0:00the large part of the industry makes medicines is this very empirically driven process. [music] Where things really fail is in the clinic because we never could see the problems until we get to the point where we put these drugs in patients and we say, "Oh no, like there's toxicity that we didn't account for." And so the really big opportunity is to have much more of that understanding before you go into patients. How can we bring all of that drug discovery work onto a computer? So how can we get out of the real world?
0:24I'd be really disappointed if we fast forward even 5 years time like the predictive understanding of disease biology has not step change in like a massive way. Do you have a prediction on when you think for example 80% of human cancers will be treatable today on no prior we're joined by Max Yadberg. Max is the president and part of the founding team of Isomorphic Labs.
0:52Isomeorphic is backed by Alphabet and a variety of other uh investors and its goal is to solve all disease which is very exciting as a topic. We're going to talk a little bit about the role of AI in the future of biioarma, human health longevity and uh Max [music] of course has a background in machine learning. He has a PhD in the topic working on machine vision eventually sold a company to Google deep mind rose up the ranks there and now is president of isomeorphics. So very excited to have this conversation. Max, thank you so much for joining us today. No priorities.
1:22No, it's such a pleasure to be here.
1:23Isomorphic, I think, is is one of the more fascinating companies right now that's happening in technology in general. Um, Alphabet is a major shareholder and sort of sponsor. Um, Demis, uh, who pioneered a lot of, uh, models at DeepMind and other places, which I believe you sold the company to before, um, you know, really helped with the inception and setup and now running of the company. and you folks are tackling one of the biggest problems that's left for humanity to tackle which is human health. Could you tell me a bit about how you came to uh work on this
1:53company and what was the motive force to starting it?
1:56I was part of the you know the founding team of isomorphic labs. Um I was a deep mind for a long time beforehand. You know personally I was working on early days of generative models, language models, deep reinforcement learning, all of the big you know game playing challenge problems. those were my jam, like Alpha Go, Alpha Star. Um, and it got to the point where, you know, we were we were going after these like, you know, quote unquote impossible problems at that point in time. You know, this is this is probably 2018 19.
2:24That's like gameplay and can AI beat video games?
2:27Exactly. You know, at that point in time, you know, we were still trying to prove to the world that AI was even a thing, right?
2:32Um, and so we were going after these impossible problems and I could see, you know, firsthand like how to like just systematically how to solve these. Mhm.
2:41And it got to the point for me personally where it's like, okay, sure, we're not at AGI yet, but we clearly have this amazing, you know, um, set of technologies, you know, in deep learning and reinforcement learning. And, you know, what are the problems we really need to be focusing this stuff on? And I was, you know, I was thinking all of this, of course, Deus was thinking all of this. the same time we're seeing the alpha folder results, you know, the first embers of that starting to emerge and really starting to see for us this
3:08was like the first proof of concept that you could get a deep learning model that generalizes in you know a meaningful space in biology. And so that was really the proof of concept that gave us conviction that actually you know now is the right time to go after this you know the big mission of you know changing the way that we think about human health you know going after solving all disease um and that's going to take like a really substantial effort and that warrants its
3:36own company its own culture you know specific people of different disciplines coming together for this and the ability to run really fast on that. So that was the genesis of isomeorphic labs in in about 2021.
3:48Oh, amazing. Your background really started off more on the deep learning side and AI modeling side and I believe you sold a company to Deep Mind and was it 2014?
3:57So quite early. I mean you were at this stuff before people realized how important it was or many people uh realized how important it was. Tell could you tell me a little bit more about sort of the journey you took, how you got there?
4:06Yeah. What got you interested in it to begin with?
4:07Yeah, I mean I I I I come from an engineering background. um ended up almost by accident doing a PhD um in computer vision and this was actually just at the turning point. I think I started my PhD um pre- AlexNet.
4:23Oh because Alex Net was 2012 was it?
4:25Yeah, exactly. And then I saw that emerge. I was like wow this is awesome.
