Putting the “Tech” in Biotech: An Unconventional Path to Building a Therapeutics Company, with Federico Paoletti, PhD

Speaker A: I had such a fun conversation with Federico Paoletti, who is the CEO and co founder of Exogene, which is a biotech startup based in the UK that combines generative AI with high throughput screening method to test best in class T cell engager drugs for solid tumors. After his PhD, Federico founded Exogene and built it from the ground up, starting from just the idea with his co founder Andrea, who he met at Entrepreneur first, which is a global accelerator, but they were based in London. He went on to secure VC fund and government grants and angel investments and got really creative with funding. We talked about so many different things, including how it was to found a company right out of a PhD, learning all the business side of biotech, and how he's really building this company as a tech first company, meaning that they are validating the idea before fully building things, which is something that's a little novel for biotech. I think a lot of founders get really excited about their idea and don't know quite how to validate it or look past the initial development into commercialization. He also has taken some great investor feedback and expanded into a larger platform based vision where they both have their assets and also a great platform that they can partner with companies on. It was a great conversation. He's really honest about the ups and downs of founding a company and learning all of these things kind of as you go, which is what most of us do. So I hope you enjoy the conversation. I certainly did. Let's jump in. Welcome to the Building Biotechs podcast. Over the years, I've helped over 90 biotech, life sciences and venture capital firms strategize and hire thousands of employees to scale companies that impact human health. We speak with those at the forefront of growing biotechs to learn their tactics on building these companies from the ground up. I'm your host, Karina Klingman. I hope you enjoy the show. I'm so excited for this conversation. Thank you so much for joining me here today, Federico.

Speaker B: Yeah, Karina, it's uh, a pleasure to be here. Thanks for having me.

Speaker A: I love speaking with founders at your stage because a lot of our audience is actually early founders or maybe even before your stage. You know, coming out of their PhD, their postdoc, they've got a great idea and thinking I want to build a company around this. And you've just gone through quite a lot of that over the last five or so years, so I'm excited to hear all about that. So first of all, what are you doing right now and why are you excited about your new company?

Speaker B: Yeah. So we're an Oxford based biotech, we're based in the uk. We are developing what are called T cell engager based drugs. So these are immunotherapies that activate the immune system to recognize and kill cancer cells. And we have a platform technology to find these molecules at scale with the right kind of characteristics for clinical application. You know, we combining AI and a proprietary high throughput screening method that we built in the lab. And I would say in the last kind of 6 to 12 months, we also really thought hard about what our technology could unlock beyond just, you know, a traditional asset story. And that's where we really thought about what could be transformational in tackling advanced cancers that currently is just not possible. And so we have this long term vision of enabling personalized cocktails of immunotherapies that would be able to target, um, all of the cancer cells in a patient, not just a small fraction, and therefore eliminate it fully and offer the potential for like a long term cure. Of course this is not currently possible because it's too difficult to discover. I mean, it's already very hard to discover drugs for single targets, let alone for a cocktail. But that's kind of our long term vision and where we see the real value, let's say the lion's share value of AI in drug discovery.

Speaker A: Yeah, and we were talking earlier too, you kind of alluded to, you don't think that tumor shrinkage is really acceptable when we're talking about sort of a standard of care. So your vision is really elimination of a person's specific tumor. And those are all personal to us. Right. All of our tumors look a little bit different. So can you explain that a little bit. And also, what is this cocktail? Are these brains brand new drugs? Are there existing drugs? Like what does that look like?

Speaker B: Yeah. So I mean, don't get me wrong, it's very valuable to shrink tumors, especially you know, when they are in the metastatic stage. Right. You can't do surgery, chemotherapy and maybe is not working. The latest immunotherapies are valuable because they do shrink the tumor, they do extend life, but eventually the cancer typically grows back because it develops resistance and typically the patient dies. So it is valuable to extend life both of course, to people, to patients, to their families and commercially. But we believe that we can do much better with the technology that we have now and that that's coming. Yeah, and just to put some numbers on that, I mean, even the latest immunotherapies like checkpoint inhibitors, antibody Drug conjugates, the response rates are like 20 to 40%. So 20 to 40% have shrinkages and tumors. The majority of patients often don't even respond. So there's still a lot to do there. And one of the key reasons for this is that traditional drugs go after one cancer target and that means that you're only really eliminating a fraction of the cancer cells. That's kind of the conventional one size fits all approach in cancer medicine. And so we believe that eventually AI will enable us to discover multiple drugs per patient, so multiple binders against cancer targets to then eliminate the tumor fully.

