theCUBE + NYSE Wired: NYSE Rx | Jacob Oppenheim, RA Ventures

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Jacob Oppenheim

Venture Partner

RA Ventures

play_circle_outline Only proven biotech business model: develop differentiated therapeutics that reach market

play_circle_outline Limited Winners in Biotech SaaS: Workflow Integration and Value-Proposition Lessons from Veeva, Schrödinger, and OpenEye

play_circle_outline GLP‑1s, mRNA and AI: Harnessing Human Genetics to Accelerate and De‑risk Drug Discovery

play_circle_outline Raven/RA Capital incubator model: sourcing ideas and building startups across modalities

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Jacob Oppenheim, RA Ventures

Jacob Oppenheim

Venture Partner RA Ventures

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(INTRO)

Gemma Allen

>> Welcome back to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen with NYSE Wired, and to date we are talking all things AI in biotech. Joining me now for a conversation on the bio bucks, that is the money and capital flowing into this industry is Jacob Oppenheim, venture partner at RA Ventures. Welcome, Jacob.

Jacob Oppenheim

>> Nice to be here. Thank you.

Gemma Allen

>> So I'm excited for this conversation because we've had a lot of fascinating founders on the show to date. We're hearing about everything from digital tumor twins, which sounds kind of very dark, but has some exciting prospects for the future of this world, to lab mice, to hibernating species. this is a fascinating space. And I've been asking them, how do you get the money? how do you go about pitching this idea, these theories, these hopeful outcomes to industry? And I've heard a lot of mixed messages on that. So no pressure, Jacob, but I'm hoping you can kind of set me straight in terms of how the investment thesis aligns in biotech, especially in the world of AI, which seems like it gets a lot of talk, a lot of hype. And sometimes people feel like not a whole lot of outcome yet. Okay. Yeah. Start there. Yeah. How do you think about the time we're in right now and the space we're in and the hype meets the reality?

Jacob Oppenheim

>> So I like to say that the only proven business model in biotech is to make drugs. And that means if you cure people, if you have a differentiated therapeutic, you will make money. So when it comes to AI or it comes to any novel technology, the question I always have, whether it's me building a company or an investor coming and saying, Jacob, what do you think of this company? The question I always have is, how is this going to get me a differentiated therapeutic, and how is it going to get there faster than the competition? I think these are questions that AI can answer. But like any technology, we need the full answer about how this is going to work. If what you're saying about AI, or what you're saying about a technology, is that it only solves one problem, it might do an experiment faster, It might eliminate a step of the process. But it doesn't tell me where that drug is going to come from and that you know how to make that drug. That makes it much harder for me to say, aha, that therapeutic, that technology, that AI model that you have, that's going to transform drug development.

Gemma Allen

>> So it's interesting you say that the only proven thesis is, of course, drugs that go to market and that have success and efficacy, right?

Gemma Allen

>> Absolutely. Absolutely.

Gemma Allen

>> It seems as though there are a lot of partners, a lot of players, a lot of folks in this ecosystem building the technical capability in somewhat of an ancillary fashion that they can then license and sell into large pharma, right? The actual tech development that's happening right now has somewhat fascinated me to date. Like I said, how do you think about those business models, those licensing or even SaaS based, which some of them are? we talk about usage all the time in the other world of tech right now. Now, these folks are just starting to see SaaS as a novel concept in some respects. How do you weigh those opportunities?

Jacob Oppenheim

>> look, this is a great question. This is the elephant in the room. The truth of the matter is that the number of really successful technology or technology style companies in biotech is very, very small. The most successful one of them all is Veeva, which was in some senses just a Salesforce spin out and didn't even need to raise very much capital to be able to build differentiated solutions as a CRM and then as a clinical trial software tool. I wish all of these companies the best of luck because if their tools and models do succeed, they can be used widely across the ecosystem. However, as somebody who's founded companies in this space, tried to raise money for them and gotten customers for them, there's frankly a problem, which is that when you're speeding up or solving one step in the 20 or more steps that you need to make a drug, it becomes much harder for you to generate the clear value proposition and the clear integration and workflows that you need to make your business case clear. And you also at the same time have to think about how much capital is going in versus how much people will pay. Ultimately our industry is built on a couple of these companies that have succeeded. For instance, there's a handful of companies such as Schrödinger, OpenEye and others that have built great tools in chemistry that basically all of us rely on to build small molecule drugs. But at the end of the day, this is a really hard business, which is why I tend to think about how does a technology get me a drug?

