Quin Wills - Ochre Bio - Part 2

Pursuing Dual Masters at Oxford & Cambridge | Moving from Genomics to RNA Research | Balancing Academia & Entrepreneurship | The Challenges of Product-Market Fit in Biotech

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Show Notes

Part 2 of 4. 

My guest for this week’s episode is Quin Wills, CSO and Co-Founder of Ochre Bio, a pioneering biotechnology company developing RNA therapies for chronic liver diseases. Using a combination of genomic deep phenotyping, precision RNA medicine, and testing in live human donor livers, Ochre is developing therapies for liver health challenges ranging from increasing donor liver supply to reducing cirrhosis complications.

In addition to his work at Ochre, Quin is also a highly accomplished academic with a medical degree from Witwatersrand University and doctoral degrees from Cambridge and Oxford in computational biology, mathematics, and statistical genomics. 

Along with his academic accomplishments, Quin also co-founded SimuGen and has worked at the University College London, the Mayo Clinic, and Novo Nordisk before he went on to co-found Ochre Bio. Quin's diverse experiences offer a wealth of insights that everyone can draw inspiration from.

Join us this week and hear about Quin’s:

  • Early interest for bioinformatics and genomics
  • Pursuit of dual degrees in Computational Mathematics and Biology at Oxford University at Cambridge, respectively
  • Exploration of single-cell genomics, RNA, and gene regulation
  • Experience founding SimuGen during his graduate studies

Please enjoy my conversation with Quin Wills.

As a podcast listener, you can redeem exclusive discounts with a growing list of biotech vendors and get $500 off your first equipment lease by using promo code “TBSP” on https://www.excedr.com/rewards.

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Episode Guest

Quin Wills
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Quin Wills

Quin Wills is the CSO and Co-Founder of Ochre Bio, a pioneering biotechnology company developing RNA therapies for chronic liver diseases. Using a combination of genomic deep phenotyping, precision RNA medicine, and testing in live human donor livers, Ochre is developing therapies for liver health challenges ranging from increasing donor liver supply to reducing cirrhosis complications.

In addition to his work at Ochre, Quin is also a highly accomplished academic with a medical degree from Witwatersrand University and doctoral degrees from Cambridge and Oxford in computational biology, mathematics, and statistical genomics. Along with his academic accomplishments, Quin also co-founded SimuGen and has worked at the University College London, the Mayo Clinic, and Novo Nordisk before he went on to co-found Ochre Bio. Quin's diverse experiences offer a wealth of insights that everyone can draw inspiration from.

Episode Transcript

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Intro - 00:00:01: Welcome to the Biotech Startups Podcast by Excedr. Join us as we speak with first-time founders, serial entrepreneurs, and experienced investors about the challenges and triumphs of running a biotech startup from pre-seed to IPO with your host, Jon Chee. In our last episode, we spoke with Quin Wills about his early years growing up in Johannesburg, the importance of seizing opportunities when they arise, and challenging the status quo. We also covered his early experiences in academia, including his time as a research scientist at UCL and his transition from medical school to studying genetic and computational biology, as well as his early exposure to and passion for bioinformatics. If you missed it, be sure to go back and give Part 1 a listen. We continue our conversation in Part 2, further discussing Quin's time at Cambridge, his pursuit of dual degrees in genomics and mathematics at Oxford University, and his early exploration of single-cell genomics, RNA, and gene regulation. We'll also cover his experience founding SimuGen during his graduate studies.


Quin - 00:01:23: Eventually, I got accepted to the Oxford Master's Program and the Cambridge Master's Program at the same time. I ended up deciding to do both, in fact. The Cambridge one was more about traditional computational biology, learning to code, learning to think about big statistical modeling, learning to work with big data sets. Whereas the Master's in Oxford was more traditional mathematics, applied mathematics, computational mathematics, a lot of mathematical biology. It was kind of two years of my life where I could just ignore the world and absorb as much as I can, not just about the science, but also about the fashions. I think what people forget is that while science might try to be objective, it's very different by fashion about where we want to place our focus. Right? in the early days of bioinformatics, it was very unclear as to the different philosophies or the different cultures that we drive in. I was like, oh, what the heck, I'll just do both. Why not? Avoid reality for another year or two.


