BioCentury This Week

Ep. 371 - Moving faster at the academia-industry interface

• BioCentury • Season 7 • Episode 371

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Getting into the clinic fast to de-risk assets has become the name of the game in biotech, and at the academia-industry interface, too.
From AI to NAMs to starting a Phase I trial in the U.S., BioCentury’s 3rd Grand Rounds-U.S. conference brought together academic innovators, biopharma leaders and early-stage investors to debate key bottlenecks in translation and how to make early-stage R&D investible.
Sam Blackman, entrepreneur in residence at GV and co-founder of Day One Biopharmaceuticals, and Aaron Coe, managing director of innovation for the Allen Institute, joined BioCentury’s analysts on stage last week in Seattle for a podcast recording to wrap up Grand Rounds and discuss key takeaways from the event.

Editor’s note: We invite you to join BioCentury and Regional Host Chairs Forbion and BGV at our next edition of BioCentury Grand Rounds, scheduled for Sept. 23-25 in Amsterdam.

View full story: https://www.biocentury.com/article/659729

#TranslationalScience #DrugDevelopment #BiopharmaInnovation #AcademicInnovation #GrandRoundsUS

00:53 - World-Class Regional Hosts
02:56 - Building Grand Rounds Community
05:21 - Two Nobels, One City
07:43 - AI Goes End-to-End
09:47 - The Data Problem
14:12 - AI, Animals, Australia
19:53 - Study Startup Bottlenecks
26:11 - Early Science Investability

To submit a question to BioCentury’s editors, email the BioCentury This Week team at podcasts@biocentury.com.

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[Autogenerated Transcript]

Josh Berlin:

Welcome to a special episode of the BioCentury This Week podcast. My name is Josh Berlin, and I lead Corporate Alliances and Business Development for BioCentury. We are taping this episode live on stage in Seattle, where we are wrapping up BioCentury's third Grand Round US conference. For those of you who don't know, Grand Rounds is BioCentury's early-stage R&D conference, where we focus on how to overcome key bottlenecks in translation between academia and industry and how to make early-stage R&D investable. We've had two-plus days this week of debate and discussion here in Seattle, plus a track of early-stage presenting companies and an academic poster session. And there's just been a lot of energy this week, from my perspective. So first off, on behalf of BioCentury, I just wanted to thank, everybody here in Seattle who's been so welcoming and so uh generous with their time. I wanna especially thank our Regional Hosts, the Allen Institute, Fred Hutch, the University of Washington, and Life Sciences Washington. We would not have been able to do this event, without their support and everybody else in the community. I also wanted to give a, a quick shout-out to um some of our previous Regional Hosts for this event. Previously, we were in uh Chicago. before that, we were in Nashville. We just announced today that next year we'll be in Houston, and, and I thought it was just really great to see folks here in Seattle talking to folks in Houston, talking to folks in Nashville, talking to folks in Chicago, and it's really… I think, you know, Simone, this is really what we're trying to help do is, is sort of foster interactions between ecosystems and, and, and- So- Allow people to build connections.

Simone Fishburn:

So, so Josh, you're kind of, for me, like the Pied Piper. You know, you do this, and then you pick them up along the way and, and they're all f- Yeah. but seriously, we have… we're really building a a community from these different Grand Rounds events. But the other thing that I think we're gonna tap into as well it is our third US one. We, of course, have had one in the UK and have one in Amsterdam, so we're doing a Europe version as well. But it is very interesting to see some themes, how some evolve, even in this short time, guess which ones, super fast, and how some seem to take a little longer to, to resolve. But yeah, and you know, I, I know you're gonna sort of Uh, sorry, I've got Aaron to my right here from the Allen Institute Yeah. I mean,

Josh Berlin:

let me, let me introduce uh the team here. So we obviously have Simone and Selina, who everyone on the podcast knows, and we're really happy to have uh Aaron Coe, Managing Director of the Innovation Portfolio at the Allen Institute, who also served on the regional host committee, and we have Sam Blackman uh Entrepreneur-in-Residence now at GV. I'm sure everybody here knows Sam from Day One Biopharma, also has been at several multinational pharmas, as well as a few biotechs here in Seattle, including Seattle Genetics and, and Juno. So really uh welcome them and we're looking forward to sort of getting their take. Aaron, maybe we'll start with you as the regional host uh spokesman. I mean, you know uh a lot of energy this week. again, couldn't have done it without you and, and Arden and, and the Allen Institute, as well as our friends at the Hutch and at Life Sciences Washington, UW. Tell us from a Seattle, um leader's perspective, you know, what, what did you see this week? What did you hear? What's your takeaway?

