Genetics Podcast 073
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[00:00:00] Patrick Short: Welcome to the genetics podcast. I'm here today with Quin Wills and Jack O'Meara. The co-founders of ochre Bio. Ochre Bio is a biotech company that's developing RNA therapeutics and using an approach called deep phenomics that we're going to talk about today. Essentially Jack and Quin, they can give a much more complete answer than me, but essentially the aim is to use a combination of genome sequencing, single cell transcriptomics, other techniques, imaging, and other biomarkers to develop really sophisticated models, understanding biology and furthering drug development. It started with the liver and their aim is ultimately to be able to develop a completely in silico liver model. So what that means basically is a computer model that allows them to run experiments as if it were a real liver.

And of course, what makes this attractive as you can do things that aren't possible in the real world. And you can also do things significantly faster and cheaper than needing to use organoid models, animal models, certainly, or having to do things in humans directly. So Jack Quin welcome to the podcast and I'd really love if you guys could maybe just take me back to when you first met and decided to start Ochre bio in the first place.

[00:01:01] Jack: Thanks, Patrick. Yeah. Rudy. Nice to meet you. I can certainly do that. Well, I guess it's a long ago and far away we actually met so Quin and I connected. I've been working in biotech in the U S for a number of years and was coming back to get closer to home to Ireland where I'm originally from, but it was a detour and went over to Tanzania to work at an NGO that my friend runs out there and I called Quin cause I'd write all those profile roles,on his work and I think we'd had a couple of messages exchanged. My first call was from a very remote middle of nowhere, Tanzania. Town and Quin had just gotten back from building three houses in the jungle in Costa Rica, which I think he is, he will tell you much more than I , but as a potential future retirement home, who knows where he's going with that one, we had this very kind of serendipitous and interesting first conversation where even though we both work in biotech and with advanced therapeutics, we spend most of the time talking about our other interests on our kindred spirits, beyond the therapeutic space.

So that's how we first connected. Very quickly met up in London and got kind of digging into our each other's work. And, uh, yeah, very quickly Bio was born. 

[00:02:01] Patrick Short: Tell us about the tree houses. And what did Jack say to you on that first call? 

[00:02:04] Quin: Yeah, it wasn't unusual Patrick, because you know, I've hopped all around education wise. Academic wise, I started my first love of genomics company before I was done with my PhD. So. I don't want to say I've seen and done it all because that's not true. But, um, I definitely, I feel had a good sense for where I wanted the science to go, where I felt the failings where I'd just stepped down from a role as head of genomics at a big pharma.

There were two things on my mind. One was, I was cautious about. Starting a company in Europe and Jack, and I can speak to some of that because we have some opinions on this topic. And so I basically went up, I spent a lot of time out in Costa Rica and went out to start building my dream tree house in some jungle and funnily enough,

the struggle is real. I actually went out to just part of setting it up, was getting wifi up and going. So I built this whole solar powered wifi system for the jungle. And one of the first call or messages that came in was from a, an accelerator in London called ETF. How is just deeply skeptical? I was like, oh, I don't know a, I mean, again, this is European.

That's going to be small money is going to be all the European attitudes, but agreed to have a call with, with the guy Johnny at that stage. And, you know, and he just made this very compelling argument for. Uh, their philosophy on aside from money, the next big thing that determines the success of a company are co-founders, you know, and I, and I've always, I've always been a big believer in founder, led companies, even in biotech, biotech needs to learn this game a bit better.

That just really melded with me. That was it. I flew home started looking about and listening to what these guys had to do. Uh, yeah. And got on a call with Jack and as he says, the rest is history 

[00:03:55] Jack: logging into this, expecting a biotech podcast, learn about solar panels and the jungle.

[00:04:02] Patrick Short: That's right. Well, we'll get there around minute at 30. We can see, we can get to the biotech part. I was going to ask about the wifi setup and all that, but I think we should, we should hear about EF because ultimately you guys did start the company. Joined EF and maybe you can share a little bit about how that process works, because it's a little bit different than some other ways that startup companies are born.

Maybe you could talk about how that came together and where you both, did you meet there or it sounds like you didn't, maybe Jack, you were thinking about it and managed to drag Quin out of Costa Rica and back to london.