Gemma Allen

>> Well, let's stay then on the drug making kingpins of this industry, right? The folks that bring drugs to market and highly, highly profitable industry too, right? We talked about GLP-1s to date. Those numbers are absolutely astounding. How do you also differentiate though, from that 8 % to 10 % of companies that are successful in this space to the 90 % of others that don't go that whole mile, right, in terms of what they can actually deliver to market? Because it seems to me, again, as someone who's only looking at this very peripherally, a lot of money and energy is spent with not a whole lot of realized outcomes, right? 8 % to 10 % is still quite low. How do you spot them?

Jacob Oppenheim

>> Look, drug development is a hard problem. There's going to be a lot of failures and there's going to be a lot of failures that you could never have predicted for reasons that you wouldn't have predicted because biology is just hard. There are ways though that you can think about the introduction of a new technology that can minimize this risk. For instance, whenever we're going to start a company and we think about a technology that we want to bring in house and build a company around, or I'm looking at some new models or some new technology, I tend to think, how can we minimize every other risk about this. So where does this technology, given biology that we know to date, given clinically proven biology, where does this move the needle? Now you might say, well that's not interesting, that's like making a copycat. I disagree. I think what's fundamentally interesting about technology and biology is it allows us to engineer therapeutics, right? Every year a new model smartphone comes out and that's a better smartphone than the one before it, right? We've gotten better at building bridges. Our world consists of physical tools and infrastructure that are built on engineering processes. I've been understanding what I need to do and I'm incorporating novel technology in it to make it better. So when I see a novel technology, I say, wait, how does that help me make a better therapeutic? And where can I apply it? What sorts of problems can I apply it to where I know I'm going to minimize all those ancillary risks and other risks and give myself the greatest chance of proving that this technology can succeed.

Gemma Allen

>> So that market opportunity around what might be seen as fast followers, right? money follows money sometimes in all industries. And we see that too, I'm sure, in drugs, right? People create one drug in the space and then we see a whole load of other pharma players who also want to sell into the industry because they're highly profitable. How do you think about that from the perspective of origination and spend into new spaces? And what are you seeing in the world of AI, especially as it relates to discovery and I guess scalability that excites you about spaces that you hadn't seen five, ten years ago?

Jacob Oppenheim

>> Okay, so this is a great question. Starting from the beginning, I don't like to think actually about fast followers all that much. And look, that's my personal opinion. I think actually when you look at the history of drug development, there's a heck of a lot of biology that's proven that never turned into a really successful drug or even made it past a clinical trial. But it's been de-risked enough that if you have a better technology, you can go out and do something about it. GLP-1s, like you mentioned, are actually a good example of this because the first few times that people tried to make GLP-1s into drugs, the peptides didn't last long enough in the body. But we did learn that the biology was effectively de-risked. it was safe to put this into people, you did see a transient effect. So that then says to you, okay, if I have better technology to make this persist in the body longer, which turned out to be a heck of a lot of peptide engineering, once you get those technologies, you suddenly open up this space of biology that you knew that you could modulate. You just weren't sure how to, you weren't sure which pieces you needed to make it work. The same thing is completely true with mRNA therapeutics, the same thing is true with genetic medicine. Now to the second half of your question, I do think that AI does open the horizons more by doing a couple things for us. If we're able to make decisions faster, if we're able to learn across data and run faster, better experiments in the laboratory, we can de-risk more hypotheses, so we can look more broadly. The second thing that we can do is that we can summarize the literature more effectively. So, one thing we think a lot about is human genetics. The greatest experiment in biology is the natural variation in humans and our natural variation in risk of disease. We put these two things together, the changes in our genome versus our phenotype of the risk of disease, we do actually start to understand what could make a good drug target. But it turns out with new AI tools, we can actually look across a wider variety of experiments and start to categorize what turns into, what characterizes biology that looks modulable, that looks interesting, that looks effective. So I do think that AI allows us to expand that surface by effectively bringing more information in and lowering the cost of exploring new hypotheses.

Gemma Allen

>> So RA Ventures.

