On this episode, we are taking a pulse-check on the state of the intersection between biology, healthcare, and technology with two scientists that sit at another intersection, that of academia and industry: Alexander Marson and Patrick Hsu, who are professors at UC San Francisco and UC Berkeley, respectively, who both use cutting edge gene editing technology to create next generation therapies, and are prolific biotech founders.
Patrick also recently co-wrote an article on Fast Grants, one of the speediest sources of emergency science funding during the pandemic, which you can read about on our media site Future.com.
But in this conversation, Patrick and Alex discuss — with a16z bio general partner Jorge Conde — what is different about this moment in bio.
Lauren: Hello and welcome to bio eats world, an a16z podcast exploring the ever growing intersection of biology, healthcare and technology. I’m Lauren and on this episode we are taking a pulse check on the state of this intersection with two scientists that sit at another intersection that of academia and industry. Alexander Marson and Patrick Hsu, who are professors at UC San Francisco and UC Berkeley respectively, who both use cutting edge gene editing technology to create next generation therapies. And are also both prolific biotech founders. Patrick also recently co wrote an article on fast grants one of the speediest sources of emergency science funding during the pandemic, which you can read about on our media site future.com. But in this conversation, Alex and Patrick discuss along with a16z bio General Partner Jorge Conde, what is different about this moment in bio.
Jorge: It feels like the world of bio has entered a phase of dramatic acceleration in a way that the entire world can see very visibly, very tangibly. So over the course of the last year, with the COVID pandemic, we were able to develop a vaccine in about a year based on an entirely new technology, the mRNA based vaccines, and that, hopefully, is what will enable us to turn the tide on this pandemic. And so one of the things that I wanted to start with is a question: are we in a new era when it comes to biology? Is this time different? And if it is, why, and how?
Alexander: I think biology has been claiming for a while to be on the brink of big breakthroughs. And, you know, I think maybe there’s been some lingering frustration that we declared war on cancer decades ago, and haven’t won that war yet. But I think that there are these moments where things become nonlinear, I think that we’re in an era where many of those sort of basic discoveries have been made. We know the building blocks, we know the basic rules of how genetics controls the behavior of cells. And all of a sudden, we’re just seeing this sort of explosion of technologies that are iteratively accelerating discovery. And the discoveries are pushing us into a phase where it’s actionable, where all of a sudden, we have the tools, and we have so many different avenues to now go after treating disease.
Patrick: We’ve spent the last several decades since the dawn of modern molecular biology, just trying to figure out the vocabulary of the language of biology. And it’s sort of like when you go and visit a foreign country for the first time in you don’t really speak the language. You can sit there in this nice cafe, you know, watch passers by and just hear them talk, you slowly start to try to repeat the words, right, and get a sense of what the cadence is what the rhythm is. And we’ve been building our understanding of that type of communication. And biology has been really always built on measurement technologies, starting with the ability to actually image the living structures inside of cells with microscopy, analyze things about chemically, and then sequence that genetic information itself. And really over the last, you know, decade or so, there’s been a massive acceleration where we’ve been able to move really beyond just measuring things, and being able to perturb things at scale.
Jorge: Is that the difference between being able to shift from observing biology to interrogating biology, where one is almost like passive and the other one’s participatory?
Patrick: Yeah. So instead of just learning the vocabulary and the grammar, now we’re trying to speak, they’re very short halting phrases, right? It’s like, I have my travel guide, and I’m trying to figure out where the bathroom is, or can I get the check, please. But this is really how we can start to actually interact with the different players in this biological world. And we’re just starting to be able to have a little bit of a conversation. And soon I think we’ll be able to no talk the way that we’re speaking out, but inside of itself.
Jorge: So I like this analogy of learning the language, learning to communicate. Do we think we understand the rules of grammar, yet? English has a lot of rules, and then has a ton of exceptions to those rules, right?
