[00:00:00] Speaker A: Foreign.
[00:00:15] Speaker B: Welcome to the latest episode of the Few and Far between podcast. I'm your host, Chris O'Brien. The phrase make tools, not toys is an important mantra for the biotech industry. Today's healthcare innovations need to be more than just cool technology focusing on critical solutions for understanding and treating complex diseases and disorders. My guest today is Marc Lajoie, CEO and co founder of Outpace Bio, a Seattle, Washington biotech exploring the curative potential of protein designed and cellular engineering enabling cells to make the right decisions inside the patients who need them. In today's podcast, we'll discuss Mark's journey from his time spent in the visionary lab environments of George Church and Nobel Prize winner David Baker to his current work moving the therapeutic paradigm of cell therapy into measurable progression free survival for cancer patients. We'll also hear his advice for budding biotech CEOs and how to manage funding opportunities in a difficult market. This is especially exciting from a guy who just closed $144 million funding round. This is a special double episode and I hope you enjoy it. Okay, let's start the podcast.
[00:01:28] Speaker C: Mark Lajoie, you are the CEO of Outpace Bio. Welcome to Few and Far Between.
[00:01:33] Speaker A: Thank you. It's a pleasure to be here.
[00:01:34] Speaker C: I've been really looking forward to this conversation. You have been on, I don't know, something of a rollercoaster of late. You've had a massive funding raise. We'll get to that a little bit later. But let's talk about Outpace and its origin at first. So you're creating de novo proteins that can be used to reprogram immune cells. This sounds like science fiction technology. Tell us a little bit about the initial insights that led you to found the company.
[00:01:57] Speaker A: What got you excited about this space background here? I did my stock in David Baker's lab, who actually just won the Nobel Prize. So kind of a fun little couple of weeks that we've had that. And I joined the lab at the same time as my co founder Scott Boykin. We both came in with this vision that we wanted to design biological function and proteins are the biological function. And so that was why we went to David's lab. He was and is the world leader in that. That was 2014. It was a time when we could design proteins that folded into exactly the right shape, but that didn't do anything. And so we really focused on developing the ability to design moving parts and precise interactions and spent the first few years doing that. And then once we had figured that out, then we started applying that technology to Useful problems. One of the collaborations that we struck up was with Stan Riddell at the Hutch, who's one of the world leaders in T cell therapies. And we were initially working on solving the T cell targeting specificity problem with him. But we pretty quickly realized that protein design and cell therapies had way more to offer each other than just that one problem. And that kind of, you know, started this whole more recent history off. And the insight that we had was that cell therapies finally were showing a potential to move from a therapeutic paradigm where we're measuring efficacy in months of progression free survival, to a world where we're measuring efficacy in the numbers of patients cured.
[00:03:23] Speaker C: Extraordinary.
[00:03:24] Speaker A: It's so exciting. It's so exciting. But at the same time, Stan was pretty frustrated, even after all of the success that he had. Juno and people may know that Brianzi actually originated in his lab. He developed the construct for that CD19 car T. But in the end, that tremendous potential patient outcome really only applied to less than 5% of cancer patients, specifically to a subset of patients with blood cancers. And so, you know, I think there was this kind of initial hope that, hey, we, we have a silver bullet here. But there was pretty quickly reality set in that there's a lot more that we need to do to be successful in solid cancers and also some of the, you know, remaining heme malignancies that we're still working on. And our insight was that there had been a lot of effort put into how do you better manufacture cell products, essentially what do you do to cells outside of the patient? And people have been kind of ignoring the fact that when you put T cells into a patient, they're inherently programmed by the immune system to turn off.
[00:04:28] Speaker C: Yeah, okay.
[00:04:29] Speaker A: Essentially, our immune system is designed to turn those off. Well, it's designed to be able to deal with infections, and it's designed to avoid autoimmunity. It's not designed to prevent cancer. And so that's kind of a long way to get back to this point that we had been starting to understand as a field. What are the actual mechanisms, the actual barriers to efficacy in the majority of cancers. And Stan's frustration was that he couldn't find somebody who could make the proteins who could solve those problems.
[00:05:01] Speaker C: So he felt like he understood what the challenge was, but he couldn't find anybody to build the tool to solve that. Is that right?
[00:05:06] Speaker A: Absolutely. Yeah. And David comes from the exact opposite side. Right. David comes at it from, I have these tools that can solve these problems that nobody's been able to solve before. I need to team up with the best minds in the world who are trying to solve these problems so we can put these two things together. And that's kind of how I've always thought about doing science as well. It's. You bring powerful technology to a problem that is well understood, then you can really make a difference there.
[00:05:32] Speaker C: Yeah, that's a fairly memorable phrase. I like that a lot.
[00:05:34] Speaker A: Yeah. So basically, the point here was that protein design finally gave us the opportunity to design past all of the baggage that evolution has given us.
Because the way that we've had to do this previously is by, you know, essentially doing protein engineering where you can flip the side chains of amino acids, but you're really stuck with, you know, the original function of that protein with some tweaks that you can make.
[00:06:02] Speaker C: The core is the core kind of.
