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When Progress Gets Penalized: Biotech in the Age of IRA · Making Medicine

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  Speaker 1: You're listening to Making Medicine Stories from the Early Stage
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  Speaker 1: Life Sciences Ecosystem, a podcast that explores the people and
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  Speaker 1: deals that have led to the medicines, devices, and technologies
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  Speaker 1: that keep us healthy. Let's get into the show.
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  Speaker 2: Welcome to another episode of the Making Medicine Podcast. As always,
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  Speaker 2: I'm your host, John Stanford, and today we're joined by
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  Speaker 2: Tom Kaiser, founder and chief Scientific officer of Avisenta Biosciences,
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  Speaker 2: located in the great state of North Carolina, and we're
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  Speaker 2: going to be talking about North Carolina a lot on
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  Speaker 2: this call.
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  Speaker 3: Tom, thanks for joining us today.
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  Speaker 4: Thanks Joan, it's great to be here. Thanks for having me.
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  Speaker 2: One of the biggest pieces of feedback we get from
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  Speaker 2: our listeners is they're always fascinated by the people we
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  Speaker 2: have on the show. Policymakers, academics, and in the case
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  Speaker 2: of this the founder of a biotech company with a
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  Speaker 2: really promising future. And the number one question we get
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  Speaker 2: is how do I emulate that? How did they get there?
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  Speaker 4: So?
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  Speaker 2: Can you start by just give us a little bit
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  Speaker 2: to your background and how you found yourself co founding
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  Speaker 2: a biotech in North Carolina.
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  Speaker 4: Yeah, my background is kind of all over the place.
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  Speaker 4: And I think that's pretty ordinary for people that start
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  Speaker 4: biotech companies. So I started off in pure mathematics. That
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  Speaker 4: was my interest. I worked in an area of mathematics
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  Speaker 4: called discrete mathematics, so group theory, graph theory, which explains
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  Speaker 4: why I'm also a chemist. These concepts are very interesting
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  Speaker 4: for their application chemistry. In fact, al Cotton was the
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  Speaker 4: first person to really apply group theory to chemistry. And
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  Speaker 4: so I was interested in the intersection of mathematics and chemistry.
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  Speaker 4: And at the time, my plan was to go into
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  Speaker 4: medical school, and I was advised by a couple of
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  Speaker 4: different medical schools, and I was looking at hey, maybe
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  Speaker 4: you shouldn't do this total synthesis thing, you should do
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  Speaker 4: something more biologically oriented. So I decided, if I have
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  Speaker 4: to choose between medicine and chemistry, I'm going to go
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  Speaker 4: be a synthetic organic chemist. And so I went where
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  Speaker 4: al Cotton did a lot of his work at Texas
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  Speaker 4: A and M. I trained in natural products chemistry, organometallics
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  Speaker 4: with goal of being a medicinal chemist. And then after
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  Speaker 4: that I did my post TALC with Dennis Liota, where
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  Speaker 4: I eventually ran the anti viral program. And that's really
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  Speaker 4: where Avisona gets started, or at least the heart of Avisona.
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  Speaker 4: So I met my co founder there, Peter Berger, in
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  Speaker 4: the Lioda lab, and we were very good at designing
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  Speaker 4: new antivirals. That's what the Looda Lab is famous for.
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  Speaker 4: I think Dennis now has something like twenty drugs on
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  Speaker 4: the market, so major innovation and HIV therapy and the like.
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  Speaker 4: And even though we knew what what a drug looks like,
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  Speaker 4: we had found things, we'd taken things through advance preclinical development.
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  Speaker 4: Still failure was fairly ordinary. Failure is common in the
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  Speaker 4: discovery of new medicines. And I thought, well, you know,
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  Speaker 4: I don't really want to do new chemistry new biology.