4:30I remember um speaking to Yan Lakun about convolution neural confets um and I was like we need to translate this into the core computer vision space that we we're working on in Oxford. Um and yes ended up developing all of these methods. We ended up having the [snorts] uh image net winning network of of 2014 best object recognition text detection all of this sort of stuff. Um and I had a company like built a company in this
4:58space uh computer vision as a service and trying to raise money and at that point in time you know uh Demis and Deepmind had just sold to Google. So trying to raise money very very quickly ended up just speaking to Demis um asking for angel investment and he was like well why don't you just come to Deepmind to solve AGI I was like what's AGI but so began the journey into Deep Mind and everything there.
5:22Yeah. Amazing. And your focus now is basically a vertically integrated company that's doing everything from models to the biology. And the idea is to find drugs that can help people different aspects of human health. Um and so if you look at the pharma industry, one of the things that I think is really striking like I' I've graphed this out in the past where if you look at the number of new drug approvals per year, it's roughly flat.
5:45But that's despite an explosion in knowledge. So all these scientific papers have sort of exponented in terms of the number of papers. You know, the numbers have gone up 10 20x during that period.
5:53um the number of new modalities, the type of new drugs that you can do has gone up. So there's antibbody based drugs, there's small molecules, there's proteins, there's peptides, there's all these new ways you can treat drugs. The price has gone way up, right? It went from a hundred a few hundred million dollars per drug to um 1.5 billion. It went from eight or nine years to 15 years. So every metric has gotten worse while the number of things have gotten flat. The knowledge base has exploded.
6:17The number of types of molecules that you can do has gone up. Why is that? Why has it been so static despite all that progress in other ways?
6:24Yeah, it's crazy to see just how it keeps getting harder and harder to make new medicines, more and more expensive, longer and longer. Um, and you know, I I I think there's probably a number of reasons behind this. Like this the thing that I really um think quite a lot about is that you know like the way that we actually make medicines in general in the industry and as a species you know forever has been a very very empirical
6:51process right at the very beginning it was you know and and and and this was you know a long time ago like a hund 100 years ago you know the medicines that we came up with they came about by chance we just like happened to stumble upon something that people realized has you know, physiological effect and people adopted it. Things got a bit more sophisticated. It's a bit more empirical trial and error.
7:14Yeah. A lot of medicines we just guessed, right? That like aspirin I think for a long time we had no idea how aspirin we didn't know how it worked. Paracetamol we didn't know how it worked. Um and the way we've been making medicines and still like how the large part of the industry makes medicines is this very empirically driven process.
7:31empirical as in you need to um you know you have a hypothesis on you know how a disease might work. You have a hypothesis on what a good molecule design could be but you have to make it and test it in the real world and do this very iteratively.
7:44That's slow. It's really really difficult to get signal out of that and you know the space of biology is really really big and complex. These are like big comp you know the cell is a big complex chaotic system super highdimensional and then molecule design whether it's small molecules antibodies peptides you know they're really again really highdimensional objects combinatorily large design problems and the only tools we've got available you know to us you
8:13know is is basically trial and error screening processes and so that limits to me the complexity of solution that you can find in in a finite period of time or finite amount of money that you spend on that empirical work.
8:27Uh and so I think you know what you've seen over time is that the lowhanging fruit of medicines that can be found with our empirical process of designing medicines are being found. We're finding the low hanging fruit, but then it's getting harder and harder to sort of ratchet up the complexity and and find things that are out of reach from this empirical process.
8:48That's interesting. But don't you think so? If I look at any other industry, often what happens is the age of the industry impacts the rate of change in it.
8:56In other words, the older the industry, the slower it moves.
8:58Things become more bureaucratic. There's more regulatory capture etc. I think the last time there was a biioarma company that was worth over $50 billion uh besides Madna and I think Madna was almost an accident of COVID was the uh 1980s I think uh with Regeneron.
9:14Yeah. And so it's been um almost 40 years since we've had a new tens of billions of dollar biotech built.
9:22Yeah. Regeneron, Vert.Ex, these you know Amjen was that era too early ' 80s.
