The Brutal Truth About Biotech: Why $2B Per Drug Is Killing Innovation

0:00:05 Since the birth of this industry, we only had increasing regulation over time.
0:00:11 I think there was like only one time in the history of biotech where we made it easier to develop an appropriate drug.
0:00:16 So when George Yankopoulos started Regeneron, it cost about $10,000 per patient in trial.
0:00:18 That’s ballooned to $500,000.
0:00:26 There is no law of physics that requires it to be $500,000 in terms of complexity and cost to dose a patient in a trial.
0:00:29 Everyone will be using AI in biotech industry five years from now.
0:00:35 Can it take $2.5 billion to offer a drug and make it into $500 million?
0:00:38 Can it make it like 4x more efficient in terms of Thailand?
0:00:43 To answer that question, we really should go back and look where most of the money is spent right now.
0:00:47 We had this enormous wave of excess and now we’re sitting on the other side of that.
0:00:50 We have an enormous amount of EV negative public companies.
0:00:54 There was a stretch of seven to eight months where there were no biotech IPOs.
0:00:58 And so I’m really excited about that sort of opportunity to make things that are just net new in the industry.
0:01:00 I think that’s where we have to go to really keep winning.
0:01:03 The biotech paradox.
0:01:07 One-fifth of public biotech companies are trading below their cash balances.
0:01:09 C-Browns have hit record lows.
0:01:13 The industry spends $2 billion per approved drug and that number keeps climbing.
0:01:16 And yet, we’re designing antibodies from scratch with AI.
0:01:18 We have drugs that are bending the curve on aging.
0:01:20 The science has never been better.
0:01:24 So why is the business of biotech collapsing while the technology is exploding?
0:01:27 Today’s guests have spent years studying this contradiction.
0:01:35 Lada Nuzana founded General Control, a startup tackling aging after writing the definitive analysis of why there are no trillion-dollar biotechs.
0:01:45 Elliott Hirschberg is a partner at Amplify, betting on the next wave of platform companies while watching American biotech companies flee to China and Australia just to run their first trials.
0:01:46 Their diagnosis?
0:01:48 We’re competing on the wrong axis.
0:01:58 China wins on speed and cost, the FDA adds friction while innovation accelerates, and the entire industry is structured around an equilibrium that no longer exists.
0:02:01 But there’s a path forward if we’re willing to invent our way out.
0:02:13 We cover why regulation might not be the real problem, what GOP-1’s reveal about blockbuster drugs, whether AI can actually fix drug development, and why the next iconic biotech companies will look nothing like Genentech.
0:02:16 This is the state of an industry at an inflection point.
0:02:17 Let’s get into it.
0:02:21 Welcome, Lada.
0:02:24 Welcome, Elliott, to the A6&Z podcast.
0:02:32 Very excited to have you here, and I’m very excited to have this conversation because where I’d love to start is I want to talk about all things biotechnology.
0:02:39 And let’s take a quick pulse check on the state of biotechnology as it stands today in 2025.
0:02:45 So question one for both of you is how do you see the state of the industry as it stands today?
0:02:50 And how do you see the state of the science as it stands today?
0:02:58 Yeah, a few AI biotech deals aside, it’s been quite a few interesting years in biotech.
0:03:05 I mean, early this year, we had something like one-fifth of public biotechs trading at or below their cash balances.
0:03:09 They had like record low number of biotechs raising their seed rounds.
0:03:14 The platform dream kind of judged more harshly.
0:03:22 And if you take a step back and look at the bigger picture of it all and ask yourself like, oh, has it ever been different?
0:03:26 It seems like this state has been ongoing for quite some time.
0:03:35 So we have Erum’s law, which is Moore’s laws, but backwards, we now spend more than $2 billion per approved drug.
0:03:43 We have a rise of Chinese biotech scene that is threatening the state of, yes, early-stage biotech companies.
0:03:48 And I guess the question is like, did we expect it to be different?
0:03:51 Because approving drugs is really hard.
0:04:00 It’s probably one of the hardest areas of deep tech broadly because it takes not only on engineering risk, but also on scientific risk.
0:04:05 And it does seem like the biotech industry started on a more positive note a few decades ago.
0:04:14 I mean, we had Genentech, Amgen, IPOs, their investors making first huge exits, more so than in tech back then.
0:04:25 And we had human genome projects that sort of promised that all the biotech will become precision medicine and will develop medicines as tailored to one patient.
0:04:30 And, yeah, it does seem like it led us to a slightly different ending.
0:04:36 I mean, I hope it’s not the ending, and I hope there are some positive things that we can find in it all.
0:04:43 But in many private conversations during these past two years, mood has essentially been like, will this industry ever recover?
0:04:46 Okay, so you have a lot of reason to be pessimistic.
0:04:47 Yes.
0:04:47 Okay.
0:04:48 At least as of now.
0:04:50 As of now, as of today.
0:04:51 I want to hear, Elliot, your take.
0:04:57 I think there’s never been a bigger disconnect between both sides of the market, right?
0:05:00 So as Lotta was saying, we had this enormous wave of excess.
0:05:04 Our last actual boom cycle in this industry was COVID.
0:05:08 You had an enormous amount of value creation and success.
0:05:13 And now we’re sitting on the other side of that, and we’re sort of objectively in a downturn.
0:05:17 We have, as Lotta pointed out, an enormous amount of EV-negative public companies.
0:05:20 That’s starting to change and resolve a little bit.
0:05:25 But there was a stretch of seven to eight months where there were no biotech IPOs.
0:05:36 There’s a logjam that goes from the public markets to the growth investors to leader-stage investors to early-stage investors that just changes milestones and makes everything super, super hard.
0:05:43 At the exact same time, there’s never been a better point to actually see the progress of technology, right?
0:05:47 So you have people developing zero-shot antibody design in labs.
0:06:05 You have incredible virtual cell projects, an enormous amount of sort of translational potential at the early stage, which I think is actually just, for an investor, for a founder, an incredibly exciting time to be building a new company because there’s this huge disconnect in the market between sort of publics and what’s happening earlier.
