Okay. Good afternoon, everyone. Thanks for your patience. We had a little bit of an elevator fracas, but we are here. We got the Generate Biomedicines team. My name is Eric Schmidt. I am going to be co-moderating this panel with my colleague, Tim Samuel, and we are just delighted to have with us the Generate team, led by the company's Chief Executive Officer, Mike Nally, as well as CFO Jason Silvers. Mike, Jason, thank you very much for being here. Realizing that you are relatively new to the public markets, Mike, maybe just start out high level with an overview of the state of affairs at Generate.
Yeah. So, Eric, first and foremost, a huge thank you for the opportunity. Generate is a company that was founded in 2018 with a premise that, at the time, was somewhat unique. Could you use machine learning-based approaches to design protein therapeutics? I think today that sounds a lot more normal, but certainly back then, to the protein establishment, this was almost heresy. The way proteins were designed historically was bottoms up, using biophysical-based means. Our co-founder, Gevorg, his work at Dartmouth led him to this belief that a data-driven approach could lead to superior answers. The company was founded with that premise. The longer-term vision is that biology ultimately could become programmable. Proteins are code, and if we could understand that code, we could ultimately program biology to drive desired effect. We did not recognize how fast the field would change.
Clearly, when the company was founded, this is a pre-AlphaFold moment, a pre-generative AI moment, and yet the early results we have seen from the technology allows us to manipulate proteins in ways that historical technologies have just struggled. The company has now put five molecules into the clinic. Our lead program is in phase III of development, for the treatment of severe asthma. We have a number of other programs in the cancer domain. The technology is very scalable, so we can have the capacity to start somewhere between five and 10 programs a year. We work not only for our own programs, but also with key partners like Amgen and Novartis. We are scratching the surface of the potential of the technology and excited by what is to come in the years ahead.
Maybe just sticking with the AI-based platform, AI drug discovery, computational design, they have kind of become buzzwords in the investment community. What really sets what Generate does apart from maybe others who have tried to make use of this newfound technology?
Well, I think it is actually this intersection, right? What we are seeing is these models are really, really powerful. I think that is what the recognition of AlphaFold was actually about, was these models can understand what is an unimaginably large search space exquisitely well. The average protein is about 200 amino acids long. There are 20 natural selections. The combinatorial possibilities is about atoms in the universe cubed. That obviously is a very hard thing to randomly explore, which is the way we have tried to design proteins in the past. What distinguishes Generate is actually a virtuous cycle of an integrated dry and wet lab. The reason that is important, if you think about where generative AI has had the greatest effect, it is in domains where you have rapid verification cycles. You can think about gaming as where DeepMind started the company.
You can think about domains like coding or domains like math, which we are seeing breakthroughs today. All of those have a rapid feedback loop. Biology does not have a rapid feedback loop, and so the lab becomes that feedback loop. A lot of our effort is not only pushing the frontiers of computational design, but also experimental throughput and pace. So we are able to survey this space in very distinctive ways.
Now, I think, Eric, you were kind of alluding to some of the hype in the field. These tools will not solve therapeutic challenges, biology, in the next five years. I know some people are saying it is going to cure all cancers on that sort of a timeframe. I also think what is obscured from the public's view is that the lead times in biology actually understate this current state of the art for these sort of technologies.
It takes us 10- 15 years to make a drug. The mere fact that our lead candidate is in phase III, about five years from design, is emblematic of how fast the field is moving. That molecule was designed on a model that predates the original release of ChatGPT. So if you think about where the current bleeding edge is, it has advanced massively. So while we agree that there is a lot of bluster in the space, we also believe that the potential of these sort of technologies could be transformative in a number of domains in drug discovery.
Eric, I would also underscore the type of data that we use to train our models is very differentiated. The scale in which, just so underscoring some of the things that Mike said, the scale at which we can measure is on the order of billions. The cryo-electron microscopy that we have, we have 4 cryo-electron microscopes that look at an atomistic resolution of protein-protein interactions on a dynamic basis. So these are not just static structures. So we are able to actually understand what is happening at binding sites and use those data on a three-dimensional perspective to help train the models. So while models may commoditize at the end of the day, the types of data will inform and lead to ultimately better results, which we think will be differentiated.