[00:06:03] Speaker A: Yeah. And so with protein design, the thing that is so empowering is that rather than hoping that we don't, you know, mess up the backbone of the protein, we're intentionally redesigning the backbone of the protein. And that gives us, you know, because proteins get their function from their shape and from their movement, that gives us the opportunity to have a lot more control over the function of these proteins. That was really the impetus that led to my previous company, Lyle Immunopharma. And then we eventually spun our team out of Lyle in order to form outpace as a way of really being able to scale the impact of the technologies that we were developing.
[00:06:40] Speaker C: Back when you're in the lab or when you're forming Lyle, are you already thinking, hey, I think there's a way to apply this technology to solid tumors? Or did that seem, you know, like. Did that seem like science fiction to you, or was that, no, no, no, I think this is going to work? Did you know that right away?
[00:06:53] Speaker A: Well, that was the whole point of Lyle. We had actually set out to work on solid tumors, not to initially work on heme malignancies. I think a lot of the technologies that we've developed could actually be extremely powerful at basically taking us from this tremendous position that we're in today with car T cells for heme malignancies and actually making it even better. But what we were trying to do in the early days of Lyle, and it's the same goal that we have at Outpace, was to take this technology that just really did not work very well in solid tumors at all, which are more than 90% of cancers, and actually open the door to the same type of efficacy that we've been able to see in those very small set of patients that have tremendously benefited from Stan's work and Carl Jun's work and others in the field.
[00:07:42] Speaker C: Yeah, it's enormously exciting. You've mentioned the Baker Lab and your experience there. You were at the church lab, too, I think, earlier. Those are two storied labs that have actually spun a huge number of really interesting biotech companies out. Will you talk a little bit about what's in the water there or the air? What did you learn, maybe, or what were those experiences like for you? There aren't too many people that get exposure to those two places.
[00:08:04] Speaker A: It was a tremendous training experience. I think I learned some really important lessons from George. I don't know if I've ever heard him say no to something.
The closest that he ever got to saying no to me was I was pitching an idea for a project to him, and he looked at me and he goes, mark, that sounds like a biology project. And this is a technology lab. So he let me tell myself no. But I think he was making a really important point that you can't solve every problem. And also, as a technologist, if you don't understand the problem that you're trying to solve, then you can't develop a technology that solves it. That's come with me for my whole career. I think a couple of other things that George instilled in the lab were projects that seem hard are usually not as hard as they seem. Projects that seem easy are usually not as easy as they seem. So you should probably just work on something that's very important.
And as a result, everything that we worked on it was we thought of, what is the problem that we're trying to solve solve, not what is the cool technology that we are creating to solve that problem, that the technology came from the problem. And I think that's really important because if you're just developing cool technologies, you're developing toys, and you have to figure out how to make them useful, Whereas if you're trying to solve a real problem, then when you're successful with that technology, you've solved the problem. That was the Church lab. And George had a really open approach to collaboration. I had this really awesome spot in the lab that was right where all the rotation students would walk past on their way to his office. And so I got the first crack at almost every rotation student that came through. And so I ended up with an amazing cohort of collaborators to work on the science that I was working on. And I think that was a really good lesson for me about how when you are trying to solve really hard problems, being able to collaborate with people who have different knowledge bases, different expertise, maybe even different working hours can be really valuable. And so that was something that I was able to take with me to the Baker lab, as well as into the companies that I've founded.
[00:10:11] Speaker C: That's great. Anything specific about Baker that you want to talk about?
[00:10:13] Speaker A: Yeah. So David is a little bit more structured in an amazing way because somehow he, even with hundreds of people in his lab, he's able to keep track of every single project that's happening with incredible detail. And one of the things that was really frustrating to me was that I had this impression that I was a really good experimentalist and that I could design experiments in really creative and efficient ways. And almost every time that David was like, it seems like you're making this more complicated. Just do it this other way. He was almost always right. So, yeah, again, you know, visionary PIs wanting to work on important problems, not wanting to work on things that were not important. And I think that David had a different but equally powerful way of thinking about collaboration. He sometimes describes his lab as like a brain where every. Everybody's a neuron and you have all these connections. When you get the right, you know, synapse forming, you can actually do, you know, incredible science.
[00:11:16] Speaker C: Magical. Magical stuff happens. Yeah.
[00:11:17] Speaker A: So.
[00:11:18] Speaker C: And.
[00:11:18] Speaker A: And I think in both labs, there's a commitment to doing amazing science, which. Which I think that has to drive everything that we do.
[00:11:24] Speaker C: Do you think that's unusual, the experience of those two labs? Do you think it's a norm in other labs? I mean, I'm sure there are others, but the idea of both taking big, aiming for collaboration, those are two themes that you just hit for kind of for both labs.
[00:11:36] Speaker A: Oh, man. I'd like to think. No, I think that there's a lot of really good science that's happening across academia. I think the scale of those labs is certainly larger. They have a lot more shots on goal. Sure. They're able to recruit really amazing talent.
[00:11:53] Speaker C: Yeah. The brands become powerful at a certain point. People want to come to these places, I think.