Jacob Oppenheim

>> Yes.

Gemma Allen

>> What is the model here? You work with companies at a certain stage in a journey, pre-seed, I guess, early stage companies, and you help them elevate, optimize, grow, synergize. I don't know what the correct bio terms are, but that's the kind of tech thesis. Talk me through it. What's a day like in your life, Jacob?

Jacob Oppenheim

>> So the funny thing about RA Ventures, or really being in an incubator in biotech is we try to be as open-ended as possible and in some ways disorganized because that allows us to capitalize on the creativity of our venture partners and our staff and the scientific opportunities that we may not have expected from the beginning. So we're a group of people that's everything from experienced operators to scientific ideators to really sort of the people that just are great at taking a company and getting it through the clinic and getting it through the regulatory process and even into commercial. We source ideas from all over. We source ideas from venture partners. Sometimes we bring in venture partners who've been chasing an idea their entire life and it's just been the one that's got away. Deprioritized by their pharma employer, deprioritized at a biotech, this thing never got funding. Sometimes it's people who follow the literature closely and say, oh, we have an opportunity here. Sometimes it's people who are so well connected, we have access to early stage ideas and teams and we can help build their companies alongside them. One thing that allows us is freedom to choose the best path for whatever we do. On the flip side of that, it can be chaotic at times, but I don't think science has necessarily been particularly predictable historically about what's going to work in advance. And the best thing that you can do is get a diversity of thought.

Gemma Allen

>> The model you describe there, it's somewhat like an accelerator meets an incubator, right? It's like shared centers of excellence around specific topics that you can use across multiple portcos. Is that typical in biotech, an industry that tends to be so niche in and of itself, or is this unique to RA Ventures?

Jacob Oppenheim

>> There's a few other incubators. We were not the first incubator in biotech. Third Rock Ventures, Foresite, Flagship, but there's been a few of these over the years, and many of them still exist. Really, the reason that you do this is that drug development is hard and needs a vast variety of people. The people who do discovery biology and come up with new ideas are not the people who do therapeutics design and often very much not the people who turn a molecule into an actual medicine. You need chemists. You need clinical folks. You need regulatory folks. You need different executives at each stage of the operation. And so really, biotech is in some ways very well adapted to having this sort of incubator accelerator model.

Gemma Allen

>> So it's very evident to me that you are a biotech guy, right? You know the language like you're completely native. Your context is so clear. Healthcare AI though and this whole investment thesis broadly is obviously seeing a whole new influx of interested enthusiastic investors and capital injections from lots of different spaces right in biotech what is actually happening are we seeing more of an encroachment from traditional tech into the space do you foresee that that will happen if it's not happening what are your thoughts because it seems to me as someone who talks about tech five days a week that now these investment theses are becoming a little bit more diluted than they were five years ago.

Jacob Oppenheim

>> Look, to the best of my understanding, these are things that are cyclical and go in waves. We saw a massive amount of investment in biotech during the pandemic, in part due to the success of mRNA vaccines and the need to cure a new disease, and in part because biotech had been very successful leading up to that. A bunch of those companies did not do so well. The money left the ecosystem slowly. I think it's been coming back in a little bit and there's oftentimes an interest from technology companies in seeing what they can do in biotech. At the end of the day, being able to do more science is going to get us more drugs in the long run and I welcome people bringing different perspectives into the market because God knows I'm wrong all the time. And allowing us to think more broadly is going to, I think, encourage everybody, even the very traditional folks in the space.

Gemma Allen

>> Okay, so Jacob, what's ahead? What does the next year, the end of 2026 look like for you and the team at RA Ventures? Where is the focus? Where are the big goals? set out the stall, finish us off here. Tell us a little bit about what you guys are working towards, what we should be paying close attention to.

Jacob Oppenheim

>> Look, I think at the end of the day, our goal is to get great companies out the door, to prove the science behind them is likely to work, to get them funded, and to start making drugs. We're working across a wide variety of modalities, from small molecules to biologics to genetic medicine. We want to be building great companies that are transformative in their effects on human health. And so our quantum of success, our measure of success, is being able to start those companies, companies getting buy -in from the rest of the market and eventually making drugs. We had a great year last year. I'm very hopeful that we're going to continue this winning streak.