Aaron Coe:

Well longtime listener, first-time caller. This has been a wonderful event. The Allen Institute is extremely happy to have sponsored this, and we are very grateful for BioCentury for building this network of networks. I think we are all speaking one language, which is scientific productivity, and we speak with many different dialects, whether it's from where we come in, in the country or around the globe, but also from where we come in terms of the stages in which we're contributing. And so to bring the institutes of, Seattle together to be able to kind of really, really shine a light on all of the interesting innovation that's happening here is really a wonderful thing. And to bring people together with folks further down the, the value chain has also been a really powerful set of conversations, and we couldn't be happier with the energy that we were able to see and all of the uh the really fun events that you guys put on, the Uh, last night we were at the Chihuly Gardens Glass Museum and- Socks. See my

Simone Fishburn:

socks… Aaron Coe: it, it was insane. Oh, those come from the… Oh. Nice. Yeah.

Aaron Coe:

Yeah.

Josh Berlin:

Simone, what, what kind of socks are those?

Simone Fishburn:

They're Chihuly socks.

Josh Berlin:

Oh, those are the Chihuly socks.

Simone Fishburn:

Yeah,

Josh Berlin:

yeah. Okay, so we, they did walk us-

Aaron Coe:

That's a good purchase… Josh Berlin: through the gift going in, so they, they got you. That's good. That's good. Yeah, no uh uh, being able to break down kind of barriers between folks and, and have them meet in the halls, meet in front of posters. We had a great group of our rising star scientists from every single one of our accelerators be able to be represented here and interact with folks all across the industry, so it's really 115 organizations that you brought into town, so this is, this is really something that stimulates a lot of great interactions and partnership opportunities. you know, we were really… It was right on the heels of a, a huge announcement for our organization, I'm just incredibly grateful for all the attention that's being paid to some of the, the great work that's being done by my colleagues, so.

Josh Berlin:

Yeah, thank you. So uh you know, the first night of this event, we had it at your institute. It was a beautiful building, and we kicked it off, you know, with I thought a great keynote and discussion with David Baker. I mean uh Simone, what, what was your take on, on David Baker's talk?

Simone Fishburn:

So actually, can I just take a step back? Because, you know, Selina and I talked about this. So we had David Baker, who's the 2024 Nobel Prize winner. We also had Mary Brunkow speak as the 2025. Nobel Prize winner, both of them from Seattle, right, or from, from the local area. So two more different people and journeys I can't imagine, right? And so, you know, this was really interesting for me 'cause I think it also captures how science works. Mm. So on the one hand, you've got something very directed. I mean, David Baker, and we'll talk about it, has just some, some phenomenal things with protein engineering and the use of AI and so on, and Mary Brunkow gave exactly a, different story about something that, you know, discovery in 2001- Mm … that now that, you know, led to FOXP3 and, and Tregs that, that now we're beginning to understand the translational value, and that is science. On the one hand, it's directional, and you can move it to accelerate things, and on the other hand, sometimes it just takes its way. And, and I just thought that that was a very kind of interesting juxtaposition

Selina Koch:

Yeah, I think one thing that was remarkable about his talk is the number of projects that have spin-out potential applications in the real world, and they're really diverse and there's a large number of them. So just that one lab alone tells you there's a lot of substrate in Seattle for biotech, even if there are hurdles, right? and I just wanna give a little plug to uh also Jay Shendure's talk. Um, he-- this is another one that's super visionary, and you can ima-imagine further in the future, these DNA typewriters where you can encode, theoretically, hundreds, maybe even thousands of cellular events simultaneously over long periods of time. Putting together that entire cellular context in which important events are happening has also many potential applications.

