Latest / The BioHub - by Avetix / Episode 173 - Jason Silvers, President & CFO & Laurie Lee, CMO (I&I) - Generate: Biomedicines

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Episode 173 - Jason Silvers, President & CFO & Laurie Lee, CMO (I&I) - Generate: Biomedicines · The BioHub - by Avetix

• 0:06
  Hi, everyone. Welcome to the next episode of
• 0:08
  the Biohub podcast. Today we have Jason Silvers,
• 0:13
  President and CFO at Generate Biomedicines and Laurie Lee,
• 0:16
  Chief Medical Officer for Immunology and Inflammation.
• 0:20
  Generate is one of the few AI native companies not just
• 0:23
  building a platform, but actually advancing a designed
• 0:26
  protein into late stage clinical development.
• 0:29
  So I'm really looking forward to getting to both the company
• 0:32
  build and what it looks like in practice with Solaria.
• 0:37
  To start, could you both give a quick overview of your
• 0:39
  backgrounds and what led you to join Generate?
• 0:42
  Sure. Thank you very much, Amber.
• 0:44
  So Jason Silvers, as you said, the President and CFO of
• 0:47
  Generate Biomedicines, I've been at Generate for four years.
• 0:51
  I've spent, before coming to Generate, I spent 20 years at
• 0:54
  Goldman Sachs and investment banking.
• 0:56
  I spent most of that career in New York City.
• 0:59
  I ran our M and a business for healthcare for a number of
• 1:01
  years. I moved to London and ran our
• 1:03
  EMIA health business out of London for about 3 years and
• 1:07
  before that I went to Med school and law school with a residency
• 1:10
  year in between those. But ultimately I've been in
• 1:12
  finance for about 24 years. What led me to Generate
• 1:16
  specifically was toward the not tail end of my career,
• 1:21
  investment banking, but having done it for 20 years, I was
• 1:23
  really looking forward to moving into industry and with a medical
• 1:26
  background on biotechnology and tech in general was specifically
• 1:31
  what I was looking for. And when I learned about
• 1:34
  Generate, met the people that Generate, saw what they were
• 1:36
  doing, I was kind of blown away by what was possible in terms of
• 1:40
  how drugs ultimately could help patients in the future and how
• 1:43
  they can be generated as opposed to simply found through
• 1:48
  traditional techniques. And ultimately, after having met
• 1:53
  the CEO and multiple people on the board and the company was
• 1:56
  forming, this was a special place.
• 1:59
  And I'm really can't be happier with my decision.
• 2:02
  And Laurie, how did you end up generates?
• 2:06
  Thank you, Amber. So I I see my career as as three
• 2:10
  main phases. First was at academia and that
• 2:13
  was very foundational for me. I trained in Pediatrics and then
• 2:17
  sub specialized in allergy immunology and I stayed on
• 2:21
  faculty at Duke for almost 11 years.
• 2:23
  After I finished my fellowship there, I was involved in
• 2:26
  clinical research for rare primary immune deficiencies,
• 2:29
  bubble way disease where bone marrow transplantation is needed
• 2:33
  in, in infancy. And I also had a really busy
• 2:36
  clinical practice seeing patients with more common
• 2:38
  diseases like food allergy, atopic dermatitis, allergic
• 2:42
  rhinitis. And, and I enjoyed the, the
• 2:45
  duality of my role. But I really thought that that
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  maybe I could have a, a bigger impact on, on patients health if
• 2:53
  I focused on one. And I thought that industry was
• 2:56
  the way to do that. So now in the the second phase,
• 3:00
  my career, I was at large pharma for 18 years and part of making
• 3:06
  essence bringing new medicines to patients all over the world
• 3:09
  for rare and common diseases. In fact, my industry experience
• 3:14
  really spanned my clinical experience with from allergic
• 3:17
  rhinitis all the way to transplantation.
• 3:20
  But I was really inspired by making new medicines for
• 3:24
  patients that could be transformative and wanted to do
• 3:27
  it over and over. And generous really stood out as
• 3:31
  the place to do that. So very similar to to Jason was
• 3:36
  really inspired by by the people we're called generators and
• 3:40
  we're all here with the same, the same mission in mind.
• 3:43
  And I was very inspired also by the late stage pram that you'll
• 3:48
  hear hear more about GBO 895 is one of the first, if not the
• 3:54
  first AI enabled molecule to go into phase three.
• 3:57
  So really excited to be part of that.
• 4:00
  It's a really exciting time for the company and and for AI
• 4:04
  enabled technology and drug development.
• 4:07
  So was really happy to join and I joined last September.
• 4:11
  Nice. It's really interesting how you
• 4:13
  both ended up here from such different environments as well.
• 4:17
  And Jason Generate has scaled quickly, including your recent
• 4:21
  IPR. How do you think about what the
• 4:24
  company is today? Would you consider yourself
• 4:26
  still a platform company or starting to look like more of a
• 4:29
  traditional biotech? I would say that we're more a
• 4:34
  platform company, but have the benefit of both in that we've
• 4:39
  now translated multiple products into the clinic.