Gemma Allen

>> Well, if your mission is transformative effects on human health, I am hopeful too. I am certainly hopeful you're successful in your endeavors. Jacob, thank you so much for joining us on theCUBE.

Jacob Oppenheim

>> Thank you.

Gemma Allen

>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is NYSE Wired: AI in Bio, and we have some great guests coming up, talking to some leaders and investors in the space. Stay tuned.

Jacob Oppenheim, RA Ventures

search

(INTRO)

Gemma Allen

>> Welcome back to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen with NYSE Wired, and to date we are talking all things AI in biotech. Joining me now for a conversation on the bio bucks, that is the money and capital flowing into this industry is Jacob Oppenheim, venture partner at RA Ventures. Welcome, Jacob.

Jacob Oppenheim

>> Nice to be here. Thank you.

Gemma Allen

>> So I'm excited for this conversation because we've had a lot of fascinating founders on the show to date. We're hearing about everything from digital tumor twins, which sounds kind of very dark, but has some exciting prospects for the future of this world, to lab mice, to hibernating species. this is a fascinating space. And I've been asking them, how do you get the money? how do you go about pitching this idea, these theories, these hopeful outcomes to industry? And I've heard a lot of mixed messages on that. So no pressure, Jacob, but I'm hoping you can kind of set me straight in terms of how the investment thesis aligns in biotech, especially in the world of AI, which seems like it gets a lot of talk, a lot of hype. And sometimes people feel like not a whole lot of outcome yet. Okay. Yeah. Start there. Yeah. How do you think about the time we're in right now and the space we're in and the hype meets the reality?

Jacob Oppenheim

>> So I like to say that the only proven business model in biotech is to make drugs. And that means if you cure people, if you have a differentiated therapeutic, you will make money. So when it comes to AI or it comes to any novel technology, the question I always have, whether it's me building a company or an investor coming and saying, Jacob, what do you think of this company? The question I always have is, how is this going to get me a differentiated therapeutic, and how is it going to get there faster than the competition? I think these are questions that AI can answer. But like any technology, we need the full answer about how this is going to work. If what you're saying about AI, or what you're saying about a technology, is that it only solves one problem, it might do an experiment faster, It might eliminate a step of the process. But it doesn't tell me where that drug is going to come from and that you know how to make that drug. That makes it much harder for me to say, aha, that therapeutic, that technology, that AI model that you have, that's going to transform drug development.

Gemma Allen

>> So it's interesting you say that the only proven thesis is, of course, drugs that go to market and that have success and efficacy, right?

Gemma Allen

>> Absolutely. Absolutely.

Gemma Allen

>> It seems as though there are a lot of partners, a lot of players, a lot of folks in this ecosystem building the technical capability in somewhat of an ancillary fashion that they can then license and sell into large pharma, right? The actual tech development that's happening right now has somewhat fascinated me to date. Like I said, how do you think about those business models, those licensing or even SaaS based, which some of them are? we talk about usage all the time in the other world of tech right now. Now, these folks are just starting to see SaaS as a novel concept in some respects. How do you weigh those opportunities?

Jacob Oppenheim

>> look, this is a great question. This is the elephant in the room. The truth of the matter is that the number of really successful technology or technology style companies in biotech is very, very small. The most successful one of them all is Veeva, which was in some senses just a Salesforce spin out and didn't even need to raise very much capital to be able to build differentiated solutions as a CRM and then as a clinical trial software tool. I wish all of these companies the best of luck because if their tools and models do succeed, they can be used widely across the ecosystem. However, as somebody who's founded companies in this space, tried to raise money for them and gotten customers for them, there's frankly a problem, which is that when you're speeding up or solving one step in the 20 or more steps that you need to make a drug, it becomes much harder for you to generate the clear value proposition and the clear integration and workflows that you need to make your business case clear. And you also at the same time have to think about how much capital is going in versus how much people will pay. Ultimately our industry is built on a couple of these companies that have succeeded. For instance, there's a handful of companies such as Schrödinger, OpenEye and others that have built great tools in chemistry that basically all of us rely on to build small molecule drugs. But at the end of the day, this is a really hard business, which is why I tend to think about how does a technology get me a drug?