[00:04:32] Jack: So maybe just for everyone's benefit, I can give you a bit of an overview, how it works in the thesis.

They call themselves a talented investor. And the idea being that a lot of the best founders are lost out there in the world and don't have the requisite or their capital or ideas or opportunities to start companies. So they try and bring together. They call super talented people. I don't know if I'd classify myself as that, but anyway, that's their thesis and essentially supercharge them and help them meet like complimentary skillsets and people who can really pull together the key ingredients to build a successful company.

And I like to analogize it a bit to love island, but for startups, but I realized that type of commentary may get me in trouble with the EF founders. But they are a great program and they put you through the ringer in terms of milestones, getting your kind of head together. And what kind of business do you want to build?

And everybody brought us together. I think that was the big, the big success for us was they released all the profiles a month ahead of the cohorts. We were already kind of very quickly honing in on each other, I think before the program actually started. And I think we officially got started before the program ever kicked off and then just spent the time building and trying to source kind of the requisite seed funding to get up and running.

[00:05:32] Patrick Short: That's interesting, so you actually saw each other's profiles and made the connection beforehand. They're like, uh, that we could really stretch this dating analogy or love island analysis is pretty far on this one can't we. And how did the actual idea come about? So did you put on the sheet looking for someone with an interest in building in silico liver models that had lightening struck or, or how did that part actually come about? And I, and I know it evolved a little bit, I'd imagine in the early stage, like every company does, because you start with a hypothesis, but you've got to, you got to test that in the real world.

So how did it actually start out? What was the kernel of insight or problem that you were both really interested in and how's it evolved? 

[00:06:09] Quin: One, it took a little bit of convincing. I had to convince Jack. I mean, I don't think Jack, you can correct me if I'm wrong, but I don't think I ever explicitly advertise that I'm doing liver science because the problem is people think wa uh, you know, everybody wants to do neurodegeneration or the latest app for this latest app for that. And 99 percent of people don't realize that the only major global killer on the rise is chronic liver disease. It has, it's so analogous to lots of other chronic diseases like neurodegeneration that we're really struggling to find therapies for. So I, you know, I've been in the space for many years and just really loved.

And I, and don't like the fact that the whole value chain is failing. It's not just the discovery science, but the modeling for the clinical trials. And yeah, I mean, I think the first conference, because Jack was really keen on the aging aspect, but it took a bit of convincing, thankfully. Yeah. He came around to loving liver, 

[00:07:02] Jack: but I guess I got on that point of like finding the right co-founder that really does fundamentally change the trajectory of a company. And I think that was as soon as we got talking and realized this, that we had a lot of the same philosophies around business building, what we wanted to achieve with our Short existence on this earth. I think that very quickly, um, roped me into whatever. Wild idea. Quin came up with for liver. I was going to get on board with pretty quickly.

[00:07:23] Patrick Short: Yeah, that's great. And talk me through that wild idea and what it was at the beginning. And also, maybe dive a little bit more into the problem. You mentioned it there, but, and we, and we've had people talk about non-alcoholic fatty liver disease and Nash on the podcast a number of times, it's kind of hard to understate.

What a, what a global epidemic it is, challenge it is. And, and also how little progress there's actually been in the last decade or so compared to many other diseases that have a comparable impact. So maybe you could just talk for people who are not as familiar with it, what the scale of the problem is, and maybe the different problems at different stages of that whole chain that you just mentioned Quin.

[00:07:57] Quin: Yeah. I'll explain from the science point of view and I'm sure Jack can pitch to that, the bigger picture here, but from, from a science point of view, there are three big problems. I feel you need to address if you're going to be serious as a serious liver therapeutics company. And the first is new biology, right.

There is very little understood around chronic liver disease. And that was what got me in my job with this pharma company, setting up a whole new genomics department to use these technologies like single cell sequencing, spatial sequencing to add a tissue level, discovered biology because. Yeah. As much as we all love genetics and genetics is doing great things for sort of de-risking targets in the drug discovery space.

It very quickly can come unraveled in the chronic disease space because it doesn't answer the fundamental questions of which cell type isn't even in the organ that you're interested in at what point in the 20-30 year disease progression, especially during the silent years, is that gene or target relevant.

