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Roivant’s Playbook for Finding Value Others Miss in Biotech · Vanguards of Healthcare by Bloomberg Intelligence

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Speaker 1: Welcome to another episode of Bloomberg Intelligence's Vanguards of Healthcare podcast,
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Speaker 1: where we speak with the leaders at the forefront of
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Speaker 1: change in the healthcare industry. My name is Sam Frazelli
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Speaker 1: and I'm a healthcare pharmer analyst at Bloomberg Intelligence, the
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Speaker 1: in house research arm of Bloomberg.
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Speaker 2: Well, I'm thrilled to have Matt Gline with.
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Speaker 1: Me today, CEO of roy Vant, a very interesting company,
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Speaker 1: twenty billion dollar market cap. I think that's a ten
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Speaker 1: X in the past, let's say four or five years roughly,
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Speaker 1: and interesting business model. So we're going to talk about that,
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Speaker 1: and as usual, I want to start with Matt's stories.
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Speaker 1: So Matt, thank you very much for joining us. We
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Speaker 1: both enjoy exchanging views on the biotech hangout sometimes and
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Speaker 1: I'm really glad that you've been able to join me
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Speaker 1: on the Bloomberg Intelligence podcast. And I love your background,
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Speaker 1: so stop by telling this where you're at.
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Speaker 3: Yeah. Thanks, I'm super happy to be here. First of all,
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Speaker 3: thank you for having me. It's funny listening to that intro.
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Speaker 3: You talked about us being a ten X in the
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Speaker 3: past whatever four or five years. I think you admitted
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Speaker 3: the fact that we're only a three X in the
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Speaker 3: last like four and a half five and a half years,
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Speaker 3: so by the sex volatile business.
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Speaker 1: But we'll be all optimistic here, so we only Yeah,
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Speaker 1: I appreciate this.
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Speaker 2: Please, it's been. It's been.
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Speaker 3: It's been a great, a great round for us.
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Speaker 2: Yeah.
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Speaker 3: Look, thanks, thanks for the question too. You know, it's
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Speaker 3: funny I feel like an outsider in biotech. Well, probably
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Speaker 3: because I am an outsider in biotech. But but but
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Speaker 3: I I studied physics originally actually, and then, like many
1:54
Speaker 3: failed physicists in the sort of pre financial price era,
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Speaker 3: I went and became a quand on Walster eaton. So
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Speaker 3: I worked at Goldbyn for a while, and then at
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Speaker 3: a bank called Lehman Brothers that you might have heard
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Speaker 3: of that went bankrupt, and then at Barkley's, and then
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Speaker 3: I started a really boring financial technology company that got
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Speaker 3: subsumed back into Goldman. And at that point I felt
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Speaker 3: like I had spent my tour of duty and finance.
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Speaker 2: And was done and was frankly, dreadfully bored.
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Speaker 3: And I connected with Rubn's, founder of VEC we had
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Speaker 3: sort of known each other through a mutual friend before,
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Speaker 3: and came to Rouven again as a total outsider to biotech,
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Speaker 3: in part frankly because it was different than what I
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Speaker 3: had done and it seemed interesting, and I came in
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Speaker 3: as our CFO or sort of a finance head that
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Speaker 3: became CFO, and then was steeped in the bizarre, silly
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Speaker 3: world that is the biotech industry for the last coming
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Speaker 3: on ten years and have redefined myself in that direction
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Speaker 3: since doing a little bit of everything before finally becoming
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Speaker 3: our CEO I think twenty twenty one. So it's been
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Speaker 3: a little bit of a wild ride.
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Speaker 1: That's any interesting one because biotech is it's such a
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Speaker 1: supposedly technical area in terms of knowledge of mechanisms of action,
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Speaker 1: knowledge of clinical trial design. What was that learning curve
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Speaker 1: for you? And as a CEO, how much do you
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Speaker 1: really need to know aside from managing the people who
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Speaker 1: know all of this?
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Speaker 3: Yeah, look, I'd like to think on the latter question,
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Speaker 3: the answer is that the more you know, the better
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Speaker 3: you are, which makes me nervous. I the learning curve
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Speaker 3: was steep. I think one of the lessons of Ruyven's
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Speaker 3: history has been you can't develop drugs if you don't
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Speaker 3: know what you're doing. But that having a diversity of
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Speaker 3: backgrounds in the room is helpful, and so trying to
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Speaker 3: try and sort of figure out the right balance between
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Speaker 3: you know, a band of outsiders who haven't learned all
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Speaker 3: the bad lessons but haven't learned any of the good
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Speaker 3: ones either, and a group of sort of stagy insiders
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Speaker 3: who only know the way things are done. I think
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Speaker 3: like striking the right balance there has been one of
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Speaker 3: the things that has been most valuable to Rouvant. I
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Speaker 3: think the thing that I'm proud of is that we've
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Speaker 3: built a company where, you know, we've successfully brought together
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Speaker 3: career drug developers who are necessary right our businesses testing
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Speaker 3: untested and potentially unsafe chemicals on human subjects. You want
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Speaker 3: cautious people with medical backgrounds who have done this before,
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Speaker 3: sort of manning the wheel, but combining those people with
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Speaker 3: outsiders who have the ability to bring a different perspective.
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Speaker 3: And I think we've done a really nice job of that.
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Speaker 3: And I think I'm just one example among many of
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Speaker 3: people who've sort of gotten involved in that melting potter
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Speaker 3: salad or whatever they're at metaphor is. You know, biotech
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Speaker 3: is definitely a business that rewards technical detail, right, Like
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Speaker 3: it's just funny. I use this in other forms, and
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Speaker 3: I really wish who I knew who to attribute it to.
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Speaker 3: When I first joined the industry, I had all these
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Speaker 3: friends who had come from tech, and someone told me,
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Speaker 3: and I just don't remember who. The difference between tech
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Speaker 3: and biotech is that in tech, all the ideas sound
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Speaker 3: stupid and some of them turn out to be really good,
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Speaker 3: and in biotech, all of the ideas sound amazing and
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Speaker 3: most of them don't work. And I think so much
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Speaker 3: the difference between tech and biotech comes down to that.
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Speaker 3: But against that backdrop, learning how to differentiate between two
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Speaker 3: technical ideas in biotech, both of which sound like they're
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Speaker 3: going to cure cancer and one of which is definitely
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Speaker 3: going to fail, is the whole struggle. And I can't
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Speaker 3: say I've figured that out yet. So I do think
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Speaker 3: that getting in the details matter, but equally important, I
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Speaker 3: think a mistake that a lot of biotech companies make
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Speaker 3: is they get really deep into the technical details on
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Speaker 3: the science, but forget to get into the technical details
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Speaker 3: on the business or into the technical details on the finance.
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Speaker 3: And I think so many of the interesting opportunities live
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Speaker 3: at like the intersection between those things, where if you
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Speaker 3: can get the right technical experts in different sort of
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Speaker 3: subdomains into the room, a certain amount of magic can happen.
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Speaker 2: Yeah. No, I love that saying.
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Speaker 1: Just I heard you say that actually at the Leering
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Speaker 1: conference too with David Reisinger.
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Speaker 2: Yeah, so I mind a lot.
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Speaker 1: I'm gonna steal that. I'm then gonna actually say it
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Speaker 1: came from that Cline.
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Speaker 2: Great, Well, no I didn't. It came from somebody else,
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Speaker 2: but I don't remember who.
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Speaker 1: So from somebody who might cliinb knows fair so so
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Speaker 1: that that takes us actually quite nicely to the Roy
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Speaker 1: Vann story. I remember, I've been around a long time,
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Speaker 1: and I remember the early days.
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Speaker 2: I used to cover a.
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Speaker 1: Company called Shire Pharmaceuticals, and they used to call themselves
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Speaker 1: a search and development company, not a research and development company, right,
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Speaker 1: so they had a search and developments The story of
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Speaker 1: Royband reminds me a little bit of that, am I right?
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Speaker 3: I think you're right in the narrow sense that we
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Speaker 3: don't do today A lot of basic bench science sort
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Speaker 3: of early discovery work. We have at times. I tried it,
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Speaker 3: and most of the reasons we don't do it is
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Speaker 3: because we found it hard and so and so we
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Speaker 3: do less of it now, we do some, but most
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Speaker 3: of our programs come from other research labs. Right, they're
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Speaker 3: partnered with big partner from big pharma companies, or partner
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Speaker 3: from academic institutions, or partner from biotech companies. And most
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Speaker 3: of the science that we do is clinical development.