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  Speaker 4: I want to find new medicines. How do I address
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  Speaker 4: this problem? And so I started getting into machine learning,
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  Speaker 4: drawing on my mathematics route. And this was back when
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  Speaker 4: machine learning wasn't a buzzword. It was part of a
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  Speaker 4: body of work called QSR and trying to apply machine
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  Speaker 4: learning to chemistry. And what we found is that if
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  Speaker 4: you want to play to the strengths of machine learning,
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  Speaker 4: you should really not use it for hit discovery or
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  Speaker 4: lead optimization. You should use it for Canada design, So
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  Speaker 4: the actual last time you touch a molecule before it
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  Speaker 4: goes into clinic, take all of that information, make the
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  Speaker 4: best compound possible for your development trajectory, and then progress
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  Speaker 4: that forward. And so the focus was, well, we can
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  Speaker 4: do this on a variety of targets, We can do
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  Speaker 4: this for a variety of chemical spaces. How should we
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  Speaker 4: best use this? And the idea was to take some
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  Speaker 4: established series, fix its limitations, and then go back into development.
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  Speaker 4: So maybe a drug had a oral by availability problem
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  Speaker 4: for a new indication. It was used in acute life
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  Speaker 4: saving intervention, but you now need to use it for
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  Speaker 4: chronic condition where IV access is not really the way
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  Speaker 4: you're going to get a drug into somebody. You can
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  Speaker 4: change the PQ the pharmaical kinetics and make it orally
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  Speaker 4: by available where you can solve some off target issue
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  Speaker 4: something like that. And so we started playing around with
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  Speaker 4: this idea, building the machine learning techniques from a theoretical perspective,
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  Speaker 4: and at that time the plan was to actually be
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  Speaker 4: an academic physician scientist. So I went over to the
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  Speaker 4: UK to study medicine. I was at Oxford while we
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  Speaker 4: were building Avicenna, and that really helped me refine my
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  Speaker 4: own understanding and power of physiology so that we could
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  Speaker 4: select targets that were really on the precipice of being
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  Speaker 4: exciting hot targets that would have a major impact in
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  Speaker 4: clinical medicine. And so that's the last piece of the puzzle.
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  Speaker 4: That's why our mascot is Avicenna. He was a Persian
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  Speaker 4: holy math that lived about a thousand years ago. He
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  Speaker 4: worked in chemistry, mathematics, and medicine, amongst a couple of
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  Speaker 4: other disciplines, and we really try to put those three
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  Speaker 4: together in an innovative fashion. Avicenta bring innovative chemistry towards
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  Speaker 4: the clinic with a very clear eye of how we're
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  Speaker 4: going to make a major medical impact and then use
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  Speaker 4: new mathematical techniques to facilitate our navigation of chemical universe.
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  Speaker 4: That's what we do.
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  Speaker 3: Thank you for that.
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  Speaker 2: And I think something you touched on there's no traditional
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  Speaker 2: path and our thirtieth or fortieth guest on the show,
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  Speaker 2: and I'm yet to hear someone say, oh, I took
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  Speaker 2: the normal path to be in a biotech startup.
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  Speaker 3: It's funny you meant.
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  Speaker 2: I was next to Dennis on a panel on Monday,
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  Speaker 2: and it's incredible what's come out of those labs and
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  Speaker 2: what it's meant for patients, what it's meant for successes,
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  Speaker 2: not just in a financial standpoint, but from a healthcare standpoint.
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  Speaker 2: So small world, and I'll have to let him know
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  Speaker 2: that all of your success that lies ahead of you
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  Speaker 2: also ties back to his labs.
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  Speaker 4: It really does. And you know that's I think the
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  Speaker 4: most important thing is finding people that you can collaborate with.
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  Speaker 4: That's the typical path, if there is a typical path.
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  Speaker 4: I was very fortunate that I he gave me the
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  Speaker 4: resources of his laboratory and we got to explore, just
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  Speaker 4: be creative, really push the frontier discovery. And my success
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  Speaker 4: really does come back to Dennis Leona. I'm eternally grateful.
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  Speaker 2: Well, we'll have him on and you can be sort
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  Speaker 2: of a guest appearance and we'll we'll do that.
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  Speaker 3: Thank you in person. I am really interested.
