The Exchanges

Q When you think about, again, just like the focusing of in-sitro, what domains? Do you decide to work in first? Because this approach should be quite horizontal of, of course, then you have, you know, complexity of what that cellular model can be.

A It for sure is. And again, uh, focusing has always been a challenge in the sense that there's so many opportunities and how do we say no to some of them? So we've, uh, what we've done is tried to go in areas where we think there is both a large unmet need in the sense that the current tools that we're deploying are just not Very effective. And at the same time, where we think that the technologies that we are developing internally, uh, provide us with the unique differentiated advantage. So one of those areas has been, um, in neuroscience, because as we know, the unmet need there is humongous. There are so very few effective therapeutic interventions in neuroscience, and that's partly because the model systems that we've been using, specifically animal models, while one can quibble about in which other therapeutic areas they are more or less Um, relevant. In neuroscience, it is very clear that they're probably not, and that's one of the reasons why things work so well in whatever, curing mice of schizophrenia, whatever the heck that means, and then not having much of an impact in human schizophrenia, because it's not really even the same disease, right? Um, the other, so that's on the unmet need side, and on the opportunity side, we know that induced pluripotent stem cells are actually, um, relatively easily Um, uh, differentiated into neurons so you can actually see cellular p…

AI assessment note: “So one of those areas has been, um, in neuroscience”

Q I was going to ask, uh, if you think about, um, something like, uh, you know, neurodegenerative diseases, Alzheimer's, et cetera, like, you know, is it single cell? Who can, who can say, but feels, feels unlikely. What, what's beyond single cell? And do you guys do organoid research? Like what, is that within the scope of in-sitro?

A Yeah, no, that's a great question. So, um, a lot of complex diseases don't, um, are not encompassed within a single cell lineage. However, I think even there, one can, um, study in many cases, not always, The disease state by looking at a cell type that is clearly relevant to the disease and perhaps pushing it out of its comfort zone. So, for example, in some of the work that we've done in metabolic disease, I mean, it's clear that hepatocytes are not the be all and end all of, uh, of what it takes to make a diseased liver, but you can push the hepatocyte out of its comfort zone by putting in the right combination of You know, fatty acids and maybe various, um, immune system factors or whatever to create a disease state that is much more, um, similar to what you see in its, uh, in its natural environment. Um, that having been said, it's clearly the case that we're not going to be able to recapitulate the entire complexity of, um, a disease state for a lot of those diseases. And so one of the things that we do, and this is in the spirit of being Pragmatic and, and prioritizing. There's plenty of things that, uh, we can do today where the, um, where the disease does manifest sufficiently in a single cell lineage, and so we go after those first, and we defer some of the other ones to a later stage, because technologies such as organoids, for example, that encompass multiple cell t…

AI assessment note: “we defer some of the other ones to a later stage, because technologies such as organoids”

Q the calibration around diagnostics and endpoints and clinical endpoints and how you think, and all those places seem like there could be real uses of AI. How did you choose what in situ is actually going to do, given how much room there actually is to innovate in this area relative to data, to your point? I mean, it's just, it's shocking how little is done, right? It's like awful.

A I completely agree. And, um, and yeah, in some sense, the, Wealth of opportunities here is one of the biggest challenges because everywhere you look, there is a big opportunity for machine learning to be deployed in a potentially quite significant way. Um, sometimes I have these discussions with, uh, the increasingly fewer number of people within biopharma who think that, uh, yeah, this machine learning thing is a fad that will go away, or maybe that machine learning is going to be this thing that helps you in a particular Point, um, area, like, you know, x-ray crystallography. It can improve this narrow little vertical, but that's pretty much what it's going to do, and my analogy is that it's not like x-ray crystallography. It's like computers. You're going to use it everywhere, and it's going to be transformative everywhere. It's not going to be the silver bullet unless you figure out how to use it most effectively, um, but, but the opportunities are pretty much endless, um, across the entire process from beginning to end. So with that, how did we pick what, um, what we end up working on? You know, I thought about this, and you could divide the process, as many do, into three large chunks. One is the original biology discovery, which is what targets do we employ in what indications, and maybe in what patient population is kind of the first chunk. Then there's turning those ta…

AI assessment note: “So with that, how did we pick what, um, what we end up working on?”