• 4:43
  And when I say that, so really elements of both.
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  But in reality I'd view us more as a technology type company
• 4:49
  that has the outgrowth of these multiple clinical assets.
• 4:52
  So as Lori alluded to, we do have multiple clinical assets,
• 4:56
  the lead assets of Phase 3 molecule and that may look more
• 5:00
  like a traditional biopharma company.
• 5:02
  Ultimately, over the last three years alone, we've translated
• 5:05
  about 5 products from concept and technology into the clinic.
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  Again, the most advanced being Phase 3.
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  And so that combination of both the technology side that has
• 5:17
  demonstrated the ability to routinely generate model and
• 5:21
  interesting molecules that have moved to the clinic and
• 5:24
  ultimately we think we'll get the patients over the next
• 5:27
  several years that is is really demonstrating the cut the
• 5:32
  combination. But I think really what we are
• 5:35
  is a true platform technology that will continue to
• 5:39
  breakthrough the limitations of traditional drug discovery and
• 5:45
  ultimately find curative solutions for patients for
• 5:50
  multiple diseases. And so using that technology to
• 5:54
  go after undruggable or hard to drug targets is really where we
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  are focusing our technology now in the very beginning stages of
• 6:03
  the company, the molecules that we started to generate were
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  really does the technology work. We go after more known biology.
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  I take less risk around the biological side of things and
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  really understand the technology risk.
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  Now that we've proven that we can do that, where we've shifted
• 6:20
  our focus is more in the hard to drug or undruggable areas that
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  as I mentioned and that's looking at some very complex
• 6:29
  technologies where traditional drug discovery can't unlock
• 6:32
  them. So if you're trying to, for
• 6:34
  example, optimize internalization, so if you have
• 6:38
  a really well known target cell with a receptor that is a known
• 6:43
  on cancer cells in particular, let's say, and really specific
• 6:46
  to cancer cells, but it doesn't internalize well, then targeting
• 6:50
  that specific receptor may prove challenging and securing
• 6:53
  disease. But if our technology can unlock
• 6:56
  internalization and allow us to create proteins that enable that
• 7:00
  receptor to internalize the drug better, then ultimately that
• 7:04
  unlocks a whole area of cancer treatments.
• 7:08
  In the example that I've given. And we're utilizing this
• 7:11
  technology in a number of other areas like whether it's pH
• 7:13
  dependent binding, where we design proteins that bind at APH
• 7:18
  of 7.4 but maybe release at APH of 6.0, crossing barriers like
• 7:23
  the blood brain barrier or gut barrier or cellular barriers.
• 7:27
  These are things traditional drug, drug technology in terms
• 7:31
  of drug development can't really go after, but these technologies
• 7:35
  that we have in our platform. Yeah, that's really insightful.
• 7:39
  And Laurie, how would you describe the company, especially
• 7:42
  in comparison to some of your previous businesses?
• 7:45
  So I agree with Jason says we're what Jason says, we're a
• 7:49
  platform company. I really see our platform as
• 7:52
  generates engine and our heart and our soul.
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  But with Gen. with GBO 895 entering the clinic in phase
• 8:01
  three and other molecules soon to enter the clinic too.
• 8:05
  We're also a biotech. I, I do think that we're a
• 8:09
  rather unique biotech. However, with the global phase
• 8:13
  three study in, in a fairly common disease like asthma,
• 8:17
  that's, that's a bit unusual. But what makes us unique too is
• 8:22
  that it's our platform, It's a, it's a robust platform.
• 8:26
  We're disease agnostic. So we're really following the
• 8:29
  the science where there are unmet needs and really tackling
• 8:34
  very complex challenges in drug development like like Jason
• 8:38
  alluded to, so platform company first, but also a biotech.
• 8:43
  Nice. And back to Jason, a core part,
• 8:47
  core part of your model is designing proteins at scale, but
• 8:51
  equally investing in the ability to test and measure at scale.
• 8:54
  How do those two sides work together in practice, and what
• 8:57
  does that unlock? Yeah, sure.