One last question on the platform, then we'll get into the programs.
Sure.
But the pace of activity or the pace of innovation here is almost head-spinning. So how do you guys keep up with the latest and greatest to maintain a cutting edge when, as you said, Mike, ChatGPT wasn't even around five years ago?
Yeah.
When we got our financing model.
We've had to tear up our infrastructure three times in an eight year company. We were a digitally native company. The hire number five at Generate was a bioinformatics hire to create a digital infrastructure for the company. The founding of the company predated the release of the LLMs. That sort of technological innovation forced us to rethink our digital infrastructure. And again, around the turn of the year, we started to recognize that a whole different agentic approach to science was going to be the future. And so part of it is, and I think this is the benefit of being an insurgent company versus an incumbent. Part of the reason I was always plagued at Merck was there are deep-seated cultural barriers to rapid technological change moments.
In a startup type environment, you have both a selection bias for the people that select into that domain, but also the company was founded with this kind of digital orientation. We embrace new technologies at a rate and a pace that larger companies, I think, simply struggle to do. Lastly, but I think importantly, the infrastructure also has been purpose-built. What large companies are trying to do right now is retrofit. It is kind of like an old home where you are trying to modernize an 1800s Victorian house. The reality is sometimes it is easier just to build from scratch. What we are finding is that given the digital infrastructure that is required to pursue biology in this way, there are huge advantages to being in this insurgent moment.
I realize it is only been about six months or so since you have been public, so the story, I am sure, is still resonating with investors, but what is it that you think the investment community is missing most about Generate Biomedicines and your opportunity set?
Well, I think this is kind of actually tying back to your earlier question around the AI bio space. I think these claims that we are going to solve all of drug discovery biology are reckless. I think they give misleading expectations, and I think what is really important in these spaces is having the discerning eye of where these technologies provide inherent advantage, and then complementing it with the sophistication of very savvy drug hunters and drug developers. One of the things we saw with some of the early startups is they missed the dose for their proof of concept studies. If you miss the dose, you can have the greatest technological underpinnings, you are still going to have a therapeutic failure.
How you marry these different domains is actually, I think, the key to success, and I think while it is one of those softer things, Eric, because it kind of ties into how you blend machine learning tribes with clinicians, with engineers, with experimental scientists. I think it is actually the key to success in this whole domain and unlocking real value is figuring out where to use the technologies and where to rely on more tried and true practices.
Okay. Let's get right into the pipeline then.
Yeah.
Your lead candidate, GB-0895. It's a long-acting TSLP antibody for asthma, COPD, potentially other opportunities. First let's start out with the unmet need. Why asthma? Why TSLP?
Yeah. When this program was started, the original question we were trying to confront was would computationally generated antibodies be immunogenic? Would designing a protein through a computer and not through using the immune system of a human, a mouse, a llama, elicit an immune reaction? Nobody had ever tested that theory, right? This was going to be the first computationally generated antibody to enter the clinical trials. We wanted to go after a target where there was actually, we could tune down biology risk, but we could identify an existing liability with the first-generation therapy. Tezspire, the AstraZeneca, Amgen anti-TSLP antibody that was the first one approved, that emerging data was coming out in 2021. We recognized that TSLP had a huge role to play in the treatment of severe asthma, but in a number of other conditions.
Based on our understanding of that molecule, we recognized that if we could drive a meaningful improvement in the binding affinity and actually a functional improvement in the preclinical assays, we could have a best-in-class anti-TSLP antibody. That is what led to the premise of the program. What Tezspire has shown is it's the only biologic that's approved for all comers in asthma. At the same time, the penetration in asthma is about 20% for biologics. Where you see normal, matured kind of immunology indications, you see 40%-60% penetration. What we've seen across a range of these different conditions is the first generation are kind of shorter acting. As you go to market maturity, you see longer acting entrants drive disproportionate market share.
