Summary

On the latest episode of The Bioverge Podcast, Ramsey Homsany, Co-founder & President of Octant, sits down with Neil Littman to discuss his unusual path to leadership of a biotech company and Octant's efforts to tackle complex diseases with next-generation small molecule drugs.

Octant, named for the nautical navigational instrument, is harnessing synthetic biology to better understand the biochemical mechanisms of its targets. Octant’s platform focuses on synthetic biology, engineering small molecules and testing them through human cell lines, biological assays and machine learning software.

Listen to learn more about Octant's platform and their collaboration with Bristol Myers Squibb to apply its platform technologies to a set of inflammation-related pathways.

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Transcript

00:29
Danny Levine (producer)
Neil We've BV BV Ramsey Homsany show today for listeners not familiar with Ramsay, who is he?

00:36
Neil Littman (host)
I'm incredibly excited to welcome Ramsay to the show today. He is co founder and president of Optin Bio, a company using synthetic biology to improve health and treat disease. Ramsay has a really interesting career path, having spent a bunch of time building high performance teams at technology companies. Before Octint, he was an executive at Dropbox, where he had leading roles in Dropbox's communications, public policy, legal and other teams. During that time, Dropbox raised over a billion dollars of capital, grew to more than 500 million users, and achieved over a billion dollars in revenue run rate. Prior to Dropbox, Remsey was Vice president at Google, where he managed their commercial legal groups and negotiated some of the Internet and industry's largest partnerships. I am really excited to learn more about his personal career path into health care and into biotech.

01:33
Danny Levine (producer)
He doesn't have the typical resume of a drug developer. Do you think that can work as an advantage?

01:40
Neil Littman (host)
Absolutely. We'll get into it but he did some undergraduate work in chemistry and biology, so he has obviously a foundation in the sciences, but approaching it from the tech side where he spent most of his career. As we know and we talk a lot about on this show, there is a convergence of technology and biology, this new wave of tech bio company. I'm sure a lot of what he's seen in terms of building and scaling technology companies can be ascribed to what they're doing at Optin. Optin in particular has a four pronged approach to using synthetic biology to develop novel small molecules. We'll get into some of those details, but we often talk about this idea of culture as it applies to building a tech bio company. I think Ramsay probably has a really interesting perspective on culture coming from the tech side of the world, and I'm really interested to hear how he merges that with the biology and chemistry side of the house.

02:42
Danny Levine (producer)
The company is really bringing next generation technology to small molecule drug discovery. What do you think the opportunity there is?

02:51
Neil Littman (host)
I think there's a huge opportunity, and I think what Octan is doing is really novel and what they're doing is done at a much higher throughput and a much grander scale than what I have seen in the past. So they're using things like synthetic biology. They're using this high throughput, multiplex assay approach. They have a synthetic chemistry arm. They're doing a lot of computational to engineer and interrogate drugs and proteins and signaling pathways at a very large scale. I'm really excited to talk to Ramsay about what they're doing and what they're building. For full disclosure, I should just mention octane is a portfolio company of ours at Bioverge. We're clearly excited about what they're doing, and I think they're taking a very different approach than what has been done historically to doing drug development and drug discovery at a very large scale.

03:43
Danny Levine (producer)
What are you hoping to hear from Ramsay today?

03:45
Neil Littman (host)
Yeah, so as I mentioned, I'm hoping to hear about the culture that they have built at Octan. I'm hoping to hear about their platform and both the chemistry engine and the biology engine that underpin their platform. I'm interested to hear what they're doing differently than others.

04:04
Ramsey Homsany (guest)
Right.

04:04
Neil Littman (host)
I mean, we hear a lot about the buzziness of AI or ML being applied to drug discovery or drug development. I think Austin is probably using some of that stuff. I think they have a really differentiated approach, which is a lot more than just applying those tools. I'm excited to dive into the inputs, the outputs that they use, how they think about building and scaling the company. Do they want to become an integrated biotech company in the future? This more of a discovery engine with a business model to license out different products? I think that there's obviously a lot to dive into, both from the platform and the scientific side, but also from the business fundamental side.

04:41
Danny Levine (producer)
Well, if you're all set, I'm all set.

04:44
Neil Littman (host)
Let's do it. Danny Ramsey, a big thank you for joining us. I am incredibly excited to welcome you to the show today.

04:53
Ramsey Homsany (guest)
Sure thing, Neil. I'm excited to be here.