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  Speaker 2: This is absolutely not why we had you on the podcast,
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  Speaker 2: which I promise we'll get to in a second. Loved
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  Speaker 2: your point about machine learning and wonder if you could
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  Speaker 2: share it because it's become this buzzword right and I
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  Speaker 2: as sort of the person engaging with policymakers on behalf
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  Speaker 2: of the early stage ecosystem. I've run into this fear
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  Speaker 2: a little bit of when I'm talking about how expensive
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  Speaker 2: bringing a drug to market is, how much time it takes,
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  Speaker 2: people have started saying, well, i've heard AI is solving
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  Speaker 2: that machine learning is going to make drugs fall from
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  Speaker 2: the sky, and we really don't need the development and
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  Speaker 2: so it's become this buzzword. And I'm very intrigued by
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  Speaker 2: your perspective that it's candidate design over the other pieces.
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  Speaker 2: Can you say a little bit more about why you
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  Speaker 2: see that as a better application of this technology. Then
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  Speaker 2: I think what's probably a little more sexy positioning of
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  Speaker 2: initial asset identification and things like that.
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  Speaker 3: I'm very curious about that.
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  Speaker 4: Yeah, so sexy is a great word, and we're going
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  Speaker 4: to come back to that. The problem with finding new
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  Speaker 4: medicines is it is so hard at the very beginning
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  Speaker 4: that it might as well be gambling. And so what
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  Speaker 4: we're trying to do at AVISENTA is turn this from
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  Speaker 4: a gambling exercise into an engineering discipline where we can
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  Speaker 4: actually rationally design new compounds and prosecute them towards the clinic.
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  Speaker 4: And the reason why I say that it's gambling. If
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  Speaker 4: you start in new chemical space and new biology, that
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  Speaker 4: work needs to happen. I'm not trying to undermine that
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  Speaker 4: that is absolutely important work, but that is not necessarily
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  Speaker 4: where you should point machine learning because you know nothing
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  Speaker 4: about the hidden unknowns, the red flags, the snares, the
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  Speaker 4: biological hurdles. None of that is understood. You have to
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  Speaker 4: spend a huge amount of money and time to find
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  Speaker 4: those red flags, and sometimes those red flags are actually
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  Speaker 4: not surmountable. Sometimes your pharmacaphor is genotoxic. The actual core
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  Speaker 4: part of your molecule is catastrophically toxic, and there's nothing
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  Speaker 4: you can do to solve that. And the only way
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  Speaker 4: you know that it's catastrophically toxic is by taking it
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  Speaker 4: into id enabling studies and finding out doing the experiment.
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  Speaker 4: At that point you spent twenty million dollars, You're five
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  Speaker 4: ten years into the program, and it's game over. And
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  Speaker 4: that's why drug discovery the old way is hard, and
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  Speaker 4: so machine learning can't really help with that because the
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  Speaker 4: information complexity is so high, there's so many degrees of freedom,
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  Speaker 4: so many moving parts on that that correctly predicting which
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  Speaker 4: new chemical spaces are going to have those issues or
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  Speaker 4: new pharmacology opportunities are going to have those issues. It
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  Speaker 4: can't be done right now, and so what we do
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  Speaker 4: is play to the strengths of machine learning and aviicent
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  Speaker 4: and we have all of this information. We have so
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  Speaker 4: many different scaffolds out there that can be tweaked and
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  Speaker 4: readjusted and tuned, and actually you can find new things
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  Speaker 4: that the original teams didn't even consider about that chemical space.
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  Speaker 4: And you know the design criteria, know what the target
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  Speaker 4: product profile has to be, you know what the hidden
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  Speaker 4: unknowns too large part are, and you can play to
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  Speaker 4: the strings of machine learning to very quickly design around that.
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  Speaker 4: But the issue is people are drawn to the unknown
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  Speaker 4: because it's sexy, because you don't know any of the wards.