We thought if we could come up with this sort of long-acting anti-TSLP, moving the dosing regimen from every month to every six months, it could have not only a huge impact on uptake in the market, but also address what is one of the, I think, major frailties of existing biologics therapy where only about 20% of patients with severe asthma are maximally adherent to their therapy. We think in the real world what we will actually see is a very clear efficacy benefit and hospital utilization benefit of these sort of longer acting therapies.
Okay, so we get the convenience advantage. That has been a winning strategy. With regard to efficacy, how do you think that convenience will be demonstratable in terms of your ultimate goal?
Yeah, I think the longer-term proof will be actually in the real-world setting. We have seen this in markets like osteoporosis, when you went from oral bisphosphonates to every six month dose biologics like denosumab. You were not able to show a difference in hip fractures in the clinical setting because these are well-controlled studies. But actually in the real world, what you see is this adherence gap leads to a marked difference in health outcomes in the real-world setting.
Programs like denosumab showed a real-world reduction in hip fractures, and we think similarly what you will see over time with this sort of long-acting anti-TSLP-based approach is that hospital utilization, exacerbation rates will be considerably lower because you will have this natural adherence benefit that is built in by the fact that for severe asthma patients, they are actually going to see their doctors every six months. The dosing interval actually aligns with doctors' visits to ensure better adherence to the medicine.
Okay. Mike, Jason, you guys chose a somewhat aggressive strategy to move right from phase I into a phase III program, which is currently enrolling. What gave you the confidence to do that, and what did you see?
Yeah. There were four key factors, Eric. Number one, we took inspiration from GSK. I think we should tip our hat. They took this exact same approach with their IL-5 strategy with depemokimab. What you normally do in phase I studies is go into healthy volunteers. They said, well, if we go into a slightly enriched population, we go into mild to moderate asthmatics with a certain EOS cutoff, what we're able to not only ascertain in the phase I study is the safety and the pharmacokinetics, but actually our ability to effectively modulate key pharmacodynamic markers that are correlated to exacerbation rates in the clinical setting. We took basically the exact same approach. We went to the exact same sites as GSK with just a different mechanism of action, and what we found was that we were able to modulate those biomarkers exactly the same degree as Tezspire.
That gave us confidence that we were modulating the biomarkers in a way that was commensurate with the approved therapy. The second piece was we could model essentially in blood how well we're able to occupy the target. What we saw was that we occupied the target at 99.9% over the entirety of that six-month course, so we knew we were fully blocking the mechanism.
The other thing that became evident in the test as we looked at the Tezspire case study is that they did dose-finding work in their phase II across a dose range of eightfold exposure. What they saw was there was no difference in exacerbations across those three different doses. They ultimately made a pragmatic choice, and we just figured rather than spend $50 million and wait two years for a phase II readout, we'd make a pragmatic choice based on those other factors that we saw.
I'd add two points. One is we hit the same epitope as tezepelumab. Number two, as Mike mentioned, we do see the deep suppression of these biomarkers all the way out to six months, similar or better than where Amgen, AstraZeneca, tezepelumab. In addition, we studied six different doses in our phase I trial in asthma from 10 mg to 1,200 mg, and once you got to 300 mg and above, you see the deep suppression is actually not really improving across the four biomarkers. Now that we have our COPD data, which we studied an additional 15 patients who received drug in 300 mg and 600 mg cohorts, there was no difference between the 300 mg and 600 mg in those patients as well. We feel really confident 300 mg is the right dose.
Maybe double-clicking a little bit more into the phase III SOLARIA trial, could you maybe walk us through some of the key efficacy populations and endpoints in the trial, and maybe what we can expect timing-wise there?
Sure. The study is it's 786 subjects in two studies. 301 and 302 are the two studies, SOLARIA- 1 and SOLARIA- 2. The timing we expect to enroll over the course of the next year and now four months or so, which will be we enroll by the end of 2027. It's a 52-week follow-up study, so by the end of 2028, early 2029, we will have our data from our phase III study in asthma. The primary endpoint is exacerbation reduction. We have secondary endpoints that will look at various biomarkers as well as impact at both above and below 300 EOS cutoffs. We've actually powered the study at about 94% on the low EOS population, which powers us at 99% on the entire study.