[00:11:57] Speaker A: Yeah. George liked to talk about how the measure of his success is if he could disappear for a month and the lab could keep on going and do just as impactful science. So that was also something that I thought both of them did extremely well. They have always been so fast to share credit, and they lift up their grad students and their postdocs, and it creates. I Think to your question, why have people been able to start so many companies? Because they're not trying to do it all by themselves. They're empowering their grad students and postdocs and really making sure that not only are they having them do the work to set it up, but they're also making sure that they have the skin in the game to benefit from it. So that has been awesome. And it's also a good role model for me to have to see, hey, this is the good that can come out of sharing value and sharing credit. And so it's something that has always been part of how I try to operate. Because I see not only is it the right thing to do, there's a lot of upside to it.
[00:12:51] Speaker C: There's a lot of value. Yeah, there's a great life lesson there, isn't there, that people who encourage others and share credit tend to attract better quality collaborators or followers, and then that results in better work. And then there's a flywheel that eventually kind of kicks in. And certainly you worked at two extraordinary examples of that. Okay, let's talk a little bit about the tools. So when in your chronology, did AlphaFold sort of cross your radar and blow your mind, I assume. And then will you take us through the development from that to openfold and how you think about protein design?
[00:13:22] Speaker A: Yeah, so we always knew that AI was going to be impactful in this space. The time when I was in the lab was pre I AI explosion. And I think that both Scott and I would need to admit that it happened faster than we thought it would and more impactfully than we thought it would.
[00:13:40] Speaker C: You're in very good company there.
[00:13:42] Speaker A: Yeah, I think it was kind of almost overnight, and I think there's a lot overstated about what we got from it, but there's also a lot understated as well.
[00:13:50] Speaker C: Interesting.
[00:13:51] Speaker A: And so, I mean, for sure, it's an extremely powerful tool. There's still a lot of value in structural biology that we still need the crystallographers and NMR specialists out there, and we need also those people to help make sense of the biology that's happening. But, yeah, I think we were ready for it and we jumped on it as soon as it became a reality. Part of the reason that we were able to do that was again, coming back to this collaboration approach that we have where we weren't going and trying to build our own protein design and protein structure prediction models ourselves. We have kept close relationship with David Baker's lab with the Institute for Protein Design more broadly and the protein design community even broader than that. And so rather than trying to build our own generative AI model, we've actually contributed our resources and our ideas to help improve those capabilities. Our view here is we all use Python when we do coding that benefits from people building packages that are open source that other people can use. And it's in a lot of ways displaced languages that are proprietary. And because it's not only free, it's actually faster and better.
[00:15:04] Speaker C: Yeah, exactly.
[00:15:05] Speaker A: So our vision has always been, if we can actually contribute to that and help that be the case, then everybody benefits, especially us. AlphaFold was a huge breakthrough. Three months later, Rosetta Fold was just about as good. And then our team helped contribute to openfold, which is really that Python view of how to. How to do this generative AI protein structure prediction.
[00:15:29] Speaker C: When you analogize to Python, then it sort of makes a lot of sense. But was it controversial at the. Were there people saying, no, no, no, we should really be trying to build on our own, or did it seem obvious to you that there was so much more to be gained by this open source kind of model?
[00:15:43] Speaker A: Well, for us, we always wanted to contribute back. I think one of the things that the pre AI Rosetta community did well was using GitHub, creating a central resource that everybody could contribute back to. And some of the early Rosetta companies that spun out, who unfortunately were not granted access to that central repository, their software actually drifted to the point that the stuff that they developed was not compatible with the stuff that everybody else developed. And so that was something that we fought really hard to change. That was actually something that our team, Brian Weitzner, who's our protein design lead, actually fought really hard to allow companies to contribute back to the code base specifically so that we could make sure that our software stayed, you know, aligned. So that's always been part of our DNA, but that's not true for every company. And how many companies are out there, are there out there that have decided that they're going to build their own AI model?
[00:16:38] Speaker C: Yeah, yeah, yeah.
[00:16:39] Speaker A: And there are reasons to do that. Potentially. Sometimes you need to build a model that's going to be, you know, set up to optimally work with the key types of data that you're generating. I think that we're taking a much broader approach to protein design where we're not just designing antibodies or antibody CDR loops, we're designing much broader classes of proteins that we're encoding into our T cell therapies. And so we get a lot more value out of the more general models that people are building.
[00:17:08] Speaker C: Yeah, that makes a lot of sense. And I think too many. Well, this is editorializing, but I think too many of the folks that are starting to build things now are focused on control of a model because that feels like strategically or economically sound. But we've got all these examples of how hard these problems are to solve. And so the collaborative models I think are really exciting. And you guys are a wonderful example of that. So are you still involved with openfold? Is that still an important part of.
[00:17:33] Speaker A: Yeah, we're one of the founding companies in openfold and we remain an important part of the leadership there. We've been seeing a lot of interest with companies large and small to join that vision as well. And it's just been really awesome to see people have been seeing the value there.
[00:17:50] Speaker C: Very cool. Mark, when you start designing to solve a problem, how long does it take you to build a protein, a testable protein? What does the process look like these days?
[00:17:59] Speaker A: Yeah, so before I jump into that, just to make one other analogy, think of the protein design software as kind of like a fancy challenging to use AutoCAD software.