0:06:07 Sort of a tale of two worlds.
0:06:18 Well, what I was going to say is I think what’s interesting is, number one, there are a couple of reasons to be optimistic today, right, in terms of the market at least showing some of the famous green shoots of optimism, right?
0:06:23 Some IPOs happening, or at least people are preparing to file some very successful M&As.
0:06:29 The biotech index is above, starting to get well above the famous $100 mark for the XBI.
0:06:29 Right.
0:06:31 So there’s reason to be optimistic.
0:06:47 I think the fundamental question is what bridges that disconnect between all of the advance and promise we’re seeing on the technology side and some of the fundamental laws of physics of the industry that have resulted in this not being a particularly effective industry to invest in over the course of the last several years.
0:06:51 You wrote a great post, where are all the trillion dollar biotechs.
0:06:54 What’s your diagnosis of the situation?
0:07:00 What is it about the fundamentals of the industry that make it so challenging economically?
0:07:07 Yeah, I mean, since the birth of this industry, we only had increasing regulation over time.
0:07:13 I think there was like only one time in history of biotech where we made it easier to develop and appropriate drugs.
0:07:22 And that was during the AIDS crisis, where AIDS patients were just laying outside of FDA and demanding drugs to be approved on a more accelerated timeline.
0:07:28 FDA did make a big change back then that allowed for certain accelerated approvals.
0:07:38 But since the birth of the industry, maybe for good reasons, we were always making it only harder to develop new drugs, all while the science was improving over time.
0:07:47 I mean, our high throughput screens for new small molecules and antibodies are like a billion times more efficient than they were 20 years ago.
0:07:55 And what Elliot said really is innate because it feels like the science continues to get better and the state of science continues to get worse.
0:07:57 And I think a lot of it is downstream of regulation.
0:08:10 FDA, just in history of regulation in general, like the way FDA started to crack down on drug development approval process is through like a huge strategy.
0:08:19 I think it was drug telling them why that pregnant women were taking that led to a number of deformities in kids that were being born from those pregnant women.
0:08:26 And since then, FDA started to require not just safety, but also efficacy in the approval process.
0:08:34 And yeah, whoever would come to FDA to deregulate this process would have to take on like the way enormous personal risk,
0:08:39 because regulation is a tension between safety and efficacy.
0:08:47 And if we deregulate the process, we would have to take on some type of safety risk.
0:08:48 I think it’s interesting.
0:08:51 There’s 100% of growth of regulation.
0:08:57 There’s a bunch of low-hanging fruit that would be just substantially better for starting trials, right?
0:09:02 So we have three companies in the next 12 to 18 months that will be starting first in human trials.
0:09:05 Guess how many are actually doing their first studies in the United States?
0:09:08 When you say we, you mean?
0:09:08 At Amplify.
0:09:09 At Amplify, yeah.
0:09:10 Oh, none.
0:09:11 Zero.
0:09:11 Yeah, right.
0:09:19 And so we’ve just come to accept the fact that everyone goes to Australia, everyone goes to Asia to do their studies,
0:09:23 because the things that we invent here, we can’t actually first test here.
0:09:25 So there is sort of a regulatory barrier.
0:09:30 But I was writing about this business, Vial, that was trying to innovate on a clinical research organization.
0:09:38 And one thing that I sort of learned in terms of the machinery of translating regulatory innovation into actually cheaper trials.
0:09:45 So let’s say that there’s a change, like whatever variable we think about, for trying to actually decrease the costs of trials.
0:09:47 How does that get implemented?
0:09:53 So it actually turns out that there’s been this enormous consolidation of the clinical research organization market
0:10:00 that’s come down to about a dozen providers who have each done roughly 40 acquisitions across 30 years
0:10:05 to really consolidate into these large clinical outsourced providers that most people rely on.
0:10:13 And so when the FDA says, we actually want to modernize the standards for trials and have electronic tablets,
0:10:19 you actually have to get the clinical research organizations to adopt those tools and technologies.
0:10:20 And they aren’t.
0:10:22 They aren’t incentivized to, right?
0:10:26 And so there is sort of a structure to the actual process of executing on the trials
0:10:31 that lags, sort of a lagging indicator from the changes in regulatory itself.
0:10:33 So it’s sort of a two-pronged beast.
0:10:37 When you think about the cost of clinical development, it’s actually sort of what the rule of law is,
0:10:39 like what you can do.
0:10:43 And then also just all of this sort of industry entrenchment of what we are doing.
0:10:48 And so I think there are some ways for people to just do things a lot faster,
0:10:52 even within the bounds of the law, than we’re sort of used to paying for or expecting,
0:10:57 which I think is in part just like a cultural component of biotech,
0:11:01 that we just assume that these things are expensive and take a lot of time.
0:11:06 So that argument would be that the challenge is not technological,
0:11:11 that in some level, the challenge might not even be the regulatory bodies.
0:11:12 Yeah.
0:11:15 It might be something structural, something incentive-based,
0:11:20 where the way the industry is structured, you have various parties, in this case,
0:11:25 the groups that, you know, help run the clinical trials that may or may not be incented to be more efficient.
0:11:26 Is that the argument?
0:11:27 Yeah, that’s right.
0:11:34 So like if you were to break it down into cultural tech solutions in terms of just implementations and regulatory,
0:11:39 you’d have to sort of assign some, you know, percentage at ease.
0:11:42 And I think it is a composite of all three.
0:11:43 Okay.
0:11:45 So now let’s add to the mix here.
0:11:45 Let’s add to the soup.
0:11:47 The China question.
0:11:48 Yeah.
0:11:52 So for folks that may not spend all their time thinking about biotech,
0:11:56 what is going on in China as it relates to the biotechnology industry?
0:12:05 What do we see as the impact that is having already and may have in the longer term to the U.S.-based biotechnology industry?