Patrick: So I think these rules are a common framework that we agree makes sense. So if you are learning English as a second language, you will make mistakes, and some things you say will make sense. People will get the gist of it. And other things you’ll say, doesn’t really make sense. And you get that type of feedback where someone doesn’t understand and when we do these experiments, we got a very similar type of feedback. Did the experiment work? Are we interpreting things the right way? Or do we fall flat on our face and we run, you know, months of western blots or our They seek experiments and we don’t get, you know, an answer back from the cell. And so we go through this process to start to understand each other. That’s a lot of what I feel we do when our scientist has just tried to understand what’s going on and, you know, start to have this conversation,
Alexander: we are sort of just at the brink of imagining all the ways that these technologies will come together and teach us new lessons about human biology. I think one of the things that we have to think about to get to what’s next is how do we structure this? I think that this new era of biology requires new modes of interactions of scientists. And it’s something I’ve been thinking a lot about, and very serious about, I think that you don’t want to dissolve individual labs completely. There’s something magical about an autonomous lab that can just explore and be nimble to try new experiments, new modes of inquiry. But I think no individual lab can seriously tackle this multidisciplinary challenge, and use these cool technologies in a way that really takes advantage of their power. And I think collaboration is not sort of a nice window dressing anymore. Serious discoveries are more and more going to be made by groups of labs working together, and sharing credit and enjoying the process of collaboration, because it’s going to open up so many more doors than any individual lab could do.
Jorge: So you’re talking about reengineering the enterprise or scientific discovery in some fundamental way? Are there other fields we can look at that have cracked that problem? Are there fields, whether it’s in science or beyond that you think, is a good model for how this could work?
Patrick: I think computer science has a lot of models for this, for example, the open source software movement, you see this with ad gene with nonprofit plasma repositories, and all these ways of sharing, right? I think one of the things that’s really helped CRISPR explode is, every time we publish a paper, we put the fundamental constructs in this centralized repository. Hundreds of labs around the world can and do order these things, test them in their lab in the matter of a week. And this has really transformed how we think about how we can share things and share information beyond just giving a talk or publishing papers, I think there’s a lot of effort into writing protocols, making videos, creating workshops to help people analyze their single cell data, or do genome editing, or run in vivo experiments. And I think there’s just so much more knowledge and a lot more knowledge sharing. And we’re starting to really reimagine what the process of discovery can look like, I think we’re starting to create new types of career models and incentive models. For trainees, it really wasn’t that long ago, that if you, you know, were finishing your PhD, you had a few options. You could do a postdoc, go work in Big Pharma, or, you know, there might be other options like going into law, or doing science communication. But staying in the primary seat of doing discovery was frankly, really economically challenging. And I think a lot of these different models are starting to shift and blend together with the rise of biotech innovation, the capital to actually enable people to make big bets, different ways that you can communicate and move between the academic and industrial settings. Ultimately, I see these as different sides of the same coin, right?
Jorge: Well, actually, let’s pull on this thread a little bit, because you both are incredibly active in your academic labs. But you’re also both very active in founding and launching new companies. Can we talk a little bit about how you think about what effort goes in which bucket to the extent that there are, in fact, two different buckets anymore?
Patrick: When I think about what should we be doing as research projects in my lab? What are things that we should be spinning out as companies, to some degree, I myself am falling prey to this dichotomy, right? I think they’re, you know, contexts where the science should be in the discovery mode, where simply just more capital, more money, better processes doesn’t necessarily accelerate you. Because you’re actually limited more by creativity. And by needing to iterate and fail more. But sometimes there’s a lifetime in a project where you really need to make something work, iterate really deeply on a set of variables and develop a process that’s turnkey. It’s reliable, it’s robust. For example, that’s what has helped genome sequencing and genome editing explode. In the early days. These things are really hard; today, my undergrads could do this. But what I have started to, you know really appreciate more over time, is you actually have to work by backwards from a central problem that you really want to solve. Otherwise, they’re just too many engineering variables. And it’s hard to actually prioritize them. If you want to look at efficiency or specificity, it can be really cell type dependent, it can be really delivery dependent. And the more clearly you can articulate the problem you’re trying to solve, you can find the different contexts that match where the science needs to be. Right? When I was finishing my PhD, my PhD advisor, spin out company from the lab and I was thinking about working there. I talked to my thesis committee, who really advise me “stay in academia, you have academic momentum, you should just go and start a lab.” But I want you to understand the other side of the coin. I feel like a lot of faculty have a lot of consulting and advising experience, right. But there’s no substitute for really putting your own neck on the line. And I’ve always been that type of person where I just, I want to actually see it. And I was that Editas for a year, year and a half, not that long. But in enough time from the beginning for series A Series B and S want to really scale from a few people in a conference room to you know, a public company that had multiple sort of programs that we were developing, and it was a fun ride. And I think I actually bring back a lot of these management principles from industry to try to help improve the way that academic labs are run and structured, and how folks can actually kind of organize themselves more efficiently.