Jon - 00:02:20: Yeah, that's hilarious. And I think that is another thing that's worth reminding is math itself also feels like this objective thing where you don't question it. And it kind of goes back to what you were saying before when you had that early classroom experience. You're just like, why is it being done this way? And if you just take that and just ask why, and you can start to unpack like, oh, we actually are looking in the wrong area right now. And during their time in your master's program, if you're jogging your merry-go-round. What were the paths bioinformatics? What was the fork or maybe multiple forks in the road at that point in time? And where were things going?


Quin - 00:02:58: I think for me, it boiled down to, do I want to do mathematical biology? And I think the answer to that was no. As much as I love Mathematical Biologists, they are incredible subspecies, but not for me. Do I want to do some of the more traditional Structural Biology? And to be honest, again, not my thing. It came back to genomics. And again, I think that was my first big move away from traditional genomics and a DNA guy to an RNA guy. That's the point at which I became an RNA guy. I'm like, oh my goodness. Again, let's go back to old technologies. This is how old I am. I was doing my M4 in Cambridge when it was gene expression microarrays. We were printing things on glass. We were not sequencing. But I felt very strongly that this is a hug. This is a really interesting, chewy space to get into. That was the problem. And that drove everything in my decision to do the PhD that I did. 


Jon - 00:03:54: Very cool. Very cool. And during this master's. You know, the dual masters. Was it basically like it was a dry lab? Or did you interface with wet labs as well?


Quin - 00:04:03: It was very dry lab. This lesson hit me in the face quite hard during my PhD is I hate analyzing other people's data. 


Jon - 00:04:15: Well, that's good that you discovered it.


Quin - 00:04:16: The way I sort of explained it, I had a very patient super body. I was like. I did not do all these studies to analyze other people's data.


Jon - 00:04:27: Yeah, yeah, yeah, yeah.


Quin - 00:04:28: But again, it was a realization you have to come to is that I'm very driven by, there is a problem out there that annoys me. I want to solve it. Data is a very big part of it. But there are many other elements that need to be solved too. And I want to be part of that. When we get to the story of Oka, it's kind of reflected a little bit in that too.


Jon - 00:04:46: Very cool. Okay, so now you're heading over to Oxford for your PhD in genomics. You've kind of started to pick your lane a little bit. But can you talk a little bit about your PhD experience specifically at Oxford?


Quin - 00:04:56: I had such a good time. I really, really did. PhDs are a slightly different beast in the UK. They're much shorter, which suits me just well. I mean, I stretched mine out into four years. But they're just very, you just get on with it, right? Which I quite enjoyed. And I was surrounded by just incredible people, very inspirational people in this very intersectional space of biology and data. And again, got to see a lot of different phenotypes and the way they, they like to do things. But I think, as I just mentioned, I think one of the biggest lessons for me was, I do not like analysing other people's data. I enjoyed it for a while because, you know, when you're coming to a PhD, because again, you're nobody, who are you? You know, here's a great data set, work with it. And I was very intrigued by the eQTL problem. You know, so connecting DNA to molecular traits, connecting those. Again, you'll see this plays out in the younger story, but I thought that was a really, really fascinating. And I was very happy to be involved in big projects and meet this very junior PhD student working on it. I just got very bored with it after a while because I didn't get the opportunity to structure the questions and the data sets and the experiments to go with it. So I really wanted to get into single cell genomics. It was the very early days. This was even before anyone else. I'll explain how I thought about this because it actually came from my first company, which I think we might get to in a little bit. But at that stage, I was really like, I think the right kind of resolution here. You know, if we want to solve fundamental problems, you need to be working at the fundamental units, cells, single cell, that kind of stuff. So there's a lot of that thinking going on. I was like, no, I'm tired of all this eQTL stuff. I didn't think I even paid for my flight. I somehow got the money to get that paid for my flight. Flew over to San Francisco and somehow got myself introduced to the fluid. I'm the very early single cell people.