0:12:12 So I think it’s worth just backing up and saying, you know, how is the biotech industry set up in the first place?
0:12:13 How does this actually work?
0:12:23 So at the very outset, in about the 19th century, there were chemical companies that decided to get into manufacturing chemical drugs, right?
0:12:30 So these were actually dye manufacturers and making dyes for textiles.
0:12:36 And they decided to take their sort of chemical manufacturing and distribution and apply it to making drugs.
0:12:37 You’re talking about the Germans.
0:12:38 That’s right.
0:12:43 And the first drugs were incredibly crude pharmacology, right?
0:12:44 So this was heroin.
0:12:45 This was cocaine.
0:12:46 This was morphine.
0:12:49 Higher retention than dye stuffs, right?
0:12:51 You know, like that’s a pretty interesting business.
0:12:57 But obviously it got a lot more sophisticated and specific over time.
0:13:02 So these groups like Merck, you know, in the 30s, they set up vertically integrated research labs.
0:13:09 So they’re in the business of manufacturing, distributing their medicines, and then also doing internal research to make new products.
0:13:14 And then the FDA comes along, writes a lot of these top pharma companies, predate the FDA.
0:13:17 And modern clinical development is formed.
0:13:24 And so there’s a three-partite component for all these businesses where there is manufacturing, commercialization, and distribution.
0:13:26 There is internal research.
0:13:29 And then there’s trials and clinical development.
0:13:40 What happened is that, as Lotta alluded to, there’s this E-Rooms law in the industry where it’s just gotten exponentially less efficient to do the internal research and clinical development.
0:13:46 And so it literally became the case that it was IRR negative to actually do internal research.
0:13:51 And so these pharma companies divested their internal research and clinical development out.
0:13:56 This is the birth of these clinical research organizations and outsourced organizations in the first place.
0:14:16 And the origin of drug-distubbery startups and biotechs is that we are the lunatics that take on that IRR negative business and say that we’re in the business of making these new hits that could ultimately realistically get bought by pharma companies and become their next armamentarium of drugs.
0:14:19 So that’s kind of the setup, like how biotech works, right?
0:14:20 These companies divested their R&D.
0:14:23 You know, biotech companies are doing that research.
0:14:30 And what happened is that we’re all doing that research with the same discovery technologies, right?
0:14:47 So I’d argue that beyond the modernization of small molecule discovery, the growth of biotech and recombinant DNA technology, we haven’t had, you know, and in subsequent modalities, you know, we haven’t had these huge changes in the fact that people have internal technologies that the rest of the industry isn’t using.
0:14:54 And so like any other technology, when it’s becoming commoditized, you can compete on speed and cost.
0:14:57 And this is where China sort of enters the story, right?
0:15:14 So China has enormous speed advantages in terms of regulation for starting and running human trials and enormous cost advantages in terms of the labor and the sort of work ethic and speed and volume of people that can be put together at these projects.
0:15:25 And so what’s happening is in this sort of loose social contract between big pharma and biotech startups, which are the ones supplying the drugs, biotech startups now make two thirds of the drugs that go to market.
0:15:36 We have this sort of geographic arbitrage where on speed and cost, China can enter the equation and compete for these types of ideas to deliver the medicines.
0:15:42 Yeah, I think what’s interesting is China didn’t start in this place 10 years ago.
0:15:45 10 years ago, no one was talking about going and running their trials in China.
0:15:50 In fact, they were more regulated 10 years ago than the US is now.
0:16:02 And what really happened is like several waves of deregulation that probably about the current modern clinical trial infrastructure that everyone goes to Shanghai for.
0:16:15 And some of the things that they implemented were the implied approval, which is when you file your IND, unless they issue a proactive hold on your IND, it will be default approved in certain days.
0:16:24 The US has the opposite model where you have to proactively approve every document, every IND document that comes to FDA.
0:16:30 They also parallelize the review of different components of that IND.
0:16:38 So you can review CMC section, you can review clinical trial design all in parallel, while in the US you have to review them in stages.
0:16:50 I think what’s even more interesting is this whole model of investigator initiated trials, which is actually what most people go to China for.
0:17:00 The actual CFDA process is, it’s more efficient than in the US, but I don’t know if it’s more efficient than just running trials in New Zealand, Australia.
0:17:08 With investigator initiated trials, your review timelines are cut 5 to 6x.
0:17:17 It’s a very fast process and it’s really specific to new modalities, high risk indications, cell engine therapy, and that’s what everyone goes to China for.
0:17:27 So I think it’s a model of people used to view China as like a need to buy a printer where they print a medicine that would already otherwise exist.
0:17:30 And so yes, I think it’s somewhat outdated view.
0:17:39 They are now leading in vivo CRTs, they are leading in gene editing, in gene therapy, and partially due to this ability to do those investigator initiated trials.
0:17:50 Okay, so given that that’s a reality today, what does that imply for the long-term future of the US biotech industry, right?
0:18:01 Because you can make the argument that if we’re competing on scale, speed, cost, and there’s still a lot of great innovation happening here in the US,
0:18:05 at some point it becomes difficult to fund that innovation here.
0:18:07 We talk about things being IR or negative.
0:18:15 It becomes increasingly difficult to fund that innovation here if we know it’s going to get out-competed over the longer term in China.
0:18:17 So does our industry get hollowed out?
0:18:18 No.
0:18:21 I think the answer is somewhat simple.
0:18:23 I think we have to invent stuff.
0:18:33 So there’s this really good China analyst named Dan Wang who just wrote a really good book on the whole structure between China and the United States right now.
0:18:40 And the one-sentence meme version is that China is an engineering state and America is a lawyer state.
0:18:43 And I think that’s simplistic, right?
0:18:45 That’s not Dan’s full argument.
0:18:49 But we forget that we’re also an incredible inventor state, right?
0:18:54 So some of our founding fathers, we have Benjamin Franklin as one of the US’s founding fathers, right?