Jorge: So, Alex, you have founded several companies. How do you decide what should be a company and what you think has potential as a startup, versus what you should continue to push and pursue within your lab?
Alexander: I think that discovery can happen in both places, translation can happen in both places. I think it’s a matter of flavor. And, you know, there is a flavor that is really important to me in the lab, which is curiosity driven discoveries. Let’s push the boundary of what’s possible, let’s see the outer limits of what we can imagine and see if we can achieve it. And that is the joy of academia. That’s why we put up with the frustrations of academia, because that is addictive to aim for even small new discoveries that are new pieces of knowledge. I think that there’s absolutely discovery that happens in industry, but the flavor is a little different. The flavor is can we have a discovery engine that accelerate just towards products? Of how quickly can we get to a specific product that is scalable, that we will get again and again and robustly manufactured towards a customer or patient, depending on what the product is? I think that you know, as this dichotomy breaks down, it opens up interesting new avenues in between. So I have recently launched this new Gladstone UCSF Institute for Genomic Technology, which is trying to take discoveries, and see if there’s opportunities to translate those into really sort of proof of concept innovative, new immunotherapies. But the idea there is, again, let’s see what’s possible, can we take all the creativity that’s happening in the lab and come up with a new proof of concept of a type of immunotherapy cell therapy, and get it into a small number of patients to open up the avenue of what’s possible. But I think that all that is still quite distinct from ultimately a company that, even as it discovers, has an ultimate responsibility to deliver a product at the end of that process.
Jorge: So tell me the story of how you both came together to start a company and how that experience has been.
Alexander: Not on my version, Patrick. Let’s see Patrick is, early on, was a wonder kid biologist doing his PhD abroad. It was looking around for his next opportunity. And we started talking about where you would do that. And I turned to Patrick to ask his advice about CRISPR early on when I was just starting my lab. But we ended up in a small room also with Jacob Korn, who’s one of our co founders, and a number of investors, but notably Blake Byers from Google Ventures. And we started really sort of exploring what was the whitespace in CRISPR? What was the high risk thing that we weren’t sure if it would work or not. But if it worked, would really open up avenues that would transform how CRISPR gets used in clinical contexts. And the question that all of us quickly settled on was delivery: how do we get CRISPR into the right cells in the body to get around these problems that have plagued cell and gene therapy around manufacturing and cost? Can we democratize CRISPR editing and make it into a simple injectable drug? And that is the sort of birth story of Spotlight Therapeutics. And this was not a product ready to go, this was an idea that needed a company to bring it into reality.