So if you're going from having to draw your parts on graph paper to being able to use render them in 3D AutoCAD software, then that's a big step. But now there are all kinds of different versions of that software out there. And so the thing that really sets the engineering firm apart is like how good are their engineers at being able to design those parts and very helpful together into the invention. Right. So that's where our unique abilities and knowledge and technologies come in. We have proprietary data sets. We don't have proprietary AI models. Right.
[00:18:45] Speaker C: Does that say that the expertise in the company or the. I guess the most valuable asset in the company then is the expertise of the individuals and the knowledge base of the individuals. Of course the data sets are valuable and ultimately you generate ip, but a lot of that resides between your ears and the ears of others of your colleagues.
[00:19:00] Speaker A: I think it's a unique thing that we have. We've built a tremendous amount of value in specific assets at this point and in code bases. So we have value at all levels. I think that the investments, $144 million that we just raised with RA Capital as the lead was not because of the stuff between our ears, it was because.
[00:19:20] Speaker C: Pretty good point, Mark. Yeah, that's a lot of money.
[00:19:22] Speaker A: It's because of how exciting our program is and how promising our follow on programs are and how enabled they are based off of the platform that we've built, which includes modular assets that we can use across many different cars and TCRs. And it also includes our enabling capabilities. So with that I can get back to your actual question, which is how long does this stuff take you? And you know, essentially from scratch for it can be, you know, depends on the problem, but it's typically about a year to go from an idea to something that's ready to go into a product candidate that's extremely fast. Fast.
[00:19:56] Speaker C: Incredible.
[00:19:57] Speaker A: If you think about what like academic groups do, that's a postdoc project essentially.
[00:20:02] Speaker C: Right.
[00:20:03] Speaker A: Everybody tries to do it faster. It's you know, a five year commitment. And what comes out of that is that usually you're not using the best technology because it takes so long and because you've got a small team working on a big problem. And then number two, it's old biology at that point. Sure, because it's five year old biology that you've been working on or older. And so being able to use our AI powered protein design, we've actually spent quite a bit of resources to build the extremely capable rapid prototyping capabilities that includes like cloning, protein production, lenti production, T cell production, and high throughput assays that utilize those T cells that allows us to, you know, number one, make better proteins faster, cheaper, with higher success rates. And then number two, be able to take that data that we're generating and iterate back into our design process so that we can make something that really functions the way that we want it to.
[00:20:55] Speaker C: That's really fascinating. I hadn't thought about speed. I love your point about you could end up working on 5 year old science because you sort of, you fixed your objectives and then you're grinding slowly towards some kind of conclusion. So you guys are then able to kind of more or less annually reset based on whatever the current biology is and then target something within approximately a year, plus or minus.
[00:21:16] Speaker A: Yeah, absolutely. And a great example of that is our outsmart IL2 cytokine. So this is a cytokine that we've redesigned so that it turns down the activity on the cells that we want to avoid. So like lung endothelial and epithelial cells that drive systemic tox of IL2 tregs that will turn off the immune response when they get stimulated by IL2. And then we've also been able to engineer this thing to turn up the activity on T effectors and NK cells in order to really drive that pro inflammatory immune response in the tumor microenvironment. We, I think people have understood for decades how important IL2 biology is. Yeah, there's been a lot of effort that has gone into making safer versions of IL2 for systemic delivery. And over the past few years, we've seen data readouts from the first companies who were advancing assets there where they essentially knocked out the interaction with IL2 receptor alpha in order to make the safer IL2. And what they ended up finding was that they kind of made a crappy aisle 15.
[00:22:21] Speaker C: Okay.
[00:22:22] Speaker A: And, you know, we recognize that from the clinical data and we also were able to see both from our own studies as well as from others, how people are able to compensate for that loss of activity by getting rid of that IL2 receptor alpha interaction and replacing it with a surrogate interaction that actually dials up the activity of the cytokine on the cell types that you want. So we were able to react to that in real time and, you know, essentially in the middle of developing this cytokine, make sure that we respond to the science.
[00:22:52] Speaker C: Basically.
[00:22:52] Speaker A: Yeah. We brought in this new scientific thread and still within that one year timeframe, we're able to make this complete asset that's now one of the central components of our OPB 101 leap program.
[00:23:04] Speaker C: Yeah.
[00:23:05] Speaker A: Crazy. It's really powerful to be able to iterate quickly like that because then you don't get stuck with old technology.
[00:23:12] Speaker C: I mean, I guess it brings you a ton of benefits, I would think. In aggregate, it lowers cost.
[00:23:16] Speaker A: Right.
[00:23:16] Speaker C: What you can get done in a year, it's not that it necessarily doubles in cost in two years, but it certainly. And more and more time from your people and all that stuff.
[00:23:23] Speaker A: Yeah, for sure. The expensive part of drug development is not discovery.
[00:23:27] Speaker C: Right, Right.
[00:23:28] Speaker A: It's. It's actually my world. IND enabling and beyond. Right, Correct.
[00:23:32] Speaker C: Life in the clinic is where you spend a lot of money.
[00:23:33] Speaker A: Yeah. Truly you do. And turns out that there are certainly things that AI can contribute to there. But a lot of the companies, particularly the AI for protein design companies, those companies are living in the discovery world with their AI. And the way that we look at it is we can make better proteins faster, cheaper, higher success rates. That means we can make more proteins that are able to solve these problems. And because we're working in cell therapies where we can actually take multiple proteins, encode them into a single genetic construct, and make a single drug, from a regulatory perspective, that becomes extremely valuable for us. So our lead program has four technologies on board, all of which we've designed ourselves. And because we've designed them ourselves. We own the royalty stack. So then that's another part of downstream costs that essentially go away because we did the upfront investment to own it ourselves.