9:27Um and and so how much do you think is just like regulatory issues or you know bure bureaucratic organizations or just like that natural aging I think that happens everywhere you know that was automotive until Tesla that was you know yeah we you know we talked about maybe the the science side of it. I think there's a big cultural aspect of um you know just how how can you like keep innovating as an industry how how can you keep moving um really really fast and I think as well and this is related to the sort of like science and
9:55technical part is um what like what are the mechanisms that actually give you economies of scale on the innovation side on the research and development side so if you go after you know a particular drug target and you're trying to make a particular medicine and you're a pharma company or biotech you're going all in on that. What do you learn from that that helps you work on the next medicine much quicker and then the next medicine even quicker after that? Um because if you could get that compounding of learnings over time, you
10:25could maybe get the benefits of scale and build something you know really amazing and big as a company. Um but I think a lot of the techniques and the way we go after medicines uh or the industry goes after medicines is really treating each one as like a segmented you know it is like a bespoke frontier science project and you learn so much about that project and there's maybe not so much that's transferable to the next like there's of course many many different learnings of disease areas and this is I think where things can
10:53shift quite a lot in the world of AI and machine learning where uh if you underneath You know, if you have this platform underneath going after these medicines that allow you to like, you know, really suck up and understand the learnings and help impact your core foundational understanding of disease biology and how to design good molecules and exquisitly engineer these medicines, then you can hope to actually get compounding improvements the more medicines you go after.
11:19Because I guess like one of the striking things that has emerged from deep learning and the sort of foundation model era that we're in right now is the generalizability of some of these things with scale. So it sounds like perhaps in biology as well if you have sufficient scale and a sufficiently um thoughtful approach you can generalize some of these problems that were bespoke before.
11:36Um what approach are you all taking?
11:39Well I I think that's the big shift that we're taking with isomeorphic compared to you know we're not the first people to try and change the way we do drug design and drug discovery with AI. One of the things that's really fascinating about um isomorphic is a degree to which you have a very diverse space of people all working together towards a common cause often who come from backgrounds that are very disperate. Can you tell me more about what sort of people you hire?
12:01What are their profiles? Who you're looking for currently?
12:03Yeah, I mean it's an interesting space because uh the the the sort of mashup of you know frontier AI and drug discovery, chemistry, biology hasn't really existed for very long. So you can't kind of go out into the world and say, "Hey, I want to hire the world experts at AI for drug discovery." Because [snorts] they basically don't exist. And if they do exist, they're not using our models using different techniques. So we think about going after literally just the best people in core AI and deep learning who maybe don't even have a background
12:32in life sciences or biology or chemistry, but [snorts] really want to see their work on deep learning translate into amazing impact for for humanity. So we hire just core people in AI. We have core people in biology and chemistry maybe who haven't had much exposure to AI and then you know we work hard internally to build that culture that everyone talks and learns from each other and then internally we get these sort of cross-disciplinary experts expert scientists emerging which has been really exciting to see.
12:59How do you think about the ratio of those things? So um I started a company a long time ago uh called color and I was co for the first couple years. My co-founder took over a decade ago now.
13:08So this was a long you know ways back and we were basically using ML and data science as applied to genomics and we were building these genomic pipelines and what we found is you could have like five ML people and then one research scientist from the traditional field and that was kind of the ratioish that we adopted. What is it like now or how do you think about how many people of each discipline do you optimally have in a pod working on a problem? Yeah, for some of these core AI, you know, superpower projects, the the AI modeling projects, um, I think that's actually
13:37not too dissimilar. You know, you have your core modelers like engineers, uh, AI scientists, and then you just need some key domain experts, people who really understand the application, the nuances of the data, like what do the metrics mean? Um, make sure that we're not, you know, chasing smoke metrics and these are real things that are going to translate into actually making medicines. Um, so yeah, those sort of ratios, one in five, like that I think those resonate quite a lot for what our teams look like.
14:06And are there specific roles that you're actively hiring for currently or just across the board, you know?