Patrick: So this gives you a sense of how long we’ve been trying to really experiment on these different models of how companies can get started. And how labs can get started. Spotlights started out as a research project, right? What are the big challenges in the field? How can we make this more like a traditional drug and less like this highly personalized, highly boutique medicine that’s, you know, difficult to access and difficult to manufacturing? But it’s something that, you know, we can actually just take more easily. And we really coalesced around this problem and thought: can we develop a really focused group of scientists and drug hunters to ultimately make an applied therapeutic product, but solve really fundamental mechanistic questions around? How do you actually do in vivo, non viral genome editing? And we’ve gone on and started other companies, you know, separately, continue to collaborate in our labs. But I think we’ve always also had a very open mind about what is the lab, and what’s the right way to start it? Both Alex and I started our labs, more or less out of our graduate training, and have really been interested in how can we innovate on the more traditional academic model? How can we innovate on the more traditional biotech model. We sort of set up the system where you have professors and research groups and incredible trainees, and you work together to solve problems, file intellectual property, and then license those two companies, right. But I feel like IP is talking out so much and set up as the value transferred between these systems. But ultimately, when I’m in a company context, wearing a company hat, like IP needs to happen, but it’s not really about the IP: what you want is people. What you want is know-how, what you want is culture. I think when I was at a class, I was one of the members of our culture committee. And coming from academia, I thought this was all a little a little soft, right? But what I realized is he nailed the culture, you’ll nail the science, and then everything else will follow. People make it very complicated. It’s very simple.
Jorge: I think that’s fantastic. I want to pull on a little thread here, this idea that when there is a powerful new technology, there is a value and an almost an obligation to explore the white spaces that are created by that powerful new technology. And so it’s really interesting to hear you both describe the process of how you thought about: “Okay, well, we have this new technology in the form of CRISPR. Now, how do we address these whitespace issues that emerge because this technology now exists?” If you sort of look forward and future, what are some big white spaces that you think remain unaddressed today?
Patrick: For me, better animal and disease models is one major area that has ongoing innovation. And I think it’s going to continue to really explode; moving beyond the traditional genetically tractable animals, like mice and rats and zebrafish, right? I think in the gene therapy field, we’ve really pioneered larger animal models, cats, ferrets, dogs. And then obviously, in gene and cell therapy, broadly, there’s a lot of work with non human primates. But NHPs, are actually really diverse. They’re old worlds and new worlds NHPS that recapitulate different aspects of human biology, whether it’s the brain or the immune system. And some of these have genetic variants that are analogous to genetic variants in humans that cause disease, and being able to use technologies like CRISPR, like single cell sequencing, to actually start to map these types of new models, understand how to manipulate them and make them genetically tractable, and turn them into more accurate models for human disease is one, I think, incredibly exciting frontier. And that’s going to be where we take all of these different tools in their sandbox and play, the measurement tools, the imaging, the single cell sequencing and the perturbations, right. Doing this in the context of more complex models.
Alexander: I think one of the next frontiers where we’re gonna see incredible progress and where we need progress is understanding how cells interact in tissues. I think we’re really good at diving deep into what how an individual cell is regulated and controlled. The next question is how does what’s happening in one cell influenced the neighboring cells? You know, whether that’s an immune cell interacting with a cancer, or multiple different cells inside a tumor, or neurons? I think that we’re just starting to imagine the ways in which cells talk to each other and understanding how those inter cellular communications can fail and disease and also how we can potentially target that with a range of different therapeutic modalities. I’ve just finished a great collaboration with Jimmy Yi and Eric Tao to start developing technologies to look at single cells interacting in tissues. And we’re now want to double and triple down on that to build a program. And some of this might be in academia, and some of this might be in industry around how do we build out these technologies to start really tackling these questions. And the other whitespace that I think really just has to be talked about, from a translational point of view is equity and access to these therapies. For a number of reasons, these resources at the technological power biology are still being narrowly focused on a relatively small set of diseases that disproportionately affect the US. And sometimes you end up with philanthropy putting a very narrow focus on certain diseases that we’re aware of. And I think we really need countervailing forces to make sure that we’re also thinking about diseases that we’re not seeing here in the Bay Area, and ways that we can take CRISPR technologies to understand infectious diseases, diseases of other regions of the world, that are limiting lifespan and limiting people’s quality of life. And I think that the ability to bring drugs and CRISPR technologies and all the things that we’re thinking about, for example, in cancer, to diseases that affect other parts of the world. disproportionally is another major, major whitespace.