Speaker A: And AI is such a buzzword right now. How far out do you think, you know, what is your time horizon for something functional? Maybe even in um, clinic?

Speaker B: Yeah, that's a great question. So I would say just to give also some context, right, the way we find these molecules now in the lab takes years. And usually it's a trial and error process. Whether it's a small molecule, an antibody there, it involves some kind of high throughput screening approach, but it's a trial and error process. You just optimize one parameter at a time. Doesn't always work and it takes years. So now I think there are a lot of enabling factors that will make this a reality in the short term. I think it's hard to say exactly when it's going to be first in, first in human, let's say. But I think our horizon, you know, in the next five to 10 years it will become a reality in terms of those enabling factors, of course, models like RFdiffusion and AlphaFold that enable us to design molecules in silico. That's kind of an obvious right as an uh, enabling factor. Recently in the last couple of years, we have also developed a pretty powerful lab technology to test these AI generated molecules at scale and quickly, specifically T cell receptor based molecules. So this is having fast and cheap lab methods to test these molecules at scale that come from the AI. That's also a key enabling factor. But also if you look more on the macro scale, right, there are like the regulatory framework of course doesn't yet exist for personalized cocktails of drugs, but it is emerging. So for example, if you look at Moderna's fully personalized cancer vaccine for melanoma, it's in phase three. BioNTech is also pursuing a personalized cancer vaccine, fully personalized. So you can see that the regulatory, um, framework is emerging. And then finally there's the big kind of elephant in the room is of course the manufacturing like how realistic is it to uh, produce a cocktail of drugs for an individual? So of course also there are some pretty interesting trends that we aim to ride there. So for example, in vivo cell therapies like AstraZeneca, for example, recently acquired eSobiotech. And that's because this approach of building the therapy inside the patient is now becoming a reality with for example, targeted nanoparticles. And so that approach of manufacturing is also likely going to become a reality and something that we will be able to leverage for our kind of cocktail, personalized cocktail approach.

Speaker A: That's great, that's. We have so many things coming together right now. I think there's been a few times in my life as a scientist that I thought like, this is the time to be a scientist, right? And this really feels like it brings a lot of that together. You know, the first was the sequencing of the human genome. It was like, this is the time, right? And then we were, I remember I'm a biochemists, so I remember we were playing with MFold M in the lab, which we were training AI. It was the precursor, right? We were training AI and thinking like, wow, this is the time to be alive. We're really shaping, you know, the future of protein folding. And I just think now when I'm talking to founders like you, you're looking at, you're looking at a multi pronged technology approach that brings all of this together and it's very exciting. What was the moment that you thought, I'm going to form a company because you came out of your PhD with this idea, right? How did you arrive at that conclusion?

Speaker B: Personally, I've always grown up in an environment that kind of stimulated curiosity, um, and open mindedness. Since kind of, you know, from the, since I was a kid at the dinner table we were having debates around history, science, politics, and always with an open mind. Initially I wanted to be a professor, to be honest. So I moved to the UK to study biology, PhD. Then shortly after. And what I kind of encountered in academia, which is probably a common story, is that you kind of start to feel smart doing stuff that is, may not really have any tangible impact on the world. And you know, I grew up in a family which also had a lot of medical doctors. So I was very, um, enthusiastic about potentially having some impact on people, on patients. Both my parents also had tumors. Fortunately they are fine. But this also had kind of an influence in bringing me towards the world of cancer. I think towards the final years of my PhD, I realized that academia was just not the place for me, I didn't like doing lab experiments. I literally heard about this um, accelerator about Entrepreneur first where you didn't really need to have a solid idea or a company, you just needed to have potential. That's where essentially I met my co founder Andrea and computer, uh, scientist. Back then he didn't know what a gene was and I didn't know much about immunotherapy to be honest. So I think the whole vision around exogene and developing T cell engagers and then the cocktail approach, it's something that we've been sculpting since inception really. So it's constantly been evolving.