Josh Berlin:

Yo uh S-Sam, I-- thank, thanks, Selina. Sam, I wa- I wanted to ask you something. So we've been doing this event now for several years and something our, our CEO, David Flores, pointed out at l- lunch today, it's um I almost think every panel this week, AI was a key focus, regardless of whether that was what was said on the title of the uh session. Much different, I, I'd say, even than a few years ago when we kicked this event off in Nashville. So what, what's your take as uh you know, on, on AI and what you heard this week on the various panels about how folks are thinking about AI for drug discovery and development?

Sam Blackman:

thanks. Well, first, thanks for having me here. It's nice uh even though I no longer live in Seattle, I live within the penumbra of Seattle, so I'm always happy to be able to come to a meeting that requires something less than a transatlantic uh transatlantic airplane ride. My recent transition from being an operating role over to Google Ventures has been nothing short of uh you know, a ten x or a hundred x increase in the amount of information that I'm now aware of regarding artificial intelligence, and that's both on the pure tech side as well as on how it intersects with either drug development, drug discovery, or patient payer-provider work. It's pervasive throughout the entire ecosystem now, and, whereas five or six years ago, or even three or four years ago, when we would see vendors and they would come and they'd pitch us on their various services and, or I'd see pitch decks for things that I was asked to advise on, and there was the sporadic sort of, "Oh, we're gonna do this with AI/ML." And you would ask people what that meant, and they would say, "Well, we have some algorithm that we're using." And you'd pull on that thread and say, "Tell me more, tell me more," and then you'd ultimately end up in some very hand-wavy,"Well, you know, we think that there's some machine learning way for us to do this aspect of drug development faster." In a very short period of time, and I was listening to the Hard Fork podcast, I'm sorry to betray uh you for another podcast, but I was listening to Hard Fork, which is my favorite tech podcast, and they reminded me that ChatGPT showed up in twenty twenty-two, so it's only been four years. And what we've seen now in the past four years is, essentially an end-to-end retooling of how we think about drug development and the inclusion of, fully mature or increasingly mature artificial intelligence systems in every aspect of what we do.

Simone Fishburn:

Sam, I, I want to pick up on that because one of the threads that came up-- So there are some things that changed and some things that seemed not to change, right? Certainly the pace at which of AI fluency, like we all know what machine learning means now and so on, right? But one of the things that came up in many, many of the sessions was the need for more data, was the need for negative data, was the need for more rigorous data and data sets. And I have a few of my own thoughts, but We've been having this conversation for so long. We, meaning the ecosystem, has been having this conversation for so long. And so I guess my question is whether the-- you know, sitting in GV, you know, is there really a sense that what's changed now is actually our ability to harness that data if we could sort it out, right? If we could get the data in there, and we've talked about making it interoperable or federated systems or whatever, but, but is there a different belief now that was coming through to you in what we could do with the data because of AI?

Sam Blackman:

Oh, absolutely. I mean, I've always been a big sort of, large dataset, empiric top-down. There are patterns, and we just can't see them. And this, this may betray my roots as a scientist. I do actually-- you know, did have some scientific training at some point in my life. But I, I'm something of a neo-empiricist. I believe that, you can understand the world equally well with a top-down approach as you can with a bottom-up approach, and that's a completely different philosophical discussion I think that we're fully bought in on that, that we generate so much data now of such depth and complexity that there are patterns in there that we can't see, that we… that the machines can tease out for us that are going to enable new ways of seeing or thinking or doing. Within that, though, there's a fundamental problem, right? Which is that we're only as good as the data that we have available to us to train these models on, and that data, to some extent, comes from things that we produce and publish and codify and put into data sets, and we're biased in how we do that. And so I think somewhere along the line, this conversation around the reliability of the output is going to be, proportional to the quality of the input data that we have and, and right now that's, that's biased.

Selina Koch:

Just to name it, you're talking about the missing negative data mostly.

Sam Blackman:

Absolutely.