04:56
Neil Littman (host)
Well, today we are going to talk about Octant bio and how you're charting new approaches in the world of synthetic biology and drug discovery by engineering biology at unprecedented scale to unlock the development of novel small molecule therapeutics. One of themes we like to look at on this show is the convergence of tech and biotech or biology or the emergence of tech bio. Octint certainly fits within this theme. Before we dive into some of the details in regards to what you're building at Octon, I would like to start with your personal journey to become co founder of Octint, because your journey is really fascinating to me. You have an undergraduate degree in biochemical engineering. You're a lawyer by training. You've gone from being a technology attorney at Wilson Censini to being VP at Google and executive at Dropbox. How did you go from those experiences to being a co founder and president of a drug development company?

05:54
Ramsey Homsany (guest)
Yeah, the short answer is a very short attention span. More seriously, I've always been really interested in technology. I was a math and science kid growing up. My parents were immigrants, and I found myself in that situation many immigrant kids do where you really just push really hard on the math and science of things because that's the thing your parents really value. I went to engineering school, and then weird stuff started happening in my career. I started doing some irrational things. Like I went to law school having no idea what I was getting myself into. Just after an engineering ethics class, it felt like it was important for people who are really passionate about technology to be more involved in law and society. And after that I really missed technology. I came out to California to work with technology firms and had an amazing few years at Wilson sancini.

06:55
Ramsey Homsany (guest)
I actually spent most of my time working with biotech and medical device companies. In 2003, my attention was captured by this. At the time, a very quickly growing company called Google just started reading about the company and was just blown away at how they were really rethinking so many things from first principles. It really captured my imagination as to what was going to happen in the next ten years with what was happening in software and the internet. I left my bio background behind and went and joined Google. Had an amazing time there. That time continued with Dropbox and there I went to More, working on the virtualization of information, seeing the growth of the cloud again, this thesis that this was going to change everything. Towards the end of that, I started to feel that way about biology again. I was keeping track of what was happening in biology.

07:55
Ramsey Homsany (guest)
It just really was blowing me away. This idea that were seeing at scale, people starting to really read, write, edit genetic code. We were accessing the information systems of life, basically. I know that sounds so hyperbolic, but it's really what it is. I'd known my co founder, Sri for a while. He was a well known synthetic biologist and I just started calling him just out of excitement, asking him questions, asking him to introduce me to people in the space. It was over the course of those conversations that we both got very excited about some of the work coming out of his lab. We decided it was important for us to stop what were doing and build a company around some of those technologies.

08:36
Neil Littman (host)
That's very cool. It's almost like you find a technology trend that excites you and follow that to the next big thing. I've got to ask, how have you found the transition from large tech companies to building a startup like Octint?

08:53
Ramsey Homsany (guest)
Yeah, so it's funny, when I joined both of those tech companies, they weren't large, so Google was about 1000 people. When I left it was 25,000. Dropbox was about 50 people when I joined. When I left it was about 2500. Seeing that evolution is really fascinating. You really struggle with questions that I think are similar to what biotech companies struggle with. Science and technology is what drives your value. How do you build your people and your talent around that? How do you build companies that attract the best talent, develop the talent how do you build cultures that make very good technical decisions? How do you build the non R and D part of the company or the non technical part of the company to also be excellent? Those are all things that I think are very similar in both worlds. I think there are a bunch of differences.

09:47
Ramsey Homsany (guest)
One of those differences, I feel like it's starting to converge . I think things in biology and chemistry are changing a lot. We are seeing more characteristics of those sciences as information sciences. People are starting to do these massive experiments, build huge experimental data sets. I'm not talking just about AI and machine learning. I know that it's very trendy to talk about those things, I think for a lot of good reasons. It's also this idea of just doing these massive types of experiments that we do at Octon for example, that are just changing the way you can really get into very fast design, build test cycles, start to treat some of these technology areas as engineering problems and then there are some things that are just very different. Biology is not software, it is just not code is deterministic for the most part. A bug in your software is the same bug today and tomorrow.

10:45
Ramsey Homsany (guest)
Your cells act differently from day to day. You don't know why. Biology is just still orders of magnitude more complicated and complex than what we think of as software technology. And you have to respect that. I think you can't run a biotech company completely like a software company. It has a lot of implications for your strategy. In fact you can wander a lot more. You can iterate in the short term, you can build something and launch it next week. In biology you're in drug discovery anyway, you're trying to hit clinical endpoints that are ten years away. You have to build back from those plans. It's just very different in those ways. The cost models are different too, which that's less interesting, but we could get into that if you'd like. I think what's really hard is doing both in the same company. It's been fun, it's been challenging, but there's a lot of excitement, as you said, about this idea of this category of companies.