14:10I mean, we want the best AI researchers um coming into ISO and helping us push on this mission. And I think there's there's literally so much work to do on on the like core simulation models like Alphold++, but also on the core reasoning models like taking all of the frontier models and and open source models and and adapting them to our space. Um so that's I think a really important area for us to be hiring on.
14:35What are the components that you tackle or what are the main areas that you all are focused on in terms of how you approach new drugs or you know you do you have your own labs? Do you run robots that do pipetting? Like what what is the approach we're taking?
14:48Yeah. So um you know if you if you go into our like uh sort of our core AI team and uh like our north star and the thing that we're really trying to achieve is how can we bring all of that drug discovery work, all of that drug design work, understanding of you know disease biology onto a computer.
15:07So how can we get out of the real world?
15:10um you know I know a lot of people are uh building ways to be like interfacing with the real world really really efficiently so you can do lots and lots of real world it iteration that's great and it's complimentary but fundamentally the the big time bottleneck the big cost bottleneck is synthesizing real atoms and then testing them out in real assays and you know uh experiments and you know just like we've seen I think pretty much
15:38every other area of engineering move into a computer uh computer AED design scenario and that's just changed the way that you know change what we can design change what we can engineer um we want to do the same thing in this space and so really we like when we think about the core AI drug design engine development side this is really focused on well how can we achieve experimental accuracy for our simulation models our world models if you can get a predictive model
16:08you know let's use Alpha fold as an example. Alphafold looks at structure prediction. The the the structure of proteins and how they interact with each other.
16:17Normally that takes, you know, months or years to do X-ray crystalallography and and and get a structure out. If you can do that with a simulation model like AlphaFold, then you don't have to go into the real world and do that. You save months and months of time. And there's many many you know drug discovery is a is a combination of many many different really hard scientific problems and engineering problems and if you can bring that you know create predictive models instead of the real worlds then you know you're saving tons and tons of time. So internally we have
16:47you know a dotted line on all of our leaderboards that is experimental accuracy and we you know the goal of all of these like superpowers that we're trying to create is to get to a point where it's experimental accuracy. At that point, you don't need to go into the real world. [snorts] You can do so much more design and iteration on a computer. So that when you do go into the real world, when you make your molecules, when you test out your scientific hypothesis, the probability of success is much much higher than it would have been traditionally. And so you don't have to
17:15do as many turns as the real world and things actually become quicker and problems fall easier.
17:20Well, so when I look at drug development, um I think it's about a 15-year process now.
17:26It cost $ 1.5 billion. My sense is the small molecule and the in vitro work, the tissue culture and m mouse work is basically say two to three years and then it's say $50 million of that order of magnitude and then the other 1.45 billion and 12 yearsish uh is the clinical trial and you know the different phases and safety and um how much of that do you think you can actually encapsulate in a computer? You know, you have the discovery phase. So
17:54where you go from idea to you think you've got a good like a good molecule that you could put in patients or before that point you have to do pre-clinical, you know, animal studies to make sure that this is safe and working as intended. That takes another year. Then you go into clinical studies. You have phase one, phase two, phase three ratchets up. And so, you know, when you look at the timeline and when you look at the failure rates, and I think these are the things that really exacerbate the costs on average, right? Why do things cost so much? It's
18:22because so many things fail.
18:24Then you have and and where things really fail is in the clinic.
18:28It's not because these are problems. um it it they fail in the clinic because we never could see the problems until we get to the point where, you know, the rubber meets the road and we put these drugs in patients and we say, "Oh, oh no, like there's toxicity that we didn't account for." Or we have a molecule and it hits the thing that we've been designing it to hit, but it hits something else, which is a really negative effect.
18:54Or the thing that you do hit, we thought it's going to solve the disease. Maybe it does even, but it actually has a really negative effect in and of itself.
19:02And so you like people only learn about those problems when they start introducing it to patients, to real patients.
19:10And so the really big opportunity is to have much more of that understanding before you go into patients.