Speaker A: And I want to touch on that founder story. Uh, that's a really unique one and I wonder if people caught that. So you didn't have an actual company in mind, but this accelerator promoted entrepreneurship in general and sort of allowed people to pursue the thought of creating a company. The idea is that pretty common. And you said it's called Entrepreneur First. This is UK and US based. Right.

Speaker B: It's global now. And I think the model has always been kind of unique, but there have been quite a few unicorns that have come out of it. Yeah, I mean the idea there was kind of bring a lot of like really talented, motivated people together that are, you know, high agency as people say now. They really encouraged us to iterate and immediately get market feedback. So as you mentioned, we were not a spin off. We are a, uh, very unconventional biotech because this whole, the whole company, everything we've done so far was fruit of an idea that we started selling something figuratively, let's say, before we actually had anything. It was a very unconventional approach to doing kind of market analysis. Um, more inspired from tech really, rather than from traditional biotech where it's like, oh, I have this cool technology, let me see where it fits in the world here. It was like, okay, let's try to figure out what's a problem we can tackle that could be valuable and then let's go and build a solution there.

Speaker A: That's really fascinating. I'm part of a founder community that's mostly tech founders and they're all scratching their head at how we build biotechs for that exact reason. They're thinking, well you have to, don't you have product market fit? Don't you know how you're going to commercialize, who's going to buy it, who's going to pay? That's all stuff that you know. There are some accelerators here like Y Combinator that so stress all of that at the outset and really help founders do that. But in biotech it's often the reverse. Somebody's really passionate about a, uh, therapeutic they think could work, could be exciting, and they build a company around it and then they learn about the commercialization and all of that afterward. And uh, for investors, and I work with a lot of investors for investors that they want to know the answers to those questions in order to give money to the companies. So it really does feel a little bit backward. But you know, we're all passionate over here in biotech. Like we want to make our mark on the disease we want to make our mark on. But it's a really cool philosophy. I'm, I'm curious if we'll see more accelerators in that vein as we go forward since the funding climate is a little bit trickier.

Speaker B: To be honest, I haven't seen anything like that in biotech yet, but I'd be excited to see it. I think getting the market feedback is very challenging in biotech. So it's all about the network.

Speaker A: It really is. And you met your co founder there, having never worked with him before. How did that come about and how did you agree to be co founders? I mean that's a, it's almost like a marriage, right? That's a pretty deep relationship.

Speaker B: Well, look, I think so. My, my, both my co founder and I, we, we grew up in Italy and of course there's kind of a language and a cultural affinity there. But you know, growing up in, in Southern Europe, you kind of develop this natural ability to hustle in life because in general you need to hustle to get ahead more. So I think in certain countries in southern Europe versus others in Europe. So there was a natural affinity and I think of course complimentary technical skill sets, but also the personalities are super complimentary. I'm. He may be more extroverted, he's more introverted. I think the, one of the unique elements in our relationship is that we're eternally curious and we're really just not afraid to ask the dumb questions, uh, when biotech people are kind of afraid of asking questions because it's also a status game. And not having had that prior kind of experience in a way was an asset because just amongst ourselves, we were constantly teaching each other and asking each other dumb questions, let's say dumb questions. And so I learned everything about machine learning, learning thanks to him. And he learned all about biology and immunology thanks to myself. And you know, going back to the hustle nature, we just using our Existing networks. That's how we got to our first kind of paid collaboration. And it was just two guys and two laptops thanks to, you know, an introduction through you, uh, know a professor that we knew from back from university. And so that kind of attitude of like, okay, we don't have anything really, but let's just go for it. Let's just see what people want in this company. Would there be, you know, some kind of product market fit for the technology we want to build?

Speaker A: I love it. Thanks for sharing that. I think you are exactly right. We don't ask enough silly questions. There is a bit of a club, a founders club, where you kind of feel dumb if you come in and you're not very well versed in things. And um, we talked a lot about that on this podcast. So I love normalizing that you are not born knowing how to run a company and spend specifically not a biotech, which is way harder than many companies to run. Does anyone stand out from your time building the company as a mentor or an advisor that just really changed the trajectory or made you rethink something?