So it's appealing to think, okay, this is a very big part of the problem that needs addressing. And it is. And we did a lot of that in my previous role. I think that the thing is, you know what next, and that's where we move on to the second bundle or the second issue. And that is just modeling. You know, we've spoken about quite a very quickly mentioned in silico live and all of that, but that all plays into this whole art concept.

What are the best models to really tease apart biology and establish mechanisms of action and to simplify it, mouse models don't predict what happens in humans. It's as simple as that. After 20 years of mouse models, I was just giving up. 

[00:09:27] Patrick Short: Mice are not humans. 

[00:09:28] Quin: They don't predict chronic liver disease. In any real way. And so it's very frustrating when you're in a pharma company, you and your team are coming up with targets.

You really have a strong conviction around, and then the absolute truth is will a DBDB mouse change. So there's that. And there's a second big part of that. And that's how this whole idea of just taking human livers and putting them on machines. I came around. I mean, I know this sounds incredible. This technology, this approach is becoming available now in the transplant world.

You know, not just for livers, but for heart lungs, kidneys. And so the idea of just going direct to human experimentation, you know, if you can answer your questions in an isolated human organ, that you can keep alive with blood and nutrients. Why not right. Because the thesis there is that that biology will still be better.

I mean, no, model's perfect, but it still be better than a mouse model. And it turns out it was, you know, we started doing this work in my previous role and it was very obvious, very quickly through the power behind these models, but in and of itself that doesn't solve the third and final issue in the space. And that is that chronic liver disease.

Like a lot of cardio-metabolic endpoints are very difficult to do clinical trials around. It's a completely different game to cancer, clinical trials. Very long-term in. Often unclear biomarkers of unclear relevance are you need huge amounts of patients for statistical significance. So just really, really messy.

It doesn't help. If you have, again, you can find your targets, you can do these atlases, you have correct models and slick ways, or very quickly coming to the right conclusions or de-risking when you still have to go through a very conventional trial. And so the realization, and this is sort of when I start thinking, oh, maybe I need to wrap somebody into this as the separate spin-out was that.

There is enough overlap in the biology between what we're trying to solve in the transplant space versus what we're trying to solve in the chronic liver disease space. And so we could do transplant trials, like you would do for any other orphan indication or region netic disease to really de risk. The final de-risking step before you need the hundreds of millions to go to very big national chronic liver disease, cirrhosis trials.

And so, so that is really it from a science point of view. That is the fundamental idea is to keep those three things going. I'm sure Jack can fill in on some of it. 

[00:11:46] Jack: Yeah, I guess my not a whole lot to add. I think that was a pretty nice overview of how we're thinking about building the initial foundations from the company from a scientific perspective.

I think the only thing I'd add is that commercially one thing that we've seen with a lot of the gene therapy companies that are now coming to the floor is that by focusing on an orphan indication or a rare genetic disease, oftentimes it's the case. You can get to the clinic quickly. You can run a tractable clinical trial.

That's not dramatically expensive. Like the last trial would be on there by de-risk the therapeutic platform or the drug discovery engine or whatever you want to call it. The portfolio of medicines that you are developing in internaly. Which allows you then to raise capital and fund more development, et cetera.

So we're thinking a little bit like, like that as a small biotech, where we can run a trial in a, in a, in an orphan indication like transplant and use the clinical proof of concept, and obviously do great work for transplant patients and for fatty liver disease more generally, but use that initial stepping stone so that we can raise more money.

Invest in a wider portfolio of therapeutics, ultimately moving them closer towards the clinic simultaneously and thereby de-risking and having a portfolio effect, which helps to sustain the company. Ultimately get more medicines into the clinic for patients with various liver disease. 

[00:12:55] Patrick Short: Yeah, absolutely.

Thank you guys. Both for the overview. So to dive into the transplant aspect, I know you guys do a lot of work around RNA based therapeutics. Maybe you could talk a little bit about what the first application of the platform is in transplant. And what you think, I know you're probably a couple of years still from ultimately wanting to launch that these things take a significant amount of time to an end investment to get over the line.

But maybe you could tell me a little bit about that plan and transplant, who is the target audience from a patient perspective and where do you see the need there? And then what's the technology. 