• 9:00
  So those Amber, those two sides are absolutely essential and
• 9:03
  frankly, probably one of the biggest differentiating
• 9:06
  characteristics of generate relative to some other companies
• 9:09
  in the field that are really just using the technology
• 9:12
  looking to unlock biology. Given biology is really not a
• 9:16
  known science. And probably we we understand as
• 9:19
  a population, maybe 5 percent, 10% of biology using just
• 9:25
  technology to try to understand what drugs are going to work,
• 9:29
  what proteins are going to work is really hard if you can't
• 9:32
  actually test them. And so one of the really
• 9:34
  interesting parts about AI is AI today gives us the tools to
• 9:40
  generate an infinite number of hypothesis, as an example, in
• 9:43
  biology, in any fields, Frankly, the problem is if you don't
• 9:46
  understand which of those hypothesis are the right ones to
• 9:49
  test and you don't have the capabilities to test them, you
• 9:54
  can't really come up in any time frame that matters in, you know,
• 9:58
  the next dozen, 100, whatever number of years to actually go
• 10:03
  and help patients. And So what we think is critical
• 10:06
  is how do you take advantage of the billions to trillions to
• 10:11
  infinite number of hypotheses that AI machine learning can
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  actually generate. And so you build what we've done
• 10:17
  from the very inception of the company, which the company was
• 10:19
  founded in 2018, it was built around creating an experimental
• 10:24
  wet lab capability that can look at every single hypothesis that
• 10:29
  the machine learning can generate and actually test them
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  to determine which is the right molecule to to molecular
• 10:35
  molecules to take forward and and design for patient use down
• 10:39
  the road. And So what that means is we
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  have capabilities using miniaturization, microfluidics,
• 10:46
  automation that enable us to get 10 to the 10th number of
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  hypothesis from the machine learning, but actually in a very
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  short period of time, we're talking days to weeks, measure
• 10:59
  and test and measure all of those in a bespoke basis.
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  So it's not random. It's literally the computers are
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  generating 10 to the 10th very specific hypotheses as to what
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  the right answer is for that target, let's say or that's a
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  problem we've provided to the machine to solve.
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  And then we test every single one of those in a bespoke basis.
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  And what that allows us to do is let's find the right 5-10
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  hundred that that we can then at much larger scale test them in
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  many more assays and many more experiments to figure out what
• 11:31
  the right answer is. And that is truly, I'd think the
• 11:34
  key differentiator for us. And that's how we look at both
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  leverage, urging, both the AI part as well as the experimental
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  part and AT generate the integration between those two is
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  absolutely essential because that is what enables us to not
• 11:48
  just come up with the hypothesis, but it's come up
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  with the hypothesis that we know.
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  We can test, test those. And then we can run a cycle
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  effectively of learning where the machines are learning from
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  what the experiments are showing.
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  So the next time you run it, you just come up with better
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  answers. And Lori, how does that show up
• 12:07
  in practice when you're making decisions on programs?
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  So it's this integration of the dry and the wet lab that the
• 12:16
  Jason has has talked about that, that is is really the key here.
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  I think what that does or what that unlocks is a solution to
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  what a lot of people in R&D are facing, which is a productivity
• 12:31
  problem. So with this seamless
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  integration, we believe that generate can design more
• 12:38
  molecules in less time. That means less cost as well.
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  And those molecules can have a very high probability of success
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  in the clinic. So in support of this, very soon
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  we will have taken 5 molecules into the clinic in the last
• 12:53
  three years alone and our most advanced one went from the
• 12:57
  clinic or went from the computer.
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  So it was hadn't even been designed yet to Phase 3 in just
• 13:04
  five years. And this is really a result of
• 13:07
  designing and testing at scale. It's the, the cycle, it's called
• 13:13
  a design, build, test and learn. So first you design the
• 13:16
  molecules and it can be, you know, billions, as Jason has
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  said. But then you really need to
• 13:22
  build them and test them and learn from them.
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  And that generative AI cycle gets better.
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  And we're learning too. It's getting faster.
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  And the, the other point about this integration is that it's
• 13:35
  not a hand off. It's not like the technology
• 13:38
  group hands something off to the biology biologists and the
• 13:41
  biologists then hand it off to the clinicians.
• 13:45
  It's, it's really a living feedback loop that's augmented
• 13:48
  by, by the the scientists and, and the clinicians here.
• 13:52
  It's embedded and and really at every layer here to unlock that
• 13:57
  full potential of the generative AI.
• 14:02
  No, Amazing. Thanks for that insight as well.
• 14:05
  And I think talking about the GB 0895 asset you mentioned, Lori,
• 14:13
  it's now in phase three, which is rare for a company of your
• 14:17
  kind. What gave you the conviction
• 14:19
  early on that this was a molecule worth progressing all
• 14:22
  the way through? Yeah, yeah.
• 14:24
  So this is an easy one for me. I followed TSLP for the last 20
• 14:29
  years or so. It's a it's a interesting
• 14:32
  cytokine with links to the innate immune system and going
• 14:36
  back to, to, you know, my my foundations in primary immune
• 14:40
  deficiency. But what gave me conviction for
• 14:44
  for this molecule in severe asthma is that there's a very
• 14:48
  strong genetic association, human genetic data to show a
• 14:53
  link TSLP and the development of asthma as well as other allergic
• 14:58
  diseases as well. And the biology is is known,
• 15:02
  it's fascinating biology. So it's been de risked and to
• 15:07
  give a little bit of the background there TSLP is is
• 15:10
  called an alarm in. So it links epithelial barrier
• 15:15
  dysfunction with immune activation.
• 15:19
  And in this case it's epithelial barrier dysfunction in the lungs
• 15:23
  as a result of exposure to trigger such as allergens or
• 15:27
  pollutants or viruses or other pathogens.