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  Speaker 4: We really try to think about ways of designing the
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  Speaker 4: medicines that is more like engineering. What do I have
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  Speaker 4: to do? What is my product profile? Basically turn it
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  Speaker 4: into a more hum drum practice, and what we're seeing
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  Speaker 4: is that we can do this. We make very few
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  Speaker 4: compounds compared to other other teams. Our first two clinical
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  Speaker 4: candidates came out of a batch of eleven compounds because
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  Speaker 4: we know what we have to accomplish from a medicinal
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  Speaker 4: chemistry design perspective, so we don't have to really explore
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  Speaker 4: the space as much. We just get to tweak it
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  Speaker 4: and fine tune it and really put the polish on it,
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  Speaker 4: if you can use that metaphorm.
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  Speaker 2: I am bottling up this segment of the BOD guests
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  Speaker 2: because I think that's the best explanation to date of
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  Speaker 2: some of the pitfalls around assumptions how AI is going
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  Speaker 2: to revolutionize drug development, and two playing to its strengths
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  Speaker 2: where it does work. And I think you very clearly
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  Speaker 2: laid out it's still based on nomes and we're running
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  Speaker 2: into unknowns. So I thought that was great, but that
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  Speaker 2: is not why we invited you onto making medicine. Obviously
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  Speaker 2: we're gonna have to have you back to decipher AI
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  Speaker 2: further for folks who aren't aware. A lot of times
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  Speaker 2: we engage people for making medicine who are in our
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  Speaker 2: network in universe known fund managers or biotechs or people
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  Speaker 2: we've seen at conferences. But what cut ri I about you,
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  Speaker 2: Tom was this op edd you wrote. And you don't
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  Speaker 2: see too many biotech leaders necessarily putting pen to the newspaper,
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  Speaker 2: and yet you took the time to write this great
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  Speaker 2: piece for Biospace, which all of our listeners can find
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  Speaker 2: it in the episode description where you talked about the
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  Speaker 2: importance of a particular policy, and I'm curious, because we'll
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  Speaker 2: get into the epic act and why it's so critical.
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  Speaker 2: What made you pick up the pen and get motivated
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  Speaker 2: to even put your thoughts out there, and why is
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  Speaker 2: this particular issue the one that's so important to you
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  Speaker 2: right now?
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  Speaker 4: Yeah? Well, I think I'm motivated to write about a
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  Speaker 4: lot of things. I guess I'm a little old fashioned.
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  Speaker 4: I don't tweet, I write, And I think the major
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  Speaker 4: thing that motivated me is drug development. Drug discovery is
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  Speaker 4: hard enough. You're already wrestling with nature. You don't really
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  Speaker 4: need to wrestle with bad policy. And so what we
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  Speaker 4: were finding is that as we were raising capital and
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  Speaker 4: even talking with large pharmaceutical organizations, this power was cast
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  Speaker 4: over small molecules by an act that was created with
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  Speaker 4: good intentions but really hamstring early stage small molecule companies.
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  Speaker 4: And I thought, I need to lay this out entirely.
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  Speaker 4: What's the problem, How did this policy completely miss the mark?
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  Speaker 4: And what can we do to fix it? So that's
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  Speaker 4: why I wrote that piece.
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  Speaker 2: And just for our listeners, what Tom's talking about is
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  Speaker 2: what we've been talking about, this pill penalty or small
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  Speaker 2: molecule penalty that in the Inflation Reduction Act world. For
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  Speaker 2: those who don't remember, that changed the game for pharmaceuticals
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  Speaker 2: by forcing negotiations or price controls depending on your perspective
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  Speaker 2: after a certain amount of time. And for our listeners
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  Speaker 2: who aren't remembering this, biologics received thirteen years of opportunity
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  Speaker 2: to sort of reward before negotiations and a price control
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  Speaker 2: kicks in, whereas small molecules those that followed the NBA
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  Speaker 2: pathway only got nine and incubator has a lot of
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  Speaker 2: materials about fifty percent of the drug's profitability coming year
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  Speaker 2: ten to thirteen. We have lots of data about why
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  Speaker 2: this is so pernicious. So for listeners, Tom wrote out
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  Speaker 2: a really compelling piece about how this is making life harder.