Speaker B: Our lead investor from, from our first round, a guy called Alistair, he's been a mentor since basically day one, since co founder and I met this guy, he raised his own fund, but he also was an investor at Leaps for Bayer, you know, Index Ventures, Lou Badla as well. So he knew the space, but he was also a biotech founder himself. And so he had the biotech experience, he also had the tech experience. So he really understood of course the space we were in. What brought us together and kind of helped us build that relationship is that he also was really bullish on kind of southern European founders. And you know, my co founder and I, we as I mentioned we grew up in Italy. And as I said, you kind of need to hustle to get ahead in life. And that then kind of naturally gives you this, I think this spark to kind of think outside the box and figure out ways to do things that are kind of non conventional. So he immediately kind of believed in us and he helped us learn the ins and outs of BD in biotech and pharma. Um, so that was super useful in terms of mentorship but also in understanding the psychology of investors, both the biotech and the tech bio. Because of course he had, he also had that exposure from the beginning. So I would say yeah, he's, he's been really uh, transformational. We talk to him like every two weeks. He's basically part of the team.

Speaker A: That's a great Mentor or story. Thank you. Tell me a little bit about the funding path. You were touching on that and this partnership. But you've had some unconventional funding in terms of you've gotten some government grants and other things.

Speaker B: Yeah, absolutely. So, well, so we raised the first investment round, you know, from VCs and angels etc. Like let's say the traditional first round and you know, and that was back in 2021, 2022. And then of course the bear market, the biotech bear market began which is, you know, still ongoing and it kind of goes back to being open minded and not chaining ourselves to the traditional funding path. We secured six UK government grants for, you know, specifically for biotech startups. And on top of that we also secured, you know, I'd say in the low six figure biopharma revenue just on the platform side. So collaborations that were useful to give us credibility in the industry, collect some training data with the ultimate goal of improving the AI platform, you know, bringing us closer to the vision, but then also generating revenue that helped us extend our Runway. So I think this is something that um, maybe some people don't, maybe they don't look into so much or they forget that uh, there are all these other options in your first rounds. Also friends and family can, with the right terms of course are also a potential source of funding because they can be match funding for non dilutive grants for example. The key kind of lesson for us was be open minded and if you see if there is uh, an opportunity to get money in a world where VC dollars are uh, scarce, especially in, for preclinical biotechs such as now, then don't put all your eggs in one basket. And we were very aggressive on the grant funding side of things and I think one of the, one of the great things it was that we just found a really good grant writer. Fortunately we didn't actually spend much of our time writing those grants because typically they're very lengthy and then there's a lot of overhead in managing that money. So first of all we used a really good grant writer that helped us just apply for as many as possible in the fastest time possible. So the point is we learned to be flexible and open minded and I think that has helped us to continuously kind of validate our technology so bringing us closer to the vision without suffering huge dilution and continuing our uh, Runway.

Speaker A: Tell us about the grant writer. How is that person compensated? How did you find them? And would you recommend this route for all companies or is it more of a specific niche that would benefit from grants.

Speaker B: Most of your audience is US based, so I think this is a uh, case by case basis. But for us we, you know, we were applying to UK grants and it doesn't really matter whether you're a med tech or therapeutics. There are, you know, there are opportunities for everyone. The way we compensated was a success fee only actually. So we never paid anyone upfront. So they just had maximal skin in the game.

Speaker A: That's really great advice. I know that applies in the United States. I've seen some companies leverage grant writers here. The grants are difficult to get right now.

Speaker B: I think the strategy overall probably uh, can be applied for the US as well. So the idea of finding a good grant writer that has had previous success, that has skin in the game with you know, success fee only approach is, I think that's the, that is a really great, great strategy. What I would say is don't necessarily be afraid of like grant writers adding a ton of like buzzwords or rephrasing things because as a technical person you read, sometimes you read these grants and you're like, what the heck? Like I would never have said this, it's so imprecise. But in reality the way these grants are reviewed is it's typically not scientists that are reading them. And so you, you need to kind of shed yourself of that mentality and kind of let the grant writer do the work. Especially if they have skin in the game like they, they will do the best they can. The important thing is that you win the money and then use it for the project.