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Speaker 1: Okay, yeah, So it's always interestingly I think you did
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Speaker 1: correct me if I'm wrong here. A licensing deal with
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Speaker 1: or a deal with Phiser, I think that brought in
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Speaker 1: was it a TIL one A target? And then of
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Speaker 1: course a few weeks later there was massive positive TIL
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Speaker 1: one A data and everyone was scratching their head saying,
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Speaker 1: why did Pizer let this go? So what is the
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Speaker 1: dynamics of doing I can understand academia because they don't
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Speaker 1: have the wherewithal to develop drugs, and you've got that.
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Speaker 1: I can't understand biotic to a certain extent. How does
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Speaker 1: this dynamic work with farmer companies where you go and say, right,
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Speaker 1: I've discovered you know, I want that drug, or they're
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Speaker 1: these deep priorities in a drug.
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Speaker 2: How does it all work? Yeah?
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Speaker 3: Look, first of all, the bioting industry, well, the bio
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Speaker 3: pharma industry has evolved a lot over a call it
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Speaker 3: twenty to longer year period, and part of that evolution
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Speaker 3: has been big farmer companies have some very good substrate
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Speaker 3: for R and D, right Like, actually, in some ways,
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Speaker 3: I think big farmer companies are like impressively good certain
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Speaker 3: kinds of basic research where you need to cast a
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Speaker 3: white net, but a lot of really good science happens
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Speaker 3: best it's smaller, more nimble, sort of bioteching environments. And
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Speaker 3: so you've seen this trajectory over many year period. The
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Speaker 3: birth of the modern biotic industry is small companies doing
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Speaker 3: sort of specific focus targeted research or development projects. And frankly,
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Speaker 3: a lot of the dynamic is big pharma companies want
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Speaker 3: to buy those companies and build pipeline around them. And
8:28
Speaker 3: one of the things that's sort of interesting is, you know,
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Speaker 3: big farmer companies continue to do a certain amount of
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Speaker 3: basic research. They buy things all the time, they accumulate
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Speaker 3: these huge pipelines of stuff. And what big pharma companies
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Speaker 3: are amazing at, at least historically, is they're amazing at
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Speaker 3: commercializing drugs. They're amazing at bringing commercial muscle to bear,
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Speaker 3: building large field forces. Using that muscle with insurance companies,
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Speaker 3: you know, there's really good at it. But part of
8:51
Speaker 3: that is they become victims and their own success. They're
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Speaker 3: very large, they're big, slow cruise ships or aircraft carriers
8:59
Speaker 3: or whatever. The sort of maritime analogy is, they're hard
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Speaker 3: to turn, hard to stee or not very nimble. And
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Speaker 3: so there's like this giant accumulation of research pipeline that
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Speaker 3: may or may not be relevant to the thing that
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Speaker 3: the Farmer company is currently doing. Right, they've decided they're
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Speaker 3: going to be a winner in oncology and an immunology,
9:16
Speaker 3: but they have a bunch of programs in respiratory disease
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Speaker 3: because eight years ago when they started working on them,
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Speaker 3: they thought they were going to be a resport disease
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Speaker 3: and now they're not for whatever reason. And a big
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Speaker 3: part of our business has been figuring out how to
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Speaker 3: create win win opportunities around that stuff, the stuff that
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Speaker 3: doesn't fit within the current strategic agreement of the company
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Speaker 3: but might still be worth investigating scientifically in one way
9:39
Speaker 3: or another. And so the dynamic is that it's that
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Speaker 3: these companies make decisions for all kinds of reasons, often
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Speaker 3: good strategic reasons. Right, they've already made a bet in
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Speaker 3: IBD as Pfizer had, or they've already made a bet
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Speaker 3: that they want to get behind in terms of therapeutic area,
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Speaker 3: and they're good stuff left behind that just doesn't fit
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Speaker 3: within that set of commitments, and it's a win for
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Speaker 3: everybody if they can find a good home for those
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Speaker 3: programs that can then get value for them and the
10:06
Speaker 3: tail when A was a sort of extreme example from
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Speaker 3: a turnaround time perspective, But Pfizer had a bunch of
10:12
Speaker 3: R and D and commercial commitments and they were looking
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Speaker 3: to manage spend I think, and you know, late stage
10:19
Speaker 3: IBD programs are very expensive and it's a competitive area,
10:23
Speaker 3: and Pfizer decided they needed to make a change, and
10:25
Speaker 3: we worked with them on a partnership around it, and
10:28
Speaker 3: then the field moved to your point, I think faster
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Speaker 3: than we or Pfizer expected, and that created the sort
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Speaker 3: of whipblash of the turnaround. But in general, I think
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Speaker 3: that's kind of that's kind of how it comes together
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Speaker 3: right now as roy Vant.
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Speaker 1: At least a half of the dame I know return
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Speaker 1: on investment right ROI. One of the points you make
10:45
Speaker 1: is that the company was formed by people who have
10:49
Speaker 1: at least some of them have got investment background, so
10:51
Speaker 1: they know what they would be looking for if they
10:55
Speaker 1: were sitting at the other side of the table when
10:57
Speaker 1: you're pitching them the company. And so that helped in
11:01
Speaker 1: structuring and putting this company together. But I think you've
11:05
Speaker 1: told me that the answer to the question I threatened
11:07
Speaker 1: i'll ask you is why do you exist? You've already
11:10
Speaker 1: explained that. What I want to understand is how does the.
11:14
Speaker 2: Operating model work? These days?
11:16
Speaker 1: We know there are other vans? Is there going to
11:19
Speaker 1: be continued production of other vents?
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Speaker 2: How does this structure work? Just tell us a little
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Speaker 2: bit about that quickly on the why do we exist? Question?
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Speaker 3: Because I think like it's sort of a funny question, right,
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Speaker 3: Like we get it sometimes, but take a step back,
11:33
Speaker 3: like why does any company exist? Right? Like why does
11:36
Speaker 3: Bloomberg exist? There's other news agencies, there's other people that
11:39
Speaker 3: gather financial data.
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Speaker 2: You know.
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Speaker 3: I think we exist because we've built a collection of
11:43
Speaker 3: competencies that are self sustaining. That is, like, we exist
11:46
Speaker 3: because we found things that were good enough at that
11:48
Speaker 3: we can create value doing those things. And I'll say, like,
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Speaker 3: irrespective of the substrate, irrespective of whether we're getting programs
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Speaker 3: from big pharma companies or in our own labs or whatever.
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Speaker 3: The actual answer to the question this point, what are
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Speaker 3: we good enough at that it's like worth it for us.
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Speaker 2: To keep doing it?
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Speaker 1: Is?
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Speaker 3: And I'm immensely proud of this. I think we've been
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Speaker 3: very good at clinical science. I think we've been very
12:10
Speaker 3: good at identifying, you know, taking programs which exist somewhere
12:14
Speaker 3: in the world, and identifying ways to develop those programs,
12:18
Speaker 3: indications patients in need, you know, clinical development approaches, end points,
12:22
Speaker 3: ways of running clinical trials, whatever it is that have
12:25
Speaker 3: created real value, that have taken programs and identified new
12:30
Speaker 3: larger patient populations, have taken patient populations in need of
12:33
Speaker 3: new science and brought science to them. And I think like,
12:36
Speaker 3: ultimately that's the same at some level as a small
12:39
Speaker 3: biotech company that identifies a new target and plows forward
12:42
Speaker 3: with it. We've just done it more in the clinic
12:44
Speaker 3: than in the research lab. But I think like that's
12:46
Speaker 3: actually why we exist. We exist because we have been
12:49
Speaker 3: good at identifying interesting places to run.
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Speaker 2: New experiments, you know, in churms of the business model.
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Speaker 3: So it's funny we have this sort of HU hub
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Speaker 3: and spoke or whatever you want to call it. REUVNT
13:00
Speaker 3: itself is a relatively small, nimble team. We're aboute hundred
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Speaker 3: fifty people kind of at Reuivant itself that are mostly
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Speaker 3: in the opportunity identification and strategic oversight and capital allocation business.