• 15:31
  And the TSLP then initiates and amplifies an inflammatory
• 15:36
  processes in the lungs that are very well characterized and by
• 15:42
  blocking the the the initiation of that inflammatory process
• 15:48
  really close to the site of exposure that makes intuitive
• 15:52
  sense. You know that that's the way to
• 15:53
  do it, to stop it at the top of the inflammatory cascade rather
• 15:58
  than downstream at at the bottom.
• 16:01
  But TSLP, it's been clinically validated as well.
• 16:04
  So there's an approved medicine for patients with severe asthma
• 16:09
  as well as chronic rhymosinusitis with nasal
• 16:12
  polyposis. The medicine is called Tezfire
• 16:15
  and it's a monoclonal antibody that's given every four weeks.
• 16:19
  And the clinical data that's been published is, is really
• 16:21
  very compelling because it really it it improves multiple
• 16:26
  facets of severe asthma. So not just the exacerbations
• 16:31
  that occur intermittently, but the day-to-day symptoms that
• 16:34
  patients are having as a result of their impaired lung function,
• 16:38
  the increased symptoms that they're having, the coughing,
• 16:40
  the wheezing, the quality of life, the blocking TSLP has
• 16:45
  shown improvement of all of those domains that asthma
• 16:49
  patients experience. So it's that clinical validation
• 16:53
  now with with GBO 895 though it was Co optimized with this
• 16:58
  generated AI mainly to increase the affinity and increase the
• 17:04
  half life. So this means that it can be
• 17:06
  dosed every six months instead of every four weeks.
• 17:11
  And so for a, you know, after years of, of clinical practice,
• 17:15
  I know that the, the medications that that patients have to take
• 17:20
  with chronic diseases really can be a burden.
• 17:23
  And to go from every every four weeks to every six months, that
• 17:28
  has to be a good thing for patients that, that are already
• 17:31
  having to take inhalers every day and continue to have
• 17:35
  symptoms. So for me, it was an easy
• 17:37
  choice. I knew that this I wanted to
• 17:39
  progress this all the way through.
• 17:41
  I see it as an opportunity to really change the practice of,
• 17:45
  of medicine or to change the the landscape for patients with with
• 17:49
  severe asthma with, with this long acting monitor and.
• 17:53
  Jason, how did you think about backing that from a portfolio on
• 17:57
  investment perspective? So.
• 17:59
  In addition to asthma, COPD is the other indication that we're
• 18:02
  pursuing. And when you look at the market
• 18:04
  opportunity and and patient need in both asthma and COPD for
• 18:09
  biologics, it's act it's very, very large and on the patient
• 18:14
  needs side. So biologic penetration in
• 18:17
  asthma is probably around 20% and bilateral penetration in
• 18:21
  COPD at this point is probably around 1%, maybe less.
• 18:24
  There's a couple of proof products in a very narrow
• 18:27
  population of COPD patients at this point.
• 18:30
  The T slip molecules which are GBO 895 is a seem to have
• 18:36
  broader implications for all comers and asthma and all comers
• 18:39
  in COPD or a larger, not necessarily all comers in COPD
• 18:44
  from an ESNFL account, but a larger number of eosinophil,
• 18:52
  amount of eosinophils that patients have in their blood
• 18:55
  than what is currently approved today.
• 18:57
  And so there's a huge opportunity for patients.
• 19:00
  And from our perspective where we allocate our capital, the
• 19:03
  platform, while we are really a platform and technology company
• 19:07
  relative to the cost of running phase three clinical trials is
• 19:11
  really de minimis. And so we can continue to
• 19:14
  execute on finding the next novel therapeutics from our
• 19:18
  platform and bringing those toward and into the clinic and
• 19:22
  still afford to do kind of the asthma and COPD trials.
• 19:26
  Now ultimately these are very expensive trials.
• 19:28
  We will continue to have to find ways to fund those, but it
• 19:31
  doesn't really impact our ability to fund the core part of
• 19:35
  our platform and coming up with the next wave of therapeutics.
• 19:38
  And therefore given the market opportunity the patient need, it
• 19:42
  makes sense at this point for us to continue to fund these.
• 19:44
  Ultimately down the road, we will still make the decision as
• 19:47
  to whether we partner them and what the business model is for
• 19:50
  these, whether it's an outside the US license, a global license
• 19:54
  or we take these all this all the way to the finish line.
• 19:57
  But that's kind of a decision that we'll make over the next
• 20:00
  couple coming years. And building on that then Lori,
• 20:03
  you've moved into a global phase three program with Solaria.
• 20:07
  What has been the biggest challenge in translating a
• 20:11
  designed protein into a real world clinical trial?
• 20:14
  So. It's, it's a great question.
• 20:17
  And actually the way a molecule was designed, whether or the way
• 20:22
  a molecule was was made, whether it was from the the traditional
• 20:27
  trial and error approach where you discover something and then
• 20:31
  you kind of work backwards and try and figure out what it does.