19:17So if you have a better understanding of disease biology, how cells work, you can maybe understand how the thing you're trying to hit and modulate is really implicated. So you're actually modeling all these other components and not just like what molecule will bind to what exactly we're modeling all of these components.
19:31How do you get the data to model those things? Like where does that data set come from?
19:34You know, we have this like ridiculously comprehensive, you know, data strategy. There's data all over the place, but I think fundamentally what we've learned over the last, you know, 5 years is there's a lot of opportunity to generate this data yourself.
19:49Um there's of course like big repositories of data. Sometimes that's really good for deep learning. Um oftentimes these repositories of data you know whether they're public or in pharma or you know um in companies they've been the byproduct of many years of passive you know data collection from doing a you know the activity of of making medicines. So they can be very very biased which often doesn't really help you if you're trying to make a general solution to drug discovery. If you really care
20:19about generalization out of these models there's maybe a different distribution of data you need. So we go out and generate this ourselves you know and we've seen some really really amazing uh data scaling laws as a result.
20:30How do you measure whether uh some of these things will have these side effects because ultimately there's the old saying I used to be a I have a PhD in biology and I used to work in a variety of fields cancer no science etc.
20:40And on the cancer side we cured cancer in mice hundreds of times and those molecules wouldn't translate into people and so a lot of the data you actually needed you had to generate in human clinical trials. How do you go about generating that data that actually informs it? Because if you're using animal models, often the data that comes out of that is incorrect relative to human biology.
20:58Well, exactly. It sounds like you experienced a lot of this firsthand.
21:01It's very painful. And you know, if you think about um you know, toxicity like sources of tox like the gold standard that you know you need to get through for regulators is you need to prove that your molecules aren't toxic on rats and other animals. Um, but still even when things are approved and they've been proved on rats, they end up being, you know, often toxic in humans.
21:25And well, why is that? Well, okay, a rat isn't actually a human. It's not actually very predictive. I think, you know, choose your study, but it's about 30% predictive of of of human clinical tox like in a rat. [snorts] Um, and you know, if if one of my like AI scientists or engineers came up to me and said, "Hey, we've got this amazing model. It's it's it's 30% predictive."
21:43You're like, "Okay, maybe do a bit better than that."
21:45Yeah. Um and so I think there's a lot of opportunity to actually rethink how we you know like what are those predictive endpoints for human clinical talks like forget animals you know they've been a proxy for the last you know 50 70 years um but we can maybe do better we can do better by um you know bringing together in vitro endpoints so things you could measure quite easily in the lab together
22:14with you know AI predictive models also together with there's some amazing new um experimental technologies coming out in the space of organoids, organs on a chip, like synthetic human tissues, which sort of approximate and create these like little microcosm environments of human organs.
22:32And so you can imagine there's probably an opportunity to bring all of this stuff together and then directly try and model human clinical talks. Now there's not much data on that side because you know there's there's only a finite number of clinical trials that have been run.
22:46Um but I think we have some really good methods now.
22:49And for these sorts of approaches or techniques, do you tend to partner for them? Do you bring them in house? Like I'm a little bit curious as a company how you operate relative to because there's so much that could be done.
22:58And a little bit curious how much you want to just emphasize the computer science side and then you know biology often there's these things called CRO or these contract research organizations that people use. Unfortunately, the quality tends to be pretty mixed in terms of the output of these types of organ. I'm just a little curious like how much you depend on external parties versus internal.
23:13Yeah. So like making our drug design engine all of the AI models, we do that all in house. Um and you know we see a big opportunity of generating proprietary data um and owning that ourselves but you know biology and you know experimental platforms like this is really complex and longtailed and there's some amazing people working on some fantastic platforms here. So you know we work with the whole world
23:43whether that's you know stuff we do ourselves contract research organizations CRO's um other innovative companies um to actually help us create the data that is you know powering our models.
