From Paint to Biotech: Turning Biology into a Predictive Science

Welcome to the Axial podcast. Axial is an early-stage investment firm based in San Francisco. We partner with great founders and inventors investing in early-stage life science companies, often when they are no more than an idea. Axial is fanatical about helping the rare inventor who is compelled to build their own enduring business. Hey guys, how are you?

Hey Josh, doing well, how are you?

Just pretty busy, but, uh, really excited to just catch up and just talk to friends. But, uh, let's do some housekeeping. Um, you can click on my profile, make me a moderator so I can protect you from trolls.

Done.

Cool.

Okay, great.

Cool.

I'm, I'm the troll. Clubhouse got kind of weird, to be honest. Clubhouse is just a tool for me, but I don't really like bringing up people I don't know to stage anymore. To be honest, Liz is cool. Liz, if you want to come up, feel free to come up. Greg, Greg's a cool guy. So Greg, if you want to ask questions, but random people, I'm sorry, I can't, I can't talk to you. To be honest, Clubhouse got really weird over the last few months. Just a lot of weird questions. But yeah, really excited.

I'm curious, but I'm going to save asking you what sort of questions you've gotten for maybe offline.

Honestly, I know the guy. He was like, talk for 10 minutes about how UV light can kill COVID. I'm like, oh yeah, yeah, yeah, this guy, is this, are you serious? Or the funniest thing I ever heard on this app, this lady was like, who invented RNA? And I was like, oh, that is such a hilarious premise. Like who invented RNA? I don't know, God. I thought that was such, that was probably the best thing I ever heard on Clubhouse. This lady asking about vaccines.

It's an earnest question. You just never know.

It's an earnest question. The answer is Bill Gates.

That's Newbar. Newbar, Derek Rossi, and the whole crew.

Yeah.

But great, let me do the intros and then we'll just get to it. And we'll just, to be honest, we might make this a trilogy, you know what I mean? So we can just like do this like the never-ending conversation.

Awesome.

James and Denny's. Everyone, really awesome to have James Siestra and Denis Kural, the founders of Seven Bridges, Tosha, and now they're executives at AbSci. And I'm sure they're going to do a lot more over the next few decades. But it's a really great conversation, not only about biotech and business development and platforms, but also just two founders who have worked together for so long and that experience of just building several companies together. And so guys, you want to do a quick introduction yourself?

We go— yeah, happy to. Uh, so maybe even before that, I just want to congratulate Josh on, uh, the recognition last week. Uh, Josh is, uh, one of the 30 Under 30 for venture capital with a focus on life sciences. So well done. Um, and especially after that, you still made time for us. Which we appreciate.

You guys have to come to Berkeley. We'll go play some golf or something. And then also the next list, I gotta get on the Midas list.

The Midas. Yeah.

That's my next list. So I gotta get that.

Give it a year. Give it a year. So hi everybody. I'm James Siedstra. It's great to join this Clubhouse this morning and be part of the conversation. My background, as Josh highlighted, I co-founded Seven Bridges. Close to a decade ago. We've been working together for a while. I see one Seven Bridges alum in the room, Benjana. Hey, Benjana, thanks for joining us. And have been working in software and life sciences for about 15 years. Before that was at Harvard with Dennis, and really excited to talk about some of the Next Wave Business Models Leveraging New Technologies in Life Sciences. Deniz?

Hi. Yeah, James and I have been working together for a long time. I'm an immigrant. I'm originally from Turkey. I studied math at Harvard. I got a PhD in computational biology a long time ago. Worked at the Church Lab briefly working on building a sequencer. Worked on the 1000 Genomes Project, and after that started Seven Bridges Genomics.

Cool. Okay, most important question, because since James and you— you and James are kind of similar but different— how did you two meet?

Yeah, so we, we were— we lived next to each other at Harvard but never really hung out, and then reconnected after college when Dennis had founded Seven Bridges and was originally looking for investors. And I had, I had founded an ad tech company right after graduation, had scaled that to a pretty successful business and was looking into different investment opportunities. And Dennis has this Jedi mind trick. And over the course of 3 hours, sort of laid out his vision for how cloud computing and machine learning, next-gen sequencing would transform cancer research from a descriptive to a predictive science. And I got super excited and he convinced me instead of being just an investor to join him as an operator. So really that Seven Bridges was the catalyst. Dennis, anything to add?

I, yeah, I think you covered it pretty well. I, from my viewpoint, I was a first-time founder in a PhD program and James already by then had some experience starting and scaling companies. So I thought he brought valuable experience apart from the small investment. And in hindsight, that was a pretty good deal for the whole company. And here we are today.

Really cool. Yeah, James, I had to put you on mute just though, some background noise. But Dennis, maybe then, why did you start Seven Bridges? You were, from my understanding, you were a grad student at BU. And this is like the mid-2000s. What were you seeing? And and, and why was it right to run genomics, run platforms, and, and what was that kind of initial premise of SevenBridge?