• 20:34
  And if it could be medicine versus the way we did with GBO
• 20:39
  895, which was designed it with intent.
• 20:42
  It doesn't really affect how you then develop it in the clinic.
• 20:47
  The regulatory hurdles are, are are the same.
• 20:50
  They're they the expectations are not higher, but they're not
• 20:53
  lower either. So I would say that actually one
• 20:58
  of the bigger challenges that that I had when I joined was
• 21:02
  really kind of keeping pace generator set a bar, a high bar
• 21:08
  with everything that they do and the in the early phase team was
• 21:11
  no exception. So the our phase one study was
• 21:15
  conducted in asthma patients rather than healthy volunteers.
• 21:19
  So that enabled them to measure the pharmacodynamic effects GBO
• 21:26
  895 in patients with asthma. So we knew that it was working
• 21:31
  on the biomarkers and, and we gained the, the safety, the
• 21:35
  tolerability and the PK data from asthma patients that you
• 21:38
  would have gotten and healthy volunteers as well.
• 21:41
  So on top of of doing that, the early phase team also developed
• 21:46
  an accelerated clinical development plan that could
• 21:50
  enable us to go from phase one to phase three.
• 21:54
  And they had the, the, the discussions with the regulatory
• 21:58
  agencies and those. So the phase three would program
• 22:02
  was informed by those discussions.
• 22:05
  So that's when I joined generate was after all that had happened.
• 22:10
  And so the talent for me was really keeping pace with what
• 22:14
  the team had done. So not only was I executing the
• 22:17
  study, but I was building the team at the time to execute that
• 22:22
  study. So it's but that's you know,
• 22:26
  that's a real opportunity and one that I haven't had at my
• 22:29
  other companies. It's real privileged to be able
• 22:31
  to hire and build the team to deliver, deliver the study.
• 22:37
  And I've made a lot of great connections over the last six
• 22:42
  months, connections that you know from previous jobs as well
• 22:45
  as new connections too. And it's it's it's it's been a
• 22:49
  really great experience. Yeah, that's.
• 22:52
  Really interesting to hear about that shift from the early stages
• 22:55
  to then how you're building out that team as well.
• 22:58
  And so Jason, would you be able to talk further about your
• 23:01
  oncology portfolio generate as well?
• 23:04
  Yeah, it's. Great.
• 23:05
  Thank you, Amber. The, so the, the next set of
• 23:08
  programs that are moving toward the clinic or into the clinic
• 23:11
  now into phase one, we have two oncology programs that are I
• 23:15
  think quite interesting that also utilized our technology in
• 23:18
  different ways. Then the TSLP molecule that
• 23:22
  Laurie has talked about, so the first is called it, we call it
• 23:26
  an MMAE neutralizer and this is when there's an antibody drug
• 23:31
  conjugate that has the antibody with a linker and the payload
• 23:35
  which is the chemotherapeutic agent.
• 23:37
  When that chemotherapeutic agent is MMAE based.
• 23:41
  The issue with those molecules is when the MMAE gets
• 23:45
  systemically circulating on its own, it causes tremendous side
• 23:49
  effects for patients and really limits the dosing of these ADC's
• 23:54
  that have the MMA payload on them.
• 23:56
  When it freely circulates, it causes peripheral neuropathy,
• 23:59
  neutropenia, skin toxicities and ultimately that leads to either
• 24:02
  reduction of the dose or interruptions or frankly even
• 24:07
  they have to stop the dose automatically.
• 24:11
  And so there are a number of these products out there.
• 24:13
  Padsev is one of the key ones for urothelial cancer today.
• 24:17
  It's given with Keytruda and is really phenomenal treatment for
• 24:22
  urothelial cancer patients. But again they get limited by
• 24:25
  this MMA based toxicity. And so we designed with our
• 24:29
  models a an antibody that binds specifically to the free MMAE
• 24:34
  that's circulating systemically, but does not bind to the bound
• 24:39
  MMA to the ADC. So the antibody drug conjugate
• 24:42
  can get to the cancer and the localized chemotherapeutic MMAE
• 24:47
  can treat the cancer and kill the cancer cells.
• 24:51
  But anything freely circulating our antibody can actually pick
• 24:55
  up, almost think about it like a garbage truck.
• 24:57
  It picks it up and it throws it in the trash and prevents the
• 25:00
  peripheral neuropathy, neutropenia and other toxicities
• 25:03
  that limit the use of the drug that is entering phase one as we
• 25:07
  speak. We've already activated multiple
• 25:09
  sites and we're going to be dosing our first patient
• 25:12
  imminently and that will have first we'll, we will study that
• 25:17
  to see the what the how much dose we will give to get to a
• 25:23
  50% reduction of freely circulating MMAE.
• 25:26
  And then we will study our drug in patients who have toxicities
• 25:32
  to see if we can limit the progression of those toxicities.