[00:13:25] Jack: Yeah, we just to give you a sense for, what's been a big challenge and transmit over the last couple of decades, is that despite improvements in technology and in a transplant setting, increasingly donor organ quality has been on the decline, largely the result of fatty liver disease and more and more donor livers that come in to centers, having some level of steatosis or us all living longer as well.

It doesn't help the donor pool. So surgeons are increasingly having to use livers for transplant patients that they wouldn't have used 10 years ago. That are on the kind of spectrum of, of, uh, increasingly diseased. And that's really where we want to have an impact is in initially in pre-treating high risk, you want to call it that are marginal donor livers with an SIOR and a therapeutic that we've designed in our labs here in Oxford to improve outcomes for patients who received these high risk donor livers and the primary end points to ultimately improve for those patients is what's peak liver enzymes. One week after transplant is the surrogate biomarker for long-term graft survival. So we'll know we've made an impact if we can improve that, which relatively Short end points Quins piece on how clinical development is different in this space.

But then also we want to follow those patients for a longer period of time to see if we have reduced recurrence rates of fatty liver disease or approved the broader cardio-metabolic profile of these patients who receive a improved or pretreated marginal liver as compared to a control group. where we're working through all of those pieces.

But hope that gives you a little bit of a sense of what we're trying to achieve with our first program. 

[00:14:45] Patrick Short: Yeah. So then how did you make the discovery in the first place and how to treat these organs? Maybe you could go into, maybe you can go into a little bit. Deep phenomics aspect and the wealth of different data types.

Quin you alluded to it earlier, that it's not just genomics, but you've got to really understand what's going on at a tissue level. Talk me through how you reverse engineer from we've got an unhealthy liver that we need to understand exactly what's going on and, and reverse engineer. What can we add to the mix to take it backwards in time or reverse its disease course so that we can make it healthier than it is today when it goes ultimately into a patient that might receive that liver. 

[00:15:20] Quin: Sure. Let me start with the, the modality. You briefly mentioned RNA therapeutics. I'll start with a modality and why we like it and then work back from there. Let's do the data and how, how, how we can, the actionable data we generate to get us to this.

So we. Fundamentally, uh, as it stands, I an RA therapy company therapeutics company, and we like, uh, RNA therapeutics, particularly a version called galvanic Sr RNA. So this is the gene silencing modality that has, uh, Galvanic sugar on the end to making it a powder site specific. And it's our prototype. It won't be our only approach but it's a, it's a very successful, very good approach. And we like it for three reasons. You know, the first is the speed with which you go from targets to therapy. You know, you, you mentioned a couple of years and sure, from a clinical point of view, uh, you know, this will be a, this is a multi-year strategy here, but from GAM moving quickly from a target point of view, it's a whole different game.

You know, we've already synthesized at gram level several lead on echos. Since we opened up our Oxford lab in June last year, that we're now beginning to use and human livers in general. Sort of human preclinical data on. So it's very fast and we love that. It's very self specific and we love that. That's very important because ultimately really why cirrhosis the end point for so much chronic liver disease is such a challenge is that it is a very multicellular disease and you need to tweak very specific biological leaders within various specific.

Yeah, small molecules, as far as I'm concerned are never going to do the trick. They will gently shine here and there, but not fundamentally solve the problem. And so we like that. And we like being on this trajectory of being able to tweak very specific genes in very specific cell types. And then from a clinical point of view, they are long acting.

I think a lot of folks don't appreciate this, these gala, these sort of highly modified S RNAs we use last for six plus months in effect with a single dose. So we can do a single ex-vivo dose in these clinical trials. So dose or liver while it's on a machine being assessed before being transplanted. Yet we can follow up the end points that Jack was talking about over many, many months.

So we love that, knowing that we love that as a good starting point for a modality. You, what is the actionable data? And so conveniently for those of us in spending many years in RNA, of course, RNA is going to be the actual data. And for us, it's at doing the kind of stuff that we love to do RNA, a single cell resolution RNA at spatial resolution and RNA at temporal resolution because we study livers on machines over time, but it's not the only data type.

Right. So it's very important for example, to have very good histology the end of the day. Nash are we talking Nash? You've spoken about Nash. Nash is defined histologically. So we have to speak to histological end points. So we have to do imaging machine learning to standardize what we do. You know, we've sequenced over a thousand livers.