11:43
Ramsey Homsany (guest)
Some people call it tech bio. It's really hard. How do you build a culture that promotes both this iterative wandering that you want to see on the software side, but also this very careful derisking over time that you need to run a good drug discovery program? How do you build a company that's good at messing around with things and harvesting that innovation but also really smart about indication selection because you could spend all your time working on the wrong thing and squander a lot of investors'money doing that. Those are definitely a lot of the challenges we deal with every day.

12:20
Neil Littman (host)
Yes, I mean Randy there are so many gems to dig into there. I'm always fascinated with the IDF culture because it does play such an important role, but I think it's often overlooked. I really appreciate your perspective on striking that balance between the tech culture and the biotech culture because I think that is really incredibly important. As you're looking to scale the business, let's jump into what optin is doing. You're using synthetic biology to do high throughput screening and a very computational heavy approach to drug discovery. Can you walk me through Octin's drug discovery approach and maybe how it's different from traditional drug discovery efforts?

13:02
Ramsey Homsany (guest)
Sure. Our basic thesis is that there are a lot of very important unsolved problems in medicine that are characterized by their multifactorial nature. What do I mean by that? Often it's how do you drug some cellular mechanism that requires modulating multiple receptors or pathways or in the case of monogenic diseases, building drugs that can act on many of the different possible mutations that a patient might have to that one gene? These are problems that historically, drug discovery aren't really optimized for. Drug discovery historically has been the secular trend towards very specific, like highly specific targeting of a single target. Right. What we're doing is building what we think of as a next generation drug discovery platform to solve these types of multifactorial problems. We're rationally building small molecules that target multiple target pathways or mutations, things like polypharmacology functional selectivity of receptors. We think it's really like the best of both worlds.

14:07
Ramsey Homsany (guest)
Between classic modes of phenotypic screening and rational drug design, many of society's best drugs, which were found largely by accident, hit dozens of targets. There aren't tremendously rational ways to deal with that. We want to build those tools and we want to use them to solve some of those problems. In monogenic disease, there are lots of diseases that share the same fundamental cellular mechanism of dysfunction. In the ones we work on, that's protein misfolding. Everybody knows about Vertexx Vertex's cystic fibrosis franchise. The thing about that franchise is that because of a founder effect, 85 plus percent of the patients all have the same variant of the gene. What about all the other diseases where the burden is spread out across many more mutations in the gene? If you could actually deal with that in parallel, you could convert a lot of monogenic diseases to look more commercially attractive and solvable the way Vertex has done with cystic fibrosis.

15:13
Ramsey Homsany (guest)
Those are the types of problems we work on. The targets are very well defined genomically, but we need new ways to actually both deconolve those targets so we know what we're trying to hit. We need chemistries that can actually alter molecules so that you can, in a systematically rational way, really dial in on the target profile you're trying to hit. Sometimes I describe this as trying to play chords rather than notes. It's really hard. The tools are finally here to do that. There's a lot of companies out there using synthetic biology tools on modality. We think there's also a big opportunity to use synthetic biology tools to tackle these sorts of problems as well, with a more traditional modality like small molecules.

16:02
Neil Littman (host)
Randy, you mentioned sort of the chemistry. You also have a biology engine. I want to get into that in a minute, but I want to actually go back to something you referenced earlier in terms of using things like AI and machine learning. If I understand correctly, you're taking an approach that is to some extent an alternative to using AI and ML to run simulations. What do you see as the limit of those approaches today?

16:29
Ramsey Homsany (guest)
I think it's a matter of timing. There's a lot of promise for AI and drug discovery, particularly in areas where there are well constrained models. So, for example, a structure problem constrained by a physics based model. AI can go town on that. There are a lot of areas of biology that just are not like that and won't be anytime soon. They're just much more messy and complex, still much more unconstrained. There's not a lot of well controlled data in those areas. We use machine learning, we use very advanced computational methods. We also don't think that's the panacea to drug discovery writ large. I think most people would agree with that. I don't think that's super controversial. We have this approach we call cellular intelligence. This is the idea that cells are these astonishingly intricate all. You can think of them almost as like, cities of complexity, right?

17:25
Ramsey Homsany (guest)
What would you do if you really wanted to understand all these interconnected information flows in a city or a cell? You wouldn't write a piece of AI to try to simulate the cell. I don't think you do that in the next ten years anyway, especially not when the cell is already doing all that work for you. Why not use all these synthetic biology tools to actually engineer the cells, to tell us what's happening inside them? And that's what we do. We really use the biology to decode itself. And so I'll give you an example. Let's say you wanted to know the functional effect of every possible mutation of a protein. This protein was roughly 400 amino acids long. You wanted to understand every single point amino acid mutation of the protein. That would be about 8000 different variants. You could go on an AI journey and try to create some classifier or model to understand what each of those different mutations, how they'll behave differently.