Simone Fishburn:

But, but I wanna pick up on that. Again, drawing on something that came up in one of the panels. So the missing data or the data that aren't there, but the other thing that kept coming up is, well, you know, garbage in, garbage out, the quality of the data, and I kinda feel like AI needs to grow up and be able to sort out good data from bad data. And that was sort of th- this idea, and you're talking about patterns, and so I sort of wonder whether there's this sense that any of you got that, like, just put it all in there and let the AI learn rather than how much curation is going into… I, I don't know, Selina, your thoughts.

Selina Koch:

I think that comment was made on the Transformer panel by one of our colleague Lindsay's panelists, that actually once you get all that data in there, it's so, it's so good at finding patterns, it can actually find the um mistakes in there, the anomalies, the bad data, and pull that out as separate. So yeah, I think so.

Simone Fishburn:

That, that was a great panel actually too.

Josh Berlin:

Yeah, and Aaron, I know you've also been making the point that some of the institutes have a role to play here in, terms of data, right?

Aaron Coe:

Yeah. We have hundreds of petabytes of data and, and we spend an incredible amount of time and effort in the quality assurance of that data. So at the earlier stages, I know there's some problems at later stages with, you know, what's missing from the clinical set. But uh one of the things that I think our announcement for that came on the second with the, the announcement of the Brain Health Accelerator, is this notion that we have a tech stack that is very robust, but we also have achieved this level of leadership in the community to be able to bring consortia together, to bring data together. And sometimes these, these different areas have siloed data. And so what we're really excited about is to be able to kind of be a marshal for some some of these efforts that will get, you know, expansive in terms of looking at neurodegenerative diseases.

Selina Koch:

And it sounds like, though, there's still some work to be done on creating, I think, what somebody called tiger teams, like people who are biology natives or tech natives. because it's interesting, with select people here, you can hear the conversation turning toward, "Okay, well, how do I create that harness around the model for my thing?" That's a term you'll hear in tech much more so than, like, our industry, or like, how do I do a very good job of creating that orchestrated set of agents with the good orchestrator at top? And not a-- you know, a lot of people don't know how to do that yet. So there's like different levels.

Simone Fishburn:

But, but Selina, like to-- go-going back to the point earlier, I wonder if in Ground Rounds in two years' time, we're all gonna be fluent in that too.

Selina Koch:

I think so.

Josh Berlin:

Okay, so we, we um we talked about the first A, AI. you know, another thing I think we heard a lot about this week is um another A, animals. And so we heard a lot about preclinical models, a lot of debate, a lot of vigorous debate on stage about where the future of animal models are going, where NAMS are going. I know Selina, you and uh and Simone spent some time on stage with uh Matt Hewitt of Charles River. I thought it was a really good discussion. So what, what's your take? what'd you hear this week that maybe surprised you about where the future of preclinical models is going?

Selina Koch:

Mm, surprised me. I don't know about that, but I definitely heard a lot of cognitive dissonance, a lot of, um , "The animal models are crap.""Actually, the animal models are really predictive." Or uh "We, we need to move away from NAMS." "No, we need more animal models." I think people are just kinda all over. And it depends, of course, on the application because what you're using, there's a lot of different things you use the models for. But uh I don't know. You wanna?

Simone Fishburn:

Actually, I wanna throw it to Sam. I mean- Yeah.

Sam Blackman:

Well, no, I, I will tell you, there, so there

Simone Fishburn:

are- But where are you? Animal models, good, bad, terrible, more, fewer NAMS? What are you gonna do?

Sam Blackman:

I mean-

Aaron Coe:

It depends… Sam Blackman: you know uh you know, You know, all models are terrible, but some models are useful. I mean- … it, it, it's, it uh you know, we, we could, we could spend the next hour, reinforcing the long-held, I think, and correctly held belief that, one of the biggest barriers to drug development that we face is the lack of predictive translatability of our preclinical models, right? I think the negative predictive value of many preclinical models is high. I think the positive predictive value is low and is evidenced by the fact that our success rate going from preclinical to proof of concept is not 100% and we have limited numbers of resources to be able to, you know, invest in building more and more reliable models. We continue to do better with organoids and slice models and, and things that are close to human tumors, but, they're all gonna be pretty terrible. And at the end of the day, it's gonna be very expensive human beings that are gonna tell us whether or not a new molecule or a new mechanism is working. That being said, one of the things that I loved out of the Charles Rivers presentation was the fact that they're using natural history data to reduce the number of animal models, and boy, did that make me happy. And it's, it's such a no-brainer. And as soon as I heard it, I was like,"How come we didn't think about this? How come we didn't think to just gather data from thousands and thousands of dogs and just have a synthetic control arm of dogs and cut down on the number of animals that were just placebo dosing?" It was… Wherever the Charles Rivers guy is, if you're out there-