Yeah, so, um, I, you know, in college, uh, I, I didn't have much of an interest in entrepreneurship. Um, as, as you all probably know, uh, Mark Zuckerberg was our year, and there were other successful entrepreneurs, and I truly had no interest in I was a math major, but taking a class with George Church was really eye-opening for me. I got really impressed by what was happening in the area of genome sequencing and synthetic biology back then. And going to grad school was a natural evolution of that. I met my PI, Gabor Marth, who's a great computational biologist, and he was working on the 1000 Genomes Project at the time, which it may sound a little silly now, but it was the largest sequencing project in the world. We've sequenced a whopping 270 genomes at that point in time, and it was a pain to analyze because sequencing data was one of the few things growing faster, or rather getting cheaper faster than Moore's Law. So there are very few things that can outpace Moore's Law, and this was one of them. And in both categories, both the amount of computation you need and the amount of storage you need. So if computers were not getting good enough fast enough, that means if, as far as exponential things go, that means you need some sort of distributed solution. And a lot of graduate students like myself, we were getting pretty tired of running around data centers writing parallel computation tools. It was just very painful to manipulate and do analysis on a large number of genomes. And I felt that no graduate student should suffer like I suffered, but— and instead should focus on the science and algorithm development and not how to, you know, coordinate running a large job across 1,000 servers. So that was the idea of Seven Bridges Genomics was essentially built to solve the problem that I saw the 1,000 Genomes Project generated. And that turned out there were going to be many, many large-scale genomics projects. And that's exactly what happened. We at Seven Bridges, we eventually ended up hosting the Cancer Genomics Cloud for NCI. Which hosts a lot of large genomics datasets, including the Cancer Genome Atlas. We built Kevatica, which is the largest pediatric genomics initiative, and it's led by Children's Hospital of Philadelphia. We work with a wonderful PI there called Adam Resnick at Seven Bridges, and the Million Veterans Program, and so on. So that was the initial problem we saw. How to scale genomics.

Cool. And two questions, and we'll talk more about Seven Bridges later in the conversation. You guys know Mark Kaganovich, any chance? I think he went to— was he your year or maybe a year older? He's from SolveBio.

Yeah, I know Mark Kaganovich, right? I know Mark.

Yeah, exactly. So like, yeah, all these stories where it's like, everybody knew Zuck, right? Yeah, everybody knew about Facebook. Everybody knew Zuck. And it's like, was it like, were you Facebook was an obvious success, right? It just— how does it feel to be like part of that class where it's like you see one of your classmates just have this huge success with Facebook? And to be honest, I don't think Facebook was obvious.

Yeah.

But like, how was that experience? Was it a catalyst?

So Dennis, Dennis. Yeah. Can you, can you hear me better, Josh? Is there less feedback?

Yeah. I think just like maybe when you stop talking, you put yourself on mute just because then I think there's like Echo Club.

Yeah.

Yep. So Dennis, I think, had the Zuck experience, was in class with him. I had the opposite experience. I was rowing heavyweight crew and the Winklevoss twins were still in the boathouse. So we brought competing visions for what entrepreneurship might be like based on who we interacted with. They were still wearing their ConnectU hats in the boathouse when I was rowing. But I think similarly, yeah, it wasn't clear Facebook would be what Facebook is today, nor was it clear that cloud computing and NGS would transform drug development as much as it has. So I think it required sort of some amount of vision for the future in any case, and neither of us were very discouraged. Unlike Dennis, I've been starting companies and failing and succeeding in various forms since I was a teenager. My first business was a paint contracting company. I worked for a paint contracting firm for 2 weeks and turned to my dad and said, "Hey, I can do this." He said, "Be a painter?" "Yeah." I said, "No, run a painting business," and quit the next day and started a competitor. So, for the record, I'm a terrible employee. I think Dennis is probably slightly better suited, but also a terrible employee generally. Um, Dennis, any comments on that?

Huh?

Um, so I guess we're both employees right now, so I, I should walk that back.

Yeah, I'll be honest with you, I'm pretty unemployable too, so I think we're all in the same type of, uh, uh, type of group.

So, uh, but second question, Dennis, what I'll say is AppSci is a great place for, uh, people who traditionally would not fit into a stodgy kind of, you know, 100,000-person pharma company infrastructure. So that was actually, and I'm sure we'll get to that in a minute, but that was one of the criteria that we looked at when we were brainstorming on exit opportunities for Atosion. We wanted, we looked, we looked beyond just the economics. We also looked for a cultural fit. That would enable us to be productive inside. Well, right.

Yeah, AppSci is a great company, to be honest, so it's going to be around for a very long time. So it's kind of guaranteed success, uh, and, you know, so, but maybe, Dennis, one question about the Seven Bridges timing. Was it obvious to you that sequencing would outpace Moore's Law at the time, or was that like a belief that you had to have? Because it seems— I don't know if it would be obvious in mid-2000s that sequencing costs would go down so quickly.

Yeah, so for me, it was an example of the future being already there but unevenly distributed. For me, it was pretty obvious because I was very lucky to see, you know, Rich Terry, Greg Perretta, Jay Shendure building the Pollinator and offshoot instruments at the Church Lab. We were essentially building a sequencer, and part of my job was to worked with another engineer called Mirko and Eric Garrison on picking, testing the different Hamamatsu cameras, the lasers, picking the fluorophores, writing Python software to control the microcontroller, control the imaging. And I had this experience of seeing how essentially a sequencing instrument would get built. And it the underlying technologies that go into that instrument were scaling so fast and the costs were dropping so rapidly. We went from the $6 billion reference genome to the $300 million Venter genome to just a couple million dollars. So it was just this amazing price drop. And it was just the 2 years after the Solexa instrument launched that we launched Seven Bridges Genomics. And the Solexa instrument was really what truly kind of kicked off this next-gen sequencing wave. So at least to me, it was fairly obvious. And, you know, I think to Illumina's credit, they really invested in the Solexa platform and made it the big success it is today.