We need to standardize that extract human interpretable features and non-human interpretable features. Do you do that? And then of course, there's just no substitute for having great clinical endpoints. And so having data sets, you know, large data sets where you have. 150 200 technical phenotypes as we have is incredibly powerful for building up this sort of multi-scale model of this is what's happening at a molecular level.

This is what's happening at a tissue level. This is hard maps tool, the clinical endpoint. 

[00:18:39] Patrick Short: I think a couple of pieces just really clicked into place for me. And I can also think I see where you guys are going. Long-term with this after a few minutes here of just hearing you break it down so systematically. Correct me if I'm wrong, but I'm gonna try to play it back to you. Basically step one is if you can really understand when you receive a liver, that's maybe not fit for transplant it's cirrhotic or has other issues with it. Yeah. High risk liver. There's two things you can do there. First is you can profile that liver and understand what's different about this liver on a real kind of granular level across all those levels that you mentioned.

So understand what are the at the RNA level and histological level, how's that different from the liver we'd be more comfortable translating. Step two is if you can insert RNA therapeutics, small RNAs to walk that back along its cellular trajectory towards a slightly healthier state, it's almost like a fine tuning or an orchestra here that you're playing that you're, you're trying to rewind that liver back in time.

But the interesting kind of step 2, 3, 4, that I think you guys have in mind, that makes a lot of sense to me is if you can do. In that context, you could probably do that in the body as well and much earlier stage. Right? So then step two is you find a patient who's not, doesn't need a liver transplant yet, and that liver hasn't left their body and you can play that same orchestra and fine tuning to make sure they never get to that cirrhotic state. For example, is that kind of more or less the two-step plan? 

[00:19:59] Jack: I love the orchestra analogy as well. 

[00:20:02] Patrick Short: It's all yours. I'm sure I took it from somewhere else. That makes a lot of sense. And I really like it. Maybe we can dive a little bit more into how you understand all those moving parts at a cellular level to begin with, because as you mentioned, Quin, it's a really complex process that involves genetics environment, everything else.

What do we know today about the processes that go that drive a liver from being healthy to unhealthy over 20-30-40 years. 

[00:20:27] Quin: Oh, you're going to start getting all the Quin's favorite theories, which is dangerous to do at this stage in the company, because of course inevitably the ones that we go to market with, won't be my favorite theories.

But let me first start by answering a little bit about the, again, the process and how we think about this. And then I'll talk to you a little bit about the biology. I think one thing. I only hear this from us that is becoming increasingly Vogue to speak like this. You've got to find the right balance between computation and experimentation.

You know, we are, we do both all the time. We sequence all the way through our pipeline. So when we sequencing, like I mentioned, we've sequenced over a thousand human livers are very high resolution in detail. We work our mechanism action lab. Our target validation lab works in primary tissues and does a lot of traditional work.

But we still sequence everything all the way through. And we still sequencing when we gather these livers. So we all building up big computational models. But at the same time, this is always very human centric and really involves a lot of biology. So finding that life balance is, is very much the motivation behind teasing apart, the right biologies, um, and even the in silico liver again, and that sort of, well, I'm sure we'll get to chat about it a little bit.

That's come up a few times is driven by this thinking of how do we find the right balance. We want to be able to screen every sub phenotype in the liver, but there are only so many livers in the world. How do we get computation the support that. Right? So knowing that, and in terms of how we think, what is tractable in, what we can do, uh, you know, we've broken down our liver biology into three big buckets, and this won't shock anyone that had largely overlaps with how others think about liver disease, but the first is as a metabolic machine.

So not just your insulin resistance. It's about the whole Samantha trophic axis in the liver and nutrient sensing and all those important nutrient sensing pathways that go haywire as you get older, which is why you tend to see this stuff as we get older. So there's all of that, but we're not just defending livers.

There. There's a bigger game, how that we playing to in, in the liver space. And that is very tractable with everything we've set up to do right now. The second big, uh, area biology. It's just how it cell state biology is, is how hepatocytes die. Don't die. You know, hepatocytes in kind of liver disease and transplant that they have so many fates it's not just senescence or apoptosis or necrosis.

It's Necroptosis, proptosis, Ferroptosis. And it's just, it's incredible that the myriad of cell states that these cells can go into under stressful conditions. And this is why. Frankly, for example, just chucking caspase inhibitors into the mix to reduce liver cirrhosis. They're not working very well because you're just hoping that they'll decide on another fate, which may be equally or even even more bad.