Gemma Allen

>> Well, let's stay then on the drug making kingpins of this industry, right? The folks that bring drugs to market and highly, highly profitable industry too, right? We talked about GLP-1s to date. Those numbers are absolutely astounding. How do you also differentiate though, from that 8 % to 10 % of companies that are successful in this space to the 90 % of others that don't go that whole mile, right, in terms of what they can actually deliver to market? Because it seems to me, again, as someone who's only looking at this very peripherally, a lot of money and energy is spent with not a whole lot of realized outcomes, right? 8 % to 10 % is still quite low. How do you spot them?

Jacob Oppenheim

>> Look, drug development is a hard problem. There's going to be a lot of failures and there's going to be a lot of failures that you could never have predicted for reasons that you wouldn't have predicted because biology is just hard. There are ways though that you can think about the introduction of a new technology that can minimize this risk. For instance, whenever we're going to start a company and we think about a technology that we want to bring in house and build a company around, or I'm looking at some new models or some new technology, I tend to think, how can we minimize every other risk about this. So where does this technology, given biology that we know to date, given clinically proven biology, where does this move the needle? Now you might say, well that's not interesting, that's like making a copycat. I disagree. I think what's fundamentally interesting about technology and biology is it allows us to engineer therapeutics, right? Every year a new model smartphone comes out and that's a better smartphone than the one before it, right? We've gotten better at building bridges. Our world consists of physical tools and infrastructure that are built on engineering processes. I've been understanding what I need to do and I'm incorporating novel technology in it to make it better. So when I see a novel technology, I say, wait, how does that help me make a better therapeutic? And where can I apply it? What sorts of problems can I apply it to where I know I'm going to minimize all those ancillary risks and other risks and give myself the greatest chance of proving that this technology can succeed.

Gemma Allen

>> So that market opportunity around what might be seen as fast followers, right? money follows money sometimes in all industries. And we see that too, I'm sure, in drugs, right? People create one drug in the space and then we see a whole load of other pharma players who also want to sell into the industry because they're highly profitable. How do you think about that from the perspective of origination and spend into new spaces? And what are you seeing in the world of AI, especially as it relates to discovery and I guess scalability that excites you about spaces that you hadn't seen five, ten years ago?

Jacob Oppenheim

>> Okay, so this is a great question. Starting from the beginning, I don't like to think actually about fast followers all that much. And look, that's my personal opinion. I think actually when you look at the history of drug development, there's a heck of a lot of biology that's proven that never turned into a really successful drug or even made it past a clinical trial. But it's been de-risked enough that if you have a better technology, you can go out and do something about it. GLP-1s, like you mentioned, are actually a good example of this because the first few times that people tried to make GLP-1s into drugs, the peptides didn't last long enough in the body. But we did learn that the biology was effectively de-risked. it was safe to put this into people, you did see a transient effect. So that then says to you, okay, if I have better technology to make this persist in the body longer, which turned out to be a heck of a lot of peptide engineering, once you get those technologies, you suddenly open up this space of biology that you knew that you could modulate. You just weren't sure how to, you weren't sure which pieces you needed to make it work. The same thing is completely true with mRNA therapeutics, the same thing is true with genetic medicine. Now to the second half of your question, I do think that AI does open the horizons more by doing a couple things for us. If we're able to make decisions faster, if we're able to learn across data and run faster, better experiments in the laboratory, we can de-risk more hypotheses, so we can look more broadly. The second thing that we can do is that we can summarize the literature more effectively. So, one thing we think a lot about is human genetics. The greatest experiment in biology is the natural variation in humans and our natural variation in risk of disease. We put these two things together, the changes in our genome versus our phenotype of the risk of disease, we do actually start to understand what could make a good drug target. But it turns out with new AI tools, we can actually look across a wider variety of experiments and start to categorize what turns into, what characterizes biology that looks modulable, that looks interesting, that looks effective. So I do think that AI allows us to expand that surface by effectively bringing more information in and lowering the cost of exploring new hypotheses.

Gemma Allen

>> So RA Ventures.

Jacob Oppenheim

>> Yes.

Gemma Allen

>> What is the model here? You work with companies at a certain stage in a journey, pre-seed, I guess, early stage companies, and you help them elevate, optimize, grow, synergize. I don't know what the correct bio terms are, but that's the kind of tech thesis. Talk me through it. What's a day like in your life, Jacob?

