Elizabeth Iorns on Biotech Companies in YC | Transcription
Transcription for the video titled "Elizabeth Iorns on Biotech Companies in YC".
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So welcome to the podcast. Thank you. How about we just start with a, yeah, you're 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 and became an assistant professor there. And then I left in 2011 to create Science Exchange. And that was part of the 2011 summer batch of Y Combinator. So that's really where I first got involved with Y Combinator. And then have subsequently continued to grow Science Exchange. And two years ago, I joined as a part-time partner, which is now called the Expit Program here to help out with the biotech companies that were starting to apply to Y Combinator. OK. And so let's just get this out of the way. Very common question is like, I'm a biotech company. Like I'm a founder. Why should I do YC? Yeah, we definitely get asked it a lot. And I think these kind of two camps. One is that there are people who are 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? And so that's more a generic answer of YC is a great starting point for companies that want to kick off their 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.
Discussing Biotech Industry
Advantages for Biotechs (01:23)
So there's that kind of avenue. And that's usually PhD students or postdocs who maybe haven't 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 biotechs or 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? And really, our answer there is around 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. So many of our biotech companies have been able to raise significantly more capital than they would have been able to if they'd just sort of stuck with the traditional biotech venture world and not taking part in the program. And that's, I think, an area where people didn't expect that there would be such an appetite. But there has actually been over $200 million of capital rates for these companies already. And so how do you see a batch play out for an average biotech company? Because one of the main questions I get during office hours is like, why see these three months long? What do I actually do in three months? This is like a decade long project. So how does it work out normally? Yeah, so normally the company is similarly, actually, to any, I think, enterprise company.
3-Month Company Focus Power Histamum (03:21)
So most enterprise companies are not going to be making really significant sort of fundamental advancements in a three month program either. But what my company does is it provides that focus to really hone what is the company doing? What's the minimal viable product that's required to get to the next stage? And so for us, a lot of our biotech companies are genuinely able to figure out what is their go to market strategy very efficiently in the program and then execute against that. So they may not make a 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 then raise funding and capital that's required to take those steps. And so 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.
Average Toast with C too Fundraising Geometry (04:07)
And so where do they put the money to work during my C? So it's not a lot of money. So-- Yeah, exactly. So they really put the money to work through things like they may do some initial critical path experiments. So if they can outsource those experiments, then they'll often be able to get results quickly. And we have had a lot of success with that with our biotech companies where they'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 to the next stage. We've seen companies work with regulatory consultants to devise a go-to-market strategy and make significant advances in terms of their filing status. So we've 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 at each stage to get to the next level. And so you kind of talk 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 a business co-founder or are they just picking up while they're going through IC? I think they pick it up. So I think there's one of the interesting differences between biotech companies and software tech companies is that the actual market, there's not really that question. 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 a great market that's obviously going to create enormous value. But the question is more of the technological risk and how you de-risk the actual innovation that's being developed. And so for a lot of the time, we really focus on that de-risking and figuring out for the biotech companies what do they need to do to really significantly de-risk the technology as quickly as possible versus the software companies, I think there is questions around market. There is questions around how big could this really get? 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 those fundamental questions where business partners and marketing and growth hackers and all those type of people come into play. But I think with the science, mostly the scientists are, I think, fairly-- scientists, people have this kind of illusion that they're anti-social or 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 everyday skill sets are already present in scientific staff. So it's, I think, actually not really a need to have a business co-founder. OK. And by nature, so many of these things are gigantic markets if you happen to-- Yeah, absolutely. So you said something that I didn't fully understand.