[00:24:27] Speaker C: And as you say, you go through the trial process and you're validating, effectively validating, all four of the component assets, right?
[00:24:34] Speaker A: Yeah. You're validating the lead, the development candidate itself. You're validating opp101 as a potential drug, and you're validating each of those technologies in the clinic as you go, which then breeds through into your future programs. And because these are such modular components, you don't have to go and reinvent them every time. You can actually take our Outsmart IL2 and plug it into the next construct. And so that's part of why we're able to be so fast. We are essentially.
[00:25:03] Speaker C: You have a library of different. Of different components.
[00:25:06] Speaker A: Yeah, exactly. And so basically what that means is in addition to essentially every new cytokine that we want to work on, taking us about a year of calendar time to do, every new program that we want to develop takes us about a year. So we can go from a binder to a DC in 12 months. We've done it twice now. And on the other side.
[00:25:26] Speaker C: That's crazy. That's crazy. That's really. That's really extraordinary. Yeah, sorry, go ahead.
[00:25:30] Speaker A: No, thanks for saying that. It's pretty exciting. As somebody who's spent a long time doing, you know, postdoctoral research. It is. It's exciting to see what happens when you can have a cohesive team come together, work on problems in parallel, smash it all together, and then develop a program where our preclinical data is showing two orders of magnitude better efficacy than people have been able to report before. And that comes from being able to combine these technologies. So if we were to take just one of them, each one is really powerful, but when we put them together, we see way more power coming out of those technologies. And so this is how it all fits together. This is why the protein design and rapid prototyping capabilities are central to all the downstream value that we've created. It's just that we're now at a phase where we've set those first bets, and we need to advance those programs into the clinic and figure out whether our preclinical data is going to translate in the clinic. And no matter how good your models are, you never know until you actually treat patients.
[00:26:29] Speaker C: Yes.
[00:26:29] Speaker A: And that's. That's where we are right now. It's a super exciting time, but we'll learn from that, and then we want to set the company up in a way where we can actually learn from that translational data and use it to make better drugs going forward. Are we going to, right off the bat, you know, achieve the same kind of efficacy that that Stan's team and the Juno team was able to accomplish with Brianzi? I'm just choosing that because that's my. My pass. Right.
[00:26:55] Speaker C: Something you.
[00:26:55] Speaker A: Yeah, definitely not promising that right off the bat, but, like, there's something between where we are right now and that future that is transformative for patients. And so it's worth doing. And if we've been able to set up our company a way that we can continue to build on that, then we've set ourselves up to really drive this field forward.
[00:27:19] Speaker B: Hi, this is Chris O'Brien, host of Few and Far Between. We'll be right back with this episode in a moment. I personally want to thank you for listening to our podcast. Now in our fourth season. It continues to be an amazing opportunity to speak with some of the top thought leaders in the clinical trials industry. If you're enjoying this episode, please leave us a review on Apple Podcasts. It really helps people discover the podcast. And don't forget to subscribe to Few and Far between so that you never miss an episode. One last request. Know someone with a great story you'd like to hear me interview. Reach out to us at Few and far
[email protected]. thank you. And now back to the podcast.
[00:27:57] Speaker C: So I want to talk a little bit about vision and where the company's going, but before we do that again, you just said you just closed a monster fundraising round in an environment where it's not easy to raise. Right. It's been a tough year for biotech companies to raise. Number one. Congratulations. That's pretty extraordinary.
[00:28:11] Speaker A: Thank you.
[00:28:11] Speaker C: Tell us a little bit what you're going to do with the money. Why go in that direction? Is this about being able to maintain your independence? Is it about developing more assets, staying with the drugs longer through the process, or how do you think about it?
[00:28:21] Speaker A: Yeah, we raised more than we expected to. We had gone out looking for a little bit over half of what we ended up with. And the goal was to essentially get initial read on that lead program that then also reads through to our platform. And when our lead came in, which was RA Capital, they said, we'd really like to make sure that you don't get caught in the middle of a milestone here. Let's make sure that you have plenty of Runway to get through it and, you know, essentially the bet was that based off of this preclinical data, this is worth testing in the clinic. Stan put it really well that I don't know what's going to happen in the clinic, Mark. But based off of this preclinical data, that data package that we've put together, this is worth advancing to the clinic.
[00:29:06] Speaker C: This is very compelling.
[00:29:08] Speaker A: It could help patients and it could change the field. It could not. Again, we could be wrong. That's the hypothesis that we're pursuing. And partially because we been able to build such a capitally efficient company.
[00:29:20] Speaker C: Yes.
[00:29:21] Speaker A: Being able to take incremental additional amount of capital allowed us to start thinking about not just advancing that one program into the clinic, but advancing two programs into the clinic.
[00:29:32] Speaker C: Gotcha.