18:24
Ramsey Homsany (guest)
Or you can do what we do, which is engineer a different living human cell line with every one of those possible mutations in a living cell model and perturb it. And that's what we do. In an experiment like that, you have this 400 amino acids times either 24 amino acid possibilities or actually we do it by codons. You can do all 64 codons and then two pathways and then ten barcodes per cell line. Now you're talking about roughly half a million cells different uniquely barcoded cell lines. Ten years ago, people would have laughed at that, right? No one would have thought that was a possible experiment. We do that kind of work here. We build those cell lines, we test them, we validate them, and we generate these very large data sets that empirically tell us what's happening in these cellular mechanisms.

19:20
Neil Littman (host)
And Ramsey. That's so cool. In my opening, I mentioned that you're doing things that unprecedented scale. To that point, that's exactly what were just talking about. I mean, the ability to do all those experiments in those cell lines, I think I haven't heard of that type of throughput before. So that's really cool. You mentioned one thing that I want to pick up on and ask you about, which is this idea of the barcode. You have these multiplex barcoded assays that you use. Can you describe what that means and what that is?

19:51
Ramsey Homsany (guest)
Yeah. So, as in vitro screening, some common screening assays that people use in biopharma are in vitro screening assays. People also use Luciferis reporter assays. When you want to measure cellular activity and in those assays, you're looking for binding or you're looking for luminescence. We basically build versions of those assays that instead of reading out with a luminescent protein, you're reading out with a barcode. A barcode is just some string of RNA, a randomized string of RNA. In our case, we're usually using about 20 base pairs long, where we've recorded and we keep track of which cell and which reporter pathway has which barcode. When that reporter is activated, one of the downstream consequences of activating that reporter is to print out this barcode. We can use very cheap next generation sequencing to actually count those barcodes as a proxy for cellular activity. We're engineering reporters to report out on biological activities like signaling function, protein abundance, protein trafficking, and then we're screening compounds against those assays.

21:04
Ramsey Homsany (guest)
One of the things that's very powerful about that is you can build many different reporters and you can put them all in the same well or all in the same dish, and you can produce these exquisitely controlled multiplex data sets so that you can measure lots of different things at the same time. It not only increases your throughput, but it also creates differential measurements. You can very quickly deconvolve things that haunt traditional assays. For example, if you're testing a compound that is toxic to cells, if you're doing that one reporter at a time, you see the reporter report activity in our assay. We'll see all the barcodes report activity. We know that whatever is happening is not specific to one of the pathways or one of the targets. We care about something that's happening to the background of the cell line. It just helps us get answers much more quickly, filter out noise.

21:57
Ramsey Homsany (guest)
In addition to actually helping us deconvolve what's happening inside the cells.

22:04
Neil Littman (host)
You've got both a biology engine and a chemistry engine at work here. You just described of both of those, but can you break that down for us? I guess really in particular how they interrelate.

22:18
Ramsey Homsany (guest)
Yeah, sure. I just covered the biology . That's our cellular intelligence engine. We really use that to chart biological mechanism both to understand what is a complex target of a biological process we want to target. We've built all the reporters and all the cell lines to figure that out. Now we can use all that technology as the actual assay infrastructure we're going to assay against. Then the next question is, okay, great. So now you have a target fingerprint. What are you going to do about it? How do you actually build a drug against that complicated fingerprint? We've built this nanoscale chemistry synthesis engine. We call it High throughput SAR where we're actually using these next generation technologies like acoustic microfluidics and fragment based drug discovery. Actually, some of these technologies aren't super new, but we're using them in new ways. What it allows us to do is build lots of new chemical entities in a very rational and systematic way.

23:19
Ramsey Homsany (guest)
Right? So we're not building random chemical space. This isn't like a Dell strategy, which Dell strategies are great. I think they're just useful for something else. What we're doing is we're taking a molecule we know has some interesting activity. Maybe it's not potent enough, maybe there's a little activity there and we're breaking that molecule up into cores and then we're using reactive chemistries to iterate on that cores with fragment library. In a typical hit to lead campaign, instead of having a few medicinal chemists build a couple hundred analogs for you, our synthetic chemistry engine builds hundreds of thousands of analogs and you just generate a lot more data and you really saturate the local chemical space to get a much better sense. For what does each potential variation of the molecule? How does that interact with the cellular intelligence part of the engine? It's really that intersection that matters a lot because we know that in some of these disease areas very small changes to the molecules can have profound phenotypic changes.