So thinking around cell state and how to guide cell states in different directions is very much how a lot of us think in the single cell space. That is, that is another big area of biology right now, mostly on hepatocytes, but watch this space. And then the third, big area biology, which we are now beginning to set up for is, you know, sort of the regenerative theme, you know, again, in chronic liver disease, we talk about inflammation, biology, fibrosis.

It's a regenerative medicine. That's what this is, is, you know, is that, is that healing scarring process that happens that eventually makes a cirrhotic liver, other players that over years and years, and how can we tweak it? How can we study a taco model of computation? And so again, to bring this all the way back into balancing computation experimentation, we've really had to think about how do we plausibly model a cirrhotic liver.

Can we measure enough cirrhotic liver, biology in a cirrhotic liver over multiple days on a machine. For example, it's all of that playing out and we'll, um, we'll hopefully have a few announcements sometime next year. 

[00:24:12] Patrick Short: Maybe we could actually talk about how you guys have gone from idea stage to, you know, raising money, to, to build a team and really start to validate some of these early hypothesis.

But there's obviously a very large vision and long way to go. And as I think Jack said early in the conversation, you all are not the typical Biotech startup founders for better, for worse. Most biotech companies are founded by industry veterans. Who've been developing drugs for 30, 40 years. By the time they decided to go do it themselves.

But there are a core of companies like yourselves that are led by very different group of people who may not have as many years of experience directly in drug development but have really deep experience in technology and think about new business models and those sorts of things. Maybe a question for you, Jack, is how do you think about how you stage your journey?

Because you all, unfortunately don't have the luxury where you can just go and ask for hundreds of millions of dollars because you've developed multiple drugs before you've got to take a slightly leaner approach. How do you think about staging that from a company perspective about what you do first and, and those stepping stones towards that big future.

[00:25:14] Jack: I think it's a great question, I guess, I could probably spend an hour on it? Um, but maybe I could try and comment that something will be useful. I think the first thing is we were very intentional about who we raised money from. So there are kind of an increasing number. I think it's still relatively small given in the economy, the overall biopharma financing industry, but there are folks who do believe in this model of founder led biotech, which is again not traditional a lot of biotech, feces kind of incubated and groomed as it were for the role. So I think that was something we were quite intentional about. And one of the reasons why we left London and flew to California to do a lot of our, our seed fundraising. I think the other thing that any investor gets excited about is results and ability to execute quickly.

And that's. Like, and was within the first eight months of us being financed and having a company were able to build the largest genomic Atlas of the human liver to date and talk about having actually executed on what we set out to do and I'm committed to do and do it at record pace. So I think that's been another big piece of it.

I think increasingly now there are more and more companies that look a little bit like us. And I say part of the reason why I keep dragging Quin into doing some of this media stuff like today. You know, you need to hear the story or you need to get out there and start banging the drum about a different model that ultimately we hope will progress more medicines to the clinic and then ultimately get to patients.

So we've been, um, I think quietly coming up with some ideas for what we're going to do over the next couple of years, now that we have the foundations in place and we have some initial results to point to. And then the last thing I'll say is just, you don't know what you don't know. So surrounding ourselves with people who have the 30 years experience in industry is very important.

That's what. We lean on a lot. I was just very incredible advisors who are around the company who helped us think through some of the challenges and questions. We also use a lot of excellent consultants for specific roles being around corporate strategy or IP, et cetera, to make sure that we are not tripping ourselves up long-term and have the best minds around the table.

I think that's been something we've been very focused on, on making sure as we set up to build a company. But Quin. I know you've got strong opinions on this as well. Anything coming to mind, you'd like to share 

[00:27:09] Quin: you hit it, bang on. I don't believe we're a biotech company. I know Jack likes to talk about us as a tech bio company.

I've I've heard of folks talking about a sort of deep tech and with this, this sounds a lot like deep tech I've never really understood the definitions of half these things like particularly deep tech. What is deep tech? It just sounds like science to me, we're a science company. We've we've figured out how to bring together technology. With a, with an idea on really wanting to meaningfully make therapies. And when you, when you have, when you're spinning plates like that, I just go with the data and the data very strongly suggests that the best model is founder led companies. And that's what we're trying to do. 