[00:29:32] Speaker A: And being able to generate efficacy and safety data that would actually read out on the timeframe of the series B phase. And so that became obviously interesting to people. And so we're now a company that has two programs that are very different from each other. One's a car, one's a tcr. So we get the diversification across modalities. Two different targets, two different indications, different technologies on board. So we have some assets that we're considering for 201 that are not on board. 101.
[00:30:03] Speaker C: Hey, interesting.
[00:30:04] Speaker A: And so we get to learn some really important things from that translational data. Again, we would only be advancing these things into the clinic if we thought that they had a chance of helping patients. But taking those learnings from each of those programs, we can think about smashing them together in the future. We can also think about making version 2.0 of each of the assets that are on board, as well as we learn what the issues may be that come up. So, yeah, so that was. In the end, we raised a much larger round because it was the right thing to do with the stage of our technology and with the opportunity that we had created. And so we're in a great position now to hold our feet to the fire and execute, which is. It's really fun to do that. And it's awesome to know that it's in at least, least the stuff that we can control is in our hands now because we have the funds that we need to be able to advance those programs into the clinic.
[00:30:53] Speaker C: Yeah, I think it's much more exciting when you have a plan you want to execute. You need funding for that plan versus you have a general sense that there's something here, if you raise money on that, that feels like a ticking clock. It's ticking very Fast. I think in a lot of cases what you've got sounds really exciting.
[00:31:07] Speaker A: Well, and the clock is ticking fast. It's always ticking fast for all of us, I suppose.
[00:31:11] Speaker C: Yeah.
[00:31:12] Speaker A: At all stages of companies too. I think that's right. Especially in this capital environment, you can't afford to wasteful with money.
[00:31:20] Speaker C: Great point.
[00:31:20] Speaker A: The way that I talk about it with my employees is we all own a piece of this company. If we're not being efficient with our capital, we're wasting our own money. We're also wasting our investors money who we're counting on to come back and want to invest in us more. So there's a lot of trust that we've built there and we have to continue to earn that trust.
[00:31:38] Speaker C: So listeners pay attention. Right. This is a massive amount of money that's just come into Mark's company and he's talking about worrying about the clock and watching the pennies. I think that's an important, important. We're going to get to advice for budding biotech CEOs at the end.
[00:31:51] Speaker A: I know exactly what I'd do with another couple hundred million dollars.
[00:31:54] Speaker C: There you go.
[00:31:55] Speaker A: If I had more money, I know exactly what we would do.
So you're always trying to do what you can with the funds that you have. And it's really important to us that we are not basically wasting money by doing duplicative work. We're always trying to make sure that we're adding value at the right time and there's a whole lot more that we need to solve as well. Right. But we can only solve a small number of problems at a time and then we can build on that. So we're focus on efficacy and safety first. We're going to need to think about building scale in the future, both on the manufacturing side as well as on the clinical side. When we essentially get to commercial stage, hopefully there's a whole lot of stuff that we still need to do here and that we're not going to do all ourselves that other companies are going to do as well. And so again, coming back to this collaboration, I see a future where outpaced technologies come together with other companies technologies to really drive this field forward.
[00:32:48] Speaker C: So does that mean, Mark, is there a version of the future where you would license some of your technologies, supportive technologies to other players that are doing related, you know, some of the things that you're going to be testing in these initial trials?
[00:32:59] Speaker A: Yeah, absolutely. Yeah, yeah, there's absolutely. And we have partnerships already. We developed a IL12 cytokine in collaboration with Lyle that they're advancing in their pipeline. And yeah, so we're focusing initially on autologous T cell therapies. Again, the goal there is to take the CMC and regulatory risk out as much as possible because we're focusing our hypothesis testing on the the hardware and software that we're putting into into the T cells to try to make them function better inside patients. But there's lots of good work that's happening in Allo. There's lots of good work that's happening in situ. There is lots of good work that's happening in other cell types outside of alpha beta T cells. Those are places that we're not playing ourselves right now. But man, there are some really good technologies that people are developing to pursue those hypotheses and they're going to need our technologies. So I think that there's a lot of upside just to share some of this downstream value as a way of being able to test some of these hypotheses that we wouldn't be able to do on our own. And also just having more assets than we can put into our first two programs. It's nice to have a capitally efficient way of being able to advance these hypotheses into the clinic. So I think there are lots of good reasons to work together. But it is a different world that we're in right now that we've raised this big Series B because we're not reliant on a partnership to test you.
[00:34:22] Speaker C: Can decisions rather than feeling like this necessity is driving some of the places you go.
[00:34:26] Speaker A: Absolutely.
[00:34:27] Speaker C: Is this then a fair analogy, folks? Talk about gold miners and people that sell picks and shovels. The old analogy being that the guys who really got rich in the gold rush were the guys that sold picks and shovels. Most of the miners went broke if you made a lot of money. And so in biotech we've got a lot of gold miners. Do you see the business as both you're developing assets, but you're also developing enabling technologies and so you're sort of both a supporting technology and the star of the show?