Getting into IC (07:29)
So if I'm de-risking a project, what does that actually mean over the course of the three months? Yeah, so de-risking is really around getting experimental data or actually looking at the go-to-market strategy for particular technology. So mostly for de-risking technology, it 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 an ASL 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. And 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. And then you'll have to do basic studies around toxicity studies that are required to submit for an initial human study. So basically all these steps happen before you're able to go into the clinic and test and people, does my intervention really work to cure Alzheimer's disease? OK, got you. And so in your experience, when people are applying to IC, how far have they gotten along that process? Because I imagine there's probably a spectrum. Huge spectrum. Yeah, a huge spectrum. And it's very interesting because actually, particularly the earliest phase of discovery, that is an area where there's significant under-investment just generally in the industry. So everybody wants these new molecules or new strategies that are at the clinical stage. So if you have initial proof of concept data in a clinical phase, then you're probably already acquired. So there's like-- Oh, wow. So I mean, people-- the pharma companies are desperate for buying an innovation. And so they're really looking for companies to get to that initial proof of concept in a human studies. So that's really where a lot of their innovation is coming from, is biotechs that have got new molecules through to that point in the process. And the investment in that earliest stage, that's where there's a lot of challenges. 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. Like that area of translation, it's called translational research, is-- there's increasing focus on it. But it's definitely an area where we see a lot of companies apply. So they apply when they have some initial data that suggests that they have an interesting approach to studying, to curing a disease, or to developing, for example, a new way to test for a particular disease. So they're 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 in the clinic that it works. So that's the gap where we really focus. So then-- OK, so I don't have a PhD like you do. So walk me through, say I'm doing a PhD program, which you said, with seven years average. Yeah, the median is seven years. In the US, I guess. In the US, yeah. Because yours was like three or four. OK, but overseas. Yeah. At what point in the process would I start thinking about, OK, maybe this is a company versus, I'm just going to round out my seven years. So much time. And like, yeah, complete it. I don't like-- Yeah, I definitely want to complete it, of course, if you've listed all of that at 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, you're thinking about what am I going to do next, of course, like that's the excitement of you 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. And that's a big shift that's happened recently. And this is 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. And so instead, you have to look for what are the alternative paths. And you've already invested all of 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. And so for them, the question is, do you go forward and just continue in academia? And 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. And there's more and more acceptance of that strategy, obviously. So people have realized that leaving academia and going into industry is not so evil as it was once thought of because the reality is all innovations that come to market through industry. So you don't see drugs on the market that came from academia. There were all-- many of them discovered in the earliest phases the basic research was done in academia.
Should you leave academia? (13:31)
But then those would spun out into commercial biotech companies or pharmaceutical companies, licensed them and took them through to commercialization. And so that is a long path, very, very expensive path. And so I think 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 distance from the actual commercial application. Of course, yeah.
Intellectual property (IP) in biotech (14:11)
And are there like other IP concerns that are specific to biotech companies that someone should be thinking about? Absolutely. So IP and biotech is very, very critical. And this is something that's very challenging, actually, with academia. So intellectual property is 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. And then you need some window of exclusivity around the 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 this space. It's not because 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 that investment. Or else it's just simply unsustainable. There is no path to actually developing those drugs. And in academia, most intellectual property is owned by the actual university. So when you-- Completely. Yeah. So when you are working in a university, your IP belongs to the university. And then there is a path to actually license it 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. We are there not subject to it. So it really depends on the specific program. And so then that's another area of complexity to have to research. But for professors and postdocs, definitely, the intellectual property generated there belongs to the university.
Fundraising after YC for biotech companies (16:00)
And so how does that work for an average YC company? Do they license it prior to YC? Yeah, so they're not always prior. They will have one of the things we do during the program is help them with strategies around licensing intellectual property, figuring out 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 a compelling business case for why they should license it to you. And usually, if you're the discoverer, then there is a compelling business case for why they should license it to you. OK, got you. And so then what about what about what happens after the three months 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 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 many companies raise pretty large rounds, straight out of YC, from a seed funding perspective. So several million dollars or more. And then it's that path of how do you get to that next stage for the institutional round. So the series A round. And 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 latest stages. But we don't have a lot of historical data to go back three or four years. We're only at this sort of two year mark. Do they tend to raise from funds out here? Or do they go, are they raising money in Germany? Like, how's it going? Oh, interesting. Mostly here. So definitely a lot of Silicon Valley funds are doing investments in this space. So even funds that people may not have thought about traditionally. So coastal eventures has done a lot. And recent Horowitz has done a lot. Data collective. Found is fund. So there's a lot of sort of funds like that. So just happening here. That are interested in science and interested in not just software applications, but really interesting companies that are combining insights, particularly from the software world into the actual biological world. And so that's an area where there's been a lot of interest in investment. So it's actually much more of a traditional fundraising path than I thought. Yeah.
Angel investors in biotech & their model (18:39)
And I don't know why I had figured that it was coming from other sources. Well, 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 one of the paths they do. It is to come and do something lightweight combinator, get some area of growth and de-risk their strategy, right? And then they can raise money. But what we've seen historically in the biotech sector is very much like the old school days of raising funding as a software company, where 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 certain stage and then spin it out and put in a professional team of a professional CEO and everything to run the company. And so that's a much more common sort of path that the biotech funds would take to developing companies.