Really interesting and helpful. Yeah, honestly, I'm trying to get the Solexa founders and be a case study of how to not sell too early anymore. You know, Selecta, like they sold for way too little, way too little. But maybe we can make the transition towards Totient. And you guys were able to set up Seven Bridges and hundreds of, I think hundreds of employees or something and all this revenue, all these customers, petabytes of data. What was that transition like to go from Seven Bridges to Totient? How did Seven Bridges set up Totient? And what was that transition like from going from SaaS genomics to drug development?

Yeah, so I'm happy to give a bit of context. So Seven Bridges, we decided to spin out right after we had raised a Series A. So we had close to $50 million in the bank. It was a successful and growing business. Josh, as you said, we had at the time, more than 200 employees, uh, a lot of whom were technical PhDs or software engineers. We were well positioned going head-to-head with DNA Nexus on lots of deals. Our largest customer was paying mid-single-digit millions annually, uh, in license and services fees. But we kept pitching these large pharma partners. We had supported two blockbuster checkpoint inhibitor programs on milestones and royalties, and they kept responding with, no, we'd prefer to keep paying you as a SaaS provider, which is fair. They were paying us millions of dollars, but we saw a lot of value creation opportunity in participating more in some of the long-term downstream economics. We had some novel ideas of our own for internal discovery initiatives within the R&D group. And had this itch to, to realize some of those opportunities or pursue some of those ideas. Talked to Rami Farid over at Schrödinger a lot about how Schrödinger structured internal development programs or discovery programs with their software business and considered pursuing a similar structure, keeping it all under one tent. Decided in the end that the two businesses had distinct risk profiles. Models, capital requirements, time horizons for return of capital. So chose to spin out the R&D team, the bulk of the R&D team at Seven Bridges back in 2016. And the way we did it structurally, I don't know if this is boring or not, but we created Toshant. We also created a holding company. Seven Bridges contributed assets to Toshant in exchange for all the shares in the company., and then stockholders were able to contribute all or some of their shares in Seven Bridges into the holding company, and then Seven Bridges redeemed a portion of its shares held by the holding company in exchange for all the shares in Toshant. The net result of all this was we had this holding company where some of Seven Bridges shareholders now held their shares that owned a portion of Seven Bridges and all of Totient. So we could go out and raise capital for both companies independently, and there was no cross-share ownership or entanglements that would potentially complicate one company's ability to raise capital. Dennis, you can speak a bit more to some of the R&D projects we were pursuing, but that's from a business perspective. That was some of our thinking and how we, how we executed on it operationally.

Cool. Seems pretty complex, to be honest. So let's not— I'll be honest with you. So let's just not— let's just kind of skip over that whole corporate stuff.

All right.

Fair. I'm sure the lawyers were really expensive, so I feel bad for you.

Yes, and it was very painful, and I don't necessarily recommend it unless you have a great reason to do it.

Yeah, just don't— yeah, I think, uh— What James said, don't do, or try to avoid. That's kind of the, that's the lesson here.

Also, be hesitant to use LLCs. There's downsides when it comes to the taxes at the, uh, the outcome once you sell one of the companies. Happy to talk offline to somebody about tax optimization regarding LLC structure, but not for this call.

Definitely. Uh, so maybe we can talk about kind of that experience of your day-to-day is focused on selling software, genomics, sequencing, and then how your day-to-day as founders shifted towards drug development and targets, and it's a whole different experience. And how did you learn, how did you update your skillset to become drug development CEOs? Who did you talk to? What mentors did you seek out? Because I think being a SaaS CEO SaaS data CEO founders is a lot different in terms of being like a biotech CEO. So how did you like update your—

Yeah, James, do you want to take it?

Yeah, sure. So we, we talked to a lot of people, uh, and read a lot, uh, and I think there is years of trial and error, candidly. Um, there is— it's a different, business. And a part of the hesitation, from some of our partners at Seven Bridges was that we had been, SaaS founder CEOs, but we had not done drug development. For anybody on this call that's looking to go raise capital from traditional life science investors, there's often a hesitation if you haven't at least brought an asset into the clinic. In funding this sort of next wave of founders in life sciences. And I think in the first few years we faced that. We had a lot of validation we had to, to show, but we found some champions. Johannes Fruhhauf, who is the founder of Mission BioCapital and LabCentral, Steve Tregay, who's the founder of Forma, mentors, Mary and Nakata over at J&J, good lawyers at Wilson Sonsini, Farah Gerdes, who just sort of knows everybody. And they helped us, I think, get acquainted with the vernacular. And as the program, especially around the human-derived antibody discovery leveraging TLS, which Dennis, you can go into a bit more deeply, as the program advanced in those first few years, uh, there were a couple of tipping points where all of a sudden those connections we'd established with not only investors but strategic partners, uh, became valuable because we could show them enough data, uh, to validate some of our novel approaches to discovery. But it's a, it's a grind transitioning from one industry to the next, and had we just been interested in sort of value maximization, there was an argument to be made to sort of rinse and repeat what we knew how to do in selling genomic data analysis software.