Selina Koch:

Matt, where are you?… Sam Blackman: well done, you. I mean, well done, Charles Rivers.

Simone Fishburn:

To- totally. That, that would go under the surprise thing. And, and we sort of… Selina and I had seen the slides beforehand, and it's effectively digital twins. It's, it's, animal digital twins. And yeah, you're absolutely right. But you still come back to the, like, you know, maybe for tox or whatever, they're still, like, crap models are crap models whether they're, you animals or not. But it, it is a short, very short jump from there to first in human, right?'Cause it's all about what you just said.

Sam Blackman:

Yeah.

Simone Fishburn:

It's like what you need is something that's gonna be predictive.

Sam Blackman:

Right.

Simone Fishburn:

Okay.

Josh Berlin:

Yeah. So that, so that was my third A that uh we had AI-

Simone Fishburn:

Those A's… Josh Berlin: we had animals, Oh, I see.

Josh Berlin:

Which I think has never been said more on stage in, in biotech, and that's sort of a shorthand for first in human studies. And uh you know, Simone, I thought you had uh just an all-star panel here on stage in Seattle. We had the, the head of the Fred Hutch. We had the head of Texas Medical Center. We had the head of the Parker Institute, a former FDA leader and, and you um sort of leading the charge. So tell us a little bit about that session. And there were so many takeaways, so but just pick out one or two.

Simone Fishburn:

So, so, so let me just say, first of all, we are gonna write about that, so we will talk about some of those takeaways it's interesting. There were a lot of passionate voices on the stage and then off the stage afterwards among, among different people on this topic. And so various things, if I think about it, so we had the heads of two major medical centers, right? Texas Medical Center and Fred Hutch, okay? And we also had a, a regulator talking about what regulators can do, and we had Karen Knudsen, who represents the Parker Institute, but really was channeling the patient voice actually in this. And so there are various things all the way along the line that are obstacles and hurdles for why it takes so long. You know, when we talk about first in human, it's not-- it is partly getting the IND approved, but it's really to get the human data. That is what it is. It's like from start to… from application to human data, right? And a lot of the things on the way seem to be obstacles or hurdles inside academic medical centers. And so some of it relates to protocol amendments and how long that takes, and s- in some parts there's a regulator role, but some of these things are really just up to the centers themselves. And so that seems to be… The good thing that's come out of this, or one good thing is we start to see an alliance between them, a meeting of minds from different medical centers that actually might be able to do some sort of you know, learning or best practices or something. And then there's a whole different thread, which is what does the ecosystem and the regulatory system need to do to make… I'm gonna, I'm gonna go there, like back to Australia kind of thing, make the U.S. competitive, to make innovators wanna run their study here or stop fleeing to do their study elsewhere. And I, I know you've got different thoughts on this that you might wanna air some of them Sam.

Josh Berlin:

Sam, I think you have some, thoughts about this. Pl- keep, keep it PG 13.

Selina Koch:

They're a little wish- they're a little wishy-washy though, they're not..

Sam Blackman:

I have- Yeah. Yeah, I, I mean, I have thoughts. So I mean, look, I've been doing Phase I for almost-

Simone Fishburn:

I have thoughts, he says.