Are biotech companies created the same as pharmaceutical (19:41)
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 that's all of those big funds, take that strategy. And it's very successful. They have created a lot of 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 in a more broad setting, which is, what else is there? Like, if you just fund-- no, well, if you just fund the same people over and over again, then, yes, they'll be 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 actually made the discoveries. And so then, if you can provide a path for them to also participate as founders and as entrepreneurs, they potentially have a lot of inside knowledge about the discoveries that they made that can help those companies succeed. And 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. And so if we can build a path that supports those people more effectively, that's going to be, I think, 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 advise folks when they maybe do office hours or maybe do a YC event or someone just emails you same a PhD student? Like, what's your advice to me? And I'm working on something that I'm excited about, but the whole thing is completely foreign to me. We were talking about before, you thought, or rather, you met with PhD students and they thought the money that they raised was alone, wasn't an investment. And then they would have to pay it back. If they'd work out-- I think that's just an education in the ecosystem thing, where 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? 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. And so I found it very interesting when I was talking to them, when I basically gave presentations to alternative careers. There's this whole alternative careers focus in the industry because for PhD students and postdocs in academia, like I mentioned, there's not often a job path that exists for them to become professors. And so there is actually a focus on saying, what are their alternative careers are out there? And so obviously, one of those alternative careers is entrepreneurship. And so I've talked to some of these groups, and I really did get questions like, OK, well, what if my company fails? Will I have to pay back that money? Will I be personally liable? I mean, so just removing those misconceptions, I can't even imagine all of the questions around starting a company. Like, you're worried about job path, and what if it doesn't work out?
Potential scary options to career progression (23:14)
And will I be able to get a job if it doesn't work out? All those questions are still there and scary. But like, so also have so much misconception around even things like, I'd have to pay back money that I raise. Like, that's, to me, a big red flag that this particular sector just doesn't even know what steps are required and doesn't understand the path to being able to start a company and doesn't realize that there's real opportunity to do this in a way that isn't as scary as it looks. 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 in, then 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. Is that what's happened with your-- because you were in Miami, right? When you started Science Exchange. What's happened with the people you're working with? How have their careers progressed, like 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 our lab, that he actually did start a biotech company with my mentor. So they started a biotech company together, which is pretty cool. That's really cool. I definitely think that there's more and more people doing this. And particularly if they see other people doing it, then it gives them one, a person to ask, like, how did you do that?
Life Science Portfolio (25:02)
How did you figure out the next steps? Can you introduce me to people that can help me? Just having more colleagues that have done similar things allows you to explore as an opportunity for yourself. And are you still reading journals? Are you still paying attention to this stuff? Yeah, I do. I love science. What's the cool stuff coming up? I mean, I guess you read applications too for YC. So maybe there's a particular focus. What are the things that are right on the edge you feel like? Like 10 years, realistically. So like, reaching market that you're excited about. Well, there's a lot of things that are really exciting at the moment. And I think science in general is like a huge board area. So I try to stay on top of the areas where I have the deepest understanding. So 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 sit between pharmaceutical companies that are outsourcing their R&D through us. And we also work with lots of early stage biotech companies. And 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, oh, that's interesting. Like, people are starting to do this differently. You're all starting to think about the next stage or they're looking at this new therapeutic area. And so I think at the very cutting edge, that's mostly still coming from academia. And 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.
Revolution in the Clinic (26:48)
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. And so 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. So historically, people would do 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're looking for efficacy. And so that's really time consuming and expensive. And 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. And so I think there's a lot of innovation in that space, which is pretty exciting. And I'm sure you've seen like everyone's talking about, you know, editing of human embryos with CRISPR and all of these things there. - Yeah, this is like the random question section. So okay, so where are you following CRISPR? Like are you into that? - Yeah, 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. Like I actually, from my PhD, I worked on RNAi screens and RNAi screens were the first technology where you could basically inhibit gene expression, but you couldn't knock it out completely. So you would be, you know, systematically knocking down the expression of a gene, maybe 50% or 60% but-- - Over generations. - No, no, just in real time. So like within a couple of, basically RNAi works within like 48 hours. So, and it's only transient unless you create like stable cell lines. But, so you can knock it down transiently and then see the effect. But it was so error prone because you couldn't really control it that well. And you would knock down gene expression like 50%. And you would be like, what does that 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 a ton of issues with artifacts and 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 I mean, it's already, I think, there's some really interesting applications that people are looking at.
