Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy, Alex Karnal
[00:00:00] S0: Everything we talked about today was discovered and developed by incredibly talented people, both scientists – people in the medical field – but then business builders that turn these things into great products and great companies. Maybe just describe so as not to take it for granted what that process is in the first place. Back to biology is complicated, it is un-freaking-believable we can do the things you've been describing so far in this conversation. [00:00:00] S1: It's pretty wild, yeah. [00:00:00] S0: And we're on a trajectory where the granularity with which we can make edits, for lack of a better term, into what's going on in biology is probably just going to continue to accelerate. So I want to understand that process itself and then we can talk about how AI figures into that discovery process.
· Alex Karnal
[00:00:00] S1: First of all, what are the two key ingredients for discovery to happen? You need scientific instinct married with tremendous amounts of capital. And I think you need both of those to converge in order for there to be a breakthrough that allows us to live healthier and longer lives. Let's break those into their pieces – the science of medicine and the business of medicine, because I think they're both independently interesting. Oftentimes what ends up happening to discover a medicine is a scientist, for whatever reason in their journey, gets inspired to spend their lives trying to crack the code in a certain disease. What tends to happen is you've identified a problem that you want to solve – just like in any consumer or tech product, what's the problem we're trying to solve? You then have to go through a tremendous amount of research to understand every single thing you can about that problem. And most importantly, what could be driving it? What's developed over time is a method, probably one of the most important methods in the history of humanity, called the scientific method, which is the systematic way by which a scientist moves from focusing on a problem they want to solve to actually solving it. And there's a couple of discrete steps in that scientific method and tremendous amount of iteration that leads to cracking that code. Once you've identified the problem that you want to solve, the first step to solving that is come up with a hypothesis for what could be driving that disease. And the way scientists tend to do that is – everything from the PCSK9 example, where scientists are observing nature. "Where are there populations of people that have an advantage? Okay, let's go figure that out. That's cool. Let's go understand it." All the way through to combing through thousands and thousands of pages of the literature to understand what people have tried to crack a code that still exists today that you want to go crack. And that's a deep literature review. Fortunately, there are tons of papers that exist on successes or tiny steps to successes that exist and are available for anybody. The unfortunate part of the literature is that what's often missing is all the failures, which would be even more informative than the successes. So generally speaking, when a scientist wants to go solve that problem, they're going to observe a population.
· Alex Karnal
They're going to start reading a ton of research. They're probably going to put both of those insights together and use that foundation to form their first hypothesis for what could be the solution to the problem. So from having a hypothesis, then the amazing scientists can then go figure out what experiments do I need to run to test that hypothesis? And even more, what's my prediction for what should happen in those experiments? And those are really important steps because the design of the experiment and the quality of the data that comes out from that experiment will either confirm your hypothesis and you move forward, or it will reject your hypothesis and then you go back through and you loop to your next hypothesis and your next set of experiments, and you continue to loop and loop and loop. So what scientific method generally looks like is hypothesis, experimental design. Execution of that experimental design. Review of the data that comes from that experiment reading out. Test that data result versus your original hypothesis. Either confirm the hypothesis or re-loop, and re-loop and re-loop and re-loop. And so there's this iterative process that tends to begin its journey from the literature, very quickly moves into what we call in vitro experiments or experiments that are done in petri dishes. And as we start to develop evidence and support for a hypothesis in cell-based experiments, it tends to move on to small animals and then larger animals. And then it gets to the point where the totality of the data and the evidence that you've collected from a hypothesis perspective to the cell-based experiments, to the animal-based experiments – you put that all together in a beautiful package that you present to regulators and you say, I think I've cracked the code. I think I can move us forward against disease A, B, or C. And here's the hypothesis that we had. Here's how we tested it. Here's all of the data we've generated that helps me, the scientific team, to believe that we might have cracked this code. And now we would like your approval to go start and agree on what studies should look like to test how toxic or how dangerous this breakthrough could be for somebody.
· Alex Karnal
And if we can show that there's good margin between really high doses and where that toxicity starts to come in, and the doses that we've proven from our experiments are likely to help somebody live a longer, better, healthier life – we want your permission to then go and actually test that in a person for the first time. And this is incredible. And that process that I just described, you can imagine most of them fail. But for the ones that do succeed and get the chance to then go into toxicity studies and ultimately into humans, those are the ones that get really exciting. But they take years and years of iterating and looping, to go from that beginning idea, through literature, ultimately to the animal study that makes you believe you've actually cracked that code. And then, of course, from there – this is so much more of a marathon than a sprint, because you've spent years trying to crack the code, and now that you have, guess what? Now you've got another five to seven years worth of clinical studies you have to do, to prove that in people, the idea you had for medicine actually has the effect that it's intended to. And that, per our discussion earlier, that benefit-to-risk equation is favorable to the point that regulators should approve it and let it come to the market.
· Patrick O'Shaughnessy
[00:00:00] S0: I have two questions here. The process itself is beautiful and has driven so much discovery and progress, not just in medicine, but scientific method-driven progress everywhere. But I'm curious about two dimensions of shortening that feedback loop or increasing the velocity of new discoveries and progress. One is AI. And the second is what I'll call citizen pharmacology, which is people. This is becoming really prevalent in what I'll call the peptide community. People love talking about peptides these days. GLP-1 is a peptide. Collagen's a peptide. There's well-known things that are peptides, but there's this whole class of things that we can talk about. But it seems like if you take to Reddit, for example, you will find groups of people organizing to test new things on themselves outside of the normal RCT, FDA-approved process. If you shorten it, you probably increase the danger, but you decrease the time to learning or something. So you've got both of these things as interesting aspects to me of what will drive forward progress in drug discovery. Riff on each, maybe starting with AI. What is real already? What do you think will become real by virtue of the increasing skill level or reasoning level of the models?
· Alex Karnal
[00:00:00] S1: Absolutely. So I think what you're basically describing on the AI side is our quest to try to get to a level of scientific superintelligence. You can imagine that from an AI perspective, if scientists are armed with an agentic infrastructure that has a scientific intelligence that's at an Einstein level or multiple of Einstein level, the force multiplication that can occur between a superstar scientist armed with that agentic resource means that they will do science faster and with higher probability for lower cost and have greater impact. So that's important conceptual framing. And so then practically, how does that manifest and why does it matter? Well, you can imagine any human scientist on their own, tinkering in the way that I just described, scientific method and that wheel of science spinning and spinning and spinning. Us as human scientists are always going to be constrained by what we can retain in our human minds and constrained by our ability to use our human hands to go run those experiments. There are super talented, incredibly smart people that can retain volumes and volumes of scientific papers. Can't retain all of them. AI can. And if humans are running experiments, not AI driven robotics, there's just a certain throughput that is going to be limited by human hands, that would be unlimited if you had an agentic, robotically driven system. At the simplest level, AI coming into the discovery of medicines is one of the biggest unlocks that we will see in the history of medicine, because it means that we're no longer constrained by our minds. We're no longer constrained by our hands. We can generate not just a few hypotheses for what the answer can be from the total sample set of knowledge that exists. We can identify all N number of hypotheses that could exist and should exist and then figure out what is the greatest and best next experiment to go run, run that, and run those in real time, 24 hours a day, seven days a week. The sheer ability to be more comprehensive and complete in our hypothesis set and then the sheer ability to run experimentation at a scale that's unprecedented, I think practically means we are going to be able to speed breakthroughs from timelines that would typically take three to five years to timelines that can take less than a couple of years.