24:25
Ramsey Homsany (guest)
This is a very systematic way to churn through that space and build molecules that are going after those nuanced fingerprints. For an example, in one of our programs, our Furthest Along program, it's a program in autosomal dominant retinitis pigmentosa. We now have some really exciting leads that are more potent than compounds reported in the literature. They have great bioavailability, they have great blood retinal permeability, and those molecules are very different than the molecules we started with. In fact, if you were to show those molecules to a chemist, I think most chemists would say like, hey, those are not similar molecules. Our high throughput of chemistry enables us to scaffold jump in very non intuitive ways to get there. A lot of that work is guided by in concert with medicinal chemist. It's not like a robot doing everything, but through this hyper experimentation, you generate much larger data sets to push the process along much faster and with more quality.

25:33
Ramsey Homsany (guest)
It's not just about speed. You also are able to dial molecules in on these complex profiles where you need to hit. In the Rodopsin program, for example, there's about 40 different variants of that gene that we'd like to target. How do you run a drug program where you're trying to target 40 different targets at the same time and optimize on permeability and optimize on bioavailability? I think it's pretty much impossible to do that. Using traditional drug discovery tools really requires this kind of multiplexed parallelization of all those things at the same time so that you can filter out a lot of the noise along the way and try to quickly get to interesting lead compounds and ramps.

26:17
Neil Littman (host)
I want to talk about some of your pipeline candidates here in a minute, but how scalable is this approach that you've developed across therapeutic areas or across various indications?

26:31
Ramsey Homsany (guest)
Oh, that's a great question. I think there's so many different ways to measure scale. Maybe I'll talk about a few that I think about. One of the factors of scale is that we really focus on cellular mechanisms. As I was saying before, ADRP, autosomal dominant retinitis pigmentosa, is a disease of protein misfolding. Basically there's a mutation in the protein where the protein is not getting to where it's supposed to go in the cell. That could cause problems in different ways. It could because the protein is not functioning, getting to where it needs to go and functioning properly. Actually, in that disease, a lot of the burden is caused by the protein just jamming up the cell. That's a common problem in a number of other monogenic diseases. It's actually a problem in more common diseases as well. I think the most famous one is cystic fibrosis, where that's a misfolding disease.

27:22
Ramsey Homsany (guest)
Vertex showed you could build a small molecule chaperone against it. It's just that there, as I said before, most of the patients had the same variant, so it was a lot easier to target just one variant to start with. You talk about scale, well, what if we could make a bunch of different rare disease look more like cystic fibrosis? We've been able to do that here. The ADRP program that took us about a year and a half to get off the ground. We built all those reporter constructs, all those gene circuits. We adapted them to another rare disease of misfolding in under two months. You have the scale of what I think of a scale of cellular mechanism where you have this repeatable toolkit you can use in other similar diseases. There's other types of scale. So, for example, the scale of our chemistry. As I mentioned in our ADRP program, in the Hit to Lead part of the program, went through about 250,000 different new chemical constructs or new chemical entities.

28:26
Ramsey Homsany (guest)
Now, that doesn't sound like a large number to a biopharma company that has chemical libraries of millions. I'm not talking about Hit finding, I'm talking about Hit to lead. That's a lot of analogues to be building in a Hit to lead campaign. And then there's the scale of biology. We're just very good at building reporter constructs. We've now run experiments with over a million unique cell lines in them. We're able to build these really exquisite reporters really quickly using these pooled synthetic biology approaches. Every time we build one of those reporters, it enters our library and we can use it going forward. A lot of our counter screens, for example, in our misfolding assays are the primary screens for the other program. Right? If you want to know, hey, is this drug actually selective to my target or is it just greasing up the Er and causing everything to get through?

29:24
Ramsey Homsany (guest)
Well, we put a bunch of our other cell lines in that assay because then we'll get to see whether the drug is being selective to the program we're currently working on or whether it's just doing something in the background of the cell. There's lots of different ways to look at the scale. Those are a few of the ways I think about it.

29:42
Neil Littman (host)
Yeah, that's super helpful. This is actually probably the perfect transition to talk about your business model. You have the drug discovery engine. Do you plan to move those candidates through clinical trials on your own? Do you plan to move those into partnerships and have others develop those candidates through clinical trials? How do you think about the future of Octant kind of moving forward?

30:11
Ramsey Homsany (guest)
Yes. This is one of the hard problems of platform biotech. I'll bet you if you asked me this question a year and a half ago before the market started to collapse, I'd answered pretty differently, just to be completely honest. Our advantages are clearly on the discovery engine and the early discovery side. That's where we drive most of our value today. We can build really high quality small molecule leads for difficult problems very quickly in a very genomically, validated way, very cellular mechanism driven. That being said, one of the things that's great about our platform is it's. Applicable in many different places. We're working on a cluster of rare diseases. We've done a lot of work and very interesting in GPCRs, which are an incredibly rich target space. We're working in kinases of BMS, so we have this luxury of being able to be choosy about what we work on.