0:18:59 We have the American research universities that are the envy of the rest of the world.
0:19:01 We’re incredible at going from zero to one.
0:19:11 And so I think that to compete on this, if you’re talking about something where it’s fast followers and sort of a specific arbitrage opportunity,
0:19:14 that’s super challenging in terms of speed and cost.
0:19:19 If you’re talking about inventing totally new modalities and sort of growing the pie,
0:19:25 things on the scale of the recombinant DNA revolution, which is a fundamentally US thing,
0:19:29 the birth of immunotherapy, which is in Texas, right?
0:19:37 You know, like these are these types of things that I think get us out of it where we have to just invent our way and actually change what’s possible with biotech.
0:19:42 I think when you get those big unlocks, that’s when you start to really deliver value.
0:19:51 So it does shift the risk profile, though, where I think that fundamentally, like, part of where you go is really pushing the boundaries of new modalities.
0:19:54 I spend a lot of time with Michael Fishback, an incredible scientist at Stanford.
0:19:58 We’re always talking about what’s the next big, interesting set of modalities.
0:20:00 What’s the new, interesting mechanism in biology?
0:20:08 And so I think that risk profile shifts and, like, you know, at Amplify, that’s what we always think about, those technical founders, right?
0:20:12 Because it starts to be the case that, you know, it’s like the regenerons of the past, right?
0:20:18 Leonard Schieffer and Georgian Coppolis, these scientists that are actually sort of going to tell us where the future is.
0:20:25 Yeah, I do wonder, though, to what extent, like, look, I think it’s a very compelling argument, obviously, I’m very aligned with that.
0:20:43 The counter argument to that would be that something you said a few minutes ago, there was this nice equilibrium that the industry has had for decades, which is, you know, U.S. biotech invests, sorry, invents, and, you know, global biopharma invests, right?
0:20:55 They in-license it, they acquire it, they partner with it, and that equilibrium basically meant that the inventions that come out of biotech had time to develop.
0:21:00 But now you have a third player in the form of China that could disrupt that equilibrium meaningfully.
0:21:07 So, yes, we can invent the next great modality, but we have to implement it faster, too.
0:21:12 So, effectively, the shelf life of an innovation has gotten far shorter before we deal with competition.
0:21:17 We talked about this a little bit, right, where it sort of, it changes one of the dynamics, which is, like, of secrecy.
0:21:18 Yeah.
0:21:28 In the industry, it’s like, like, secrets become more important, it’s like, when you want that longer time horizon to actually invent something, you probably need to keep it a little bit closer to the chest initially.
0:21:34 I do think that there’s some nuance in terms of how we think about regulation for this, right?
0:21:44 It’s probably not biosecure where you are totally trying to restrict relationships and transactions between the U.S. and China because that’s just sort of net negative.
0:22:00 But if there’s, you know, really direct, fast followers, you know, and like you’re saying, that sort of half-life on an initial invention, there, we might need to rethink, like, what that actually, like, how to extend that horizon for inventions.
0:22:15 Yeah, and sort of to push back with one example, I keep thinking about this professor, Irvin Weissman, who is a legendary sort of cancer biologist and stem cell biologist from Stanford, launched a few companies into existence five years ago.
0:22:26 So, he published a new paper on the new cancer targeting mechanism, which is what ultimately we think that U.S. biotech should differentiate themselves on, is, like, new biology, new modalities.
0:22:40 Immediately started a company, well-funded, they developed the asset, were taking it to the clinic, and before they were able to initiate their clinical trials, Chinese biotech, with the same identical mechanism,
0:22:45 bids onto the clinic, and launch trials, not only in China, but also in the A.S.
0:22:48 So, I think, I do think that secrecy really matters.
0:22:56 I think many people are starting to ask themselves whether they want to present at conferences, whether they want to publish papers, whether they want to file patents,
0:23:01 which wasn’t the case when Genentech was created.
0:23:05 Back then, people were just, like, putting everything out there for everyone to see and share.
0:23:15 Yeah, that’s a real negative externality, right, in terms of just the mean substrate of communicating science, of open source, of these sort of, this proliferation of technologies.
0:23:18 A lot of technologies are built on other technologies.
0:23:25 And if everybody has to keep it closer to the chest for that exact reason, that, like, your time to actually commercialize your own invention shrinks,
0:23:29 that’s probably one of the, that’s, like, a consequence of this trend that we have to think about.
0:23:38 So, speaking of inventions, one of the great inventions that we’ve seen over the course of the last few years is the rise of artificial intelligence.
0:23:46 Artificial intelligence is obviously being heralded as being transformative across a broad range of industries, a broad range of applications,
0:23:52 creating new experiences, new consumer products, all kinds of things.
0:23:55 Lots of use cases that we would have never imagined.
0:24:02 Where do you view the impact of AI when it comes to developing new drugs?
0:24:09 How is that going to, is that going to, is that the invention that actually makes biotech competitive again,
0:24:15 that makes biotech an investable, an investable asset again?
0:24:20 Yeah, I think it’s sort of not a question of whether AI will be useful.
0:24:24 I’m definitely in the camp of, like, everyone will be using AI in biotech industry five years from now.
0:24:26 For me, it’s more of a question,
0:24:33 can it take $2.5 billion to approve a drug and make it into $500 million to approve a drug?
0:24:36 Can it make it, like, 4x more efficient in terms of timeline?
0:24:38 And I think to answer that question,
0:24:43 we really should go back and look where most of that money is spent right now.
0:24:45 And most of it’s spent is not in preclinical stage.
0:24:49 It’s spent in validating safety and efficacy in humans.
0:24:54 It’s spent on commercialization stage, which is what happens after phase three clinical trials.
0:24:59 And I think so far, and maybe Elliot’s take would be different from mine,
0:25:06 a lot of the efforts that we are seeing are concentrated on the preclinical stage of doing toxicity.
0:25:11 Can we make toxicity studies in mice much faster?
0:25:17 Can we have in silica talks for cell lines?