One of the key inclusion criterias will be two exacerbations in the 12-month period leading up to enrollment as enrollment criteria. That's kind of where we are. We started enrolling, or we opened the study up in December of last year, of 2025. We now are open or have regulatory approval in 39 of the 42 expected countries across six different global regions across the world, and that is ongoing.
This is a competitive space, not just for other biologics, but for other TSLPs. I think that you're the most advanced long-acting TSLP, but how do you look at the rest of the field? And in particular, there's some bispecific approaches out there that are trying to do a little bit more.
Yep. I think when you look at the field, having started the phase III study in December of last year, we're at least 9- 12 months ahead of the other long-acting TSLPs. There have been some more recent announcements over the last couple of days coming out of the European Respiratory Society conference that says a few others are in process of starting their phase III programs. We think we have, let's just say 9- 12 month lead on the field on the long-acting anti-TSLP antibodies. That's really important because historically, order of entry drives market share performance in these sort of classes. And so, having the first-mover advantage is of distinct value, and part of our focus is executing on the trial.
And the benefit of being first also was the fact that we were able to select which sites we wanted to go to, and enroll patients in a less competitive field, at least for the first year of the study. And so we feel pretty good about the lead we have in this space. You noted the emergence of a number of biosimilar entities. One of the data sets we were looking at very closely earlier this week was the Sanofi IL-13, TSLP data. What we saw in that data set yesterday was that there was no difference, really, between the ability to reduce exacerbations or improve lung function between the bispecific and the monotherapy. And so for us, one of the, I think, perceived threats was would combination therapy ultimately unseat the monotherapies in asthma? And the data to date has been less compelling on that front.
I think you're not seeing synergistic activity. In this case, you weren't even seeing additive activity between the two mechanisms. And it's somewhat not surprising because, given the overlap in the pathways between TSLP and IL-13, in our studies, we saw a 50% reduction in IL-13 in the TSLP studies. And so we believe that the overlap, in some ways, masks the individual components.
You yourselves were at the European Respiratory Society meeting over the weekend.
That's right.
What did we learn from those presentations?
Well, I think there's a huge amount of excitement in COPD. I think what the clinicians around the world were telling us is the COPD patient population is a much more frail and vulnerable population with many comorbidities. A six-monthly dosed antibody in a market where current biologics therapy is about 1%-2% penetrated across most of Europe. It's just a huge market opportunity. COPD is the fourth greatest burden of any disease in the planet. The ability to have a meaningful reduction in asthma exacerbations and hospitalizations with a medicine like ours is really satiating a big unmet need. The other thing that we've seen, and this is, I think, coming out of some of the data, up until recently, the biologic eligibility was only in the high EOS population.
About 28% of the population has an eosinophil level of greater than 300, and that's where biologics like Dupixent and Nucala have been indicated. What was exciting at ERS is there's been strong signals that both with the IL-33s and with TSLP, there's the opportunity to expand the access to these medicines to a much broader population, and I think there was a lot of excitement on both of those fronts at ERS.
Have you begun doing a deeper dive into these subsets within the COPD population that you've started to treat?
Very much so. In our phase I-B study where Jason alluded to with the 40 patients, we use an EOS cutoff of 200. We don't think TSLP will go down and work well in the less than 150 EOS cutoff. We think you're going to probably want to try and figure out this 150 and higher, because the nature of the TSLP mechanism is it responds to type- 2 inflammation. And so that's where you see the vast majority of that sort of inflammation.
You referenced your good biomarker data for GB-0895 in COPD. What's next steps for that molecule in this indication?
Yeah. We feel really good about the data. We've had regulatory interactions, which will be continuing and ongoing. We have from our cash balance, which sits as of June 30, just under $460 million. With the asthma trials well underway, we have oncology trials in phase I at this point, and we have a number of other things that are coming. We're going to put all the totality of that information together and look at the competitive landscape, which obviously the information really came out yesterday, and ultimately decide what the next steps will be. With COPD, we believe there's a path very similar to asthma to move into later-stage trials, but that's something we'll be evaluating over the course of the next several weeks or months.
Do you need COPD to compete effectively in asthma and respiratory disease, or do you think these two are sort of idiosyncratic to themselves?