Jon - 00:06:46: Yeah.


Quin - 00:06:46: Yeah. Wow. I kind of made a piss to myself. I was like, . I'm a PhD student. That's it. I want to do stuff. 


Jon - 00:06:53: Yeah.

Quin - 00:06:55: And this was very early, you know, to synthesize PCR, but they had the technologies, the microfluidics to do bigger RAID PCR. So you could look at a lot of genes, right? It wasn't quite sequencing yet. And somehow it comes to supervise it to go with this crazy pattern. And I loved it. And it was exactly how I like to do science. And there's a problem. I see a technology, I think really to solve it. And we really just chewed apart this idea of genetic noise and how it plays out. And we still do and actually still today, we really think about gene regulation in very kind of Lego block mechanical ways. But there's a lot of control theory around noise in systems and promoter bursting genes and that kind of stuff. And this is stuff you can do with single cell. So we just played with a lot of cute ideas. And I came out of my PhD feeling right, again, talking about science and then the phenotypes of the culture and how things work. You know, it's amazing how equally brilliant scientific universities will have very different science cultures. And there's often this comparison between the two Campbridge and Oxford. Because Oxford's a quirky place. It is a very quirky place. But there are a few things that Oxford does very well. And this embracing of wanting to solve clinical problems. If you think you can get hold of samples and new technologies and pair them up and think about a clinical problem, I think people are a lot more unbeatable to that in my experience. And I think others have said the same. And so somehow I managed to convince people that I think this was the next step if you want to think about gene regulation. And gene targeting, clinical problems, etc, etc, etc. Somehow they agreed to shut me up and we got a paper out of it.

Jon - 00:08:30: That's actually awesome. I love when you're talking about like these different phenotypes in science, especially for those who may be more outside looking in might just be like scientists all come in one phenotype. But there's just like such a diversity and approach and even like institutions. And I was thinking about my time at Berkeley, and Berkeley has a very specific style of we're always compared to Stanford, obviously, for sports reasons and stuff like that, and just like historical norms, but just such a very different place when it comes to scientific entrepreneurship. And my time at Berkeley was just like pure academia, pure academia. And like you're talking about Oxford embracing if there's a potential for clinical impact, maybe let's go for it. And so at Berkeley during my time there is different now. But the concept of starting a business and spinning it out of academia was like shunned.


Quin - 00:09:18: Oh, it was bad. It was bad.


Jon - 00:09:21: They're like, oh, you're going to the dark side. I did you know, that's like me verbatim quoting it. I was like, not the dark side, trying to help. I'm just trying to help. Um, but the tides have turned and it is a little bit more open now.


Quin - 00:09:41: People forget hot it wasn't that long ago. There's so many universities were like this, Oxford was very much like that, very much like that form good.


Jon - 00:09:41: Well, yeah, yeah.


Quin - 00:09:41: You were made out to be a failure. If that is what you wanted to do, I think the thing, and again, this is personal experience. This is a, you know, I'm always cautious to generalize too much. But yeah, having started my own company, and being in pharma and now another company of my own, if I compare a lot of big academia and the politics of big academia versus my time, I find the science we do not only fast-paced but a lot more authentic because it just has to work. You don't have to do hand-waving in a paper. And I find, at least in the genomic space, in industry, we are very collaborative, very collaborative. I would argue more collaborative than a lot of academic setups. So I think I'm with you. But Oxford is slowly, slowly. There are a few really fantastic champions around Oxford that are also changing that narrative and getting people far more enthusiastic.


Jon - 00:10:34: Yeah, and I'd love to see that. And it's not to pick sides either. There's room for it all. And it doesn't have to be antagonistic. It could be like, can we work together on this? And it always was previously butting heads, but it's just like it feels far more collaborative now. But this kind of dovetails nicely. So I know you started a business during your graduate studies. So it's SimuGen. Am I pronouncing that correctly?