23:55Uh so yeah that's cool. How much uh so Enthropic recently announced that they're doing a wet lab in San Francisco and you know reading between the lines without any special knowledge just reading external papers it kind of looks like there's some potentially robotic system or closed loop system or something else they're trying to build and in general if you look at AI models as you know better than me um you want closed loop systems to be able to iterate quickly in other words if you can get rapid feedback like if you're writing code and it can instantly get tested then you have a fast way to test if the model's
24:25working well or not right and So in biology, one of the issues is you set up an experiment, you wait 3 weeks, then you get the results, you have to interpret the there's a big loop and so that slows down progress in the ability to iterate on data and building these these robotic platforms or other things may expedite that. um how do you approach that problem or do you think that's part of the solution that you all will be pursuing in terms of Yeah, I mean it's been really exciting to see like how much better these you know automated labs how how much better these robotic solutions for experimental
24:54labs have been getting and you know we we we've been working with um these sort of automated labs for a long time [snorts] now everyone should and we should keep driving down that like iterate iteration time with the real world and fundamentally I want to get out of the real world right And and you know, can we actually because there's going to be just like you're going to hit the finite laws of physics at some point like how long does it take to synthesize a molecule?
25:20At some point you have to just wait for the reaction to happen and that's how long the reaction takes. How long does it take to culture a cell?
25:28At [snorts] some point in time you just have to wait those days for the cell to get to the right state before you can test things on that. And so yes, we can, you know, I think do a lot of work and we should and we are as a as a species as well is on on on you know improving automation and improving the way you want to bring it in silo. It sounds but I think we should also be driving really hard at bringing that in silicon on a computer so that we can reduce the amount of real world time so that we can do much more predictive work on a computer so that
25:56when we do go into the real world yeah it looks like we go into real world to validate not necessarily to learn and iterate. We still need to go into the real world to validate because we're coming up with novel, you know, scientific hypotheses and we want to test them out in people at the end of the day. So, you have to make sure that they work and they're safe. So, you have to go into the real world to validate. But the question is, how much can you reduce the amount of time you go into the real world to experiment and to iterate?
26:21How much of your approach is focused on partnering with existing pharma companies versus developing your own drugs?
26:27We make the AI drug design engine in house and then we we actually, you know, don't sell the model. We we don't license out the model. We don't sell tokens in that way. We use it internally to to make medicines.
26:38And then we do that in two ways. We have programs, drug discovery programs that we fully own and fund ourselves and work on ourselves, our internal pipeline. And then we have programs that we work on with, you know, amazing farmer companies, biotech, um where we partner on these on these projects. And those are really exciting as well because often those companies uh and those teams they've got you know amazing ideas and amazing expertise in specific disease areas and so they come
27:07to us with like these are the hardest problems maybe problems that they've been working on for sometimes 10 20 years not making much progress and then together using our drug design engine we can make a huge amount of progress. What are the indication areas or areas of human health that you all are focused on?
27:23Internally, we're focused on, you know, much more on oncology, immunology, and inflammation.
27:30Um, but the cool thing is, you know, the drug design engine, all the models, they're general. They're disease agnostic. They're target class agnostic.
27:36They're modality agnostic. So, they work across multiple modities. You're starting with like cancer and is it autoimmune disease or something else or Yeah. So you know um immunology inflammation they're often quite related um so it's that sort of space and when we go after these things internally whether it's oncology you know cancer inflammation you know I think there's a big opportunity to go after some of these root node problems the the sort of things that could affect you know positively if you have a solution could
28:06affect you know lots and lots of different patients for different downstream indications um an example of that um I If you if you think about cancer, like this is a really really complex and longtail disease and it shows it itself up in in patients in many many different ways.
28:24But we also know there are some like maybe there are some core driving mechanisms that are common to many different things.
28:31Um, you know, we saw this amazing example earlier this year. Um, you know, Durax and RAS, like Kraz inhibitor, standing ovation at a big cancer conference like drastically improving um, you know, the the the the lives of of of sort of like latestage pancreatic cancer patients from Revolution Medicines. And, you know, again, great example of like going after some sort of root node.