0:25:22 And a lot of those things are valuable, but they don’t necessarily bridge the gap.
0:25:28 And they don’t necessarily improve the failure rate of clinical trials,
0:25:32 which is like the highest failure rate right now is phase two, which is efficacy.
0:25:42 So if we were, if I started to see those same companies use and generate more human data to apply to those models,
0:25:44 I think I would become more optimistic.
0:25:55 But I actually think many big bio problems and open questions will be solved with AI way before we can predict efficacy for drugs.
0:25:59 But I think virtual cells is actually not that far out.
0:26:02 I think we have all the necessary data to generate something like that.
0:26:07 The question is like, how can we make it useful for predicting efficacy of drugs in humans?
0:26:11 Yeah, I mean, I think Lott is right.
0:26:15 The answer is probably unequivocally yes.
0:26:23 It’s like it’s becoming consensus that AI is a pretty important new experimental tool for biology and biologists, right?
0:26:25 Just as software was.
0:26:39 The way that I like to evaluate where it’s useful right now is if you sort of break down like the three horsemen of Eeroom’s law of the time and cost of clinical development, right?
0:26:42 That’s an enormous pillar that’s sort of hard to tackle.
0:26:47 We sort of talked about that of tech, probably software 1.0 can help a lot there.
0:26:49 Regulation, culture.
0:26:58 One layer deeper, you have the challenge of phase two failure, which is basically a readout on the fact that we don’t understand biology, right?
0:27:00 And we can’t predict what’s going to work and what’s not going to work with high fidelity.
0:27:06 Yeah, so sort of efficacy prediction on net new targets or sort of hypotheses for a mechanism.
0:27:15 And then the third being that there’s an enormous amount of interest in ideas that we have, but we actually can’t express those ideas in molecules and sort of make new drugs.
0:27:20 So that’s sort of a third pillar of why it’s really hard to make exciting new medicines.
0:27:30 For all the reasons we talked about, it’s sort of hard to dramatically change the time and cost if you’re doing preclinical discovery when it comes to clinical development.
0:27:44 But when it comes to making things that are otherwise impossible medicines, so either finding really interesting new targets and having higher confidence in predicting the efficacy of a drug, there are some exciting directions.
0:28:07 And then especially when it comes to expressing ideas in molecules, I think that a lot of these platforms and capabilities to take new interesting data sets, to take what’s coming out of molecular machine learning, and just make things that are, you know, unequivocally impossible to make without these tools, make some really, really exciting medicines.
0:28:10 And so I think that, like, the ambition of a TPP will go up.
0:28:17 I think that’s also really important, right, because, like, the portion of Eroom’s Law is predicated on the better-than-the-Beatles problem.
0:28:24 We keep adding to this ornamentarium of medicines that we have, and that stacks up and up and up over time.
0:28:27 We’re sort of continually in pursuit of beautiful medicines.
0:28:40 And to actually make something more beautiful and more potent, at this stage of the game, the alpha probably is in really interesting data sets, new modeling tools and capabilities.
0:28:45 And I’m really excited about the sort of categories of medicines that could be made with this type of approach.
0:28:49 So just let’s talk about the three horsemen of Eroom’s Law.
0:28:52 I like this, I like this, the way you’ve framed it.
0:28:57 The structural problem we have with getting drugs approved is one, it’s a big horse, totally.
0:29:04 The lack of understanding of biology and the ability to predict efficacy seems like a very big horse.
0:29:10 Is the ability to, are we design limited in our ability to make molecules?
0:29:12 Is that a big horse or is that a pony?
0:29:17 You know, I mean, I think it’s pretty substantial, right?
0:29:20 So we’ve gotten really good at making monoclonals, right?
0:29:24 We’ve been making monoclonal antibodies for 50 years, half a century.
0:29:32 And we’ve got exquisitely good experimental tools for sort of panning and finding these types of molecules.
0:29:37 And so if you sort of have a specific, no-nonsense target,
0:29:43 but if you’re talking about a really complex poly-specific molecule that’s hitting multiple components,
0:29:47 you know, you see the beauty of multivalency with PD-1-VEGF.
0:29:56 What else is out there that is like two, maybe three interactions that exquisitely tunes the immune system or the state of a cancer?
0:30:01 As we learn more about moving cells around on their sort of manifolds of different cell states,
0:30:10 I think that there actually are target product profiles and medicines that we just genuinely can’t get to with our existing discovery technologies
0:30:13 that could be really, really big products, right?
0:30:19 So you see this like very low-hanging fruit, like without this tool, combination of PD-1-VEGF,
0:30:21 and like that’s beating Keytruda, right?
0:30:25 Like that’s like this enormous step change.
0:30:33 And so our ability to sort of potentially get those types of results from these models seems possible to me.
0:30:40 Yeah, I think there are many targets on pharma’s most-wanted list that have been on that list for many decades now.
0:30:45 I mean, targets like top 53 that so many companies tried to target,
0:30:48 and no one really managed to get the molecules that really works,
0:30:51 or like it worked, but the target product profile wasn’t desirable.
0:30:57 Yeah, I think until we clear out that list,
0:31:04 there’s definitely a bottleneck on the ability to design certain therapeutic modalities.
0:31:08 The question is like, do we always want to have an oral for every antibodies that exist out there?
0:31:10 Maybe antibodies are actually not that bad.
0:31:19 I think the whole GLP-1 story definitely altered my perception of how much people are willing to do like injectable drugs on themselves.
0:31:24 Yeah, if you’re like every quarter you take an injection,
0:31:29 and you don’t have to think about it, you don’t have to sort of adherence risk of a small molecule,
0:31:34 I think some of that is totally right, that that’s not always the canonical rule of thumb.
0:31:38 Yeah, it’s also true that new modalities often struggled,
0:31:43 even though they were better in some shape or form.
0:31:45 I mean, the whole generation of CRISPR companies,
0:31:50 I don’t think we’ve really seen through the CRISPR dream quite yet.