[00:27:50] Patrick Short: I know you guys have been running your own show and, or at least we're on clubhouse the other day with Ethan Pearlstein right. Talking about this very concept. How, how did that come about? And I'd love to hear more about this core of founder led biotech companies. And what are the, especially what are the advantages that groups like you all have over the existing model? And I know Ethan has been shouting from the rooftops about different ways of doing drug development and in particular, going very granularly down to the motivated patient level to give them the tools on the ultra rare disease level. I'd love to hear more about that. And maybe you could give a plug for the clubhouse show if you guys are doing it fairly regularly, 

[00:28:25] Jack: Well, firstly, it's a, huge, huge fan of Ethan. I think he's just an inspiring character and massively driven and doing great things for the world and incredible moral and ethical competence.

I think we really deeply admire and he's an investor, in us. So, clubhouse join us Sundays. 1:00 PM EST 6:00 PM. UK. We do interviews like this with other biotech founders. 

Hear a little bit about their story. I do have a slight bias, but not, not necessarily the case, but slight bias towards folks who are kind of do this founder led model.

I'm trying to prove it out. And some of the people I think I, I really admire in the field who are doing that is I think Trevor Martin is a fantastic. CEO and founder of mammoth, bio sciences, I think an OBS client. It is Joe with me and Selena was another just rock star in the field. And then of course there's many, many more I will go on and on and on, but I think it's an interesting model.

And I guess, do you ask your question about what are the advantages I can kind of straight off the bat, tell you what some of the disadvantages are, some of the things where we're not advantage and that. Your play drug buying in IP and just developments. I think. That's not our, our strong spot but I think where companies like ourselves really flourished through this founder led model is on the edges of the innovations of new technologies that converged to really give you a differentiated approach and a really competitive space.

I think that, and you've seen it in tech, right? I mean, not that tech is great analogy for biotechs, such different industry and regulatory and ultimately patient centric space. Um, but a lot of these really new innovative technology companies really flourished from the people who know the science or closest to the technology, and really understand what we're trying to bring together are in the helm and really driving deep innovation.

And then it also allows you to think kind of creatively. And I guess this is more, my role on the business development side is like, how do you think about novel business models that suit this really deep, innovative science? You know, this whole hub and spoke model is quite interesting or setting up the business model is such that you can bring in the clinical development experts when you need them on for a very focused programs or thinking about creative ways as we move closer and closer to clinical and commercial organization, but definitely for where we're at now in terms of coalescing, a lot of really interesting RND I think we're right at the front of it, but Quin, you might have additional comments, 

[00:30:25] Quin: I'll say one or two things. Cause I know you Jack and I can talk for hours on this topic, personal passion. But I think at the end of the day, I don't like to use the word disruptive either because you know, what, what does that mean? But if you are trying to achieve a step change in your field, you need to be able to join the dots.

And if you don't have the dots, you can't join them. And that's the problem with parachuting people in rather than having founder led companies. 

[00:30:50] Patrick Short: That's a really good way to put it and also you, I've got to imagine that there's a tendency to be a little bit reactive and behind the curve, especially when very new technology paradigms come across.

Occasionally there are exceptional people who can kind of break out of that paradigm that they might have been in for, for their entire life. But more often than not probably, it's really hard to think truly outside the box. If you've been in the field for to long. 

[00:31:13] Quin: We'll go with that for sure. 

[00:31:15] Patrick Short: We're running up on time here.

I did want to just circle back to the, in silico liver and hear a little bit more about that. Cause it sounds like that's a big piece of the puzzle in the, in the next couple of years, I can say the very first research lab I worked in was a wind tunnel and I thought it was going to be fun, but it was actually ended up being really boring.

I was watching leaves flap in this wind tunnel and measuring something. The second lab I worked in, which I really enjoyed was. Uh, we were building a computational model of, um, of the mitotic spindle, which anyone who does cell biology, it's the thing that helps cells kind of split apart. But what was really powerful about that is it was right next to a wet lab where we could build the model and then they could actually test the models, predictions in the lab.