Sam Blackman:

I have thoughts. Uh, and opinions. Um, l- I… look, I've been doing Phase I research for almost 20 years, and n- nothing that came up in that panel discussion was news to me and, and they're not new problems, and nor is the idea of going to Australia for, some time savings. And it's… And by the way, it's not years of time savings, it's months of time savings. I actually think that the U.S. IND system, if you work through it properly, if you have a pre-IND meeting or you're going in with a drug that… where the mechanism of action's been well-characterized, and you write a clean and responsible and thoughtful protocol and, you know, you interact with the FDA appropriately You can get your IND cleared in 30 days. I mean, it happens automatically unless you've done something wrong. And I think if you do things right, then the system works. What gets annoying and expensive, both in terms of dollars and time, is the nuts and bolts stuff. It's all of the site activation steps required at a site, and each of those is expensive and requires human interactions. It's the budgeting and contracting for which there are no universal templates. And every time you're going through this exercise, if you're running a trial at 10 different sites, you're having 10 different budget and contract negotiations with 10 different formats. You're having site qualification visits and having to inspect every single pharmacy and where they store the investigational product, and you have to train the staff and so on and so forth. And you've got to run the gamut of 10 different scientific review committees and 10 different IRBs for the same protocol. And these are all, for the most part, NCI Conference of Cancer Centers. They're all, you know, experts in what they do. And at the end of the day, and I think Tom Lynch brought this up in his talk If you get through the IRB at MD Anderson, from his perspective, that's good enough for him. That should be good enough for the Fred Hutch. I thought that that was a brilliant insight into a way in which we can easily cut out months of time in the… not the IND part, the IND part's short. It's the study startup part that is killing us.

Josh Berlin:

Yeah, I thought that point on stage, and I think Karen of the Parker Institute also mentioned it, is sort of using NCI designation perhaps not only as a, as a carrot, but also a stick in the sense of if you're uh you're in that class, you should be able to recognize-

Sam Blackman:

Well, y-yes, and because it does require resources and, it's to some extent an unfunded mandate. It's one thing if we say to an, you know, to be an NCI Conference of Cancer Center, you've got to start a protocol within six weeks of getting the regulatory document packet from the sponsor. Sure, it's doable, but you need to resource all of the systems there to be able to do that. And of course, we're doing the opposite of that today. We're de-resourcing our NCI.

Simone Fishburn:

Right. And, and so, and so the overhang of this is obviously the way that China has just, bent the curve on this and changed the pace. And so I think that you're right that a lot of these issues that were discussed on the panel are not new issues, but there does seem to be-- It's sort of a little bit like AI's just created a, a new motivation to, let's say, get the data or whatever. This has created a, a new urgency to solve this because I think people are looking at that efflux of, potential going elsewhere.

Sam Blackman:

Well, I think we're, what we're realizing is that there are faster and cheaper ways of doing this. Now, they're not perfect, and of course, you know, Endpoints and STAT and every other newsletter this week was talking about now the federal government is saying, "Well, wait a second here. We're watching China You know, generate all the molecules and we're watching companies running to China to run early phase clinical trials, and they're about to do what government typically does, which is have some type of blunt tool to fix what is a very subtle set of problems. You know

Simone Fishburn:

what- Yeah, I would encourage you to read what BioCentury is writing about this, of course.

Sam Blackman:

Of course. Oh, I was… I wasn't reading any of it. I was just… You know, I was paying, I was paying rapt attention to the panel so I could be prepared for this.

Josh Berlin:

Claude uh Claude was reading it for you.

Sam Blackman:

Claude was… Uh, my agent was reading it for me, 100%. Um, but uh but I, I think, the thing about China is you've got very large institutions that are running investigator-sponsored trials where the data's not necessarily going to be fit for submission to the FDA and needs to be verified. And there are problems with going… There's certainly advantages time-wise and scale-wise to going to China that we can't replicate here in the United States with the current system that we have.

Simone Fishburn:

Yeah, and so, Josh, I'm actually very surprised you haven't plugged our China conference at this point. Please, please. But uh n- so well, which takes place 2nd to 4th of November in Shanghai. But I, I think it… I mean, that's a, a conversation that I anticipate. That and the animal models one. This is a conversation that I anticipate being in future Grand Rounds for a while, right? But I thought, you know, there were so many really deep science conversations. I thought your Precision Neurology panel, Selina had some interesting things. Do you have any, like, top take-homes from that one? You gonna kill me for that question? Okay. That

Aaron Coe:

That was one of my favorite panels, by the way.