28:55I see. Yeah. RAS has been popping up in the cancer literature for like 30 years, 20 years. Yeah. But these these problems are devilishly hard from a like drug discovery and design point of view like just and also has a natural function. So being able to modulate. Exactly. So you really and and of course then you know uh in cancer things mutate very quickly. So you think you've got a solution but actually clinically it doesn't work out because uh it just mutates around the solution.
29:20It's actually an interesting point because um for a lot of different drugs, for a lot of different areas of health, this also was HIV, right? You end up with a cocktail of drugs that you give eventually and that's actually what allows you to suppress AIDS or HIV. In cancer, similarly, a lot of drugs are sort of developed as as individual drugs versus cocktails. And to your point, often within a tumor, you have a spectrum of mutations that you need to suppress simultaneously if you really want to cure the thing because otherwise it'll sort of evolve out under given the sort of
29:50substrata of mutations that exist in a distribution across the cells. Are you thinking about those more complex approaches of cocktails or multi-drug approaches or so? So why aren't we giving more cocktails drugs? Well, the problem is like often these molecules they are they work but they have some toxicity or they exhibit some effect on the liver such that if you're taking multiple of these molecules these medicines at the same time they have these drug drug interactions that can be really harmful
30:20And so it's actually because we're not great at engineering exquisite molecules and medicines that means that we can't combine them together in a safe way for patients. And so we're maybe limiting like the the surface of applicability.
30:35Yeah, I guess you only have so many patients to and so many people you can enroll a clinical trial and so the combinatorics of it my sense is also kind of adds up in terms of the clinical trial side especially when there's a lot of um like risk and uncertainty about how these things will combine and so that's where we see an opportunity. Yeah. If you can actually much more accurately engineer these molecules to be safe, to not have these effects on the liver, to not uh interact with each other in bad ways, then you can feasibly come up with actually, I would really hope some quite
31:05complex, you know, combinations that can modulate not just a single target at a time, but a whole vector of targets that span these like very complex biological pathways at the same time and so have a better effect on disease. There's a very good YouTube video of uh this guy Jansen who started Jansen Pharmaceuticals. It was acquired eventually by J&J and he was one of the big pioneers of drug development and pharma and I think he was one of the um they had one of the most productive pharma companies in terms of actual
31:34drugs launched and in it he his view that he expresses pretty strongly is that the main constraint on drug development is an overemphasis on safety and that regulators kind of curtail progress. And he pointed out how during World War II when St. Churchill tried to find a cure for soldiers of some venerial disease uh in the field. And so they they rediscovered um antibiotics because penicellin was discovered in the 20s by Fleming and then kind of put on a shelf and forgotten. And then in a
32:019-month period they rediscovered and um commercialized uh penicellin from scratch specifically to treat this and it was because he removed all regulatory obstacles. So that was an example that Jansen pointed to one could argue the COVID vaccine was similar in terms of being able to rush it. Um, how much do you think there's too much of an emphasis on safety? Like Jensen's point was it's about riskreward.
32:22You know, you need to make trade-offs and you can't just monolithically say it's only safety. You should also say, well, it's about benefit and in some cases risk should be taken as well. Like how do you think about that?
32:30I think it's like centrally important to just always have patient safety in mind to be honest. Um, and but then you need to understand like what are the risks and the appetites of patients and that can be different for different indications.
32:45uh you know COVID's a good example where the risk appetite for patients because it was so pervasive and so dangerous just changed dramatically and that changed I think the demand and the force for doing things slightly differently. You even see this across different disease areas.
33:02If patients have you know latestage cancer and not many options in front of them there's a very different risk appetite.
33:10Yeah. compared to if you're trying to treat a chronic low-level, you know, uh, inflammation um, uh, indication. And so I think it's actually really really important that like we keep focusing on patient safety, but that we can modulate that, you know, depending on what the patient need is. So ultimately, you know, ultimately we want patients who are living healthier lives and they're receiving that treatment as quickly as possible. Mhm.
33:37And you know we we we speak a lot to regulators here um you know FDA here but also all over the world and I think the one thing in common is like you people really want to get patients the best drugs and the best solutions possible.