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Speaker 3: That is, we find drugs birdied somewhere in the world,
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Speaker 3: we find good science with which to develop those drugs,
13:19
Speaker 3: indication ideas, et cetera. And then we build operating units
13:22
Speaker 3: we call them vance, call them companies, whatever you want
13:25
Speaker 3: within the family, to actually run those development programs. And
13:28
Speaker 3: the main reason we do that, actually, I've joked in
13:31
Speaker 3: other settings it's sort of a Peter Pan complex. It's
13:34
Speaker 3: that we don't know how to be a five thousand
13:36
Speaker 3: or two thousand person company, and so if you want
13:39
Speaker 3: to grow, you have to find some way to do
13:40
Speaker 3: it while preserving the entrepreneur environment that we know how
13:43
Speaker 3: to exist in. And so the way we've done that
13:45
Speaker 3: is by sprouting appendages, right, instead of growing Reuyvant at
13:48
Speaker 3: the center. We build these nimble teams, We give them
13:51
Speaker 3: their own leadership teams. We provide a lot of strategic partnership, oversight,
13:55
Speaker 3: et cetera. But Ultimately, the idea is that those units
13:57
Speaker 3: can be independent, they can be nimble, they can be fast,
13:59
Speaker 3: they can have their own culture, they can be sort
14:01
Speaker 3: of super focused on a specific thing while we and
14:04
Speaker 3: so they get to be small entrepreneurial and we get
14:06
Speaker 3: to be smaller entrepreneurial instead of the whole thing sort
14:09
Speaker 3: of ossifying or calcifying into something different.
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Speaker 2: And so that's that's just how we operate.
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Speaker 3: And sometimes I get the question why, And obviously there's
14:16
Speaker 3: some answers prespos in that scription, but the real answer
14:18
Speaker 3: is because every time we deviate from that model, we
14:21
Speaker 3: get worse. Every time we get bigger at the center,
14:23
Speaker 3: we get top heavy, we get slower and clunkier. And
14:26
Speaker 3: so we just keep reminding ourselves this model, despite all
14:30
Speaker 3: of the attendant complexity internally and externally, it's just the
14:33
Speaker 3: thing that works best for us. And so, yeah, I
14:35
Speaker 3: think you're to answer your question. When we see new opportunities,
14:39
Speaker 3: we don't even really think of anything else other than
14:41
Speaker 3: like continuing within this model.
14:42
Speaker 2: At this point.
14:43
Speaker 3: If we're looking at a new program, especially if it's
14:45
Speaker 3: in a new therapeutic career or a new medication, our
14:47
Speaker 3: general view is like we're gonna build a new van
14:49
Speaker 3: around that program because it's what works for us, and
14:51
Speaker 3: so I do expect as we do more BD, as
14:54
Speaker 3: we find more opportunities, we'll continue.
14:56
Speaker 2: To grow in that way.
14:57
Speaker 3: I don't know that every outside audience needs to think
15:00
Speaker 3: of us in those terms, as some that do. Right,
15:02
Speaker 3: our parmer partners need to know that they're going to
15:03
Speaker 3: work with a small, nimble, focused organization. But you know,
15:06
Speaker 3: from an investor perspective, is it that different than a
15:10
Speaker 3: company saying we're going to build a new therapeutic area
15:12
Speaker 3: under a new leader or something like that. I mean,
15:14
Speaker 3: it sort of depends how you think about it. Different
15:16
Speaker 3: in the how, but it's not so much different in
15:17
Speaker 3: the why. But anyway, it is definitely what works for us.
15:21
Speaker 2: But you do have some internal projects that are not
15:24
Speaker 2: in an external event.
15:25
Speaker 3: Right, What external means in that context, basically everything we
15:29
Speaker 3: do is a van. That is, if we are doing
15:32
Speaker 3: drug development work or drug discovery work or whatever, it
15:35
Speaker 3: ultimately winds up boxed within a VENT. So I think
15:38
Speaker 3: the answer is like basically every project or we work
15:41
Speaker 3: on of any real scale winds up living within a vent. Now,
15:43
Speaker 3: whether that van is externally visible as an entity or
15:47
Speaker 3: not sort of depends on where it is, and almost
15:50
Speaker 3: all of them are to some degree either wholly owned
15:53
Speaker 3: or controlled by us. But like you know, on the
15:55
Speaker 3: one hand, you have companies like Immune Event, which, for
15:57
Speaker 3: various historical reasons, Immune Events are publicly traded company that
16:00
Speaker 3: we kind of only own sixty yes percent of, or
16:03
Speaker 3: pri Evant, which is a effectively JV between Reyvent and
16:06
Speaker 3: Pfizer where we own seventy five percent and they own
16:08
Speaker 3: twenty five percent. Or then you of Palmamant, where we basically,
16:11
Speaker 3: other than the play team, we own the whole thing.
16:13
Speaker 3: There's sort of a spectrum of ownership. But actually that's
16:16
Speaker 3: that's not so much a useful descripture of how these
16:19
Speaker 3: vans operate as just a literal statement of who owns them.
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Speaker 1: How do you do you think that the last question
16:25
Speaker 1: about structure here that you know sell side that lists
16:28
Speaker 1: I used to be one have their methods for vetering
16:30
Speaker 1: companies or investors a value. Do you feel that complicates
16:34
Speaker 1: their ability to value the company correctly?
16:36
Speaker 3: I mean yes, is the short answer to that question.
16:39
Speaker 3: Look at I think it creates a lot of complexity.
16:41
Speaker 3: I think, first of all, it means they need to
16:43
Speaker 3: track mechanically some of these ownership structures and things like that. Now,
16:47
Speaker 3: is that so different than tracking the royalties or milestones
16:50
Speaker 3: under partnership agreements. I don't know, but it is. It
16:53
Speaker 3: is nonetheless like an additional thing to keep track of.
16:55
Speaker 3: But I also just think it looks complicated right, Like
16:58
Speaker 3: you know, it creates visual complexity and distraction. It creates
17:01
Speaker 3: people with the title of CEO who you feel like
17:03
Speaker 3: you're supposed to meet, that exists within the organization. That
17:05
Speaker 3: creates sort of multi tiered dynamics that I do think
17:08
Speaker 3: have been a source of complexity, and for a while,
17:11
Speaker 3: I think they were a value impediment. That is, like
17:13
Speaker 3: I think look Private existed within Reuvent developing repositative for
17:17
Speaker 3: several years, and up until a year and three months ago,
17:21
Speaker 3: no one paid any attention to it. And then all
17:23
Speaker 3: of a sudden it sort of popped up on people's
17:24
Speaker 3: radars and we started getting credit for it. And I
17:27
Speaker 3: think some of that was because the van model sort
17:29
Speaker 3: of hides these things a little bit in terms of
17:32
Speaker 3: how investors or seal side analysts perceive them, and it
17:36
Speaker 3: causes people to want to write about structure and want
17:38
Speaker 3: to write about ownership models and want to write about
17:40
Speaker 3: sort of hub and spoke instead of just thinking about
17:43
Speaker 3: these things as programs that marithrone investigation. So I think
17:46
Speaker 3: it is both a literal distraction as well as like
17:49
Speaker 3: creates mechanical complexity that absolutely obscures the story. And again,
17:53
Speaker 3: the reason we pay that price is because every time
17:55
Speaker 3: we try and do something the simple way, we fail,
17:57
Speaker 3: and so we continue to do things the complicated way
18:00
Speaker 3: as it works well.
18:00
Speaker 1: So the representing it is one of the drugs that
18:03
Speaker 1: the way I thought about doing this is I looked
18:05
Speaker 1: at the consensus estimates by drug within the Bloomberg terminal.
18:10
Speaker 1: Twenty thirty one is the year that I picked randomly.
18:14
Speaker 1: It's five years from now, representing it two point seven billion,
18:18
Speaker 1: mostly sequat about eleven hundred or say a billion, and
18:22
Speaker 1: Emily event fourteen or two, I know it's gott a
18:25
Speaker 1: name also about four hundred million. So I thought I'd
18:28
Speaker 1: just pick those three drugs and ask you to not
18:31
Speaker 1: tell me whether you agree with those.
18:33
Speaker 2: What you can if you want to.
18:34
Speaker 1: With those for forecasts sell Side has, but just to
18:39
Speaker 1: talk through why these are the I mean, how is it?
18:42
Speaker 1: Obviously it's the stage of development of the drugs where
18:44
Speaker 1: they're when they're expected to come to market. But is
18:47
Speaker 1: that the right focus area for cell side? Are there
18:51
Speaker 1: assets in there that you think people should be paying
18:53
Speaker 1: a bit more attention to talk around these drugs and
18:56
Speaker 1: this subject as much as you.