• 25:35
  Those will be our proof of concept studies.
• 25:36
  We should have data on those probably in 2027 and really
• 25:41
  excited about those because the MMA based their ADC's.
• 25:44
  There are a number of them out there in addition to PAD 7 in
• 25:47
  development today and many of them have a real great success
• 25:51
  on cancer patients, but you're not able to dose them for a long
• 25:55
  period of time or if at all because of these side effects.
• 25:58
  That's one. The second one that's entering
• 26:00
  the clinic, Phase one clinic as we speak also is a partnership
• 26:04
  we have with Roswell Park. Roswell Park is a Cancer Center
• 26:08
  in Buffalo, NY. We have a 5050 collaboration
• 26:10
  with them. This is for CAR T, So for Cell
• 26:13
  therapy, Roswell Park, some of the real founders of cell
• 26:19
  therapy have moved from Sloan Kettering to Roswell Park and
• 26:23
  our we're partnering with them on this program.
• 26:25
  They asked us can we produce a highly potent binder for the
• 26:30
  cell therapy that they have an armored car to treat, in this
• 26:35
  case bladder cancer. And these are solid tumors.
• 26:38
  So cell therapy has not been very successful in solid tumors
• 26:41
  in the past. We think we have the one of the
• 26:44
  highest if not the highest potent molecules out there.
• 26:48
  Again, it will it's going to be in platinum resistance bladder
• 26:52
  cancer patients. Muck 16 is the target on the
• 26:56
  bladder cancer cell. We should have dosed our first
• 27:00
  patient certainly in 2026 if not multiple patients and again in
• 27:03
  2027 with cell therapy you'll find out very, very quickly
• 27:07
  whether it's successful or not for these patients.
• 27:09
  And so we're very excited to see because this is also a
• 27:13
  technology, not only will it help these patients, but it's a
• 27:16
  technology and cell therapy that we can apply to other cell
• 27:18
  therapy molecules. It's designing framework and
• 27:23
  binding of the protein that's part of the cell therapy that
• 27:28
  could be applicable to many other things.
• 27:30
  We also have some other oncology programs that are preclinical
• 27:34
  that we hope will be on the come over the next couple years
• 27:37
  utilizing some of the technologies that I mentioned
• 27:39
  upfront like inter improving internalization or other
• 27:42
  technologies that once you get to a cancer cell then you get
• 27:46
  internalized to pill that cancer cell.
• 27:48
  And so I think some of the next wave of therapeutics may be
• 27:51
  around oncology for us. Amazing.
• 27:54
  Well, thank you for sharing all of that detail about your
• 27:56
  clinical studies. And maybe taking a step back
• 27:59
  slightly for both of you then as you scale both the platform and
• 28:04
  the pipeline, what actually gets harder?
• 28:06
  Would you say it's the technology, the biology or more
• 28:10
  of the operational side of running these programs?
• 28:14
  It's it's an interesting question.
• 28:16
  I think our technology allows us to go after more de risked
• 28:21
  biology. So the biology can be more
• 28:24
  complicated if you want it to be, but I don't know if it has
• 28:27
  to be. And so I we think that there are
• 28:29
  a lot of lower risk biology that just can't be drugged based on
• 28:35
  using traditional technologies like I've talked about before.
• 28:38
  And therefore we can use our technology to go after those to
• 28:41
  solve patient problems in a way that doesn't have to
• 28:46
  significantly increase biological risk.
• 28:48
  That is an area those are areas that we will start to folk.
• 28:50
  We will continue to focus on down this downstream.
• 28:53
  We certainly can raise the biological risk using these
• 28:56
  technologies, but we don't have to today because there's a lot
• 28:58
  of unmet need for patients in areas where there may not be as
• 29:02
  high biological risk. The technology risk I think is
• 29:05
  getting less and less, although we are even through our
• 29:09
  partnerships. We have a partnership with Amgen
• 29:11
  that we've had for five years. We have a partnership with
• 29:13
  Novartis that we've now going on the second year of both of those
• 29:17
  companies. I think would it would even
• 29:22
  corroborate that they gave us some of their hardest protein
• 29:25
  challenges, protein development challenges and we've been able
• 29:27
  to use our technology to unlock many of the challenges that they
• 29:31
  provided. And now we can utilize those
• 29:33
  unlocks for ourselves in terms of where we apply those across,
• 29:38
  you know, technology. And so technology risk remains,
• 29:43
  I'd say something that we will continue unlock, but I'm not
• 29:45
  sure that all is the the greatest challenge right now.
• 29:48
  Although we are going after some very, very hard challenges like
• 29:52
  trying to, you know, bind ion channels, which is not something
• 29:54
  that traditional techniques can do.
• 29:56
  Again, maximizing internalization, not something
• 29:58
  traditional techniques can do, crossing barriers, not something
• 30:01
  that easily that traditional techniques can do.
• 30:04
  So I'd say that's technology risk.