Yeah. And I think what was really helpful personally for me was the having the life sciences PhD where you become accustomed to the pace of research and the risks involved. It's— so there was a good understanding on my part about the overall difficulty involved and the low chances of success. And I think any PhD student sees a lot of failure day to day in the lab and things just not working out. So I do think having some sort of life sciences academic background is actually a good introduction to the job of being in a life sciences company and getting comfortable with risky research projects and doing things on the cutting edge. And in fact, I think to some extent, some of the larger companies over time lose the appetite for doing novel and risky things without, with an uncertain outcome. And to double down on something James said. We really benefited from finding the right early backers, the early believers. We primarily relied on seed funding at Toshant, and beyond just the initial investment, our, our early seed investors truly believed in the mission. And I think what they saw was a balanced approach combining algorithmic data-driven approaches that we are essentially kind of digital natives in life sciences, combining that with more traditional drug discovery approaches. And I think despite some of the, you know, our lack of experience, the novelty of that combination made it exciting enough for people to kind of approach us as an experiment. And a lot of Early backers did call us their experiment in this type of computational and, let's say, less computational approaches to drug discovery. Cool.

Let's make this transition, shift gears, and talk about drug development platforms and use Totient and AbSci as kind of the case studies. And so when you guys started Totient and you guys are coming from like a SaaS background, genomics background, and then you try trying to make drugs. You know, how did you think about your platform at Toshint versus platforms in the past, especially the value-add?

Yeah, so we had a very specialized approach to human-derived antibody technology, and I think that really helps. I can maybe briefly describe that. We were looking for patients with a specialized type of immune response called a tertiary lymphoid organ, and this is a mature germinal center present inside the tumors of patients, and they produce these affinity-matured, clonally expanded antibodies, and we used bulk RNA-seq data to mine heterogeneous tumor sequencing and reconstruct antibodies from these rare patients with this type of immune response, and then figure out if these antibodies did anything. So we are not the only human antibody discovery company in the world, but we are one of the handful primarily human-focused companies And among all the human-derived antibody discovery companies, we had a really unique approach and a unique platform on how we went about doing that. It was heavily reliant on computational biology and sifting through the patients and antibodies with a specialized algorithm. So in that sense, I think it was a differentiated platform and it could it could add something to big pharma, something they didn't necessarily have in-house or had time to build out. So I think that, that makes it particularly attractive. And humanization remains a big challenge in biologics discovery. So starting with fully human materials still to this day has its advantages. There's a lot of focus these days, justifiably, on, de novo design and fully, you know, ML-derived design, but all those resulting proteins still need to get fully humanized to avoid various immune reactions and downstream issues.

Awesome.

I think, so we had Jacob Oppenheim, I think last week, and you guys did a deal with him at EcoRx. And so Jacob was discussing about, you know, acquiring datasets and then standardizing them and then generating their own data. How did Toshint think about acquiring public data or which public data sources to use versus internally generating your own dataset?

Yeah, I think it's very problem-specific. So, um, in our case, there was just so much published bulk RNA-seq data that, that it just made sense to start there and start using those datasets. And at the time, and actually still to this day, there just weren't a lot of efforts or success at reconstructing correctly paired antibody sequences from those datasets, and we were uniquely able to do that. So there wasn't really much of a downside on relying on public dataset because the way we used and transformed it was still quite unique for our company, but we did not want to stop there. So we did actually establish partnerships with hospitals. We speak about our Avera partnership quite a bit that James set up for us, and we are rapidly expanding the kind of partnership side and getting our own dataset as well. For some applications relying on big data, it does make sense to generate bespoke data and close that loop of generating data, training your models that generate new hypotheses, and just kind of generating that flywheel effect. So I think both approaches make sense, and public data is an excellent place to start bootstrapping that. And once you churn through all the public data, you can expand from there.

Interesting. So I think sequencing data is commoditized enough and antibodies are well characterized enough that you just need a unique question or hypothesis like you guys had, which is finding antibodies from intratumoral B cells. Right. How many antibodies did Joshua end up having? At the end before you guys got acquired? Was it like 10,000 or 100,000?

It was a big number. Yeah, so we, we are very selective and filtered down significantly, and we've actually observed millions of, millions of unique CDR3 sequences in our datasets, but ultimately we were looking for high-quality germinal center-based, clonally expanded affinity matured antibodies. So ultimately, at the time of our acquisition, and this is fully public information available on the S-1, we had roughly 4,000 antibodies at the time of our acquisition that are based on processing over 50,000 patient samples. Cool.

Well, congrats on that. Now let's talk about use that to set up the AbSci acquisition. Maybe James, because James is honestly everyone— James is a master negotiator. You know, whenever a portfolio founder, it's like, oh, just talk to James. James knows, he's been doing business. James, I know you did paint, so he's been doing business for the last 25 years or something, uh, from paint to biotech. Uh, that's gonna be the title of this podcast. Um, but, uh, how did, uh, thanks, Josh.

Seriously.

But how did you two think about getting— why did you take— why did you—

can you just talk about that experience of building Totient and then getting acquired by Appian?