18:59
Speaker 3: Yeah, Okay, Well, look, those are the three late stage
19:02
Speaker 3: drugs in our pipeline right now, and I think they
19:04
Speaker 3: all merit focused and investigation and consideration by the cell
19:07
Speaker 3: side and by side, etc. I think those are the
19:09
Speaker 3: right drugs. There's other stuff that sort of will rise.
19:12
Speaker 2: Eventually up through.
19:13
Speaker 3: But I think part of what we've learned as a
19:15
Speaker 3: public being a public company sucks in a lot of
19:17
Speaker 3: ways as we start with that. But one of the
19:19
Speaker 3: nice things about it is it's very good at focusing
19:22
Speaker 3: you in that you can talk about as many things
19:25
Speaker 3: as you want, but you ultimately reduce to a small
19:28
Speaker 3: number of things that investors care about. Investors are lazy
19:30
Speaker 3: in myopic and can't think about more than two or
19:32
Speaker 3: three things at a time, and so no matter how
19:34
Speaker 3: excited you are about things four through fifteen, you will
19:36
Speaker 3: never ever ever get investors excited about things four through fifteen.
19:40
Speaker 3: And they will routinely remind you of that in meetings
19:42
Speaker 3: when you try and get them to focus on those things.
19:44
Speaker 3: So you know, it's a humbling experience from that perspective,
19:47
Speaker 3: but I think those three things are liveit this way.
19:49
Speaker 3: I think you can certainly get as excited as any
19:51
Speaker 3: investor needs to get about reven from a value perspective
19:54
Speaker 3: just by thinking about those three drugs. So I think
19:55
Speaker 3: in that sense, that's like a reasonable place to be.
19:58
Speaker 3: And I think we are immensely fortunate in that each
20:02
Speaker 3: of those drugs has the potential to well exceed the
20:05
Speaker 3: numbers that you quoted for them from a sort of
20:07
Speaker 3: peak sales potential, because they are broad, foundational mechanisms of
20:12
Speaker 3: action that can be used to treat a wide variety
20:14
Speaker 3: of patient populations, patient populations.
20:16
Speaker 2: With high unmet need.
20:18
Speaker 3: And they appear and we know this to varying degrees
20:22
Speaker 3: depending on what the drugs you're talking about, but they
20:23
Speaker 3: appear to be safe and efficacious agents across different indications,
20:28
Speaker 3: which gives us a lot of substrate to work on, right,
20:30
Speaker 3: Like the thing that's nice about our pipeline, and this
20:33
Speaker 3: is especially true about rerepsit and the thin fourteen oh
20:36
Speaker 3: two but it also could be true about Mosley siguad is.
20:39
Speaker 3: These are drugs to use some tride examples like renvoke
20:42
Speaker 3: or Humera or whatever, where the biology is broad enough
20:46
Speaker 3: that creative development can just continue to reach sort of
20:48
Speaker 3: concentric circles of additional patients. And that's a lot about
20:51
Speaker 3: how we're developing. The program is right now, right represent
20:54
Speaker 3: you know. For example, the first indication is demandamisitis. We
20:59
Speaker 3: produced our registration data last September. It's under review by FDA.
21:02
Speaker 3: We have a PADUFA date in the third quarter, but
21:04
Speaker 3: we will get registrational potentially registrational data and non infects
21:07
Speaker 3: uviitis later this year. We have another registrational study soon
21:11
Speaker 3: to be ongoing, and containeous arcedosis. We have additional indications
21:13
Speaker 3: of working on and it's kind of thing we'll be
21:15
Speaker 3: able to continue to stack indications. Each of them areas
21:19
Speaker 3: of great unmet need with very sick patients who are
21:21
Speaker 3: looking for new opportunities. And that's because rerepessit NIM is
21:23
Speaker 3: a great drug. It's a very very powerful anti inflammatory
21:26
Speaker 3: across multiple targets Jack one and TIP two that are
21:29
Speaker 3: useful signalers of psyite kinds across a wide variety of diseases.
21:32
Speaker 3: Same thing for fourteen oh two, right lead indication there
21:35
Speaker 3: as I would call it as Graves disease where we
21:38
Speaker 3: talk a little bit more about it because it's it's
21:39
Speaker 3: a It's a good example of two years ago we
21:42
Speaker 3: said we were studying Graves disease and there was a
21:44
Speaker 3: lot of like huh. But now I think there's twenty
21:46
Speaker 3: companies studying Graves disease. Everyone agrees it's going to be
21:49
Speaker 3: a huge indication. There's hundreds of thousands of patients with
21:51
Speaker 3: unmet need. You know, I think we've been able to
21:53
Speaker 3: sort of lead a field into a new area, but
21:55
Speaker 3: you know, a huge biological opportunity. But interestingly, right fourteen
21:59
Speaker 3: oh two was an FC or an inhibitor. Actually, our
22:01
Speaker 3: Genics is the lead company in the FCR and inhibition space.
22:04
Speaker 3: They weren't even in Graves disease until a few months ago.
22:07
Speaker 3: So it's a topic of the target that has all
22:09
Speaker 3: this different biological lookabilit If you would ask six months
22:12
Speaker 3: ago what are the biggest indications in FCR, and people
22:14
Speaker 3: would set my SNEA, gravis or CIDP or something. We've
22:16
Speaker 3: now added graves to that list. But there's just like
22:19
Speaker 3: a ton of places to go and again mostly Cigarlotte.
22:21
Speaker 3: You know, inhaled vaser dilator should work at a whole
22:24
Speaker 3: bunch of indications. Right, we are studying in PHLD. We
22:26
Speaker 3: have phase one data in PAH. There's recent data for
22:29
Speaker 3: another inhalevisor dilator to prostenol in pulmonary fibross. Again, these
22:34
Speaker 3: are just like mechanisms that if you study them the
22:36
Speaker 3: right way, have broad applicability. And so I think from
22:38
Speaker 3: that perspective, plenty of opportunity even just kind of within
22:42
Speaker 3: those three to think about a big portfolio, and you
22:45
Speaker 3: don't need.
22:46
Speaker 1: A large form of partner to do what you said
22:48
Speaker 1: they're good at, which is eight stage development and getting
22:50
Speaker 1: products to market, because these are to begin with, at
22:53
Speaker 1: least except for the graves disease that you mentioned, these are.
22:56
Speaker 2: Markets that you could manage yourselves.
22:58
Speaker 3: I mean, I think they're all markets, including grades, that
23:00
Speaker 3: we could manage ourselves as whue by manage yourself you
23:02
Speaker 3: mean commercially, yes, And look, I think most of these
23:07
Speaker 3: indications have in common that they are call it big
23:10
Speaker 3: orphan indications. They are tens to maybe low hundreds of
23:14
Speaker 3: thousands of patients, which puts them in a sweet spot.
23:16
Speaker 3: And by the way, this is like a thematic trend
23:18
Speaker 3: that we can spend more time on. I'm sure you've
23:19
Speaker 3: heard me talk about this in other settings. The biotech
23:22
Speaker 3: industry in the past five to seven years, and this
23:26
Speaker 3: is not something that Reudan can take any credit for,
23:28
Speaker 3: really has shown great success in launching drugs, especially in
23:33
Speaker 3: these large orphan indications Mycethenia gravis thyroid eye disease, you know,
23:37
Speaker 3: COPD in the case of Verona. You know, there's like
23:40
Speaker 3: a bunch of indications CIDP in the cases of e loogenics,
23:45
Speaker 3: a bunch of indications that have like tens to low
23:47
Speaker 3: hundreds of thousands of patients, a relatively high price point,
23:50
Speaker 3: very high on met lead need, relatively lower competitive intensity.
23:54
Speaker 3: And over and over again, biotech companies have been hitting
23:56
Speaker 3: it out of the park launching drugs and these indications. Again,
23:58
Speaker 3: Rouven has never done this success, so I can't take
24:00
Speaker 3: any credit for it. But all of these, all of
24:03
Speaker 3: the diseases that we are studying rhyme with that sort
24:06
Speaker 3: of series of recent commercial successes in a way that
24:09
Speaker 3: makes me very excited about what I think we're going
24:10
Speaker 3: to be able to do, but we have to prove
24:12
Speaker 3: it for sure, and we have a number of programs
24:15
Speaker 3: and indications of that sort of size and inscription.
24:18
Speaker 2: It's interesting, Matt.
24:19
Speaker 1: One of the topics that came up recently was I
24:21
Speaker 1: don't remember where it came out.
24:23
Speaker 2: I've just been to China, so I've.