0:31:54 Ten years ago, when the first CRISPR companies were launched,
0:32:00 the dream was really we will develop a tailored gene editor for every rare disease that exists out there.
0:32:03 And it’s partially bottlenecked by the regulation.
0:32:07 But even for well-known targets like PCSK9,
0:32:11 the question is like, do you really want to do a gene editor?
0:32:17 Is a ripasta or inclination run actually good enough?
0:32:24 So I think many new modalities will be bottlenecked by the Beatles problem.
0:32:28 Yeah, it is a fascinating thing because the armamentarium, as you were saying,
0:32:35 has gotten like so large that now you’re really slicing at specific patient preferences, right?
0:32:38 Someone may prefer an oral to a weekly injection.
0:32:44 Someone may prefer an infusion, you know, once a year to, you know,
0:32:46 taking an injection every month, that kind of thing.
0:32:52 It starts to get very challenging to tease out which product profile wins in the market.
0:32:56 This is where I’m sort of an unequivocal platform bull still,
0:33:01 is that I think like one of the most exciting opportunities for AI and biology
0:33:07 is this sort of world that we’re going to where the platform is the product, right?
0:33:10 So if you think of like Moderna’s and BioLintex cancer vaccines,
0:33:16 this is a product that’s in clinical trials where it is part next generation sequencing,
0:33:19 part AI and machining.
0:33:22 There’s actually a neural network that processes the sequencing data.
0:33:26 And then there is a specific mRNA cancer vaccine designed for that patient.
0:33:26 Right.
0:33:29 And so in that case, it’s hard to even disentangle.
0:33:33 Like it totally breaks our conception of what the target is, what the drug is.
0:33:37 The drug is, you know, part information product, part diagnostic.
0:33:42 And that in theory, that type of really personalized approach
0:33:46 could sort of open up and actually be a total pan indication solution.
0:33:48 Again, in the limit.
0:33:50 And so one of the really exciting things is like
0:33:56 if you have a fundamentally generative platform that is the product,
0:34:00 does that open up a much wider indication base?
0:34:01 Yeah.
0:34:04 So speaking of indication bases, big indications,
0:34:07 you talked about GOP-1s.
0:34:09 Obviously, one of the most extraordinary drugs
0:34:11 in terms of the impact it’s had on the broader society
0:34:13 that we’ve seen in a very, very long time.
0:34:16 It’s also a drug that, you know, if we talk about,
0:34:22 you know, health span, longevity, all of these things,
0:34:24 it might actually be bending the trend, right?
0:34:26 In terms of some of these chronic diseases,
0:34:29 whether it’s metabolic disorders, obesity, and the like.
0:34:34 You, in your piece on the, where are the trillion dollar biotechs,
0:34:38 talk a little bit about, well, where will the next big wins
0:34:39 for the industry come from?
0:34:41 And you talk about, you know, the genetic, you know,
0:34:43 finding diseases based on genetic basis,
0:34:46 rare diseases where we can have a big impact.
0:34:49 You have a couple of other examples,
0:34:51 but really, if I’ve read your conclusion correctly,
0:34:54 where you land is the real big nut to crack for the industry
0:34:57 is to go after the disease of aging
0:35:00 and to solve, to the extent that we can, longevity.
0:35:05 What’s your view on what the industry is doing right
0:35:07 and where the industry is still lacking
0:35:10 when it comes to all things aging and longevity?
0:35:12 Yeah, I think, unfortunately,
0:35:15 the incentive to develop aging drugs
0:35:17 is still not quite there
0:35:20 because if you look at the U.S. payer system,
0:35:29 the payer that pays the most for diseases of age population
0:35:33 is Medicare, which kicks in after 65.
0:35:36 Before then, we have a multi-payer system
0:35:41 where patients tend to rotate their insurance every few years.
0:35:45 And so there’s really not much incentive
0:35:49 for someone to cover preventative medicine early in life.
0:35:52 And once you hit 65, it’s no longer preventative medicine.
0:35:56 It’s treating the disease itself.
0:36:01 But I think a lot of it would be downstream of fixing
0:36:03 how we pay for aging drugs,
0:36:04 how we pay for preventative care.
0:36:08 I don’t know if we have a way to do that now,
0:36:10 not for chronic medicines,
0:36:13 especially not for one-and-done solutions
0:36:15 to age-related diseases.
0:36:19 I do, I am very excited about GLP-1s.
0:36:22 I’m probably not the first person in aging space
0:36:24 to say that maybe GLP-1s would be
0:36:26 one of the first aging drugs.
0:36:27 I think this month,
0:36:30 Lily is reading out their semaglutide
0:36:31 in Alzheimer’s trial,
0:36:33 which is, to me, is a real test
0:36:34 of whether it’s an aging drug or not
0:36:36 because it’s well outside of
0:36:38 the metabolic spectrum of diseases.
0:36:43 And, yeah, at the same time,
0:36:48 Medicare refused to cover GLP-1s for obesity care.
0:36:52 So can we cover GLP-1s for aging diseases?
0:36:53 It’s not super clear to me.
0:36:55 I do think there’s one
0:36:57 interesting component of incentives
0:36:58 where it’s like a,
0:37:00 it’s like a commercial better than the Beatles,
0:37:04 where once you have such an enormous revenue
0:37:07 in sort of sales generation from a product,
0:37:09 you’re going to fight like hell
0:37:11 to have something in your pipeline
0:37:13 that could potentially replace that.
0:37:14 And so it does have this,
0:37:17 this property of sort of driving
0:37:19 and sort of pulling on the ambitions
0:37:20 of the industry where it’s,
0:37:20 you know,
0:37:22 people are honestly thinking it
0:37:23 at Lillian Novo,
0:37:24 like what is it going to take
0:37:28 to actually fill the patent window?
0:37:29 What’s the Act 2?
0:37:30 What’s Act 2?
0:37:30 Yeah.