Yeah. Dennis, were you about to— No, no, I was going to say the roots of the pharma industry actually came from dye manufacturing. So the paint to biotech is the story of pharma over the last 150 years. So it fits very well that, you know, it's not actually a bad—

This is what I was thinking about when I was on the side of those houses pushing my brush back and forth. My vision for computational biology all the way back in 2002. So Josh, your question, and thank you for the credit. I— we originally weren't looking to sell the company, so it's, it's been a very productive year for the Toshian team. We, we went into 2021 with a lot of leverage. We came out of stealth for Toshian last September, so it's been just over a year since we came out of stealth. And we announced $10 million in seed funding, um, from some of the names that we've highlighted previously: Mission BioCapital, Sands Capital, uh, Jonathan Milner was an investor of ours. He's the co-founder of Abcam, still deputy chairman. Um, we were a JLABS company. We had a strategic, uh, antibody discovery partnership with Ginkgo, which we had announced fall of last year as well. So we were well positioned to raise a Series A, and we kicked off a process with the target of raising $50 million, uh, to advance one of our candidates into the clinic. And by March, we had a highly competitive process running with multiple blue-chip VCs, strategics, um, we were running a bunch of things in parallel. And, uh, one day the CEO of a strategic who we had asked for some, some help in, uh, sort of backchanneling with one of the VCs that we were, uh, serious about said, well, don't raise capital. Um, sell your company to us instead. And one of the things we learned at Seven Bridges, um, a, a lesson that we, uh, learned painfully, we said no to a term sheet. From a strategic, and in hindsight, we probably should have said yes, or at least seen it. We told the CEO, great, appreciate the interest, show us a term sheet, please. And we then communicated to several other strategics that this was in play, and we found ourselves then having two competitive processes running in parallel, one of which was Series A, the other was an acquisition. And AbSci, for lots of reasons, was the best possible fit for us. Um, it combined our discovery strategies with AbSci's platform for optim— optimized therapeutic protein design and biomanufacturing. It allowed us to de-orphan all these antibodies we'd computationally assembled and really build the, the largest collection of human-derived antibodies, tissue-specific human-derived antibodies and target antigens ever. And we, we found not only the science really exciting, but also the, the culture. And Sean McClain, I mean, you've had him on here, Josh, built his company over the last 10 years from a basement lab. And it really is, if we were going to be employees anywhere, not a bad place for us to be employees at., uh, uh, we get along well with the team and the, the, the vision is just really exciting. It reminds me of early days at Seven Bridges when we sort of predicted what the next few decades would look like. Um, and then we, we merged with AppSci 48 days before their IPO. Um, and, uh, sort of rode that momentum and we sold. So based on the stock price, during the first 10 days of trading, uh, the cash and stock total consideration to acquire Totient ended up being somewhere between $101 and $124 million. And, um, we sort of did the math and saw the upside in, in combining our technologies and decided it was worth selling earlier than we had originally thought. We thought we were going to take this into building a public company or be acquired you know, once an asset was in the clinic. So it was, it was unexpected, but a great, a great turn of events. And, you know, couldn't be happier with the outcome.

Awesome. Yeah, I think, well, first of all, congrats. And yeah, I think, you know, you have a pile of cash and you work in a great company, and now you get to do a lot more. You know, you get to— you two are leveling up, right? I think as founders, as a duo, you know, each 5 years, each company, You're leveling up, and so I think that the best is yet to come. But maybe to talk about, especially for James, maybe specific tactics around how do you engage partners? I think what you just said, I'm gonna put a little snippet, and you know, like how to talk about fundraising and create the right type of momentum. How do you think, seriously, how do you think about engaging partners?

Partners?

How do you think about closing deals? Are there any little tactics or little kind of little tricks you've kind of collected over the years in terms of like, you know, talking to one, you know, just like talking to VCs and getting them engaged with you? I think you alluded to just, you know, taking the money, right? I think some founders, you know, when there's money on the table, they kind of bungle it and they don't take the money. And that's always a mistake. If there's money on the table, we should get it, you should get it. So any little tactics you've picked up along the way in terms of like engaging partners and closing?

Yeah, I think it's a delicate balancing act. I'm a big fan of taking the money on the table, but on the flip side, the single best thing you can say to an investor or potential acquirer is no. There's nothing that gets a buyer more excited than you saying I'm not going to take your money. I don't need it. So I think you have to— if you can walk a fine line between making the buyer or investor fight for participation with not, you know, turning them away, that's a fine line worth walking. But there's no sort of magic formula for doing that. The, the best way to position for that is to have good validation for your asset or whatever you're selling. If it's a discovery platform, have a resultant asset that is well validated in a data package. So have your science check out. And in addition to that, I think having competition so you can, you can posture and you're not beholden to a single potential buyer. Um, both of those things enable you to run, uh, efficient processes and sort of get maximum value for what you're selling. And that's true if you're trying to do a licensing deal, that's true if you're selling a company, it's true if you're bidding on a paint contract. I mean, these are less— it's actually less true if you're bidding on a paint contract. Um, but I think these are sort of generalizable ways of engaging competitive processes and selling things.

Dennis, do you have anything to add to that?

No, I think, I think we got it. Cool. I think competition is the key word here, you know, competition between bidders. And that's always, you know, as an investor, I'm always trying to avoid auctions, but then if I invest in a company, I want to send my portfolio companies to the auction. And so it's kind of, that's how I think about things. And I think, you know, it's always nice when you have a lot of people who want you and then, and then if you want something, you're one of the few people who get it. So it's kind of always a balancing act, but maybe we can shift gears and talk about platforms and now in the context of Toshin, AppSci. And so maybe, you know, can you talk more about how Toshin fit into AppSci? AppSci had also bought a company called Denovium 2 years before. And can you just talk about the premise of AppSci and then, and now how's Toscient adding?