24:24
Speaker 1: Got a jet lag and all sorts of things going on.
24:28
Speaker 1: So somebody said that, I think it was one of
24:31
Speaker 1: the companies that going forward, we're going to give only
24:34
Speaker 1: a super conservative guidance.
24:35
Speaker 2: And try It was on. Yeah, Yeah, that was on.
24:38
Speaker 3: That was one of the conversations you guys had, right,
24:40
Speaker 3: I was on a biotech hangout where we talked about this.
24:43
Speaker 2: That's two two weeks ago. Yeah, two weeks ago.
24:45
Speaker 3: Yeah, I is that how.
24:47
Speaker 1: I'm not sure you agreed with that or didn't agree
24:49
Speaker 1: with that? Is what's your what's your thinking on this subject?
24:54
Speaker 3: Look, I think at some level, the way you want
24:58
Speaker 3: the world to work is that question is irrelevant? Right,
25:02
Speaker 3: What does guidance matter anyway?
25:04
Speaker 2: At some level?
25:04
Speaker 3: Right, like, ultimately the drugs will do what they're going
25:07
Speaker 3: to do and in the end of days, right, whatever,
25:10
Speaker 3: there's like the famous comments about the market moving from
25:12
Speaker 3: a voting machine to a Wang machine or Wang machine
25:14
Speaker 3: to voting machine or whatever. In the end of days,
25:16
Speaker 3: these drugs are going to reach patients, They're going to
25:18
Speaker 3: generate sales. Investors will judge us on the outcome of
25:20
Speaker 3: those sales and the trajectory along the way, and how
25:23
Speaker 3: we communicate it about it and how anyone assessed that
25:25
Speaker 3: trajectory is tangential to the ultimate answer the weird moment.
25:30
Speaker 3: I don't know. This is not unique to the current moment,
25:33
Speaker 3: but it is a topic of conversation. The weird thing
25:37
Speaker 3: about the way the markets work is there's sort of
25:41
Speaker 3: this shell game around expectations. And by the way, this
25:43
Speaker 3: is not specific to sales. This is also like one
25:45
Speaker 3: of the dumbest conversations in biotech is what.
25:48
Speaker 2: Is the bar?
25:48
Speaker 1: Right?
25:48
Speaker 3: You have a clinical trial reading out, and there's like
25:50
Speaker 3: this intense debate among investors and analysts and short sellers
25:53
Speaker 3: and loans and whatever about like what the trial needs
25:55
Speaker 3: to do to matter? And the US center is like,
25:57
Speaker 3: who cares what any investor thinks about that question?
26:00
Speaker 1: Right?
26:00
Speaker 3: Like it matters to the near term on valuation, But
26:02
Speaker 3: like the trial is going to deliver certain data. Whether
26:05
Speaker 3: it met the street bar or didn't meet the street bar,
26:08
Speaker 3: it either will or will not be approved, and it
26:10
Speaker 3: will or will not get out to patients and doctors
26:12
Speaker 3: will or will not use it, and eventually the street
26:15
Speaker 3: will be right or wrong based on the outcomes, and
26:17
Speaker 3: everything along the way is just people trying to figure
26:19
Speaker 3: out figure out what's going on. So, you know, I
26:23
Speaker 3: think in some ways this question of guidance collapses into
26:26
Speaker 3: the reality of like the world is what the world is,
26:29
Speaker 3: and guidance is just a is just a weak lens
26:32
Speaker 3: in which to interpret it. Along the way, having said that,
26:35
Speaker 3: you know, there's this game that we are expected to
26:38
Speaker 3: play as public companies, where you know, sell side analysts
26:41
Speaker 3: write reports and the market establishes some both explicit consensus
26:47
Speaker 3: estimates for what we're going to do and then some
26:49
Speaker 3: like shadow set of consensus estimates that exist around that,
26:52
Speaker 3: and investors are happier when whatever that set of estimates
26:56
Speaker 3: is both explicit and implicit we beat it. And so
27:00
Speaker 3: in the near term whatever investors call us and they're like,
27:03
Speaker 3: you should call yourself an analysts and send bag the
27:05
Speaker 3: numbers so that you perform better, And you know, do
27:09
Speaker 3: I think that's like a good strategy or a bad strategy.
27:12
Speaker 3: I don't know. To me, I think what we benefit
27:16
Speaker 3: from is reasonable expectations that are not so high that
27:21
Speaker 3: you know we can't live up to them, nor so
27:23
Speaker 3: low that they defy credibility. And then you know, we're
27:26
Speaker 3: supposed to just execute as best we can and hopefully,
27:30
Speaker 3: if we're good at what we do, people will be
27:32
Speaker 3: happy with the outcomes. In the end, I think there
27:35
Speaker 3: is no benefit. One of the weird things about biotech,
27:38
Speaker 3: and then I'll start talking is biotech companies are intensely
27:41
Speaker 3: dependent on capital markets, especially sort of pre commercial biotech companies,
27:46
Speaker 3: and so there is a big portion of the life
27:48
Speaker 3: of a pre commercial biotech company that does a sales
27:50
Speaker 3: job basically, and your customer is, as it were, is investors. Right,
27:54
Speaker 3: You're trying to convince investors to support your business, and
27:57
Speaker 3: what that requires in practice, because there's so many biotech
27:59
Speaker 3: companies competing for capital, is convincing investors that your program
28:03
Speaker 3: has the biggest potential, the ear program is the one
28:05
Speaker 3: worth investing in it. And so you wind up with
28:08
Speaker 3: this necessary puffery at some level where biotech companies are
28:12
Speaker 3: supposed to convince people that their opportunity is huge, that
28:14
Speaker 3: they're going to cure cancer, that this disease that you've
28:16
Speaker 3: never heard of is a huge disease. And I think
28:19
Speaker 3: that dynamic sort of compounds into commercial, where, you know,
28:25
Speaker 3: in some ways, I feel like setting reasonable expectations and
28:28
Speaker 3: beating them is a novel idea for bioteo companies that
28:31
Speaker 3: become commercial companies because they've spent so much of their
28:34
Speaker 3: previous decade of life convincing people that the opportunity that
28:38
Speaker 3: their drug is the next two mira, that it's going
28:39
Speaker 3: to be the biggest drug, And I feel like that
28:41
Speaker 3: hits the cold hard reality of commercial launch along the way.
28:44
Speaker 3: So I think like that's that's where this whole discussion
28:48
Speaker 3: comes from. Is this idea that as you become commercial,
28:51
Speaker 3: you're supposed to move from the pre revenue convince everyone
28:55
Speaker 3: that your idea is huge to the post revenue convince
28:58
Speaker 3: everyone that your idea is modest and easily atchievable.
29:00
Speaker 2: Moment of transition. Yeah. Look, so I used to be
29:04
Speaker 2: a self sidentist.
29:05
Speaker 1: I remember very well in the early noughties or.
29:08
Speaker 2: Before the early noughties.
29:10
Speaker 1: So in ninety seven, ninety eight, we dealt with a
29:13
Speaker 1: company called Cambridge Antibody Technology CAT and they had a
29:18
Speaker 1: drug called Adelumo mab dnf antibody. I heard of it,
29:23
Speaker 1: and we also were scratching, I as, why are you
29:25
Speaker 1: bothering with this? And Abbott Abbott took it that it
29:29
Speaker 1: wasn't like a Johnsthan Johnson or Advisor or a rush
29:32
Speaker 1: took it. And then we thought it's a pointless one.
29:34
Speaker 1: And of course what happened history everybody knows the story
29:37
Speaker 1: of Himaira right, And I'm right now looking on the
29:39
Speaker 1: terminal for the consensus estimates for q Truda for the
29:45
Speaker 1: six Currently their expectation is thirty two point four billion dollars.
29:51
Speaker 1: What was it in twenty seventeen for twenty twenty six.
29:54
Speaker 2: That does seem pretty good. Twelve billion, yeah, yeah. So
29:58
Speaker 2: all it chose is that sales size analysts have a
30:01
Speaker 2: really tough life to deal with because if you got
30:03
Speaker 2: too high, people laugh at you.
30:05
Speaker 1: If you go too low, you don't get any company
30:08
Speaker 1: to do any deals with you or anybody listen to you.
30:10
Speaker 1: So it's a difficult space to be and and and
30:13
Speaker 1: buyside needs to focus on a particular angle or two
30:17
Speaker 1: or three angle to try and find whether they can
30:19
Speaker 1: find an investment, what's a conviction in something, because they're
30:22
Speaker 1: also looking at another fifty companies or a one hundred companies,
30:25
Speaker 1: so they can't focus on every detail, although some some
30:28
Speaker 1: folks seem to be able to do magic with that.
