I always wondered how that like correlates to even just fundraising—like what gets funded here? People that can live forever finally?

Have you been following, like, you know, calorie restriction? You know all of us, obviously you work with cancer too as related.

Yeah. What is real and what is just like hype right now that people are paying attention to regarding life extension?

Yeah, I think this—it's a challenging space as a scientist for the main reason being that there isn't really a—so, you know how I was just talking about earlier on, like, models that you can use to understand the biological mechanism of a disease?

So for example, in cancer, you can say if I create certain genetic mutations in a normal cell it then turns into a cancer cell, and I can tell that because it does certain things that it wouldn't do if it was not a cancer cell. So it will form a tumor and a mouse, or it will grow and in suspension we are a normal cell wouldn't, and so these, like, these kind of basic assays that you can use to understand better what you're looking at with aging or longevity, that is, you know, more challenging in the sense that, you know, in model systems we have C. elegans as a good model that they use a lot where they're looking at these nematode worms and saying, like, how long they normally live and can we extend their life?

But a lot of that research—it's difficult to know how it translates into humans. So you don't have like really a good endpoint in the human system. So people have used, like, telomeres as telomere length. It's like an endpoint, but I don't think that is very well established, so you actually, you know, see a lot of noise in that particular endpoint. So it's hard to know if you do this intervention in a nematode and it extends the lifespan, if you do that intervention in humans, you're going to have to wait a really long time to know whether it actually worked, and so that clinical study is very difficult if you don't have a secondary endpoint you can measure.

Okay! So think about cancer research—you're always looking at, you know, extension of life or reduction of tumor size, and these are in patients where, you know, they have advanced disease, so you're going to see in a very short window if most of them are going to die in one year, you can quickly see if this drug extended their life, and you can actually monitor their tumor size in real time and say, does the drug shrink the tumor? So those are like the endpoints you're looking at, so you can quickly tell if this drug works.

So for their longevity research, it's like, what is the endpoint we can use to say that our interventions are having an impact in the clinical setting? I think there's like a lot of interesting research that's happening, certainly. I mean, Silicon Valley even made fun of it that the parabiosis work is obviously fascinating—very, very interesting work!

And I think there's, you know, the calorie restriction stuff's been around for a while. There's actually like a lot of controversy there because there are mixed effects that you see in different models.

So for example, in some strains of rodents if you do calorie restriction, it actually decreases their lifespan, and there are two primate studies that were done, one which it extended lifespan and one which should decrease lifespan. So the work and the preclinical setting and animal models is pretty mixed for calorie restriction, so I don't know if it's you know—again, we don't have that endpoint to really measure and see if it works or not.

And then with parabiosis, which is the other kind of big trendy area right now—advantage—might not, just in case people don't know.

Yeah! That's basically, it's kind of terrifying sewing to animals to get like an old mouse and a young mouse in like connecting their blood streams so that they—the old mouse and the young mouse are receiving each other's blood.

But that's been done in a less like sort of dramatic way through actually just harvesting blood from young mice and injecting it into old mice, so that's been done that way. And so there you also see an effect. What hasn't been very successful is trying to figure out what causes the effect.

So when people have tried to analyze the blood of young mice and old mice and say, "What is the growth factor or the hormone, or what is it that's causing this?" there's been controversy over what it is, so some people have published certain factors that other groups have not been able to reproduce the effect, and so that area is still unknown.

Okay, and obviously if you could find out what it is that would be huge because then you can make a recombinant of it, and you wouldn't need to take, like, young blood and inject it into old people. You could just take this recombinant factor or group of factors and just use that as a supplement, as an injection or whatever it needed to be.

Okay, so right now it's just like time for snake oil basically?

Right now it's still very, you know, very much in that stage of figuring out how to apply it; but there's—I mean, the hard part is getting that initial robust result. Like the fact that people are consistently seeing that if you take young blood and inject it into old mice, that has an impact is actually like very exciting.

Most things—what you most of the time when you're doing fundamental research—you’re like finding something that seems interesting, but then the more you study it, you realize it was just an artifact. So it's mostly disappointment. The fact that, you know, people have consistently seen this is pretty interesting!

Interesting!

Okay, last question. Are you applying any of the stuff to your daily life? Do you have any weird bio habits that you're like taking different, whether it's like medicine or supplements or you're fasting? Anything like that? Are you doing any of this stuff?

So the one thing I am trying to do—but it's so hard—is to do fasting. So I used to try to do it every couple of weeks, but honestly, I only managed to do it like once a month. For me, if I don't eat for a whole day, I end up like just unable to function properly. Like, I'm really—I have to choose like a Saturday or Sunday where I don't have to do anything because I can't really function well enough to do it on a workday.

But I definitely think there's a lot of science there around fasting improving your metabolic control, and just in general for me when I do it, I feel like my appetite control is a lot better, and it was just like—it just seems to have a good impact going forward. So I definitely—like that's one that I do think people are like, "Oh, it seems kind of hype," but I think the science is there.

So I have 24 hours; I do it from the evening through like the whole next day, through breakfast the next day.

Okay, sounds like 36...?

Thirty-six hours!

Yeah! Interesting! But yeah, you're kind of like slower during that fasted state?

Oh, my brain just doesn’t work at all—completely! It's completely, you know, just have to watch TV or do nothing useful because I can't think properly. But that's the downside of it.

But I do think it seems like it has some benefits. But yeah, I don’t take like supplements or nootropics—none of them?

No, none of that! I should probably research more into it, but I haven't so far. I'm very interested. I have some, you know, theories myself around these things, but one of the things I'm very interested in is individual responses to food.

So I think that, you know, all of the research has been done on diet and dietary interventions. If you actually look at the clinical data, it suggests that there’s a subgroup of people that respond to that dietary intervention very well, but the vast majority don't really at all, and so you get this kind of modest effect.

So if you do things like, you know, Paleo diet or, like, you know any of those diets, you have like a small group of people who actually lose quite a lot of weight, and then you have, you know, most people don't really lose much weight, and so you just have a small effect.

And so I think that it would make sense to me that there's actually, you know, different responses as individual people because if you think evolutionarily about how to have a diverse population of people, you would never want to be—everybody responds the same way to certain food availability because if there was, you know, famine of a certain type of food, you would lose like the whole population.

So it kind of makes sense that you would have diversity in the types of diets that people respond well to, and so that's an area where I think there's some initial data that looks pretty interesting around personalization of response to foods.

What are those studies called out of? This is fascinating. What are what would I look for to learn more?

I mean most of what's really like at the cutting edge there is actually around measurements of things like insulin response to certain food types.

So if you were actually wearing like insulin monitors—like continuous insulin monitors—to look at things, glucose, insulin levels, trying to understand like people's response to food for those kind of key hormones, and so that's an area where that you could probably research it, but it's pretty cool.

That was pretty cool!

Yeah! Alright, well thank you for coming in!

Of course!