[00:34:52] Speaker A: Oh yeah. There are all kinds of good analogies that you could make. In the end, we are a drug development company. When this company is successful, it'll be because we developed drugs that are helping patients and therefore are valuable and can be, you know, commercial products. That's where ultimate success lies for us. Along the way to get there again, we can't do everything ourselves. We got collaboration within the company, we got collaboration outside of the company and where we we don't have conviction, we also not have conviction. We just don't know yet what is the actual ultimate cell type and manufacturing approach that is going to really become the thing that you develop the blockbuster T cell therapies of the future with. We kind of have to rely on if we want to pursue that now before we've been able to check off the risk from the initial efficacy and safety stuff that we're focusing on. We have to rely on collaborations to be able to test some of these other things.
[00:35:51] Speaker C: Makes a lot of sense in the.
[00:35:52] Speaker A: Future, say that we get to the point where this stuff is working extremely well, then we're going to be really incentivized to work on those other problems ourselves if somebody else hasn't figured it out first. I'm a firm believer if somebody has a good solution to something, don't go and try to reinvent it.
[00:36:09] Speaker C: Plenty of problems.
[00:36:10] Speaker A: There are plenty of problems to solve. One of the analogies that I like to make is going back to the 70s with protein therapeutics, where you just come off of this really exciting recombinant insulin that was able to help a really niche patient population population, but a really important one and transform their lives. But you couldn't actually apply it beyond diabetes. And then you have Genentech then comes along and says, okay, well here's monoclonal antibody technology. And then over the next couple of decades it explodes. And now you have many of the most important drugs of today are based off of antibodies. And so it was this foundational technology that opened this potential to be able to treat all these different types of diseases. But then there were incremental improvements made along the way, following that around, you know, going from very low titers to having, you know, grams per liter production of antibodies. That didn't happen overnight. And it wasn't all Genentech. Right. And, you know, humanization of antibodies, not relying on mirine antibodies, there are all kinds of these things that others contributed to the space.
[00:37:19] Speaker C: Genentech opened the door and then a lot of people went through it and added value in different ways totally.
[00:37:25] Speaker A: And it would be very egotistical to say that Outpace was going to do the equivalent of all of that ourselves. Right. So we're going to just work on the part that we think is the primary problem that needs to be solved and one where we don't see others solving it in the same way that we are. And then we're going to work with others on the things that we think that they're doing a really good job on it.
[00:37:46] Speaker C: Does sound like you think you guys are opening an important door here.
[00:37:49] Speaker A: Oh yeah. I think that time will tell whether this is right. But I hope that protein design will be the equivalent of monoclonal antibody technology for self therapy therapies.
[00:37:58] Speaker C: That's incredibly exciting. Do you see a future? I guess you do. But tell me, do you think in the future most biologics will be designed in this way? Will everything be engineered using AI models? Is that kind of the future we're headed for?
[00:38:09] Speaker A: That's a very timely question that is not currently knowable. Certainly you're seeing the second generation of AI powered drug design. Yes. Both for small molecules and for protein therapies and also, you know, frankly from through us for cell therapies. And certainly there are lots of advantages of being able to bring these workflows and I think that they will be an essential aspect of how we develop drugs in the future. I think they'll. The way that I think of them is a way of augmenting the scientists. I don't think that they will displace the scientists. And so scaling to hundreds of drugs may be still challenging to do for the foreseeable future. There may be a future where we are able to accomplish that. But again, as we talked about earlier, the expensive and most challenging part is actually the downstream clinical development of these drugs. And so I think the goal here is get better starting points that are worth taking into clinical development and to put yourself in a situation that you can do more iteration to get it even more right in the time that you've got allotted to actually develop that candidate that's moving forward.
[00:39:23] Speaker C: Terrific.
[00:39:23] Speaker A: And then the other thing is, it's not just about hey, can you make a better molecule? It's also can you make a better molecule that is able to address a better understood mechanism? So I don't think that it's just, you know, in a vacuum like we have the same structural knowledge and suddenly we're going to make better drugs. It still goes down to we have to be able to build really deep understanding of the mechanism of the disease, disease so that we can actually design the solution to that mechanism. That's how we do everything at Outpace. If you look at anything that's in our platform, it's based off of very well understood biology. We're not trying to put proteins into T cells that have never been in T cells before, going based off of extremely well understood mechanisms that have clinical data behind them. And we're just seeing that people haven't been able to make the molecules that is able to accomplish what they articulate the mechanism needs to be. And that's where I think we've been able to see a lot of advantage with our approach, to be able to move backbone structure, to be able to get that biology exactly right.
[00:40:28] Speaker C: Mark, you have a really interesting chair for thinking about AI right now. And I think you said up front that in some ways it's overhyped, in some ways it's underhyped. Tell us, how fast is the pace of change for you? How far ahead maybe do you look in terms of the technology, the AI technology, do you see it as. It's hard to Predict. More than six months out, 18 months out, three years, what's the right distance? How far out do the headlights go?
[00:40:50] Speaker A: You always underestimate that.
[00:40:51] Speaker C: Yeah. Okay.
[00:40:52] Speaker A: Certainly again, AlphaFold came out three months later. David had something just about as good.
[00:40:59] Speaker C: Right.