30:29
Speaker 1: But yeah, so that's that's I mean, you know, your
30:32
Speaker 1: story looks pretty well. You know, I think the market
30:35
Speaker 1: cap tells the story, whether there's three extra ten x
30:37
Speaker 1: over the past five years or seven years or whatever.
30:41
Speaker 2: Or whatever. Yeah.
30:42
Speaker 1: But also you know, I was talking to a few
30:45
Speaker 1: self side by side friends before our podcast and say,
30:48
Speaker 1: how do you view Roivat and do you view it
30:51
Speaker 1: as a as a as an orphan drug company? I thought,
30:55
Speaker 1: I think it does look like to begin with at
30:56
Speaker 1: least most of the assets certain in a norphan drug space.
30:59
Speaker 2: I think that's right. Yeah.
31:00
Speaker 1: Yeah, so that takes us to the un interesting comment.
31:02
Speaker 1: Now going to the more broader subjects I want to
31:05
Speaker 1: talk about, and Matt, if you want to go back
31:07
Speaker 1: to anything specific to the company, just tell me and
31:09
Speaker 1: we can go back and talk about it before I
31:11
Speaker 1: move on to the broader subjects, which takes me from
31:14
Speaker 1: orphan to the US and FDA and how things are working.
31:17
Speaker 1: But before we do that, is there anything else you
31:19
Speaker 1: wanted to touch on specific to.
31:21
Speaker 3: The cook People can hear me draw around about the company,
31:23
Speaker 3: lots of forums, and it's fun to pontificate on bigger subjects.
31:26
Speaker 2: So let's move on. I think that'd be fun. So
31:28
Speaker 2: we've got this, We've got what.
31:30
Speaker 1: I think you didn't vehemently disagree with, at least that
31:33
Speaker 1: there's a lot of the assets that you're developing at
31:35
Speaker 1: the beginning, at least or most of the indications on
31:38
Speaker 1: orphan indications.
31:38
Speaker 2: So let's say you're dealing with the orphan drug Gay Playbook.
31:42
Speaker 1: How do you think the current FDA scenario that's happening
31:47
Speaker 1: where there's a lot of positive talk and we get
31:50
Speaker 1: CRL left to CRL, left to CRL and we just
31:53
Speaker 1: had another one today or yesterday, I can't remember.
31:56
Speaker 2: Now, just tell me how you how your interactions.
32:00
Speaker 1: I know this is a sensitive subject because, of course,
32:03
Speaker 1: at the other end of this podcast, the FDA may
32:04
Speaker 1: be listening, and I hope they are listening because I
32:07
Speaker 1: think we need to be realistic. There are things that
32:08
Speaker 1: they need to fix. And so whatever, however way you
32:12
Speaker 1: want to address this.
32:13
Speaker 2: Please go ahead.
32:14
Speaker 3: I mean, look, first of all, and this is in
32:16
Speaker 3: part in case FDA is listening, but actually not really.
32:18
Speaker 3: I like, look, FDA has a difficult job, and they've
32:21
Speaker 3: always had a difficult job, and the sort of symbiosis
32:25
Speaker 3: between FDA and pharma is like a complicated relationship. And
32:29
Speaker 3: you know, mostly I think FDA is populated by you know,
32:33
Speaker 3: career technocratic civil servants who we are trying really really
32:36
Speaker 3: hard to make.
32:36
Speaker 2: The right decisions on things.
32:38
Speaker 3: And I think even many of the CRLs are a
32:42
Speaker 3: function of FDA trying to do the right thing and
32:45
Speaker 3: companies trying to do the right thing and everybody working together.
32:48
Speaker 3: And it's you know, it's obviously when the rubber meets
32:50
Speaker 3: the road, if there's a disagreement about whether a drug
32:53
Speaker 3: should approved or not, that's going to be a tough situation.
32:55
Speaker 3: And one of the things I made a joke on
32:57
Speaker 3: a bout tocanning Out episode. You know, I think one
32:59
Speaker 3: of the dynamics of this which can feel particularly intense
33:02
Speaker 3: when you're staring down the barrel of a CRL or
33:04
Speaker 3: just at these moments where some decisions may be more
33:07
Speaker 3: difficult to understand than others is ultimately. There's there's a
33:11
Speaker 3: like an apocryphal parable that circulates on the internet about
33:14
Speaker 3: like a Coastguard ship in the fog and they get
33:17
Speaker 3: on the radio and there's like someone in front of
33:19
Speaker 3: them and there's like a debate about and they're going
33:22
Speaker 3: back and forth. There's like, oh, like, we're a Coastguard cruiser,
33:25
Speaker 3: like you're in our way, you need to move, And
33:27
Speaker 3: the other station comes on and they're like, no, you
33:29
Speaker 3: need to move, like whatever, and then eventually it becomes
33:32
Speaker 3: kard of the coast Guard cruisers negotiating with a lighthouse
33:34
Speaker 3: about who's going to move, and of course the lighthouse
33:36
Speaker 3: is going to win that debate in the end. The
33:37
Speaker 3: FDA is the lighthouse obviously, like ultimately, as a pharma company,
33:40
Speaker 3: like they are the final word. If they reject your drug,
33:42
Speaker 3: they reject your drug, they approve your drug, they prove
33:44
Speaker 3: your drug, and there's not that much recourse. So that's,
33:46
Speaker 3: to your point, what makes the dynamic difficult. But everyone
33:48
Speaker 3: is mostly trying their best. I'll say this, we are
33:52
Speaker 3: mostly not in the crosshairs of the stuff that has
33:56
Speaker 3: been whatever has been debatable at FDA lately. We're not
33:59
Speaker 3: developing therapies. Most of our studies have relatively larger and
34:04
Speaker 3: you know, if they fail, they fail, and if they succeed,
34:06
Speaker 3: they succeed. But these are not like they're not the
34:08
Speaker 3: kinds of things that have been so intensely debated lately.
34:11
Speaker 3: In our interactions with FDA have been the same as
34:14
Speaker 3: they've always been. Roughly, they've been positive, they've been technocratic,
34:17
Speaker 3: they've been detail oriented, they've been broadly on time. I'll
34:21
Speaker 3: say since twenty twenty, FDA has clearly been strapped. That is,
34:25
Speaker 3: they've been overwhelmed, they've been understaffed, it's been harder to
34:28
Speaker 3: get meetings, it's been harder to have constructive discussion with them,
34:32
Speaker 3: and you know, I think against that backdrop again, they've
34:34
Speaker 3: been working really hard to fix that and do their
34:36
Speaker 3: job where they can, and it's been productive. I can't
34:39
Speaker 3: say that any of our interactions with FDA have reflected
34:42
Speaker 3: any chaos or change.
34:44
Speaker 2: Okay, I mean they have been.
34:46
Speaker 3: The other one you left up was vaccines in terms of, yeah,
34:49
Speaker 3: we don't have any vaccines, for example, so you didn't
34:51
Speaker 3: make some money out of vaccines. We did, but we
34:54
Speaker 3: didn't have an interact with FDA to make that money
34:56
Speaker 3: that was on a litigation of course.
34:58
Speaker 1: So actually, so you don't have a particular wish list
35:02
Speaker 1: for the FDA, I mean, I'm gonna I'm gonna ask
35:04
Speaker 1: you about a wish list.
35:05
Speaker 2: Oh, it's a good question.
35:06
Speaker 3: Look, I do think and by the way, like I think,
35:10
Speaker 3: say what you will about current FDA, and there's been
35:13
Speaker 3: some complexity for sure. I think a criticism of prior
35:17
Speaker 3: FDAs could be they weren't trying to change or improve, right.
35:21
Speaker 3: A criticism of prior FDAs could be they were pretty
35:24
Speaker 3: status quo biased. And the whole topics of like should
35:29
Speaker 3: we have one trial instead of two? What should the
35:31
Speaker 3: boundaries be for that? Like, they were moving slowly on
35:33
Speaker 3: some of them. Should we accept you know, simulated data,
35:36
Speaker 3: should we be trying to move away from animals or whatever.