Right. So I'm very excited overall about the premise of synthetic biology and where it's heading. At the very beginning, we started with this idea of how do you turn biology from a descriptive science into a predictive science? Because initially, it kind of started out like stamp collecting, right? Collect different specimens and make different observations, a lot of them being molecular observations. But we had very poor tools perturb a biological system and truly do predictive experiments and test how those will be. And the better a toolkit we get with synthetic biology, the better we can actually design things and turn biology into a predictive science. So reading DNA, we're now pretty good at. That's what the sequencing revolution has enabled. It's very cost-effective to read DNA. And now with synthetic biology, under which— under whose umbrella I include DNA editing, DNA synthesis, and other kind of genetic manipulation technologies, we're now able to write genomes as well. And AppSci, broadly speaking, provides us with that ability to also write into organisms and complete that loop of read-write genomics for biological engineering. The deNovium acquisition was also very important because not only do we need to be able to read biology and write biology, but we kind of need the brain in between to design and figure out what we want to write, right, and make sense of what we just read. So that's where a lot of AI comes in. Humans are still really, really bad at designing biological systems and designing proteins. We don't really know how to design the right antibody for the right target or the right enzyme. And AI is probably the best tool we have alongside wet lab biology to actually design the stuff we want and improve upon nature. So that's where the De Novium acquisition and the focus on machine learning comes. And the overall vision for AppSci is through combining Totient, AppSci's existing business, and De Novium, it kind of makes progress on this journey synthetic biology is having of doing more and more of these steps in silico of making sense of genomes, designing novel proteins, and ultimately carrying out the lab work to generate organisms to produce these proteins. The most lucrative application of this technology currently is in kind of therapeutics and drug development. So a lot of our focus at AppSci has been helping our partners develop better medicines focusing on next-generation biologics. And Tocantin had, had this focus on targeting tumor-specific membrane antigens that are really a key aspect of enabling a lot of next-generation biologics and A lot of the antibodies we discover create content that can go into a bispecific or chimeric antigen receptor and a number of different modalities, which AbSci is really good at engineering and optimizing.

Absolutely. I think, you know, AbSci might have the best, one of the best marketing terms for antibodies. You know, you guys have bionic proteins.

Bionic.

There you go.

Bionic.

Whoever, I can't believe AbSci is the first one to thought about that. But could you talk more? I think running for AbSci is like, you guys are doing like a one-stop shop for antibody discovery, you know, you, AbCellera, maybe Alloy. And I think there's a really compelling slide in the S-1 where you guys compress the timeline from years to months because AbSci not only discovers the the E. coli that's humanized, or no, that's a yeast that's kind of made to be human-like. And then you also discover the antibody all in one step. And so that's kind of really compelling. So I think AbSci is gonna do very well in the next decade at least. And so could you talk more around kind of this topic of platform partner fit? You know, AbSci definitely has a unique business model and Toshin definitely had a unique business model. And so how do you two think about building platforms out, getting partnerships? How do you think about making sure that you retain the value of the platform and not give away a crown jewel? How do you think about data in that context? But this overall discussion around platform partner fit, how does AppSci and /Toschen think about it?

Yeah, that's a, that's a great question. And I think it will remain an active conversation within AbSci. It has been for us for years leading up to the acquisition at Toschen. AbSci is a bit different from some of the traditional biologics discovery platforms in that there's no interest in a fee-for-service model. We are relatively choosy about the programs we partner, and we want to have conviction around the biology because we expect downstream economics on those programs. So even though we're, we're preclinical partners, typically we're looking for sort of the next 10 years for these assets to advance and be clinically successful. So we, you'll see more, I think, partnerships like EQRX where they're multi-target deals and where we have the potential to realize some of that long-term value creation and we're deeply invested. So the reflective of sort of what Dennis and I have done in the past, both at Seven Bridges and Toshint. AbSci is investing in moving more and more to in silico design and optimization. I, I think arguably we have the largest AI team in biologics in the industry, at least from a sort of non-internal perspective. And that investment is sort of the beginning. We intend to do a lot of platform and infrastructure investing to be able to continue. Josh, as you said, sort of compression of timelines, not only compressed timelines, but be able to offer technologies that are truly differentiated from other companies in the space. But it's not going to be a prolific engine insofar as we're not, we're not looking for fee-for-service arrangements and to do small projects with hundreds of companies where we're looking to partner selectively and participate in the downstream economics.

Absolutely. Yeah.

You just want to work with the best. So you work with Jacob at EcoRx, you work with— I think you guys work with Merck or Novartis. You work for a bunch of companies, maybe. So you guys work for everyone. So everyone good you work with. So I think Appsite's in good company. And also, I'm gonna be honest with you, you guys hired a really— you hired a— I don't know if it's announced yet. I won't announce it, but you guys just hired a genius from Facebook, so congrats on that. That guy is a genius.

Josh?