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  Speaker 2: And Tom, I think you know you have the first
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  Speaker 2: hand experience of investors large strategics pharmaceutical companies saying hey,
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  Speaker 2: we are less interested in this, not because of anything
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  Speaker 2: you did, not because of science, not because of your data,
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  Speaker 2: not even because of your thesis, but because of an
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  Speaker 2: arbitrary policy out of Washington, and this is something that
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  Speaker 2: Incubate immediately latched onto and the epic Act introduced by
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  Speaker 2: some fellow North Carolinians would fix this. So so everyone
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  Speaker 2: knows there'd be thirteen years for biologics and small molecule
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  Speaker 2: and Tom can go back to fundraising and partnering the
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  Speaker 2: way he should be. So if we don't address this policy,
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  Speaker 2: if it's left not fixed, where do you see small
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  Speaker 2: molecule innovation? Where do you see specific drug areas Parkinson's, MS, pancreatic,
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  Speaker 2: some of the ones we've talked about. What's the downside
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  Speaker 2: risk here in your mind? If we don't correct this.
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  Speaker 4: Yeah, well, hopefully Greg Murphy and Don Davis really champion
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  Speaker 4: this policy forward. But if we don't correct this, I
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  Speaker 4: think we thought very very seriously about the epic Act
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  Speaker 4: in Avicenna, and you really have to focus on absolutely
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  Speaker 4: gangbuster small molecules for rare diseases that are transformative. So
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  Speaker 4: fortunately for absent, we're already shooting for that threshold. But
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  Speaker 4: it really raises the bar on what a small molecule
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  Speaker 4: has to do in order for it to attract development.
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  Speaker 4: And we've seen this internally, like we're very fortunate to
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  Speaker 4: be in a position where we're having very advanced conversations
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  Speaker 4: about potential partnerships. Our fundraising is going really as well
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  Speaker 4: as it can in this day and age. It's a
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  Speaker 4: bit crazy out there right now. We're doing all right. Really.
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  Speaker 4: I think that's a testament to having good backers like
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  Speaker 4: DCVC Bio, and I think because we've we've been very
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  Speaker 4: considerate about how we're going to find these revolutionary small molecules,
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  Speaker 4: we don't face as severe of a penalty as a
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  Speaker 4: second or third line oncology agent for a tumor that's
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  Speaker 4: resistant to first line. Like those medicines are still needed,
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  Speaker 4: don't get me wrong, but because they're perceived as being
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  Speaker 4: less innovative, and I think unfairly, they face harder I
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  Speaker 4: guess market propositions in order for people to be attracted
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  Speaker 4: to them. And I think that's really where we're going
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  Speaker 4: to see the fallout of this over the next ten
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  Speaker 4: to fifteen years, is that these innovative therapies that are
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  Speaker 4: perceived as being less innovative but are still solving major
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  Speaker 4: medical needs, they're not going to come to market and
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  Speaker 4: patients are going to suffer as a consequence.
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  Speaker 2: I want to stay there because I think you just
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  Speaker 2: laid that out so well that for avicenna, because there
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  Speaker 2: are some real home run I mean, you are swinging
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  Speaker 2: for the fences on small molecule innovation, there can still
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  Speaker 2: be appetite. And this is something I try to explain
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  Speaker 2: to policymakers whose heads are it's binary, Well, either all
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  Speaker 2: small molecules gone or all small molecules okay, And we
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  Speaker 2: try and try and try and say, there are so
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  Speaker 2: many shades of gray in an area as risky and
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  Speaker 2: complex as drug development. And so I think your point
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  Speaker 2: is a really really good one that the real risk,
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  Speaker 2: at least the biggest risk here are those products that
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  Speaker 2: are small molecule in oncology that might have a really
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  Speaker 2: small patient population who's not responding to an existing medicine.
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  Speaker 2: We could do something for them, but now we won't
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  Speaker 2: because it's not attractive enough. And I don't think that
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  Speaker 2: light switch is flicked for enough people who will eventually
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  Speaker 2: hopefully cast votes for the epic Act. And so I
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  Speaker 2: really like that you laid out that some small molecules
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  Speaker 2: will still march forward, but it's this stuff that is
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  Speaker 2: deemed less innovative.