Speaker A: Yeah, that's great advice. I have another hat I wear. It's a hard market. So I've been helping m some folks around resume writing and things like that. And that advice you gave of just like it's not usually a scientist reviewing your resume either. It's not usually a scientist reviewing the grant. They're marketing documents. And that at the end of the day you are trying to sell your product which is your company's vision. So that's great advice. Hard to do as a scientist. It is really hard to use what we think of as like fluffy language. I want to talk about this San Francisco trip that you took. So you came to, was it JP Morgan?

Speaker B: Actually during jpm, that's when we decided to go to San Francisco. And you know, back during J.P. morgan this year there was two stories. One was ah yes, it's going to be great. Going to be a biotech boom. Things are looking great. Trump is going to be super pro biotech. Great Two months later, we land in San Francisco and tariff war begins. That's when the stock market collapsed. Right. We went there with the idea of fundraising and basically VCs were in, in those few weeks, they were all kind of panicking and they were like, no, we're not doing any deals. Okay, so much for looking for the, you know, long, for long term kind of gains. Anyway, so we had this whole situation and at some point we ended up in a meeting with a, uh, tier one kind of tech bio vc. And so by then, you know, we hadn't raised anything in San Francisco. We go into this meeting and we were wearing our kind of asset centric hat and we were pitching this asset centric story. And essentially we're kind of following this mainstream advice, which is in bear markets, it's all about the assets and don't pitch the platform. No one cares about the platform. That's just for frothy bull markets. And you know, there is truth to that, of course, because pharma are purchasing assets. So yeah, that, that makes sense. But most what One of the things we forgot is you also need to know your audience really well. And it turns out that, well, this investor especially was, you know, when we pitched our story, oh, you know, we're developing novel T cell engagers for solid tumors and they have these great app qualities. You know, we, we were just perceived as okay, yet another T cell engager company with like, you know, 1 to 10 billion exit potential, low probability of success. How are you guys any different? And you know, how is your technology really going to change the world? And the idea is, okay, how can you become a $100 billion company? This was the scale, uh, that we were missing. And in that case it was more about uh, a grand platform vision that perhaps we were missing. Anyways, that whole conversation was super useful because then it forced us to adapt and to think hard about, okay, if our technology becomes really powerful. Right. And eventually, of course it will, with the right amount of training, data, et cetera, what will it unlock that will be transformational for patients? That currently is not feasible.

Speaker A: Right.

Speaker B: And you know, that's where we came up with this idea of unlocking cocktails, personalized cocktails of drugs to eradicate tumors.

Speaker A: That's a great story. Thanks for sharing that. Everyone has little battle wounds from the funding process. So you've come through it successfully. I love that. How did this platform thesis change your decision making after that set of meetings?

Speaker B: Yeah, I mean, I wouldn't say it changed our decision making process necessarily. I think it's more like it helped us create a grand vision on top of the asset story and therefore we have kind of a much more clear understanding of what to emphasize based on our audience. So uh, of course if we talk to the biotech VCs, we start saying, ah, uh, yes, personalized cocktails. They're going to be, oh, so wait a minute, but how are you going to manufacture this? And the fda, how are they going to prove it? It's a much more conservative mindset. But for example, tech, bio VCs, this is something that, that resonates deep down. This is our vision for the future of medicine and we need to know of course how to pitch it and who to pitch it to. So I wouldn't say it has changed our decision making process, but it rather provides a framework for how we see our future and how we pitch it that makes sense.

Speaker A: What does then a good partnership look like for you?

Speaker B: We are focused now still on advancing our lead asset. And so an ideal partnership for us now is where a pharma comes and purchases our asset using a traditional kind of bio bucks deal framework of upfront milestones and royalties. Of course we are also open minded on the platform side and kind of opportunistic. So we are also receiving a lot of interest to use our, especially our wet lab technology which we invented in house to create and generate large data sets, large training data sets. The important thing is kind of matching the economics of these kind of side deals or partnership deals with of course the effort that we need to put in and deciding whether the deal helps us move closer to our vision and to the near term kind of inflection point. You know, ultimately we want to produce better assets in the short to midterm beyond generating revenue. That's the key goal. And so we keep an open mind for on the partnership side, but we are laser focused on, on advancing our asset.

Speaker A: Let's talk about that wet lab technology that you built. What does that flywheel look like right now for generating data, learning from it and then going back.