Yeah, Josh, man. You guys, I can't believe you hired Josh. I don't know if it's public, but seriously, congrats on hiring like Josh from Facebook. I don't know, I won't name his last name. Let's keep it a mystery, but he's a legit protein engineer, so, um, You guys, next hire is Bart, right?

So get Bart on board. Uh, we, we are fortunate. We're fortunate to— you get sort of, uh, network effects, right, where people want to join that team. And, um, yeah, we, we've got a really strong— on multiple, uh, layers, a really strong, uh, set of people contributing to building for the long term, which is pretty neat.

Yeah, seriously, capital. Yeah, I mean, uh, I mean, AppSci is such a great story. You know, Sean, he moves, he moves back to his parents' house in Vancouver, Oregon, and he's like doing his work in a basement lab and he's trying to make E. coli human-like. And he spends a year tinkering. It's really honestly a story of a great inventor tinkering, having a vision, and then like taking a lot of business model risk and social risk. And it worked out, which is pretty cool. And so being part of that, I'm sure that the culture today kind of is connected to that founding story. It is kind of probably persists. There's probably small little things that you aren't talking about that like, like are really unique because of how the company was founded. And because AppSci, you know, and plus Vancouver, Oregon, great place to live.

I mean, in terms of taxation. So, so We— Sean is absolutely tenacious. Uh, part— a big part of the reason Dennis and I chose AbSci was, uh, Sean, Matthew Weinstock, uh, Greg the CFO. We met the executive team and, uh, we just felt so confident that, uh, through sort of the next however many years, the next decade, the company was well positioned and had the vision to execute, take risks, and then just tenaciously go after it. Sean is unwilling to take no for an answer, which is fantastic when you're selling into organizations with lots of inertia and bureaucracy. And you're right, Washington State Governor Inslee came to our grand opening recently, and we have lots of support. It's a great place to live. It's beautiful. The facilities are great. Dennis can comment on sort of the labs, but we rocked up after we had sold the company and both of us were pleasantly surprised at just the extent of the capabilities. We sold Toshint to AbSci without ever having seen the place. So it was nice when everything checked out.

Yeah, I mean, you mentioned the location and the lab is quite impressive. It's 77,000 square feet of purpose-built labs for AppSci. And the first time I visited there, I was just constantly doing the math of how much that would set a company back to have a lab of that size in either Cambridge or either San Diego or San Francisco. And I think that's one of the advantages of our location is to have such a high-throughput, automated, and large-scale lab facility that the kind of the more computational technologies ultimately rely on and benefit from. And we can just generate a massive amount of data in-house now through both our E. coli technology and other high-throughput assays.

Yep, I mean, I love trees. That's why I live in Berkeley. And so I would go live in Washington State. There's no sun though. That's the issue though. But a lot of trees. But maybe we can shift gears. One last question on the platform side. At AbSci and Totient, part of it, you guys have the ability to discover antibodies and validate new targets, but you also have an E. coli platform to manufacture this stuff. That's very rare, right? Most antibody discovery platforms just discover the antibody, but they can't help you make it. Where AbSci is one of the, I think maybe one of the few that can discover the antibody and the chassis that's going to make it. How does that help you in BD discussions and partnership discussions? Is that a major advantage and be able to have discovery and manufacturing all in one product?

Yeah. What kind of advantages Yeah, so maybe I'll just speak quickly to the BD side, and then Dennis, it might be worth just sort of expanding on some of the capabilities beyond antibody discovery and optimization. We have— it's both an opportunity and a challenge, Josh, to take this drug creation platform with a stack of complementary technologies for next-gen biologics and figure out, you know, with J&J or with Novartis where the matches are. It's a many-to-many problem. The nice thing is if we establish a beachhead, say with an asset deal, we're out-licensing a reasonably well-characterized antibody, and then it naturally leads to a cell line development conversation, a lead optimization conversation. These beachheads are particularly useful to us because AbSci has so many complementary capabilities. The challenge, however, is focus. So we need to be, and as part of the BD organization, now leading the BD team, we need to be disciplined about how we focus on our outreach and how we match sort of the initial engagement with where R&D is focused, within the organization. Dennis, anything to add on sort of the broader tech stack?

Yeah, I think speed is an important component here. That's one of the major advantages of this type of vertical integration where we can take something from target to discovery to building that chassis and scale the tighter of the product we're developing very rapidly. And I think COVID is a good example where this type of vertical integration is very helpful in terms of meeting rapid deadlines and pandemic rapid response requirements, where having all these capabilities in-house allows us to go from a patient who's infected with the disease to a manufacturing-ready cell line in a low single-digit number of months.

Yep. I mean, I think invest in a few antibody platform startups and the biggest barrier is like the $5 to $10 million you need for manufacturing to get to IND. And so you discover an antibody and then you have to pay Lonza a bunch of money to get to IND and then that helps you raise more money. Whereas AppSci and your partners, you kind of solve that problem from day one, or at least really early. Totally. And then that allows you to have more shots on goal. And then to be honest, in drug development, I don't know if you could predict, one, antibodies have a higher success rate in the clinic than other modalities by a pretty big margin. And it's hard to engineer success in the clinic. Right now, but at the very least, you can like engineer more shots on goal. And if you have more shots on goal, you have more drugs, just based on the numbers. You said it well. That's it. To be honest, that's it. And then I think app side, a big theme, the thing I'm looking for, one of the biggest events I'm looking forward to in biotech and next decade hopefully is like the first bio bond. Like the first company that securitizes a bunch of drugs in a bond and then like sells the bond and it gets money from on it. And I think AppSci could do it, right? I think one day you guys could make a biobond. And so whoever makes the first biobond, I think it's gonna be a big pivotal event.