[00:41:00] Speaker A: And that's one of the big challenges in this business. If your business is the AI model, as soon as you put it out there in enough detail for people to understand end, other people are going to be able to kind of, you know, find that path and go after it. And if they have enough funds to be able to generate the training data sets or to leverage the existing public ones that everybody uses in this space to train the model, which is also expensive to do, then other people can play there too. I think one of the reasons that we've seen such an explosion, particularly in the protein design space, is that we have this massive resource in the protein data bank that is a publicly available resource that essentially these models can go and scrape that information, that data, and train it itself based off of that. So what you're seeing a lot of these new companies doing that are trying to build on top of that is generating their own data sets now. And those data sets are going to be way more niche. So you'll see companies that are focusing on antibody design that are generating specifically antibody data sets that could be really, really powerful for that specific product problem. But I think that we've kind of gone over that ledge of really being able to take a big step forward based off of the protein data bank, and there will continue to be other innovations that allow us to leverage that even better. I think that what you'll see is kind of punctuated growth opportunity here. So. And then again, our strategy here is to keep our ears close to the ground, contribute when we can, both technically as well, well as with our ideas, so that people can be working on the problems that are important to us and that will also be valuable to them.
[00:42:41] Speaker C: Okay. In closing, I want to ask for some advice for budding biotech CEOs, you've achieved an enormous amount getting these assets as far along as you have, raising huge amounts of money, managing a team, all this stuff. So tell me a few things that you think you wish you had known or you think are important to focus on for somebody who is sitting in a lab somewhere thinking about starting a company.
[00:43:03] Speaker A: So, yeah, so I definitely learned things from all three companies that I've started. My cbo, Eric, likes to explain that every company is an overreaction to your previous company.
In many ways, that is true. That doesn't mean that our overreaction was the right overreaction.
[00:43:21] Speaker C: Sometimes it's yes, sometimes it's a reaction, sometimes it's an overreaction. Right. I think we all recognize that.
[00:43:28] Speaker A: So I think for me, the most important thing is that when you are thinking about starting a company, is doing that because there's a problem that's worth solving that you think that you can solve better than anyone else. If there's an existing company that you think is going to do it better and you can contribute to that in a way that your ideas can actually see the light of day, it's way more efficient to work with that existing company. Company to solve a problem. Again, this all comes from a mentality of problem solving, more so than wanting any kind of particular structure. And full disclosure, I never wanted to be a CEO. I kind of was forced into this situation and I've grown to like it quite a bit. It was never about that for me. It was about solving problems. And I think that has served my companies well. Second thing is, no matter how cool the idea is, don't try to build something new with there's an existing solution that may be less cool that's just as good or maybe even better. That comes back to what I was saying earlier, where you don't want to make toys, you want to make tools. Number three, make sure that you build an amazing team that has skills that are complimentary to yours specifically, that fill the holes that you lack. We hired a tremendous CSO who had filed seven inds compared to my zero.
[00:44:47] Speaker C: Yeah, yeah.
[00:44:48] Speaker A: So. So we hired a chief development officer who has led the Phase 1 clinical development of several innovative CAR T Cell programs in solid cancers. And those things were not things that we had on the team. And they were huge, huge important things for us to be able to get to where we are Today and also to collaborate with others, as we talked about, through company to company collaboration, and company to academic collaboration can be hugely valuable as well. And then the final one is focus on doing good science, because good science will lead the way to creating actual value. And there's, I think, particularly in this climate, you can raise a seed round on an idea that is really exciting ideas. Series A is where you actually have to be creating like tangible value that's based off of data. Data in series B is you basically, at least right now, is you're generating clinical data to show that that value is real. Right. So this is part of what I was saying before. You never have the luxury of wasted time because you have to be essentially thinking about each of your rounds of the company, getting to a real inflection point for the company, a race to.
[00:46:08] Speaker C: Get to the next inflection point point, which is the next round, kind of.
[00:46:11] Speaker A: Yeah, exactly. And it. And it happens on a timeframe that. That's not typical for your academic research. Right? So in academia you write papers, sometimes they take you five years to write, sometimes they take you two years to write, and then you write a paper. And that's like your opus, right? And in industry, you live and die by slides, individual slides, individual data sets that you generate in a week, sometimes in a month, sometimes in six months. And so recognizing that you have less time to do that, you have to be really thoughtful about which experiments you do that are answering very specific questions that you need to answer as you're on this path to value creation. It's a different way of thinking. Some people don't like it. Some people prefer the ideation, the creativity that comes with being in academia. Some people really like the very focused and goal oriented approach of being in industry. I happen to like both, which is a little bit tough for me sometimes. But one thing that can absolutely be accomplished is in both academia and industry is really good science, really creative science that nobody else has done before. And that's where you create value.
[00:47:23] Speaker C: Mark, we'll leave it there. What a pleasure. I feel like we could go for another hour. Thank you for coming on Few and Far Between. This was really an exciting, interesting conversation.
[00:47:30] Speaker A: Thanks, Chris.
[00:47:35] Speaker B: Thank you for listening to the latest episode of Few and Far Between. Our podcast is now available on Apple Podcasts and other major streaming services. Please take a moment and leave us a user review and rating today. It really helps people discover the podcast and we read all the comments. Those comments help us to make Few and Far between better and better. Also, be sure to subscribe to Few and Far between so that you don't miss a single episode. Got an idea for a future episode? Email us at fewandfarbetweeniorasi.com or contact us on our website at bio rossi.com I'm your host, Chris O'Brien. See you next time.