31:06
Ramsey Homsany (guest)
We don't invest in programs unless we feel they would be attractive candidates to take into the clinic ourselves. That's sort of our threshold. Could this program be a lead program for us? If not, we don't really want to work on it now. That doesn't mean we're going to take all of them into the clinic ourselves. Just realistically, some of them aren't going to make it. If there are opportunities along the way to do good deals where we learn from a partner, we get to diversify some of our risk, we get to leverage the platform more, we'll do that. We do hope to one day be a commercial drug discovery company. Our primary lens through which we see that is building our own pipeline over time. We obviously are also open and active in business development and always very excited to meet with partners who we think we would work well together with.

31:58
Neil Littman (host)
I want to talk about one of the partnerships that you do have but I just want to ask you a follow up question in terms of your pipeline and thinking about the future. I think there's a couple of ways to think about building value for a platform drug discovery company. One is to really concentrate on the platform and spitting out a bunch of molecules, moving those forward and rinse and repeat. The other, which is more of the tried and true biotech playbook, is you find one molecule, put all your effort into that molecule and deriving value for moving that through clinical trials. How do you think about those two as you're thinking about building value for the platform?

32:39
Ramsey Homsany (guest)
Yeah, unfortunately I don't think there are easy answers here. It's something we've definitely struggled with a lot. I think one of our added challenges is our platform is really well suited for some disease areas where you have to go into the clinic to prove value. This is something we think a lot about and we try to calibrate carefully. We definitely want to be a multiprogram company. We're not doing the just one put all our wood behind one arrow, but on the other hand, it's also not enough to just spit out a bunch of molecules, claim they're in lead optimization and hope to get value for them. I think we do have to show and validate that our technology is producing quality drug candidates and that's a hard bar. We've been through this past two or three years of just this massive biotech exuberance and there's a lot of companies out there making a lot of claims about a lot of things and in the end, we all have to show up with our data and show that.

33:54
Ramsey Homsany (guest)
Yeah, first of all, it actually is the platform that's building these drugs. It's not some drug and we have a platform next to it. Second, there's real quality here and we're generating something that is unique. I think it's very hard to do that if you don't at least have some proofs of concept that you take pretty far yourself.

34:20
Neil Littman (host)
Yeah, I totally agree. I think a lot of these questions are questions that a lot of tech bio companies struggle with. Right? As you're building a platform, you need to validate the platform through a case study or through clinical data. These are big questions for our industry in general. Ramsay I want to circle back to a collaboration that you have with BMS, Bristol Myers, that you announced at the time of your Series B financing, which is focused around a set of inflammatory related pathways. Can you talk about that collaboration and the goals?

35:01
Ramsey Homsany (guest)
I'll try to say what I can. Obviously, there's a bunch of confidentiality around that agreement. We're just really excited to be working with BMS. I think they're really one of the companies that we think get it. They really understand and are forward thinking about the role of genomics in drug discovery and in drug development. We had been talking to them for a little while in conversations that were put together through our primary lead investor in our Series A and then a big investor in our Series B, Andreessen Horowitz. They are very excited about our deep but genetic scan technology. We're excited about it too. This is that technology I was talking about earlier, where you take a target you really care about is interesting, and you build every possible single amino acid mutation for that target. You can bar code the different downstream pathways of every variant of that target and you can start running both drugs, tool compounds and dodging the sligans, small molecules, peptides, et cetera, against that target.

36:12
Ramsey Homsany (guest)
You can start to learn a lot about the target. It basically becomes a proxy for SAR in some ways. You can use the data with like GWAS data to better understand what the function of that target in the human population is. The BMS team is really a leading force in this area and we're partnering with them to work one of their immune programs and doing some of this DMs work. It's really exciting, I think, and trying to be humble, as I say to this, I think we are the best team in the world to do these deep metrogenic scans and I'm super excited about what they're going to mean for the future of medicine. Our first foray into that is this partnership with BMS. After that, they approached us after we had signed the deal, their scientists were just really excited about technology, and they asked if were investing anytime soon and if they could invest in the next round.

37:15
Ramsey Homsany (guest)
They also participated in our Series B as a separate standalone engagement. That was the greatest flattery for us. Just the idea that they were impressed enough that they also wanted to be invested in the company and that was really exciting.