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  Speaker 4: I think there's another thing that like, I really want
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  Speaker 4: you to champion this idea to policy makers. There are
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  Speaker 4: things that are so transformative, like metabolic syndrome in stage
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  Speaker 4: renal disease, where because there'll be a major burden on medicare,
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  Speaker 4: those also won't be invested in because of the epic AC.
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  Speaker 4: So you could have a small molecule that completely changes
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  Speaker 4: the game for renal replacement therapy, but if it's pre clinical,
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  Speaker 4: people are less likely to capitalize around that molecule because
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  Speaker 4: of the EPICAC price disincentives. And that's a consequence of
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  Speaker 4: some of the policy positions where and I want to
• 17:38
  Speaker 4: make sure that I get this exactly right, the stipulation
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  Speaker 4: that the part D revenue, that part D spending. If
• 17:44
  Speaker 4: you're going after major consequences of diabetes and things like that,
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  Speaker 4: major medicare spins and you're on the Part D market.
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  Speaker 4: Even if you change the game on renal replacement therapy
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  Speaker 4: as a small molecule, you're still going to be subject
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  Speaker 4: to price controls, which is catastrophic for people in.
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  Speaker 2: I can't stress enough. If you are a policymaker or
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  Speaker 2: staff listening to this episode right there, right there is
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  Speaker 2: you are hearing from a biotech leader, Why would I
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  Speaker 2: fix something from a financial and economic standpoint, and free
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  Speaker 2: markets being what free markets are, that's what they'll reward
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  Speaker 2: end stage renal diabetes, other things. And as you look
• 18:24
  Speaker 2: at this administration and they want to put a focus
• 18:26
  Speaker 2: on chronic disease, and I think Congress on both sides
• 18:29
  Speaker 2: of the aisle want to see medicare spending come down
• 18:34
  Speaker 2: and let innovation solve some of that problem. And what
• 18:37
  Speaker 2: we're sitting here saying is we're not going to do it.
• 18:40
  Speaker 2: Why would we pursue that when you sort of gutshot
• 18:43
  Speaker 2: this so much, and sometimes I make it even simpler.
• 18:48
  Speaker 2: Three hundred billion, that's the CBO estimate of what's coming
• 18:51
  Speaker 2: out of this industry. And it's hard for me to
• 18:53
  Speaker 2: look someone in the eye and have them say back
• 18:55
  Speaker 2: to me, I think it's okay that we're taking three
• 18:58
  Speaker 2: hundred billion out of this industry, changing the incentives, and
• 19:02
  Speaker 2: yet we still expect the level of innovation we've seen
• 19:04
  Speaker 2: over the last two decades. So I think what you
• 19:07
  Speaker 2: hit on there is just a gross disincentive for something,
• 19:11
  Speaker 2: even if you don't care about patients and you're just
• 19:14
  Speaker 2: a scorekeeper in the basement room of the capital complex
• 19:19
  Speaker 2: trying to get Medicare's costs down. This policy is driving
• 19:22
  Speaker 2: Medicare's costs up, and we will continue to pay for
• 19:27
  Speaker 2: not innovative treatments in these spaces and have people admitted
• 19:31
  Speaker 2: to the er as opposed to innovation that really could
• 19:34
  Speaker 2: be transformed. So, Tom, I think you do a better
• 19:36
  Speaker 2: job with my job. But I like that you're doing
• 19:39
  Speaker 2: what You're putting your genius to work to create drugs.
• 19:43
  Speaker 2: But you've got a great career as a lobbyist.
• 19:45
  Speaker 3: If you want to fall back on it.