Speaker B: One of the key building blocks in developing our T cell engagers is finding what are called T cell receptor molecules. And they need to have some specific characteristics. So they need to be high affinity, so they need to bind very tightly to the cancer target and not bind to anything else. And so our platform helps us find these kind of needle in a haystack molecules better than if we were to do use like a random trial and error screening approach. And so the three step process is like AI to generate the variants where we modify, uh, CDR loops of our molecules, biophysics based methods to filter them still always in, in silico. And then we take the most promising candidates and we test them in the lab. And we invented a new methodology to test up to 100 million t cell receptor candidates in just a few weeks straight from the AI at a low, uh, cost as well. So our technology kind of makes it fast and economically feasible to test up to 100 million AI generated candidates, which is important to highlight because if you were to print 100 million candidates, you know, using like twist libraries, this would be not, this would not be feasible. But we have found a way to do this in yeast, which makes it feasible to do. And so we can collect these large data sets and then we feed those data sets from the yeast display and other validation methods in the lab to improve the AI. And so we have this kind of lab in a loop cycle.

Speaker A: So you have a really lean team. Tell, uh, me about the stages of team building kind of from the beginning. I know it was just you and your co founder for a while and what is your team today and what are your plans for growing that team, but staying lean because, yeah, cash flows, cash flow and funding are going to remain a challenge.

Speaker B: I'm sure we're around 10. And so we have a lab team based in Oxford in England. And the computational team is kind uh, of AI computational team spread across Europe. Another unconventional thing we did was of course we were set up during the pandemic and so we set ourselves up to be hybrid and to be able to hire the best possible talent also from other geographies. And so that gave us economic leverage to be able to hire really good talent at slightly lower rates versus what you could find locally beyond the computational and lab team. You know, we recently hired a really fantastic industry veteran, a guy called Sebastian Bunk, who was at a pretty um, relevant German biotech called Imatix. They're basically one of the leaders in our space. And you know, this guy, he brought, discovered two drugs, developed them from completely from scratch, put them into first in human trials, and he also unlocked from a scientific point of view, a large $150 million upfront deal with Bristol Myers Squibb. So he has a lot of expertise, technical expertise that will, I think will be really game changing for us.

Speaker A: That sounds like a fantastic hire. I do a lot of talent map planning for early stage companies and there's always the question of where to spend and where to save when you're looking at a Runway and you're thinking of, you know, who do I have to hire and what should we spend on them versus how quickly is that Runway going to end? You know, sometimes those maybe pricier hires that really give you an unlock, like a huge network, like the industry expertise are worth it. And so, uh, that sounds like a really great hire for you. And then I like the geo arbitrage of being able to think about where you hire great ML AIML folks, uh, within the United States. That's a big conversation right now as well is, you know, where are these people, how can we hire them? And a lot of them aren't in the U.S. so you know, you were ahead of the curve there as we're all scrambling for that talent.

Speaker B: We used actually an employer of reference and they've been super helpful to hire kind of talent all over. And with regards to like, what are kind of more senior or expensive hires, I think there's also a clever way to hire these people so that they give you the value you need especially to get to that next milestone without having to pay huge sums of money. Usually startups are equity rich and let's say relatively cash strapped. So equity is usually a good incentive. And sometimes you don't even need to hire them full time.

Speaker A: Right?

Speaker B: Especially in the beginning. You want them to prove themselves and so sometimes you might be able to hire them part time and that's already enough for them to give you a lot of value so you get to the next inflection point. I think there is again there. Some of these dynamics are in a way probably promoted kind of indirectly by VCs that look for specific things and kind of maybe you are rewarded because you say you have this person full time, rather than maybe having the courage to explain, well, it doesn't make sense to hire this person full time, but we still get the value we need. There's that dynamic there with VCs where sometimes you need to push back and kind of explain that some things don't need to be done in the way that they've always been done. You can just think differently.

Speaker A: Luckily we're starting to see that shift at the VC level. Some of the really canonical biotech VCs that have been around for a while, we're a little slower to evolve, but the tech bio VCs are very much on that same page, which is great. One thing that I do for a lot of clients that are on the funding trail is I'll review their talent map, I'll do the benchmarking and all that, and Then we'll put together slides that specifically lay out that as a story as to how we're attacking talent from a cost perspective and also that expertise. But the change is there. It's just been slower with some VCs than others.