• 30:06
  But with our models, we're finding it's not as risky, so to
• 30:09
  speak, as I think you might have once believed 7 years ago.
• 30:12
  And therefore, to me, operational risk I think is the
• 30:15
  biggest thing or execution is the biggest thing.
• 30:18
  Can you actually execute on the timeline and make really hard,
• 30:24
  good decisions about what you move forward?
• 30:26
  Drug development is extraordinarily costly.
• 30:28
  You cannot bring everything forward.
• 30:30
  Our technology can produce well more than we could possibly do
• 30:36
  on our own and prosecute in clinical trials just given the
• 30:39
  cost of these. And therefore, we have to keep a
• 30:41
  very high bar in terms of what we select to move forward and
• 30:45
  have to be very thoughtful about the execution because execution
• 30:50
  in our minds, particularly on some of the later stage products
• 30:53
  that we have today is really what will make the difference
• 30:56
  between getting the patients faster and solving curing
• 30:59
  diseases faster versus maybe not getting there at all.
• 31:02
  I don't know, Lori, if you would add anything to that.
• 31:05
  I don't have much to add to that, Jason, very much in
• 31:08
  agreement. I think that the technology is
• 31:11
  getting better. I've already indicated it's
• 31:13
  getting faster. And as far as the biology goes,
• 31:17
  I do think that we will take on bigger challenges, the ones that
• 31:21
  that Jason has covered and that might enable us to get into more
• 31:25
  complex diseases that maybe don't have established clinical
• 31:30
  trial infrastructure so that that operational risk could
• 31:35
  increase there. It's also an opportunity as
• 31:39
  well. So with the with the pace and
• 31:42
  the with the that the platform has produced molecules if we
• 31:48
  keep pace certainly increase it. I think the tough thing will be
• 31:52
  making the choices as as Jason indicated, we could potentially
• 31:56
  have lots of good molecules in areas of very high unmet need.
• 32:02
  And it'll be, we'll have to decide how we can operationalize
• 32:06
  those in, you know, unique ways or or perhaps partnerships.
• 32:11
  Yeah, that's a really interesting insight there.
• 32:14
  And one thing that you both mentioned earlier in the call is
• 32:18
  culture. And maybe Laura, you can start
• 32:20
  on this question, but what you, what would you say the kind of
• 32:25
  mindset or culture is required to actually thrive in a
• 32:28
  fast-paced company like Generate?
• 32:31
  Yeah, happy to start there. So we have one thing that I've
• 32:36
  learned very quickly is that I have to be wake up every day
• 32:42
  ready to face anything that comes my way.
• 32:46
  I'm not, I can't rely on what's in my Outlook calendar to tell
• 32:50
  me what is going to happen. You know, that day as in, in
• 32:54
  biotech, you know, we have to be very solution oriented
• 32:57
  entrepreneurs, you know, every, every every day.
• 33:01
  But what drives us, I think, is generators.
• 33:06
  Regardless of where you sit in the organization, it's this
• 33:09
  reminder that there are patients in need.
• 33:12
  That's what dictates what we do every single day.
• 33:15
  So whether there's an uncertainty about, you know,
• 33:19
  what we're going to do that day or if one of our programs is
• 33:22
  going to progress or not, we're all driven by the patients in
• 33:27
  need. And that doesn't matter if
• 33:29
  you're sitting in technology, if you're a biologist or a
• 33:32
  clinician or, or, or we're, we're all focused on the same
• 33:38
  thing. And I think it's very unique
• 33:40
  that in, in a company like this, the technology, the biology and,
• 33:44
  and that and that and that the medicine, we're all joined at
• 33:48
  the hip and working together. And that's, that's, that's what
• 33:52
  we had. The culture exists that we're
• 33:55
  all seen as important parts finding solutions for patients.
• 33:59
  Nice. And Jason, what are your
• 34:01
  thoughts on the culture? I agree.
• 34:03
  I think we have a phenomenal culture, but the culture is of
• 34:06
  curiosity. It's of constantly raising the
• 34:08
  bar, constantly asking questions, making people feel
• 34:11
  comfortable that they can ask ask questions at any time.
• 34:15
  It is always looking for great solutions for patients.
• 34:18
  It is very patient centric thesis and that's even at
• 34:22
  frankly at the at the technology because and I think one of the
• 34:26
  other unique aspects of generate is we do have the technology
• 34:30
  group, which is fairly sizable. It's almost 100 people.
• 34:33
  We have an experimental group, which is also almost 100 people.
• 34:36
  And you asked the question earlier, Amber about the
• 34:38
  technology and the experimental, so dry and wet lab and how they
• 34:41
  kind of come together and what the purpose is.
• 34:43
  And then we have a clinical group which we we had a
• 34:46
  preclinical group that was here before Lori Lori's done a great
• 34:50
  job of building out our clinical expertise here.