Selina Koch:

Do you have take homes from that one- Okay,

Simone Fishburn:

okay… Selina Koch: 'cause you Okay, this is, okay, but this is how it goes, right? Well, yes. So Aaron, did you have any take homes from that panel that Selina ran?

Aaron Coe:

Well, I, I thought it was extremely well run and I was very proud to see my colleagues on the panel. the thing that struck me was the positive tone in terms of how each of these different contributors had aggregated tools that are now kind of coming into, into their own. And so I encourage everyone to sort of follow what we're doing at the Brain Health Accelerator because there's a lot that is going to be moving in a very translational direction relative to our history in, in building all these, you know, foundational data sets. And so it's uh it's an exciting time.

Selina Koch:

Yeah. I think it's clear from that. I think somebody used the word humility. You do have to have a lot of humility because incidents Capsida's we, can't predict just yet. You know, they might look good in a non-human primate and then, you know, kill somebody in the clinic. But when it comes to, avoiding places in the brain where you don't want your transgene because it's gonna cause a problem, or needing to have a, a low dose that you can actually manufacture, there are several layers of precision and a toolkit across each one that you can kind of mix and match and tune to even, with the limits that we have today, start getting into precise brain regions. And I do think that's a very optimistic message.

Aaron Coe:

Yeah.

Simone Fishburn:

Can I, can I just call out a, another couple of panels? So first of all, want to thank a couple of our other moderators, Melissa Bonner for a really interesting CAR T panel, and Rob Hershberg for a really interesting panel on, like, I guess what makes it investible, what makes the early science investible.

Selina Koch:

Can I just say, what, his recipe, his solution to all of you looking for early-stage investment was, I believe, to start an AI company to de-risk assets out of China that have had fast proof of concept, and then you're good.

Simone Fishburn:

You're good. Then you're good. And then, and then-

Josh Berlin:

Problem solved. Yeah.

Selina Koch:

Yeah.

Simone Fishburn:

But, but also a couple of really key workshops, like a target product profile workshop, which I think is just, like, under… I don't know if I should say under-discussed, but this is a thing that I really feel that the academic, industry interface is a really important place for that conversation to start. Sam?

Sam Blackman:

Yeah, I mean, that, I saw that, a theme that we haven't touched on here that ran across the whole meeting, certainly any time an investor was up here, was you need to tell me how this becomes a product. Mm-hmm. You need to tell me how it becomes a product even if you're just talking about an idea. During the session on the DNA Typewriters I thought that there was a really interesting set of interactions between the scientist who was leading the technology and investor who was sitting there and trying to understand, I don't understand how, how- How would

Selina Koch:

you go about evaluating?

Sam Blackman:

Yeah … how, how is this investable, right? How does this generate a return on investment? And you literally had… It reminded me of something from when I was at pharma. We used to joke, this was when I was at, at Merck, that the scientist would make something in the laboratory and sort of throw it over the transom, and they would think it was perfect, and we on the development side would catch it and we'd be like, "What am I supposed to do with this?" And to some extent, it was like that with the scientist and the investor, which is, "I've made this thing, and now I'm gonna throw it over to you because I wanna…" And the investor's like, "I don't, I don't know what to do with this. How do I make money with it?"

Selina Koch:

There's, there's a whole lot of bridging still needed there.

Sam Blackman:

So, yeah. And so the TPP, the thinking about the how does this turn into a medicine for somebody as early as possible is one of those themes that ran across all three days of the meeting.

Simone Fishburn:

Yeah, I mean, I, I do know uh and actually an, an academic innovator who did start a company who would then tell the story that they did this incredible work and they published it in Nature, and then they sat by the phone waiting for it to ring kind of thing, you know. And so, so I, I think that probably that, that was a while back.

Aaron Coe:

Jay is not sitting by the phone waiting for it to ring. Yeah. He's making a lot of phones ring, for sure. Yeah, yes.