37:29
Neil Littman (host)
Yeah, I mean, that's obviously a huge road of confidence. You had mentioned about interest in other types of BD partnerships going forward. I'll just ask about that. Probably a hazard of a lot of the time I spent in my career doing business development. What are the types of things that you're looking for in a partner? What would attract you to a potential partner in the future?

37:54
Ramsey Homsany (guest)
Sure. I think we just have these very unique abilities to iterate on these hard problems. This could be applying the DMs technology to either genomically risk targets or to derisk targets sorry. Or to work on a target someone already really cares about to really better understand that target. So, for example, you can use the technology to look for allosteric binding sites on the target. All of this is not by measuring binding activity, it's by measuring functional activation of the downstream pathways of the target. You really can really understand the target better by doing that. We also are very open to drug discovery, more common drug discovery partnerships. So, for example, a lot of times you have interesting chemical matter and you get stuck because you've shown some level of potency or you've shown something interesting that the target is doing. You've tried to build a bunch of analogues to that target, and you're just getting stuck with the way that's going.

38:56
Ramsey Homsany (guest)
Our platform is a really great platform to build living human cell models for the activities you're trying to modulate and then go in and really just explode the space around that chemical matter and thoroughly test it so that you can get a much better idea of what potential opportunities in your existing chemical space might be. The other area I would say, is in GPCRs. We really have some cutting edge ability to build genetic reporters for GPCR pathways, the various different downstream pathways that GPCRs act through. If you're a company that really cares about Gprs and is working in that space, we can do some really interesting things, both finding hits against those targets, but maybe even more importantly, optimizing polypharmacology and bias against those target classes. We know that a lot of the most valuable drug areas over the course of human history have been Gpr targeted areas.

39:55
Ramsey Homsany (guest)
We know those targets are extremely the drugs in those areas are extremely promiscuous. A lot of them were discovered by mistake. We think it's this massive exciting opportunity for the future to be more rational about how we develop drugs in that space. That's been really hard for a long time. We're excited to work with partners who have biological expertise or existing chemical matter that they want to further optimize in this new kind of way to solve some of those problems.

40:24
Neil Littman (host)
And these are hard problems to solve. It sounds like you have obviously developed a, a pretty powerful platform to try to overcome some of these challenges. So, Ramsay, you and I could probably talk for another couple of days about some of these topics. I do want to be cognizant of your time and ask you one final question, and that is, what is your vision for the future and where do you see optin in, let's say, the next five years or so?

40:49
Ramsey Homsany (guest)
It's so hard to think five years ahead when we joined this company or when we started this company. Funny, I think of it as joining because I feel like we joined a movement. We both left behind very successful careers. Sri was a tenured professor at UCLA. I had built a pretty good career in tech. We both deeply believe that these new technologies are just going to change the future of medicine in a lot of ways, and that someone needed to take these bets, someone needed to apply these synthetic biology tools and now these high throughput synthetic chemistry tools in concert with automation. We didn't even talk about our automation today and our computational approaches to just crack some of these hard problems. There are problems that are extremely important in society. They're huge disease burdens areas we're not making enough progress in. In five years, I hope that we have multiple candidates in the clinic in different therapeutic areas.

41:54
Ramsey Homsany (guest)
I hope we've developed the platform both in its capability and its scale. That's been going really well. It's been really exciting to add new chemistries to the platform, add new biological capabilities to the platform, but also to just increase the scale. About a year ago, we could run one to one and a half programs at a time on the platform. Now we can run six or seven programs at a time on the platform. There's really no ceiling to that over time, and that we're generating progress in a bunch of disease areas that portend hope for patients. I think that's the ultimate goal. One of the things Sri and I have been clear about in building this company is that we're trying to take the bets that we think other people aren't taking and that society really needs to take. We're less interested in being in crowded spaces where there's lots of other bets.

42:45
Ramsey Homsany (guest)
I think there's something we learned during COVID is there are times when society just needs you to do that thing that could make a difference, but if you don't do it, nobody else is going to and really target the company of those things. Five years from now, hopefully, we're doing this again and we have multiple candidates in the clinic and we're well on our way to really being a full stack biopharma company.

43:06
Neil Littman (host)
And Ramsay, I for 01:00 a.m. Extremely grateful that you and Sri decided to take the plunge and build Octant because I think there's a huge need for it and there's so much potential with what the company is building. So certainly applaud your efforts there. With that, Ramsay, I want to say a big thank you for joining me on the show today and your time today.

43:27
Ramsey Homsany (guest)
Thank you. There's been a lot of fun. I really enjoy the podcast. Thanks for having me on.

43:35
Danny Levine (producer)
Well, Neil, what did you think?