Q COVID that we can really expedite both drug development, vaccine development, everything, right? We, we did things in six months that normally would take 10 years during COVID because we decided we could do it. How much time do you think an ML first company or ML first approach can really cut out of drug development? Or do you think it's purely a regulatory issue in terms of those timelines?

A I think that's a complicated question, and I think has elements of both. I think first, there does need to be a discussion with the regulators around, uh, what, what might be feasible, um, from, from a regulatory approval perspective about different kinds of biomarkers. There's also elements that I think are very legitimate questions, like how do you collect the relevant biomarker in a robust reproducible way from different patients? What kind of lab protocols one would need in order to have that be collected robustly? That's not always trivial. You can have the most beautiful, sophisticated biomarker that works in a very carefully designed research environment, and it's not going to work in the wild as part of the standard of care. So I think the regulator does have legitimate questions that need to be answered there. Um, so, uh, and so, but I do think that with that Um, with that discussion, and especially if you can front load that and have the discussion with the regulators, not at the very end when you show up with your whatever NDA package, but in an earlier state saying, okay, what would it take in order to make this, uh, reasonable from your perspective? What questions would you like to see answered? I think there is a legitimate opportunity to actually accelerate things. Having said that, I think one needs to be realistic about what is and is not feasible. In COVID, we…

AI assessment note: “I think that's a complicated question, and I think has elements of both.”

Q We have a lot of, um, tech people, uh, engineers, founders, researchers, as listeners. Um, what would you be working on if you weren't working on in secret? Like, what else are you paying attention to in digital bio or AI, assuming people are, uh, attuned to having that culture of openness and respect and constructive thinking?

A So, um, I think that's a great question, and this really is the golden age of, um, AI and machine learning, and there's just so many different ways in which that can be deployed in useful ways. I mean, my personal compass has always been that we should be deploying this towards areas where we make life better for people, so I've tried to veer towards applications that are really about Improving life, improving health versus, you know, selling more ads or whatever. Not that, you know, I mean, I guess selling ads is good too, but, um, but for me, it's really about how do we make life better? Um, so I think there's a lot of really exciting opportunities right now. I think that intersection or that interface, if you will, between biology and technology is one of the richest areas that exist today because each of these fields has been making A huge amount of progress in its own right. We all hear about, you know, AI much more in the news because of Chad GPT and so on, and it's something that everyone can really relate to and understand, but the toolkit that biologists have available to them with CRISPR and pluripotent stem cells and the huge advances in microscopy and such are maybe not quite as visible to the everyday person, but they are equally dramatic, I think, in terms of what they unlock, and so bringing those two together creates so many opportunities for Change in, uh, not …

AI assessment note: “in agriculture technology, in, um, environmental technology, in energy, in biomaterials”

Q went to Calico right after that, right? And I, you know, a few years after that. I'm sort of curious, what made you decide to go into Calico? Because you mentioned your career was split between Life sciences and computer sciences, and so you went down the computer science online learning route, and then you went back into biology, so I'm a little bit curious what drove you back in.

A So actually I'm going to go back and answer the earlier part of that, which is what took me to Coursera in the first place, because I think it feeds into what took me away. Um, so the, throughout much of my career at Stanford, I had an increasing sense of urgency that I needed to make an impact in the world, a real impact on real people, not something that was at one step or two steps removed by training great students and having them go and do amazing things, but by something that I get to experience myself. And so when the work that I was doing at Stanford, On technology-assisted education gave rise to the launch of those first, um, Stanford massive open online courses, and we saw just how much impact those were having. I felt like it was too amazing of an opportunity to pass up and just assume that if I didn't do this, then somehow other people would take on the flag and carry it forward. I felt like there was an incredible need to go and actually have that impact myself and make sure that it was done right, and so that led to what My departure from Stanford on what was supposed to be a two-year leave of absence to go and found Coursera, and I had the full intention to go back to Stanford at some later point and resume my faculty life. Um, that didn't happen. Stanford has a very strict leave of absence policy, and when they came two years later and said, so are you coming ba…

AI assessment note: “I'm going to go back and answer the earlier part of that”