35:39
Speaker 3: I'm not saying I have a view on any of
35:41
Speaker 3: these questions, but a lot of them were not really
35:45
Speaker 3: actively discussed. And I think like the current FDA has
35:48
Speaker 3: been trying to make changes. You know that the CNPV,
35:51
Speaker 3: the the voucher program, the Commissioner's Prior Review voucher program
35:54
Speaker 3: like again complicated from an implementation perspective, Lots of debate
35:59
Speaker 3: about it, but like a cool idea to try and
36:02
Speaker 3: get some drugs reviewed faster and approve faster, And like
36:05
Speaker 3: I respect them for trying. I respect them for like
36:07
Speaker 3: throwing ideas out and iterating. I think that's like a
36:09
Speaker 3: good thing. Obviously that needs to be balanced with the
36:12
Speaker 3: exgitent realities of the need for careful view and all
36:14
Speaker 3: these other things. And I don't mean to take away
36:16
Speaker 3: from people who disagree with any of these individual progress
36:18
Speaker 3: I think it's like impressive that the FDA is trying. Obviously,
36:21
Speaker 3: from our perspective, faster reviews would be better if they
36:23
Speaker 3: were sort of magically.
36:24
Speaker 2: Achievable, you know.
36:25
Speaker 3: I think one thing that's on my wish list that
36:27
Speaker 3: FDA probably used to be better at than it is
36:29
Speaker 3: now is just more consistency around guidance. That is, like,
36:33
Speaker 3: when you go in and talk to FDA about something,
36:36
Speaker 3: irrespective of how their policies may change over the next
36:39
Speaker 3: few years, it would be helpful if they stuck to
36:41
Speaker 3: their promises or at least like had some knowledge of
36:44
Speaker 3: what they had agreed to four or five years ago. Again,
36:47
Speaker 3: that hasn't affected us specifically, but we're making long term
36:49
Speaker 3: investment decisions and the more variability there is around that stuff.
36:53
Speaker 3: Over time, the harder it is for us to adapt,
36:55
Speaker 3: you know. I think there are situations where the FDA's standards.
37:01
Speaker 3: I actually think there's some logic to moving in general
37:03
Speaker 3: to a one trial standard versus a true trial standard.
37:05
Speaker 3: I think like the two replicate clinical trial standard doesn't
37:08
Speaker 3: have a lot of like statistical meaning and frankly, if
37:12
Speaker 3: you want better powered studies, lower the P value threshold
37:15
Speaker 3: versus requiring sort of two studies within add a whole
37:17
Speaker 3: bunch of administrator complex I think like there's useful things
37:20
Speaker 3: they are. I think being able to interact more with
37:22
Speaker 3: FDA during the process would be productive, just like on
37:26
Speaker 3: like sort of modes of working, you know. I think
37:28
Speaker 3: a little bit more flexibility and dynamism with respect to
37:32
Speaker 3: end points would be helpful. I think like there's certain
37:36
Speaker 3: dogma within various divisions, within various indications.
37:39
Speaker 2: You get.
37:40
Speaker 3: There's like dogma around end points that comes from a
37:42
Speaker 3: this is the way it was always done with this indication,
37:45
Speaker 3: and it's hard, right, Like if ten drugs were approved
37:47
Speaker 3: down to give an end point, and over time that
37:49
Speaker 3: endpoint just like less well reflects either the performance of
37:52
Speaker 3: drugs in the clinic or the needs of patients. It's
37:54
Speaker 3: hard to switch because you worry that like you're incentivizing
37:59
Speaker 3: a change in pain or advantage, or creating a disadvantage
38:02
Speaker 3: or an advantage for a given company. But I think
38:05
Speaker 3: like over time those things do change. Placebo responses have
38:07
Speaker 3: moved a lot on some of those endpoints, and I
38:09
Speaker 3: think like a little bit less dog more other respec
38:11
Speaker 3: endpoints would probably be helpful. Those are probably the things
38:13
Speaker 3: that are on my.
38:13
Speaker 2: Wishless Okay, that's quite a big wishless there amount, yeah,
38:18
Speaker 2: but all of it very wishy washing. So I hope
38:22
Speaker 2: that it will listen. Maybe you should send it to that.
38:24
Speaker 2: So actually, in terms of interactions and more interaction, et cetera,
38:28
Speaker 2: do you think AI will help there?
38:30
Speaker 1: Do you think there's any way because they're talking about
38:32
Speaker 1: it if you're able to analyze data sets. So you
38:36
Speaker 1: did say that they're constrained from a manpower perspective since
38:40
Speaker 1: twenty twenty, I mean AI is supposed.
38:43
Speaker 2: To help that. Look, I lilearly.
38:45
Speaker 3: Among the lowest hanging fruit for AI everywhere is information
38:49
Speaker 3: synthesis and sharing, right, Like if you think about what
38:54
Speaker 3: is an NDA, like what is a new drug application.
38:56
Speaker 3: Actually it is hundreds of thousands of detailed technical information
39:01
Speaker 3: shipped over, and the idea is that a large team
39:05
Speaker 3: of technical reviewers are going to be able to read
39:07
Speaker 3: different subsets of that document and find things and understand them.
39:11
Speaker 2: I don't know that.
39:12
Speaker 3: I think today FDA is going to stand in for
39:14
Speaker 3: the comprehension layer. That is, I don't know that, Like,
39:17
Speaker 3: if you are a career expert in small molecule manufacturing
39:21
Speaker 3: quality at FDA, I don't know that, like you're going
39:25
Speaker 3: to get replaced by an AI agent that understands small
39:28
Speaker 3: molecule quality issues as well as you do. But what
39:31
Speaker 3: I do think is, in reading twenty thousand pages of
39:33
Speaker 3: quality review documents for manufacturing facilities, AI ought to be
39:37
Speaker 3: able to help you zero in on the things you
39:39
Speaker 3: really care about and be more efficient at your job.
39:42
Speaker 3: And conversely, you know, we as filers of the NDA,
39:45
Speaker 3: ought to be able to use AI to better sift
39:48
Speaker 3: through tons and tons and tons of manufacturing quality records
39:52
Speaker 3: and generate the summary tables that FDA reviewers really care about.
39:56
Speaker 3: And so I do think AI in sort of a
39:59
Speaker 3: boring process oriented just like data synthesis and data review
40:03
Speaker 3: sense ought to be able to facilitate faster, faster human
40:06
Speaker 3: review by technical experts. Look to use it for these
40:09
Speaker 3: things all the time, right you You know you have
40:12
Speaker 3: a large volume of event from by whatever, a six
40:15
Speaker 3: hundred page legal contract, and you want to know what
40:16
Speaker 3: it says about a certain idea.
40:17
Speaker 2: You give it to it. You give it AI.
40:19
Speaker 3: It'll give you a pretty decent summary of like those provisions.
40:22
Speaker 3: I think stuff like that translated to the reglatory sphere
40:24
Speaker 3: could be very powerful. So I am hopeful that AI
40:27
Speaker 3: should be able to help relieve some of the workload
40:30
Speaker 3: demands on the creer civil servants at FDA.
40:33
Speaker 1: And how close are you to the you did have
40:35
Speaker 1: an AI company data event or connect?
40:37
Speaker 2: Yeah, well we had a data company. Yeah yeah, How
40:40
Speaker 2: close aw you to that story? I mean in the
40:42
Speaker 2: end that's ended up being part of a very successful story.
40:45
Speaker 2: And identify the.
40:46
Speaker 3: Closure we to data Vent. Yeah, we're a ten percent
40:49
Speaker 3: owner of data Vent at this point, the data van,
40:52
Speaker 3: I want to say something that data Van would disagree with.
40:55
Speaker 3: I don't know that data Vent is per se an
40:57
Speaker 3: AI company. They're a data company. And I think the
40:59
Speaker 3: beauty thing about data companies in the AI era is
41:02
Speaker 3: that in the in the AI era, data is everything,
41:05
Speaker 3: and data event lives like at the pinch point of
41:07
Speaker 3: a whole bunch of healthcare data coming together. So that's
41:09
Speaker 3: part of the reason they've been successful is they've been
41:11
Speaker 3: helping all kinds of people manage the mess that is
41:14
Speaker 3: healthcare data spread across a whole bunch of different places.
41:17
Speaker 3: And I think that's going to be necessary to unlock
41:19
Speaker 3: some of the quote unquote power at AI within the
41:22
Speaker 3: US healthcare system.
41:23
Speaker 1: Yeah, So, Matt, the other topic that I mean, obviously,
41:26
Speaker 1: how do how do you because a lot of folks
41:30
Speaker 1: I speak to see one of the key values of
41:34
Speaker 1: application of AI today pretty is in the pre clinical
41:38
Speaker 1: lead nomination, lead development, some of the chemistry, et cetera.