And so I think AppSci has— yeah. Couldn't Axial do this, Josh? Isn't this, uh, uh, something, a creative structure you could put together?

Yeah, but I don't, I don't generate IP. I just read all day. So I don't know. This is tough, right? I don't like, I don't do anything but read and talk. You'd have to broker some partnerships.

Uh, I don't know.

I think honestly, AppSci plus Axial. Yeah, exactly. I might have portfolio companies, but to be honest, within the whole— this is a side note— like for securitization of biotech and like bio bonds, I bet there's going to be like a Michael Milken type of character. It's not going to be me, but like there's going to be some sort of like Michael Milken who like brokers a bunch of assets in a bond and like talks, you know, is really charming and can get Merck and Novartis all to work together and like create these like, you know, high high-yield debt of drugs. I'm sure that happens actually, but yeah, that's something I'm really looking forward to actually. And I think AppSci has a unique platform to get more shots on goal, move more quickly, and kind of reduce the barriers to initiate a trial. And so I think AppSci's gonna do very well. So congrats on selling to AppSci. And I think the future looks really bright for the company and its U2 in particular, I think it's pretty obvious how well you two complement each other. Seriously, I think it shines out pretty well. Maybe to wrap it all up, what are you two looking forward to over the next year?

That's a great question. I am looking forward to— well, I don't know. I guess I can't make any predictions since we're a public company about the number of programs we're going to close. So I'm looking forward to being successful at AppSci in my role, and I won't, I won't translate that into any sort of metric since now we're trying to get used to being a public company and just executing. It's, it's really exciting, the complementary nature of the technologies AppSci had with what Toschin brought, what Denovium brought. We've got the capital, we've got the infrastructure, we've got the team. There's, there's no reason we can't be really successful. And hopefully, hopefully more trips up to Washington. And on one of them, Josh, you can come with us.

Yeah. And while we have the audience, maybe what we could say is we're looking forward to working with innovative startups and innovative structures. We're a very inclusive platform company, and there's still lots of areas in which we love bringing on partners. So I guess that would be a good takeaway in the next year. James and I are actually looking forward to meeting new companies, new founders, and learn more about what they're doing and see how it could complement what we offer at AppSci.

Great point. You know, Dennis, I'll send you an email. I have a bunch of portfolio companies, so I'll just send you a long list of startups, and we can go from there. But cool, guys, let's hang out. If you guys are in Berkeley, let's just get some coffee, walk around town, and have fun. But always great chatting, and yeah, thanks for taking the time to talk to us, and have a great day. Thank you, guys. Congratulations again. Midas List next.

Yeah, exactly.

I gotta I think we have an investment. We're in business with a guy named Aaron Ring. I think we should talk to Aaron Ring, actually. I think AppSci and Aaron could do something together. Because Aaron, in my opinion, he's the best protein engineer in the world, in my opinion. I'm very biased. And so he's going to start a bunch of companies. So hopefully, I think his companies are going to get the biggest. So my model is to just stick close to great scientists and great founders and just hold on for dear life and be useful, and then like, you know, good things happen. Uh, it's like being a movie agent for a scientist. So it's a pretty— it's a pretty sweet life, actually.

So, uh, I appreciate it.

That's a good metaphor. I love it. Movie. Honestly, you gave me a great analogy. Honestly, when we spoke, I, I looked in, I started studying Ahmet from Atlantic Records. I, I read his biography.

I—

when every single We spoke like a year ago about it. Yeah, I remember. And I really studied the guy and it was really fascinating. He actually wrote songs. He spelled his name backwards and he would write songs. And I think the key lesson is his success was he took a lot of social risk where he's like this son of a diplomat in Turkey, but he loves blues. So he goes to the American South and maybe, you know, since what, the '40s or something, probably a lot of racism and stuff, but he takes a lot of social risk to like work with, I don't know, Ray Charles or something. I think that's a big lesson in history is just like smart people who take social risk. And that's like AbSci, right? I think it's you too, right? James, you did heavyweight rowing, right? But then you link up with Dennis, right? You take a lot of social risk. I'm gonna be honest with you.

I'm gonna be honest with you, right?

I think—

wow, that's a—

how do you feel about that, Dennis? I'll get him later. You get me later, Dennis. I'm sorry, Dennis. But yeah, I think it's, you know, that's why I think you guys work out so well together, because you guys are very similar but kind of different too. And so, uh, you guys complement each other really well. And so I think, you know, a lot of, uh, I think that's why you guys—

I think she's been so successful because of that reason.

I'm sorry.

You're good.

I'm sorry.

That was cool.

What a way to— I thought you read up on Ahmet Ertegun, founder of Atlantic Records.

Yeah, that's— I did. I mean, I studied David Geffen. Who else did I study? I studied Ari Gold. You know, as you go to watch Entourage, you know, Ari Gold, the case study. That's great. But a few other folks. But cool, guys. Let me— I know you guys are busy, so we'll talk soon, man.

And have a great day. Awesome.

Thanks so much.

Thanks everybody. Bye.

Talk soon. Bye.