0:37:32 And I think that, you know,
0:37:34 there is a component where it’s,
0:37:34 it’s these,
0:37:36 it has to be something
0:37:38 that is a large enough indication
0:37:41 to take that vision seriously.
0:37:42 Yeah, I think it’s a good,
0:37:43 I mean, it’s a great argument
0:37:46 that GLP-1s have really achieved
0:37:47 two important things.
0:37:48 It’s, you know,
0:37:51 potentially given us ways
0:37:52 to treat some of the most endemic,
0:37:54 challenging conditions
0:37:54 that affect society,
0:37:55 whether it’s metabolism
0:37:58 and obesity and other factors.
0:38:00 And the second one is
0:38:00 at some level,
0:38:01 it’s given the industry
0:38:02 its mojo back, right?
0:38:03 To go after big problems,
0:38:05 to go after the big indications,
0:38:06 to find the Act 2s,
0:38:08 because you’re absolutely right.
0:38:10 You’re, you’re going to have to replace
0:38:12 at some point, you know,
0:38:14 this product with the next big idea.
0:38:16 I mean, Alex Telford’s
0:38:17 written a great piece on this,
0:38:19 of the sort of cyclicality
0:38:20 of trends of what produces
0:38:21 a blockbuster, right?
0:38:23 Where you had like the Lipitor era
0:38:25 of these enormous pills
0:38:26 for big indications
0:38:28 that were phenomenal products.
0:38:30 And then we sort of moved
0:38:32 into this era of specialty medicines,
0:38:34 the birth of biologics,
0:38:37 very, very big focus on rare diseases.
0:38:38 So sort of making up
0:38:39 for smaller population sizes
0:38:41 with larger price tags.
0:38:42 And I think in terms of
0:38:44 the general pressure on pricing,
0:38:46 plus the sort of carrot
0:38:48 of the success of GLP-1,
0:38:50 there is this big swing
0:38:51 back into big indications.
0:38:52 And potentially,
0:38:53 there’s such big indications
0:38:55 that you have to take
0:38:56 being a direct-to-consumer
0:38:58 business really seriously.
0:38:59 Where you think of,
0:39:00 you know,
0:39:00 Lily Direct,
0:39:02 where these are so big
0:39:03 that you’d actually
0:39:04 break the payer system
0:39:05 if you were distributing
0:39:07 something for weight
0:39:09 metabolism for aging,
0:39:11 where the sort of arc
0:39:12 of our business model,
0:39:13 I did not expect this year
0:39:14 to have John Merrick-Nore
0:39:17 talking about a direct-to-consumer
0:39:18 biotech company.
0:39:19 But that’s kind of where
0:39:20 the vibes are right now,
0:39:21 which is actually exciting.
0:39:23 Where is this,
0:39:24 what is the state of the science
0:39:26 when it comes to aging?
0:39:28 Because you talk about
0:39:31 some of the structural challenges
0:39:33 in terms of reimbursement
0:39:34 and maybe even regulatory.
0:39:36 But let’s go all the way
0:39:36 back to the beginning.
0:39:38 What is the state
0:39:39 of the science
0:39:39 when it comes?
0:39:41 Like, do we understand
0:39:42 what aging is?
0:39:44 And do we have credible theories
0:39:45 as to how to intervene?
0:39:48 Yeah, I wouldn’t say
0:39:49 we know what aging is
0:39:50 or even how to measure it
0:39:51 because if we had a way
0:39:52 to measure it,
0:39:54 we would have clinical trials
0:39:56 run based on surrogate endpoints
0:39:57 and maybe we would have
0:39:58 multiple aging clinical trials
0:39:59 run in parallel.
0:40:00 Right now,
0:40:02 the way we approve
0:40:04 or like move in the direction
0:40:05 of approving aging medicine
0:40:07 is we’re on multiple trials
0:40:08 for multiple diseases
0:40:09 the way we are doing
0:40:10 with GLP-1s
0:40:11 and then we are sort of
0:40:12 deriving the conclusion
0:40:13 that, well,
0:40:14 maybe if it delays onset
0:40:16 of multiple diseases
0:40:16 at the same time,
0:40:17 maybe it’s an aging drug.
0:40:19 I do think regulation
0:40:20 is lagging behind science.
0:40:22 I think we have multiple drugs
0:40:23 that extend lifespan
0:40:25 in mice and monkeys
0:40:26 that have never been tested
0:40:28 for lifespan indications
0:40:29 in humans.
0:40:32 I think some companies
0:40:33 are doing very exciting
0:40:34 regulatory groundwork,
0:40:35 companies like Loyal
0:40:38 where maybe for the first time
0:40:38 we would have
0:40:40 an aging drug approved
0:40:41 for dogs
0:40:42 and maybe it’s not
0:40:43 that far out
0:40:43 for approving
0:40:44 the first aging drug
0:40:45 for humans.
0:40:49 I think we would be able
0:40:50 to treat aging
0:40:51 before we understand
0:40:51 how to measure it
0:40:52 or what it is
0:40:53 or why it happens.
0:40:56 And the way I really view
0:40:59 sort of the future
0:41:01 reveling of lifespan drug
0:41:03 is it should come
0:41:04 in several waves
0:41:05 where the first wave
0:41:07 is sort of small effect sizes,
0:41:08 very established
0:41:09 therapeutic modalities,
0:41:10 small molecules.
0:41:11 If it’s preventative medicine
0:41:12 and it has to be
0:41:13 squeaky clean,
0:41:14 very safe
0:41:15 because if you’re preventing
0:41:16 some future disease
0:41:17 there is no room
0:41:20 for side effects.
0:41:21 after that
0:41:23 we would have
0:41:25 more exciting
0:41:26 therapeutic modalities,
0:41:27 maybe genetic editors,
0:41:28 gene editors,
0:41:30 maybe gene therapies
0:41:31 and
0:41:34 as we progress
0:41:35 it will only get
0:41:36 I think the variance
0:41:36 of medicine
0:41:37 and
0:41:39 therapeutic modalities
0:41:40 that we apply to aging
0:41:41 will increase
0:41:42 over time.