41:44
Speaker 2: You don't have any of that.
41:45
Speaker 3: Do you have a use for AI business?
41:49
Speaker 1: I mean, this is I think we get a bit
41:53
Speaker 1: bored with asking the same question over and over, but
41:56
Speaker 1: it's good to know, and you know, yeah, I think.
41:59
Speaker 3: It's great question. Say two things about it. One is
42:01
Speaker 3: we used to do and have done more sort of
42:03
Speaker 3: AI and drug discovery of the kind you're talking about.
42:06
Speaker 3: And my view is over time, absolutely AI as applied
42:12
Speaker 3: to problems of basic biology and chemistry and intersect betem
42:15
Speaker 3: those things should make a big difference. It's really really hard.
42:19
Speaker 3: And the interesting thing that I see happening is drug discovery.
42:23
Speaker 3: Pre clinical drug discovery is a composite of one hundred
42:25
Speaker 3: and fifty different hard problems. It's binding and permeability and
42:29
Speaker 3: talk screening and you know, you know, like binding confirmation
42:33
Speaker 3: and protein confirmation, all these like very specific CMC synth.
42:36
Speaker 3: This is all these very specific problems. And most companies
42:40
Speaker 3: working on AI and drug discovery are solving one or
42:44
Speaker 3: two of those one hundred and fifty problems. And the
42:46
Speaker 3: challenge is if you become the best in the world
42:49
Speaker 3: those two like you build that you build a supercomputer
42:51
Speaker 3: that is the best. Literally you're better than any human,
42:53
Speaker 3: better than any company at one of those one hundred
42:55
Speaker 3: and fifty problems, you're still probably mediocre at best. At
42:58
Speaker 3: the other one hundred and forty nine you have an
43:00
Speaker 3: alpha issue. Even if you are exactly average one hundred
43:03
Speaker 3: and forty nine things and way better at one of them,
43:05
Speaker 3: unless you're running a thousand drug discovery programs, it's going
43:08
Speaker 3: to be really hard to show that you're better than
43:10
Speaker 3: somebody else because you're only better at one thing. So
43:13
Speaker 3: what I think is going to happen in part is,
43:15
Speaker 3: over time, sixty of these companies are going to solve
43:18
Speaker 3: sixty of the most foundational problems in drug discovery. All
43:22
Speaker 3: sixty of those companies are going to have an uphill
43:25
Speaker 3: battle to prove that they're better because there's so many
43:27
Speaker 3: things at which they're not better at, but there's a
43:29
Speaker 3: couple of things that they're really, really really good at,
43:31
Speaker 3: And many of those companies may fail, but we are
43:33
Speaker 3: all going to be beneficiaries as you take those sixty
43:35
Speaker 3: tools from the carcasses of those companies and you build
43:38
Speaker 3: a composite out of them that's going to enable everybody
43:41
Speaker 3: to be better at drug discovery. But it's hard to
43:43
Speaker 3: be any of those individual companies because you're only best
43:45
Speaker 3: in the world at one or two things. Having said that,
43:48
Speaker 3: in terms of your broader question about AI, I think
43:51
Speaker 3: if you think about the hierarchy of use cases for
43:54
Speaker 3: AI currently in drug development or pharma, generally, the very
43:59
Speaker 3: very very hardest problems are chemistry, right like that is
44:04
Speaker 3: not if you think about low hanging fruit solving problems
44:07
Speaker 3: of chemical binding. Are the apples that are at the
44:10
Speaker 3: very very top of the tallest apple tree. They are
44:13
Speaker 3: the hardest problems to solve at the bottom of the
44:16
Speaker 3: apple tree, the low hanging fruit. It's the same stuff
44:19
Speaker 3: that you're using AI for. I get too many emails,
44:22
Speaker 3: I have too many meetings. Making presentations is hard. Reading
44:26
Speaker 3: legal documents, writing legal documents, summarizing complicated documents, summarizing data,
44:31
Speaker 3: to rough understanding all of the things that everybody everybody's
44:35
Speaker 3: that's the low hanging fruit, right, Like you know, I
44:38
Speaker 3: think about Like on the commercial end, one of the
44:41
Speaker 3: things that has developed in the field is that for
44:44
Speaker 3: orphan commercial launches, you have to help patients and physicians
44:48
Speaker 3: get drugs through the insurance system. You have to help
44:52
Speaker 3: payers understand why a given patient is appropriate for a drug.
44:56
Speaker 2: What does that involve?
44:57
Speaker 3: Reading dozens or hundreds of patients of that pages of
45:00
Speaker 3: that patient's medical record and helping them synthesize a well
45:03
Speaker 3: drafted letter to an insurance company explaining why their medical
45:06
Speaker 3: record supports the use of a drug. AI is really
45:08
Speaker 3: good at that. It's not going to be the be
45:10
Speaker 3: on end. All the patients can have to read it,
45:12
Speaker 3: that doctors can have to read it. Every reason you
45:14
Speaker 3: have to work. But the first step of reading the
45:15
Speaker 3: medical records and providing a first draft gosh chat, GPT
45:19
Speaker 3: is really good at doing that sort of thing. That's
45:21
Speaker 3: low hanging fruit managing you know, a CRM system that
45:25
Speaker 3: covers how reps or medical affairs people talk to a provider,
45:29
Speaker 3: keeping on top of medical literature. All of that low
45:32
Speaker 3: hanging fruit is incredibly valuable and in my opinion, like
45:35
Speaker 3: that's where AI is first going to have an impact
45:37
Speaker 3: on commercial on managing cro relationships for clinical development. That's
45:43
Speaker 3: like the low hanging fruit. And then like you get
45:46
Speaker 3: into biology and into understanding clinical data, and into understanding
45:50
Speaker 3: pre clinical data and into understanding chemical data, and then
45:53
Speaker 3: like somewhere way at the top of the tree, the
45:56
Speaker 3: hardest problems to solve are these problems of conformational genetic
46:00
Speaker 3: ex or biology or chemistry or whatever that will matter
46:03
Speaker 3: in drug discovery.
46:05
Speaker 2: So, Matt, thank you for that. It was a really,
46:07
Speaker 2: really good detail. They need to go back and listen
46:09
Speaker 2: to it. I've got a I've got someone knocking on.
46:12
Speaker 1: The door here wanting to get in and do whatever
46:14
Speaker 1: it is I want to do, But I have to
46:16
Speaker 1: ask you one quick question before we end China, is
46:20
Speaker 1: it a threat to the United States? You can do
46:23
Speaker 1: a yes and no and then move on to the
46:25
Speaker 1: I mean us biotake.
46:26
Speaker 2: I don't mean that sort out. Yes, I think is
46:30
Speaker 2: the answer, but it's also an opportunity.
46:33
Speaker 1: For you specifically, it could it should be an opportunity
46:36
Speaker 1: in terms of finding assets, right.
46:38
Speaker 3: I think China is a little bit of a red
46:40
Speaker 3: herring in that it is a place that has figured
46:43
Speaker 3: things out that we can learn from in order to
46:46
Speaker 3: like in terms of like making certain parts of drug
46:48
Speaker 3: discovery more efficient and faster, and that is that is
46:52
Speaker 3: both awesome, but also it ought to be repeatable in
46:56
Speaker 3: other places. It ought to be leverageable, and I think
46:58
Speaker 3: I think that's where.
46:59
Speaker 2: Our focus should well.
47:00
Speaker 1: So that's Matt Gline, everybody, CEO of roy Van. I
47:04
Speaker 1: had some other topics, but we might have to get
47:06
Speaker 1: you back for episode two. Thank you for so much
47:09
Speaker 1: for joining us. Matt, it's been great to have you here.
47:12
Speaker 1: Please don't forget everybody to give us a review and
47:15
Speaker 1: make sure to click the follow button on your favorite
47:19
Speaker 1: podcast app or site so you never missed a discussion
47:22
Speaker 1: with leaders such as Matt. I think this was really
47:24
Speaker 1: interesting and exciting. So I'm Sam Fazelli again one of
47:27
Speaker 1: the Bloomberg Intelligence Farma lists, and you've been listening to
47:30
Speaker 1: the vangors of healthcare podcasts by Bloomer Intelligence. Until next time,
47:34
Speaker 1: take care.
48:09
Speaker 4: Uses asses, US and moss, cots and bases.