43:36
Neil Littman (host)
I thought that was a really great discussion with Ramsay. I mean, a wideranging conversation. I loved his perspective on the culture and merging the biology and the chemistry and the technology culture. Obviously, given his background at places like Google and Dropbox, he comes from the large tech company side. You heard him say when he joined those companies, they were much smaller. He has that perspective, what it takes to build and scale a hugely valuable technology business, and they're merging that with the scientific, biology, biotech part of the equation. So that was really interesting. I love the nuanced discussion around the platform that they developed. Right. There's a lot of pretty complex technology that goes into their platform and what they're building. You heard them talk about what they call cellular intelligence and how they integrate this barcode assay and what that means. Those are just really cool approaches to drug discovery and drug development that, to my knowledge, no other folks are doing.

44:43
Danny Levine (producer)
He certainly seems to understand that tech and biotech are different beasts. In the case of optant, like other tech BIOS, you need to be of both, as he said. How difficult is finding that balance?

44:59
Neil Littman (host)
I think it's extremely difficult, and you heard Ramsay say as much, right? I mean, there is a balance between the tech and the biotech aspect, and there's a balance between building the platform and the drug discovery engine and building a pipeline of small molecule candidates and then actually validating those candidates with meaningful POC data, with clinical data. And so there's that balance as well. I think there's a lot of challenges, and these challenges are not unique to oxygen per se, right? I mean, I think these are challenges that our industry and tech bio companies face as a whole. You heard Ramsay's approach to derisking some of what they're doing, and I think that's the right approach, right? I mean, you need the engine, you need to be able to rinse and repeat, so to speak. You also need to validate those molecules, those candidates with some preclinical proof of concept, some clinical proof of concept, and that all helps validate the platform.

45:58
Neil Littman (host)
There's a whole host of challenges here, but I think they're approaching it the right way.

46:04
Danny Levine (producer)
Octon has an agreement with BMS. They've become investors in the company, which is certainly validating. The challenge for companies with powerful discovery engines, though, is getting value for what they do. You talked about this with them, but in terms of building that value, can they partner their weight.

46:27
Ramsey Homsany (guest)
There or are they going to really.

46:28
Danny Levine (producer)
Have to become drug developers who carry their molecules to the market?

46:33
Neil Littman (host)
Yeah, that's the billion dollar question, Danny, and I don't think there is one right answer. I think there's a multitude of ways that you can create massive amounts of value in this industry. Right. The traditional biotech playbook is to become an integrated biotech company, develop your own candidate or candidate and move that through clinical trials to demonstrate value with a single asset or a couple of assets, right. There's no question that acquirers pay massive billions of dollars for single assets. Now, that's not the only way to do it. I think what a lot of the tech bio companies are doing these days are building these massively scalable platforms where they could likely spit out a bunch of candidates. Whether they want to move those for themselves or they want to partner those, I think we're going to see a lot of new models emerge where the platform itself creates a ton of value and tech biocombies can be valued for the platform and not necessarily for an asset that they happen to be developing.

47:34
Neil Littman (host)
If you think about the partner business model, there could be some future state where OCTIn is developing a bunch of candidates, taking them through I'm just making this up, but preclinical proof of concept and then partnering them off. They do that in a higher throughput fashion. They have a whole bunch of molecules that they have some milestones on or royalties on in the future. Maybe that's a business model, maybe that's how they're creating massive value. I'm not sure which way they're going to go necessarily, but you heard Ramsay state of his opinion about a few different ways they could capture a ton of value.

48:07
Danny Levine (producer)
Well, until next time.

48:08
Neil Littman (host)
All right. Thank you, Danny.

48:12
Speaker 1
Thanks for listening. The Bio Verge podcast is a product of Bio Verge, Inc. An investment platform that funds visionary entrepreneurs with the aim of transforming healthcare. Bio Verge provides access that enables everyone to invest in highly vetted healthcare startups on the cutting edge of innovation, from family offices and registered investment advisors to accredited and nonaccredited individuals. To learn more, go to bioberg.com. This podcast is produced for Bio Verge by the lead media group. Music for this podcast is provided courtesy of the Joan Levine. Collect all opinions expressed in this podcast by participants are solely their opinions do not reflect the opinion of Bioberg, Inc. Or its affiliates. The participants opinions are based upon information they consider reliable. Neil bioverge.com, its affiliates, warrant its completeness or accuracy, and it should not be relied on itself. Nothing contained in accompanying this podcast shall be construed as an offer to sell, a solicitation of an offer to buy, or a recommendation to purchase any security by Bioberg, its portfolio companies, or any third party.

49:26
Speaker 1
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