Jacob Oppenheim

>> So the funny thing about RA Ventures, or really being in an incubator in biotech is we try to be as open-ended as possible and in some ways disorganized because that allows us to capitalize on the creativity of our venture partners and our staff and the scientific opportunities that we may not have expected from the beginning. So we're a group of people that's everything from experienced operators to scientific ideators to really sort of the people that just are great at taking a company and getting it through the clinic and getting it through the regulatory process and even into commercial. We source ideas from all over. We source ideas from venture partners. Sometimes we bring in venture partners who've been chasing an idea their entire life and it's just been the one that's got away. Deprioritized by their pharma employer, deprioritized at a biotech, this thing never got funding. Sometimes it's people who follow the literature closely and say, oh, we have an opportunity here. Sometimes it's people who are so well connected, we have access to early stage ideas and teams and we can help build their companies alongside them. One thing that allows us is freedom to choose the best path for whatever we do. On the flip side of that, it can be chaotic at times, but I don't think science has necessarily been particularly predictable historically about what's going to work in advance. And the best thing that you can do is get a diversity of thought.

Gemma Allen

>> The model you describe there, it's somewhat like an accelerator meets an incubator, right? It's like shared centers of excellence around specific topics that you can use across multiple portcos. Is that typical in biotech, an industry that tends to be so niche in and of itself, or is this unique to RA Ventures?

Jacob Oppenheim

>> There's a few other incubators. We were not the first incubator in biotech. Third Rock Ventures, Foresite, Flagship, but there's been a few of these over the years, and many of them still exist. Really, the reason that you do this is that drug development is hard and needs a vast variety of people. The people who do discovery biology and come up with new ideas are not the people who do therapeutics design and often very much not the people who turn a molecule into an actual medicine. You need chemists. You need clinical folks. You need regulatory folks. You need different executives at each stage of the operation. And so really, biotech is in some ways very well adapted to having this sort of incubator accelerator model.

Gemma Allen

>> So it's very evident to me that you are a biotech guy, right? You know the language like you're completely native. Your context is so clear. Healthcare AI though and this whole investment thesis broadly is obviously seeing a whole new influx of interested enthusiastic investors and capital injections from lots of different spaces right in biotech what is actually happening are we seeing more of an encroachment from traditional tech into the space do you foresee that that will happen if it's not happening what are your thoughts because it seems to me as someone who talks about tech five days a week that now these investment theses are becoming a little bit more diluted than they were five years ago.

Jacob Oppenheim

>> Look, to the best of my understanding, these are things that are cyclical and go in waves. We saw a massive amount of investment in biotech during the pandemic, in part due to the success of mRNA vaccines and the need to cure a new disease, and in part because biotech had been very successful leading up to that. A bunch of those companies did not do so well. The money left the ecosystem slowly. I think it's been coming back in a little bit and there's oftentimes an interest from technology companies in seeing what they can do in biotech. At the end of the day, being able to do more science is going to get us more drugs in the long run and I welcome people bringing different perspectives into the market because God knows I'm wrong all the time. And allowing us to think more broadly is going to, I think, encourage everybody, even the very traditional folks in the space.

Gemma Allen

>> Okay, so Jacob, what's ahead? What does the next year, the end of 2026 look like for you and the team at RA Ventures? Where is the focus? Where are the big goals? set out the stall, finish us off here. Tell us a little bit about what you guys are working towards, what we should be paying close attention to.

Jacob Oppenheim

>> Look, I think at the end of the day, our goal is to get great companies out the door, to prove the science behind them is likely to work, to get them funded, and to start making drugs. We're working across a wide variety of modalities, from small molecules to biologics to genetic medicine. We want to be building great companies that are transformative in their effects on human health. And so our quantum of success, our measure of success, is being able to start those companies, companies getting buy -in from the rest of the market and eventually making drugs. We had a great year last year. I'm very hopeful that we're going to continue this winning streak.

Gemma Allen

>> Well, if your mission is transformative effects on human health, I am hopeful too. I am certainly hopeful you're successful in your endeavors. Jacob, thank you so much for joining us on theCUBE.

Jacob Oppenheim

>> Thank you.

Gemma Allen

>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is NYSE Wired: AI in Bio, and we have some great guests coming up, talking to some leaders and investors in the space. Stay tuned.