Longevity Research Challenges And Nuances
What are the key challenges in longevity research? Why (29:46)
Obviously, the most obvious application is to edit genetic defects. So if there's like a lethal genetic defect being able to correct 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, 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. All right. I guess I have one more question. So people in Silicon Valley seem to continually be obsessed with life extension. So-- Yeah, they are. Yeah, they're really-- Yeah, I always wondered how that correlates to even just fundraising, like what gets funded here? People are like, oh, I can live forever, finally. Have you been following like calorie restriction, all this-- obviously, you worked with cancer or two is 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 there's-- it's a challenging space as a scientist for the main reason being that there isn't really assays. 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 in suspension where a normal cell wouldn't.
Steve Ages cantate (31:31)
And so there's like these kind of basic assays that you can use to understand better what you're looking at. With aging or longevity, that is more challenging in the sense that in model systems, we have CL again. It's a good model that they use a lot where they're looking at these nematode worms and saying how long do they normally live and can we extend their life. But a lot of that research is difficult to know how it translates into humans. So you don't have really a good end point in the human system. So people have used like telomeres as telomere length. It's like an end point. But I don't think that is very well established. So you actually see a lot of noise in that particular end point. And so it's hard to know if you do this intervention in a nematode and it extends a 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 that you can measure. So think about cancer research that you always are looking at extension of life or reduction of tumor size. And these are in patients where 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 there's the drug extend their life. And you can actually monitor their tumor size in real time and say, does the drug shrink the tumor? So those are the end points you're looking at. So you can quickly tell does the drug work?
Longevity, Robert, untelevised 229 Euphoric Metabolic Imbalance (33:13)
So for that 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, but the parabiosis work is obviously fascinating, very, very interesting work. And I think there's, you know, the calorie restriction is all spin around for a while. There's actually like a lot of controversy there because there is 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's two primate studies that were done, one which it extended lifespan and one in which it decreased lifespan. So the work in the preclinical setting in 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 say if it works or not. And then with parabiosis, which is the other kind of big trendy area right now, they have-- - Can you explain that just in case people don't know what that work means? - Yeah, so that's basically, it's kind of, I don't really-- - It's kind of terrifying. - Yeah, I don't know what to say. It's like, it's sewing to animals to get like an old mouse and a young mouse and like connecting their blood streams so that they, the old mouse and the young mouse are receiving each other's blood. But that's then 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. Well, it hasn't been very successful as 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. And obviously if you could find out what it is, that would be huge 'cause then you can make it a common invention 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 a 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. Well, right now it's still very, you know, very much in that stage of figuring out, yeah, figuring out how to apply it. And, 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 it 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. - Oh, okay. - So it's mostly disappointment. And so then 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 like bio habits that you're like taking different, whether it's like a medicine or a supplement or you're fasting or anything like that? Are you doing any of the 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 because 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 work day. 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 just like, it just seems to have a good impact going forward. So I definitely like that's one that I do think people like, oh, it seems kind of hypey, but I think the science is there. - So like 24 hours? - I do it from the evening through like the whole next day through the breakfast the next day. - Okay, so it's like 36 hours. - 36 hours, yeah. - Okay, interesting. But yeah, you're kind of like slower during that fasted state. - Well, my brain just doesn't work at all. - Oh wow. - It's like completely, it's completely, you know, just have to watch TV or do nothing useful because I can't think probably. But so that's like the downside of it, but I do think it seems like it has some benefits. But yeah, I don't take like supplements or-- - Nutropics, none of that stuff. - No, none of that. I should probably research more into it, but I haven't so far. I'm very interested.
Fasting Research (36 hrs since last food) 36 Individual Response to Food (38:16)
I have some 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 that's 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 evolutionary about how to have a diverse population of people, you would never wanna 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 who, 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? I do this fascinating. What are, what would I look for if I wanna 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 people are actually wearing like insulin monitors, like continuous insulin monitors to like 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 you could probably research it, but it's pretty cool.
Forward. No Sponsorships Today. (40:17)
- That is pretty cool. - Yeah. - All right, well, thank you for coming in. - Great, well thanks for having me. - Of course.