Patrick: One other whitespace, I would add, that we’ve touched on today, but I’d like to expand on is really the human element of how discovery happens. Science is done by people talking and thinking together and collaborating and creating. And making the lab a place that’s more human centric is something that I’ve spent a lot of time thinking about. Fundamentally if people do their best work when they feel happy, and you feel happy, and creative, when you feel safe and comfortable, right? People talk about lives being these really sterile intellectual environments, flow, confusing and metallic looking equipment and all kinds of weird plastics around and making it more of the human place where we can grow together. And we’re fighting for bigger goals beyond individual training, or individual papers, and actually make discoveries together. I mean, that’s, that’s what I love about science, I can think through problems deeply and openly with my friends, right? And hopefully, you know, help out in some way.
Alexander: It’s something I think about a lot is that there’s this sort of stereotype of the science nerd, that gets propagated, which is like, a loner, who likes to memorize formulas and sort of like, know the right answer and remember that. And that’s so different from the people I see her succeeding in science, who like to work together, are communicators, and are driven by curiosity and exploring the unknown. In an open minded way, it’s so different from the very, very limited stereotype.
Jorge: You’re both great examples of that. So we’ve spent a fair amount of time talking about, you know, how the cycles of innovation are accelerating, how you know, some of the advances we see in this field compound on top of each other, which takes us beyond linear advancement and an acceleration. How do you folks think about what we’re seeing in terms of the rate and speed of translation? Because that historically, has always been the bottleneck? Right, a meaningful amount of time passes before that discovery goes from bench to bedside? When it comes to things like therapeutics? What do you think is the state of translation today? And what do you think will meaningfully accelerate that? Or will that just always be a natural speed limit to how quickly you know we can get innovations to have positive impact on patients?
Alexander: I don’t want to give the impression that I think that the only things that are out there are cell therapies and gene therapies. But it’s something I think about a lot in this space. I think translation is wildly different than what we saw in the past with small molecules. The process to develop a new small molecule is inherently more time consuming and laborious than what’s required to come up with a new gene edit to make a cell behave that we want. And as soon as we discover the gene modification that makes a T cell better at translating cancer, there is a very direct path to taking that cell in manufacturing in the clinically compatible way and putting it into a patient. What I’m learning is that that doesn’t happen automatically. That requires a really specialized group of people that know how to do that. And, you know, I’ve now been privileged to work with a team UCSF that’s building up this capacity. The chancellor at UCSF is invested in a living therapeutics initiative to build a team and a physical infrastructure to start manufacturing this. But the power of that investment is that it is not for one product, it is for cell therapies. And that was learned to make a car T cell with you know, gene editing makes a T cell directed against a tumor target. The same manufacturing for that genetic will also be useful for gene replacement. In a regulatory T cell therapy to treat autoimmune disease, there is a modularity of just changing out a guide RNA and a cell type that is inherently faster. And so we need the teams that can build on that modularity and move from one product to the next product nimbly. And we need to be conversant about how this is going to happen through interactions with regulatory agencies. I think that there is an openness in regulatory discussions in theory, and we’ll see how this plays out over time to start seeing that clinical trial might not be for just one gene editing in the future. There might be baskets of gene edits that get addressed in a comprehensive way by the FDA. And that kind of basket approval for clinical trial will allow us to try more things and learn lessons and hopefully help more patients faster.
Jorge: Yeah, and the hope is, you know, I think what we’ve seen with the example of the COVID vaccine, that the regulatory apparatus was able to adapt and adjust to get us, at least an emergency use authorization within a year process that, at least historically, people assumed would take many, many, many years to get to.
Alexander: And I think that the further analogy, I think the COVID vaccine is a great roadmap here. I think if we need booster shots that cover the variance, that’s where we’ll really see this kind of ability for modularity. It’ll be really interesting to see how quickly we can get to version two, version three, to make sure we’re prepared for variants that may come up and escape immune protection. And I think that the FDA has signaled a willingness to be nimble to make sure that we can get those approved fast and keep us productive.