Quin - 00:10:57: SimuGen.


Jon - 00:10:58: Can you talk a little bit about that? Like one, did you sleep at all? Like how did you?


Quin - 00:11:03: No. 


Jon - 00:11:04: So can you talk a little bit about the business you started in grad school?


Quin - 00:11:07: Yeah, this is why I wanted to sleep now when it comes to New Year's.


Jon - 00:11:10: Yeah, exactly. Again, come to the dark side.


Quin - 00:11:13: I'm old and wornout.


 Jon - 00:11:14: Yeah, yeah, yeah.


Quin - 00:11:14: Yeah. So this is why my supervisor was an absolute saint, my PhD supervisor, because I started SimuGen. Co-founded, coming out of my master's program. And I was due to start my PhD and sent my supervisors, or both of them, I sent them an email and was like, can I start my PhD six months later? And I can already tell both of them were like, oh, this one's going to be a spicy problem. And it was fine. I mean, it made no real difference to them. I mean, I did a lot of SimuGen during my PhD, did a lot of things in chunks. I can't multitask. I have to focus on things in moments. So I spent six months really getting a lot of it up and going. And then at points in my PhD, there were times it was, yeah, all right, I'm going to spend two weeks in Malaysia now working with a computational team out there. So then I'll come back, carry on with my PhD, and then I'll do two weeks here. And, you know, it's kind of a little bit like my life is right now. I can't imagine being any other way. I really love it. It's really fun engaging lots of different teams all over, getting on with it, moving fast with stuff. I have no regrets when it came to SimuGen.


Jon - 00:12:21: And can you talk about, were you the sole founder? Did you have like a co-founder? And like, what did Simogen do or does?


Quin - 00:12:27: Yeah, it was co-founded. So scientific founding CEO, but then became CSO of SimuGen. And it brought this idea that, okay, we want to do liver. Liver tox is a big issue. It still is. Drug development, you know, looking for earlier stage models. And this was when high content imaging was becoming really, really vogue. And everyone was like, wow. And I was like, well, I could do better than that with gene expression arrays. This was going to be either in parallel with or replacement in many cases, high content imaging. And it was the same idea. You take some models, but instead of just imaging, you put the stuff on arrays and you see, so you're getting mechanistic information and hopefully making better predictions of tox endpoints than imaging. That really was the idea, I think. But it was, there were, there were many, many great lessons in SimuGen. Not least of which was, you can have a great idea, but if the market doesn't want it. People were not prepared to pay for it i mean genie's fresh and raised were new tech they're like why how much yeah i could do this for two cents with a microscope and a few clever stains and you know and it was a really great sort of wake-up call for business but in life really too right even in developing therapies you know in the liver space there are a lot of areas like NASH will people pay for it there's incredible therapies for these fat and will they pay for it and how much and so there was a lot of that and there was a very big lesson product market fit.


Jon - 00:14:02: Yeah. So there's so many directions. I kind of want to go with this, but basically, was this your first time you've tried your hand at sales? It's like where you're starting to like talk to the market interface with like potential customers. How was that experience?


Quin - 00:14:16: You know, you should probably ask the people who work for me. I want that experience.


Jon - 00:14:20: Okay, okay, okay, okay.


Quin - 00:14:21: What's it like working for a first-time scientific founder? Yeah, yeah. And you'll get some real horror stories. I'm blushing every time I say stuff like this. But I made all the classic mistakes. I really, really did. You know, the product not paying enough attention to product market fits and just clever take. Big mistake. And another one is I was very driven to make this work. And I think one thing I came when I left SimuGen, I deeply regretted certain things I said and did. Not that they were unethical. I think what a lot of first-time scientific founders forget is that science is 10% tech, 90% people. And you have to spend a lot more time in empowering. And I mean, I'm still sucking at it. So don't pretend that I'm lecturing. But I try much harder to really focus on the people side of things. And I think it was a very, very big lesson for me coming out of SimuGen.