33:50Um and I do think that over time we will see a difference in our you know pro you know the success rates and the safety profiles of molecules coming out when they're designed with much more understanding about how they work with you know we're be able to search the design space to engineer something you know much better than you could do from an empirical empirically driven process and I hope to prove out over
34:18time that this is very stat statistical different and that will change the way that we think I think about about regulation and and getting drugs to patients.
34:26When do you think you'll have your first clinical trial?
34:29I would love to give you like a a hard day. This is like really hard frontier science.
34:34Um we've got some really exciting um medicines in the works. Um and these are going really well.
34:42So we're pushing really hard to get these to patients as quickly as possible. We've been building up our clinical development team. So we're getting ready for this. in there.
34:48Do you have a I know these are always impossible questions. Do you have a prediction on when you think for example 80% of human cancers will be treatable or you know some other metric of why this is so important for the world? I'd be really disappointed if we fast forward, you know, even five years time, if we don't have the base tools like the like the predictive understanding of disease biology has not step change in like a massive way. Mhm.
35:15Um our ability to design molecules that are much more precise, much less toxic, has not step changed in such a massive way that you know still drug development, discovery and development takes years and that'll reduce over time but it takes years now. Um but we should have the tools in place to really start be like start cracking these large large portions of disease in a different way.
35:41um if you remove uh if if you removed cancer from society and heart disease, I think the stats suggest that the average person would live I can't if it's five or seven years longer. And so the actual impact to longevity for two of the biggest killers um is on some in some sense quite large and another point of view is kind of small. Uh there's a number of companies that have tried to focus on aging or longevity as a more general process which seems to clearly be genetically programmed. Right? If you knock out
36:09certain genes in seaggans or drosophila or in mice, they'll live 20 to 30% longer. Or in the case of seelagans, the the little worms, they'll live three times longer, right? If you knock out certain genes in certain pathways, I actually worked on those pathways as part of my PhD.
36:23It was really fun. Um, how do you think about that in the context of human lifespan? Like should we just hit a point eventually where people live three times longer, five times? like what how do you think about longevity in the context of drug development and you know all these tools that will now exist?
36:39Yeah. No, it's interesting like there there are some of these major diseases that afflict so many people that if we could start to make impact on those that that would have a concrete impact on on how long people are living. I think even outside of um um things like cancer and heart disease, you just have the um sort of chronic afflictions uh of aging that start to add up and the root cause is literally aging.
37:08In other words, like the root cause is um the fact that you're allowing these systems to degrade over time.
37:13Great. And stem cell compartments.
37:15Yeah. And they and they combine in a way that means that people's you know even if people live to a lovely long age of 80 or 90 like they maybe the last like 10 years of their life or even 20 years of their life isn't not isn't necessarily as great as it could be because they've you know because of um you know your bones start to degrade your muscles start to degrade like you have a fall and suddenly you can't you're not as mobile as you want to be that has impacts on your mental
37:44health and suddenly your quality of life has dropped dramatically. You still might live another 10 years.
37:50In the aging community, they talk about that about as lifespan versus health span.
37:53Exactly. So, we think a lot about that.
37:55Um, and but all the all the aging models, if you impinge on aging and you delay it, the health span goes with it. In other words, if all lives three times longer, it's actually a healthy adult for three times longer. It's and then it crashes out at the end. But you actually have extended both simultaneously. So, I'm a little bit curious how you think about like life extension and you know that kind of stuff.
38:15I I think it's going to be like it's going to be really fun to see everyone working on this and and um yeah, as you said, I think we can attack this from every different angle, right? we can think about improving um you know the things that afflict us and and and help maximize our health span and also be trying to hit some of the root root causes of aging and degradation um of our bodies and cells and organs so that we actually you know can actually extend that over time as
38:43well and and I there's yeah I mean there's such exciting work happening in this space and um yeah it'll be interesting to see how this pans out.
38:51Amazing. Thank you so much for joining us today. No, it's been a fascinating chat.
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