Simone Fishburn:

No, but I, I, you know, I th- I think you're right. I think that interface has really come a long way since the story I told. But I think there's still a lot to understand because the concept of a target product profile is so often thought about something that is just industry relevant and, you know, happens from Phase II on and is all about commercialization. And so breaking down some of that is, is really interesting.

Josh Berlin:

Yeah, I, I I think that's spot on um what you're saying. You know, there's a, there was a lot of folks in the room this week that wanna be first-time founders. Brilliant scientists but haven't done this before. and so that is one of the missions that uh you know, Selina and Simone and the whole BioCentury team is really trying to, to help further along this discussion, trying to help get folks like you Sam, like you, Aaron, and others in the room together to learn from each other and to talk about how to, how to build companies and how to take medicines from discovery all the way to patients. Yeah.

Aaron Coe:

And to that end, I think these last three days were a tremendous success. You know, lots of discussions in the hallways, just like you'd set this up, so

Simone Fishburn:

Yeah. So-

Aaron Coe:

Kudos… Simone Fishburn: you know, it, I did have one investor tell me, you know, they, they actually met a bunch of companies and they are going… Or c- company wannabes maybe uh, and they, and they will have follow-up calls. We had how many posters? We had, like, I don't know, 30 plus- We had uh

Josh Berlin:

thir- I think 35 posters. We had uh 25 presenting companies. You know, most of them pre-seed or seed companies.

Simone Fishburn:

Yeah… Josh Berlin: uh, some, some with some with first-time founders. And I thought it was- And a Rising Star that we wrote about in BioCentury.

Josh Berlin:

And this year's Rising Star was actually not, uh- It- Uh, what was it last time? It was a, a Setting Sun-

Selina Koch:

Setting Sun… Josh Berlin: in Chicago.

Simone Fishburn:

Yeah. So yeah. But I, I, I told Josh, so you know, the image that came with the story was of this beautiful statue, almost like an Oscar. So now he has to go out and uh source. We'll need a sponsor for the Rising Star statue.

Josh Berlin:

That, that is true. So if anyone listening on the podcast wants to sponsor that, please, uh-… please send me a note on LinkedIn. We'd be happy to- Yeah … to speak with you.

Aaron Coe:

That's excellent.

Josh Berlin:

So we're almost out of time. I did wanna also talk a little bit just real briefly about where we're going next. So Simone, you know, our next Grand Rounds event is our second Grand Rounds Europe. We're gonna be in Amsterdam September 23 to 25 with Forbion and BGV as our regional host sponsors and committee sponsors. So uh what, what are you looking forward to? I know you're gonna have a discussion with Emer Cooke of EMA and-

Simone Fishburn:

You stole, stole my line, Josh

Josh Berlin:

Yeah… Simone Fishburn: you stole my line. Okay.

Simone Fishburn:

Yeah. So first of all, like, who doesn't wanna go to Amsterdam, and who doesn't wanna go to Amsterdam in uh in September? So I'm very excited about that. Yeah, so we have Emer Cooke from EMA who I will be having a conversation with. If you don't know her, she's a good conversation, that she's an interesting and, and lively person to speak to. And we are putting together an agenda, which we're actually gonna post We'll be, we'll be posting that in this coming week. I won't reveal too much now about the nature of some of the other sessions. We hope to have a debate. Let's see. but we will have, as we traditionally do, some very kinda in-depth… It's, it's a science conference where we're really talking about translational issues that are, are coming out, like bottlenecks. And so we're very excited to engage with Forbion and the local ecosystem. I think we're talking of the Benelux kind of area, right? It'll, it'll be a showcase for the Benelux region. So uh Sam and Aaron, we expect you out there. We expect everyone here in the audience out there. We'd love to see you. And then we will also as we announced today on stage, be back with this event. the US edition of Grand Rounds will be in Houston next April, working with the Texas Medical Center. It'll be a showcase for the whole state of Texas, and we hope you'll all come out and join us for that. So I think with that just wanted to thank Aaron and Sam. you know, obviously uh Simone, Selina, and our whole team which just did an amazing job this week. And, thank you all for being here and for being part of the discussion and the dialogue and the debate. And we hope you'll join us again next time. So thank you.

Aaron Coe:

Thank you too, Josh

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