Speaker B: I agree, I agree. I mean the biotech world is a little bit more culturally conservative and I mean it's probably, you know, you can speculate as to why, but there's a lot of regulation and so there is this uh, mindset that is more, more takes more time to change even for simple day to day activities. So yeah, I think the influence of tech in that sense of culture and company building I think is super positive.

Speaker A: What's one misconception about AI and drug discovery that you wish would just go away?

Speaker B: There's so many, but maybe the most relevant one, the last 15 years have been focused on applying AI to improve traditional drug discovery of one size fits all medicines. So the idea of making traditional medicines slightly cheaper and slightly faster to develop, but so far, um, unclear whether they will be significantly better for patients. Hopefully they will. But uh, I think this is a misconception because people believe that this is where a lot of value is in applying AI to drug discovery. My vision and opinion is that the lion's share of value that we can unlock is in rethinking medicine, imagining new categories of medicine that are not currently feasible to develop. And so that's the, I think AI will unlock personalized medicine. That's my strong belief. Because when you bring down the cost of developing things and the cost of building units, whether it's a drug or something else, when you start to bring that cost and time to zero, then new things start to happen that were previously unmanageable. And so, yeah, personalized medicine is, uh, the future.

Speaker A: What is the hardest decision you made in the last year?

Speaker B: The decision to go to San Francisco to raise it was a tough decision, um, because of course, you know, I was leaving like friends and family for a good amount of time, going to the other side of the world. And it was also really tough moment because as I said, we couldn't raise anything because of the, that very challenging macroeconomic situation. So I think mentally it was pretty tough. Of course, as I mentioned, the silver lining is that we built that vision and we kind of, you, uh, know, picked ourselves up and thought even more ambitiously. Those are the moments that really test your resilience and your, your mental kind of capacity. I was also lucky that I could rely on friends and family to, to share my frustrations. And ultimately kind of come out to the other side.

Speaker A: I love that I always say you win or you learn and sounds like you learned a lot. I think it's a pretty big silver lining because honestly those early and I know it wasn't early for you, but it was early in your US based fundraising journey. Those early fundraising fails often do really shape people for better or worse. But if they shape you for better, then you just get really good at raising money, which is a great skill to have.

Speaker B: Yeah, absolutely. I think, you know, as I mentioned, we were kind of left out of the room, which is kind of painful if you can, if you can think about that, you know, flying across the other side of the world and then having that happen to you. But again, you know, you also have to have a good sense of humor and I think my co founder and I are, we like to think we have that and I think it helps a lot.

Speaker A: What is one metric you watch that others might ignore at this stage?

Speaker B: This is something that I check kind of daily and I actually look at Nvidia stock price and trading volume on a daily basis. AI in itself is not a bubble, but I do believe there is a forming a kind of LLM bubble that is, you know, it might last for a long time, it might pop. And I think Nvidia is at the top of everything. So if Nvidia collapses, that's a really good indicator that the stock market itself might go down. And that's really useful to know, to have your kind of finger on the pulse there because stock markets affect startup fundraising and biotech especially by having my finger on the pulse there, that really helps me to plan short to midterm.

Speaker A: Yeah, that's really timely too. A lot of people are looking at this Nvidia OpenAI deal and going, wait, the money's just flowing back and forth and both their stock prices raised like that's total bubble reactionary. But you know, I guess we'll see.

Speaker B: Biotech is not so resilient and you know, we are struggling because of high interest rates whilst the tech world, it generates revenue and it's more resilient and it's currently thriving. But we, it is important to keep an eye on, on the trend and on whatever bubble is forming.

Speaker A: What's one partnership deal you most want to sign this year?

Speaker B: I can't say that much but I will allude to the fact that we will soon be announcing something really massive that will be, I think transformational for Exogene. Hopefully early next year we'll be announcing that.

Speaker A: Amazing. We're rooting for you. Well, this has been so much fun, and I just so appreciate your candor. It was really lovely.

Speaker B: Oh, uh, thanks so much, Karina. Really enjoyed the conversation.

Speaker A: Yeah. And where can people get in touch with you?

Speaker B: Uh, LinkedIn is probably the best place.

Speaker A: Great. I'll drop that link in the show notes and people can reach out there. Thank you so much.

Speaker B: Thanks so much, Karina.

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