Jon - 00:15:17: That resonates so much with me because you're nailing a bunch of concepts that I think, for whatever reason, isn't really touched upon much. You know, the saying, like, if the science is sound, it all works itself out. But we sometimes can easily forget that who's driving the science. It's a group of people who, like, it can quickly turn if the people element isn't right. And exactly, we're getting back to, like, the science can feel objective, but it's far more multivariate. And far more subjective than we would like to tell ourselves. And also another thing, too, you know, in addition to people, the concept of product market fit in science, people just assume that there's already product market fit. Like, coming out of the gate that it's already done. But that can be, like, a brutal reality where you just muscle through it. You white knuckle through it. You're just like, we're going to make this work. And then you're so proud of the science and the product. And then when you bring it to the market, and it's just like, nope. I absolutely took it personally, early days. And that was a hard lesson learned.


Quin - 00:16:17: It's very tough. You know, a slight tangent to that same lesson is that just because something's valuable doesn't mean it can be monetized. And I think this was a big lesson a lot of us learned in what I call the First Wave of Bioinformatics. You know, now we're in the second wave of bioinformatics and everyone seems to have forgotten all the lessons we learned. Because everyone's like, we're going to generate lots of data. It's going to be valuable. I'm like, oh, how's that business model coming along? How much are people paying for your data? People want assets. People want de-risk assets. And it's just that having just a clearer vision for what that journey could be at the start, I think, is a far better investment to your time than having a perfectly formed idea. Because scientists always want a perfect idea. We need a brilliant idea to start it. It doesn't need to be brilliant. You just need to see what the journeys could be and you'll really be doing far better than everybody else.


Jon - 00:17:09: Yeah. Also, I think, too, just like being willing to adjust on the fly. I think a lot of the time you can easily get wedded to your idea, your initial idea. And that can just blind you to exactly what you're saying, the multiple paths that are in front of you. And I'll say I'm a pretty stubborn person. So I'm speaking for myself. Like, if I look back at my past, I'm like, John, you probably should have pivoted a little bit earlier. And it probably would have been far less painful. But those lessons, too, are lessons best learned firsthand. It's one thing to hear someone say, like, yeah, be open to change. And the market might not like what you have to offer. You're like, yeah, yeah, yeah. They're going to like what I have to offer. Like, because I. But one of the things I have internally, again, kind of this broken record thing is like, I can tell you how hot fire is, but you don't truly know until you touch it yourself.


Quin - 00:17:57: Yeah. 


Jon - 00:17:58: On paper, it's hot. I can tell you that. But you don't really know.


Quin - 00:18:01: No, no. And I think a lot of us thought about this when we were much, much younger. Yeah. I just sort of thought like this when we were much younger. I definitely felt like you could think your way through anything. This whole thing of life lessons. I don't know. You know, if you're just smart, it's like, no, no. And here's why. When you're dealing with complex systems where there's not a tidy equation, there are so many feedback mechanisms, so much happening that the only way to learn and get your internal AI to recognize these things and respond at the correct time is to go through it yourself. It has to be learning by experience.


Outro - 00:18:38: That's all for this episode of the Biotech Startups Podcast. We hope you enjoyed our conversation with Quin Wills. Be sure to tune in for part three of our conversation to learn more about his journey. If you enjoyed this episode, please subscribe, leave us a review and share it with your friends. Thanks for listening and we look forward to having you join us again on the Biotech Startups Podcast for part three of Quin's story. The Biotech Startups Podcast is produced by Excedr. Don't want to miss an episode? Search for The Biotech Startups Podcast wherever you get your podcasts and click subscribe. Excedr provides research labs with equipment leases on founder-friendly terms to support paths to exceptional outcomes. To learn more, visit our website, www.excedr.com. On behalf of the team here at Excedr, thanks for listening. The Biotech Startups Podcast provides general insights into the life science sector through the experiences of its guests. The use of information on this podcast or materials linked from the podcast is at the user's own risk. The views expressed by the participants are their own and are not the views of Excedr or sponsors. No reference to any product, service or company in the podcast is an endorsement by Excedr or its guests.