• 19:47
  Speaker 4: Well I'm happy to support you. Yeah, it takes a
• 19:49
  Speaker 4: team that it really is an ecosystem, So I'm glad
• 19:52
  Speaker 4: you're out there really trying to get people to understand
• 19:54
  Speaker 4: this very complicated problem. It's an incredible thing to design
• 19:58
  Speaker 4: a new medicine and take it into the clinic. You'll
• 20:00
  Speaker 4: have ten thousand people by the time you're done, and
• 20:04
  Speaker 4: billions of dollars spent over a decade to design a
• 20:07
  Speaker 4: new medicine. And it takes a healthy ecosystem in order
• 20:12
  Speaker 4: to do that. And I'm glad that people like you
• 20:14
  Speaker 4: are lobbying. I truly am, because sometimes government gets it
• 20:18
  Speaker 4: wrong and lobbyists can be there to fix it.
• 20:22
  Speaker 2: That might be our bumper slogan over at Incubator. I
• 20:25
  Speaker 2: want to close on a positive note and one that
• 20:27
  Speaker 2: literally comes close to home. It's North Carolina. You mentioned
• 20:31
  Speaker 2: doctor Murphy, you mentioned Congressman Don Davis in the Senate.
• 20:35
  Speaker 2: Senators Tillis and Bud author the Epic Act in the Senate.
• 20:40
  Speaker 2: You're in North Carolina. We're advancing good policy, we're advancing
• 20:44
  Speaker 2: good companies. What's making the magic happen? Do you think
• 20:48
  Speaker 2: in this state? Because North Carolina is on the rise.
• 20:52
  Speaker 4: Yeah, I think it's investment. People had the foresight to
• 20:57
  Speaker 4: build a research Triangle Park as a mecca for the
• 21:02
  Speaker 4: blending of academian industry to bring technology and technologic solutions
• 21:07
  Speaker 4: into the lives of people, make their lives better. And
• 21:11
  Speaker 4: that investment is paying off, especially as the biotech spheres
• 21:16
  Speaker 4: in San Francisco and Boston, they're getting a little saturated,
• 21:19
  Speaker 4: like real estate is hard to find. People are moving
• 21:22
  Speaker 4: out for cost of living reasons, and the ecosystem was
• 21:25
  Speaker 4: ready to thrive from North Carolina, and people are coming
• 21:27
  Speaker 4: to North Carolina realizing that there's a lot of innovation
• 21:30
  Speaker 4: here and there's a healthy ecosystem here to build companies,
• 21:33
  Speaker 4: and I think that's a big reason, is foresight and
• 21:36
  Speaker 4: investment in infrastructure.
• 21:38
  Speaker 2: Well, you have a very busy day job, but I
• 21:41
  Speaker 2: hope you'll come back and join us, whether we talk
• 21:44
  Speaker 2: AI policy, avicenta. We're excited to hear the progress that
• 21:48
  Speaker 2: you're making there. But really I want to say thank
• 21:50
  Speaker 2: you on behalf of Incubate the Ecosystem for being motivated
• 21:55
  Speaker 2: enough to put your voice out there on the Epic
• 21:57
  Speaker 2: Act and why we have to take action. It's not
• 22:00
  Speaker 2: easy with all being the founder of a biotech company
• 22:03
  Speaker 2: and take the time to get your voice out there,
• 22:05
  Speaker 2: and we're so grateful for you doing that. We're grateful
• 22:09
  Speaker 2: for you taking the time today to be on making medicine.
• 22:12
  Speaker 2: Building off our conversation today. Incubate is currently running an
• 22:16
  Speaker 2: ad campaign to raise awareness of the Epic Act to
• 22:20
  Speaker 2: President Trump and to Congress. You can learn more about
• 22:23
  Speaker 2: that at endpill Penalty dot com, and you can always
• 22:27
  Speaker 2: find out more about Incubate at Incubatecoalition dot org. While
• 22:31
  Speaker 2: you're there, give us a follow on LinkedIn, x and
• 22:34
  Speaker 2: YouTube to keep up on policy and life sciences. Tom
• 22:38
  Speaker 2: any closing thoughts you want to throw out there.
• 22:40
  Speaker 4: I think this was great. Thanks for the opportunity to
• 22:42
  Speaker 4: talk with you. John really enjoyed our conversation.
• 22:45
  Speaker 2: Well, we're thrilled to have you. Thanks again, and as always,
• 22:48
  Speaker 2: to our listeners, keep innovating.
• 22:52
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