Patrick: Yeah, and I think we’re gonna see this across different classes and types of genetic medicine, right. I think we’re taught to think about drug discovery as this forward linear process: we do preclinical studies, ind enabling, and then Phase One, two, and three. But I think oftentimes, you know, it’s really important to also not just think forward, but think backward, right? As Alex was saying, if you can anchor based on your ability to deliver these gene editing or perturbation molecules to a given tissue, or to a given cell type, specifically, you can just swap out the guide RNA, and go after different genetic mutations in the liver, or in bone or an eye or muscle, right. And this gives us I think, unprecedented determinism compared to small molecule campaigns for other types of trucks, where you would have to find a composition, and then also find the genetic target, and then do all the safety and toxicity studies, right? I think the evolving conversations with these regulatory bodies is going to really be advanced. Also, by measurement technologies. It’s the same thing that we’re seeing that’s driving discovery, there are all these swirling conversations and the gene and cell therapy world around. What does immunogenicity look like? What does safety look like when you’re making irreversible covalent modifications to DNA? How do you actually measure and understand the impacts of these long term? What’s the impact of expressing these things for years in the body, and being able to actually develop the right platforms and pipelines for measuring off target effect, measuring immunogenicity measuring these transduction and delivery technologies, I think are going to allow us to actually answer these blackboard questions with data.
Jorge: I totally agree. I think there’s a lot of reason to be incredibly optimistic on multiple fronts, and our ability to measure and test and regulate these innovations, is going to be critical for our ability to make sure that we can keep up with the state of innovation. Given where we are in this new world. What advice would you give a future Patrick Hsu or a future Alex Marson that comes to you and says, “Hey, I’m interested in maybe working at a startup” or “Hey, I’m thinking about staying in academia,” like what advice do you give to the next generation of scientists that are coming up behind you?
Patrick: The way that I do this for folks in my lab, is what I would call almost science therapy. It evolves from pretty emotionally vulnerable conversations. And ultimately, what I’m trying to do in these chats is to try to understand you understand where you come from? Why do you do this? What motivates you? What makes you light up? What are the things that you don’t like? What are the experiences that you’ve had that give you this heuristic on how things are and how things might be? And do they match up with my own heuristics and experiences and perspectives, and trying to figure out a way to meet in the middle. And I think a lot of people kind of get these ideas in their head of “Oh, I should be doing X or Y,” without necessarily really thinking it through really systematically. And this is actually shocking, right? Because in the process of doing research, you’re trained to be hopefully really thoughtful and careful and systematic about your problem. And people don’t, oftentimes I found apply that lens to themselves, right, and their own career developments and their own, like, maximization of potential. And ultimately, it just comes from having these conversations with folks on what do they really want to do. And it might be primary research, it could totally not be. I actually feel very open minded because I feel like I could be doing many things. If I weren’t a professor and a researcher, I would probably be in music talent scout, it’s a big world out there.
Alexander: You know, there’s been many stages where I needed advice and went around to people, and have benefited from people giving me some really good advice. But I also realized, over time, generally people are giving advice based on their own experience. And they’re saying either what worked for them or what didn’t work for them. But ultimately, my biggest advice is, you’ve got to just do what you’re passionate about. And like, there’s no formula for success, that is more important than following passion. Like when people do what they think they’re supposed to do. Like, this is gonna be the next hot thing. This is the strategic thing that I should be doing. Those are fundamentally eliminating things and put caps on success, I think, rather than enable it. I don’t know what’s next. I think that the people who are going to show what’s next are the people who are the most curious and the most passionate about what they’re doing.
So you know, if the last year has shown us anything is that on the dark cloud side of things that you know, some of the biggest problems that we have, as a society will be biology based. On the silver lining side, it’s been wonderful to see how impactful we can be when we thoughtfully find ways to discover and develop new technologies rooted in biology. As we’ve seen with our response to the pandemic, one of the things that I’ve really enjoyed about this conversation, is seeing the work you have done, the career paths you’ve taken, the work that comes out of your labs, that is so varied and impactful in so many fields, makes me doubly optimistic that the future is indeed bright. Thanks to folks like you that are contributing so much to what we understand and what we can do in this world. Thank you so much.
Patrick: Thanks so much for having us on.
Jorge: Thank you, Alex. Thank you, Patrick, for joining us on the a16z podcast.