• 34:52
  So we can handle not only the phase three trials that we have
• 34:55
  ongoing, but also the Phase 1 and oncology, their upcoming and
• 34:58
  future trials. And I think the critical piece
• 35:01
  for our culture is the integration of those three.
• 35:05
  And Lori touched on this earlier, you can't really run
• 35:08
  technology and machine learning and design anything without
• 35:11
  understanding not just what assays and experiments you need
• 35:14
  to run and where we think will be more or less successful, but
• 35:17
  you got to understand what the clinical implications are.
• 35:20
  And does it make sense for patients, Is there a need for
• 35:22
  patients, What target should we be going after, where we're
• 35:25
  going to be more or less successful?
• 35:27
  And everybody in those three groups really talk to each other
• 35:31
  constantly such that we can design the right molecules with
• 35:35
  the goal of getting them to patients.
• 35:37
  So it's not just the purpose of designing it to go and partner
• 35:40
  it, it's the goal is actually to get it to patients.
• 35:43
  And so with that mindset in place, that culture, I think
• 35:46
  it's hard to replicate, but it is another one of these things
• 35:49
  that that makes us pretty unique.
• 35:51
  And then you wrap around the execution on that.
• 35:53
  Everybody's focused on actually executing with speed, with
• 35:57
  purpose, with creativity, with patient in mind, and it makes it
• 36:02
  makes working here an incredibly rewarding experience.
• 36:06
  Nice. Great.
• 36:07
  I think you could tell as well because you've both mentioned
• 36:10
  the culture multiple times throughout the call about that.
• 36:13
  And I think finishing off the podcast here with our last
• 36:17
  question that we ask everyone who joins the Bio Hub, if you
• 36:21
  could have dinner with any scientist or innovative past or
• 36:24
  present, who would it be and why?
• 36:26
  And Lori, I'll come to you first on this.
• 36:29
  Yeah, thanks. Amber So I would absolutely love
• 36:32
  to meet Doctor Virginia Apgar. Back in the 1950s, Doctor Apgar
• 36:38
  created the Apgar score and, and this is a very simple pragmatic
• 36:43
  assessment of newborns right after they're born.
• 36:47
  They're scored on a score of five different criteria up to
• 36:51
  upfront, starting from zero to 10.
• 36:54
  And it's scored at one minute and 5 minutes.
• 36:56
  And this is really a sign of how healthy that the the newborn is,
• 37:01
  whether that baby needs immediate medical Care now.
• 37:04
  So the interesting thing about Doctor Apgar is that she wasn't
• 37:07
  a pediatrician as you might expect.
• 37:09
  She was a surgeon 1st and then as an anesthesiologist.
• 37:13
  It was in the delivery room that she recognized how important
• 37:17
  those first few minutes of life were, and she decided to
• 37:20
  standardize the way babies are assessed.
• 37:24
  And that was back in 1952. The score was adopted widely,
• 37:29
  the Apgar score still used today.
• 37:31
  If you're a parent, you might recall the score from the
• 37:34
  delivery room. You know, the, the, the weight,
• 37:37
  the length and the Apgar score is, is, is often what, what
• 37:41
  parents talk about. And I, I would really love to
• 37:45
  meet her to talk about career journey like from surgery to
• 37:49
  anesthesiology and be really interested in whether she knew
• 37:55
  how much the simple score was going to change clinical
• 37:59
  practice and really impressive that it's still used today.
• 38:03
  I, I really am inspired by simple, pragmatic things that
• 38:07
  are are able to be instituted easily within a very complex
• 38:12
  medical care system. And, and I do believe that it's
• 38:15
  still a very important way to assess newborns and, and
• 38:19
  determine the ones that need some extra support.
• 38:22
  Amazing. What a strong choice with that
• 38:24
  one. And what about you, Jason?
• 38:27
  So for me, I, I would love to have a table of like 100, but if
• 38:31
  I had to select one, I think Charles Darwin would be the one.
• 38:36
  My family and I just recently went to the Galapagos.
• 38:39
  I've never, I've never been it's, but it's a dream trip of
• 38:41
  mine for since probably birth. I've always loved biology and I
• 38:46
  was a chemistry major in college, but love biochemistry
• 38:49
  and biology in particular. And actually working with my
• 38:52
  three kids when they went through middle school and high
• 38:54
  school, going through even just kind of the concepts of natural
• 38:58
  selection and evolution and things like that has been a
• 39:02
  reminder to me of what Charles Darwin studied.
• 39:05
  And then going to see it in the Galapagos first hand and going
• 39:08
  to the Charles Darwin Center was fascinating.
• 39:12
  So that's, that's my choice. What an.
• 39:15
  Amazing experience. Well, thank you both for joining
• 39:18
  me on the podcast today. It's been really great to learn
• 39:21
  more about the work that you're doing at Generate Biomedicines
• 39:24
  and if everyone can remember to like and subscribe the Buy Hub
• 39:27
  podcast. Thank you SO.
• 39:28
  Much, Amber. I really appreciate it.
• 39:30
  Thank you, Amber.