So maybe you could talk a little bit about the in silico liver. And I noticed on your website, you've got some people say wet lab on their title. Some say dry lab, and some say no lab, which I thought was super. I think Jack, maybe you're one of the note lab, crew. Maybe you could talk about that setup and the power between having those two pieces under one roof.

[00:32:12] Quin: Sure, absolutely. It's I'll speak a little bit. So you, the wetland dry land mix, and then I'll briefly mention the in silico liver and why, so the wetland dry lab thing, it's a tough game. I'm sure you know this as well as we do. It's a very tough game to get right. I think one thing that I've come to learn over the.

Is the whole idea of putting wet lab scientists and dry lab scientists into the same space and hope magic happens is generally not a good idea. Again, it's joining the dots. You need to have enough people with the dot doing that. And so, you know, we do focus on having individuals, you know, some people are more wet labs.

Some people are more dry lab, but also having individuals who. Both so that, you know, that process happens in a better way. It doesn't guarantee it, but it helps a lot. So yes, we do focus on a lot of that. And part of that, we do think about things like the in silico liver. Now, you know, people have been wanting to build in silica organs for some, since computation began in earnest in our space, I think for me, one thing had to happen and that was, we had to be doing biology at the fundamental unit of life.

So until single cell sequencing kicked in and the technology we have now, what is it? We really do in the space. Okay. So now that we can do this and now that we can do this on livers, that we keep alive on machines, what do we do with it? Do we want an all singing or dancing or do we want something that really augments what we do?

Explain it experimentally. And that's what we've gone for as a company, you know, we want to. And again, this is not to discredit some of the sort of biology at scale companies out there that are using huge robotics, but we just feel for our space the big compromise there's complexity. And we don't want to compromise that you do need to understand complexity.

And so what we're doing with this in silico level, with all those data that we generating in Oxford type fusion centers some stage next year, we predict that there, particularly the single cell sequencing data will be large enough and powerful enough to start making good predictions about genes before we've tested them.

So that means we don't have to, every time there's a new phenotype, like maybe we want to study. So in essence, we don't have to now get 30,000 livers and screen each one of them with a gene and measure the phenotype. We can test a couple, put it into the model and the model will tell us, which are the next ones we need to be looking for.

So that's the basic idea. And again, it comes back to the balance. 

[00:34:24] Patrick Short: There's a really powerful flywheel there, right? Because you can then do hundreds of thousands, millions of livers in a totally software experiment. Right. And test it in the real world on a couple and, and make sure you're on the right track. Jack, are you going to add something to that?

[00:34:37] Jack: No, I think that was already a nice, a nice summary. 

[00:34:39] Patrick Short: Yeah, no, I, I think so too. Maybe last question here. I'd love to hear from both of you all just before we close out, is if you're successful at Ochre bio, let's assume everything goes your way over the next two decades. I know it's a long way out and I won't hold you to this, but what does the world look like?

What's different. Quin, maybe you're retired in your tree house and you've got Starlink beaming, a nice high quality wifi, but I assume that was a live when you were down there, but maybe it was I'd love to hear what, what, how's the world different? In 20 years, if you guys are successful. 

[00:35:09] Jack: I think one of the things we think about internally a little bit careful how to articulate this, not to get myself in trouble is this idea. I guess you could call it preventative medicine or health span, but ultimately therapeutics that help people stay healthier longer and the liver being the brain of the metabolism. If you want to call it, that is a great place to start thinking about broader systemic health and modulating, potentially cardio-metabolic health, et cetera, such that we can ultimately aspire towards a different type of medicine.

That's less sick care and responding to cirrhosis per se, but ultimately bringing therapeutics that help us never reach that point and stay in a, in a healthier state for longer. 

[00:35:51] Quin: That's exactly it for us it's this problem of, or the challenge of, you know, in the UK, for example, one in six women born now can expect to live to 100.

What does society look like? Where we should all expect to be hundred healthy, a hundred year olds, because the current paradigm of preventative health, where it's just, you know, eat less, move more, not really doing great. So where does that go? And we do think liver is a very big part of that. 

[00:36:15] Patrick Short: 100% thank you guys. Both. I really appreciate you taking the time. This is one of the favorite conversations I've ever had. It was great to hear about your founding story in particular at the beginning. Sounds like you guys were a match made in heaven from the get-go and thanks everyone for listening as always. 

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