I don't think you need it necessarily, Eric, but I think it certainly is beneficial from a contracting perspective. I think the broader your label can be, especially in the United States, the better you're able to effectively compete from a formulary positioning perspective. I do think there's some benefit of a broader indication strategy. At the same time, I think the unmet needs in both asthma and COPD stand alone, right? We know the mechanism works in both populations, and so we think there are real severe unmet medical needs in both that we can potentially address.
We've only got a few minutes, but let's move on to the rest of the pipeline. You've got two other candidates in the clinic, GB-4362, the MMAE neutralizer. What is this, and why is it interesting?
You want to take that?
Sure. The MMAE neutralizer basically is an antibody that binds specifically to free MMAE, which is the payload on Padcev and other MMAE-based antibody-drug conjugates. The big issue with those drugs, antibody-drug conjugates with the MMAE payload, is cytotoxicity. When the Free MMAE circulates systemically, so you see a lot of skin toxicities, neutropenia, and importantly, what you saw in the Padcev trials is two-thirds of patients getting peripheral neuropathy.
As that peripheral neuropathy progresses from grade one to grade two, it becomes irreversible for those patients, and that leads to significant discontinuation, interruptions, or reduction of doses for those patients. What you've seen, frankly, even in the community, is only about 50% of patients with urothelial cancer are getting Padcev, even though it's extremely beneficial and effective for treating those cancers because some of which physicians are less comfortable dealing with those side effects. Our antibody binds specifically to an area on free MMAE where it binds to the linker, so you're not actually impacting the intact antibody-drug conjugate.
The antibody-drug conjugate intact can get to the cancer cells, the urothelial cancer cells, and kill the cancer cells. But when the free circulating MMAE is exposed, our antibody can bind it and neutralize it and therefore reduce the toxicities. We're in phase I clinical trials right now at multiple leading cancer centers around the country, MD Anderson, City of Hope, Memorial Sloan Kettering Cancer Center, for example, et cetera. We have dosed our first cohort of patients at this point. We're in a dose escalation trial to determine what dose of our drug will reduce free MMAE by 50%. The reason we're targeting that is in our preclinical studies in mice and non-human primates, we saw that we can go all the way to 80% reduction in free MMAE, get reduction in all these cytotoxicity like neutropenia and skin toxicity without impacting tumor killing.
Above 80%, we did see some impact on tumor killing, so we've given ourselves a wide range below that, which is why we're targeting 50%. We believe that by over the next several months or early in 2027, we'll have that dose. The plan is to expand the cohort of about 40 patients or so who have grade one peripheral neuropathy already on Padcev and Keytruda, give them our MMAE neutralizer in commensurate with the cycles of treatment to see if we can reduce or stop the progression or reverse the progression of peripheral neuropathy. We believe we'll have those proof of concept data in 2027.
Eric, you mentioned earlier, what is one of the things that people are underappreciating? This program could actually get approved faster than the
Okay.
TSLP program. I think the registrational path after the data that Jason just talked about could be very, very rapid. You're probably talking about 200 patients, and your ability to slow the progression, you could see that very, very quickly.
Okay. We just have 1 minute remaining for a question on your other program, CAR T and solid tumors.
Yeah. The CAR T program is a program that one of the founders of the Juno Technology came up to us and said, "Could you use your platform to optimize the binders in the constructs for CAR T?" We took a very small team. We put four scientists on it. Within 3 months, we saw that we had one of the most potent CAR Ts that would ever be tested. We're excited to try and take on. One of the holy grails for CAR T has been solid tumors. This is just starting phase I right now. The first patient should be dosed almost imminently. We think this could be dosed without lymphodepletion, which would be a major advantage versus other CAR T therapies. The unmet need here is extraordinary.
If the thesis is right and the potency of this molecule makes a difference where you're able to fight through the tumor microenvironment and kill cancer cells for ovarian cancer, it could be transformative for women's health. We're excited to work with our partners at Roswell Park and hopefully have a breakthrough therapy for patients with ovarian cancer.
Mike, Jason, thank you for coming out and sharing the Generate Biomedicines story with us.
Thanks for the opportunity.
Thank you so much.