0:41:44 If you guys
0:41:45 could wave a magic wand
0:41:46 what would be
0:41:47 the
0:41:50 aging stack
0:41:51 that you take
0:41:51 every day?
0:41:52 You know,
0:41:53 like you have
0:41:54 the Brian Johnson
0:41:55 blueprint
0:41:57 don’t die
0:41:58 sort of protocol.
0:41:59 In your mind
0:42:00 what should
0:42:01 the average person
0:42:02 be thinking about
0:42:03 that they should be
0:42:04 taking on a regular,
0:42:05 maybe not every day,
0:42:06 on a regular basis?
0:42:07 It does seem like
0:42:09 we kind of hit a point
0:42:09 where
0:42:10 actually like
0:42:11 our generation
0:42:12 shouldn’t die
0:42:13 of heart attacks.
0:42:13 It seems like
0:42:14 we have everything
0:42:15 to prevent
0:42:16 high cholesterol
0:42:17 on people from
0:42:18 like you can take
0:42:18 antibodies,
0:42:20 sRNAs,
0:42:21 small molecules,
0:42:22 sunjin editors,
0:42:23 we have full stack
0:42:24 for that
0:42:26 and heart attacks
0:42:27 is I think
0:42:28 the primary cause
0:42:29 of death
0:42:29 in the United States
0:42:30 it’s like
0:42:31 people die around
0:42:32 72
0:42:34 remove that
0:42:36 people would
0:42:37 start living
0:42:37 to 75
0:42:38 maybe 80
0:42:39 I think
0:42:40 a good benchmark
0:42:41 is Japan
0:42:41 because in Japan
0:42:42 people don’t die
0:42:43 of heart attacks
0:42:43 they die of cancer
0:42:45 and I think
0:42:45 the median lifespan
0:42:46 there is close
0:42:47 to 80
0:42:48 so that’s
0:42:48 plus 10
0:42:48 years to
0:42:49 lifespan
0:42:50 I think
0:42:51 most of the
0:42:52 things would be
0:42:53 the things that
0:42:53 are already
0:42:54 approved
0:42:54 GLP-1s is
0:42:55 obviously
0:42:55 a big
0:42:56 big one
0:42:57 like the only
0:42:58 documented effect
0:42:59 for lifespan
0:42:59 that we have
0:43:00 in monkeys
0:43:01 is caloric
0:43:02 restriction
0:43:03 we know
0:43:03 we know
0:43:03 that
0:43:04 we can
0:43:05 add
0:43:05 about
0:43:06 two
0:43:06 and a half
0:43:07 years
0:43:07 to
0:43:08 25
0:43:08 year
0:43:08 median
0:43:09 lifespan
0:43:09 in monkeys
0:43:10 by
0:43:10 calorically
0:43:11 restricting
0:43:11 them
0:43:12 it really
0:43:12 depends on
0:43:13 the controls
0:43:13 that you use
0:43:13 in your
0:43:14 study
0:43:14 I think
0:43:14 there were
0:43:15 like two
0:43:15 big studies
0:43:16 that were
0:43:16 run
0:43:17 one of
0:43:17 them
0:43:17 used
0:43:18 monkeys
0:43:18 on
0:43:18 high-fat
0:43:18 diet
0:43:19 this controls
0:43:19 the other
0:43:19 one used
0:43:20 healthy monkeys
0:43:21 and if you
0:43:21 compare it
0:43:22 to healthy monkeys
0:43:23 caloric restriction
0:43:24 doesn’t add
0:43:24 that much
0:43:25 but I think
0:43:26 if you live
0:43:27 in the United
0:43:27 States
0:43:27 you’re likely
0:43:28 a monkey
0:43:28 on a
0:43:28 Western
0:43:29 diet
0:43:30 so I
0:43:30 think
0:43:31 GLP-1s
0:43:31 will be
0:43:31 broadly
0:43:32 impactful
0:43:32 for
0:43:33 everyone
0:43:34 all right
0:43:35 so magic
0:43:35 wand
0:43:35 you put
0:43:36 Lipitor
0:43:37 and GLP-1
0:43:37 in the
0:43:37 water
0:43:38 PCSK9
0:43:39 inhibitors
0:43:40 and
0:43:41 GLP-1s
0:43:41 in the
0:43:41 water
0:43:41 okay
0:43:42 I think
0:43:42 it’s
0:43:42 I think
0:43:42 it’s
0:43:42 like
0:43:42 the
0:43:43 104
0:43:43 year
0:43:43 old
0:43:44 lady
0:43:44 who’s
0:43:44 like
0:43:44 it’s
0:43:44 just
0:43:45 one
0:43:45 cigarette
0:43:46 a
0:43:46 day
0:43:46 and
0:43:46 like
0:43:47 one
0:43:47 piece
0:43:47 of
0:43:48 chocolate
0:43:49 no
0:43:49 I
0:43:50 think
0:43:50 it
0:43:51 seems
0:43:51 to be
0:43:51 the
0:43:51 case
0:43:51 that
0:43:52 caloric
0:43:52 restriction
0:43:52 is
0:43:53 important
0:43:53 right
0:43:55 you know
0:43:56 moving
0:43:57 we’re a
0:43:57 very sedentary
0:43:58 society
0:43:58 so I
0:43:58 think
0:43:59 there’s
0:43:59 just a
0:43:59 lot
0:43:59 of
0:44:00 benefits
0:44:01 in
0:44:01 just
0:44:01 being
0:44:02 active
0:44:03 and
0:44:03 it’s
0:44:04 interesting
0:44:04 just
0:44:04 seeing
0:44:05 the
0:44:05 level
0:44:06 of
0:44:07 personal
0:44:07 health
0:44:08 monitoring
0:44:09 downstream
0:44:10 of
0:44:10 whatever
0:44:11 Brian
0:44:11 Johnson’s
0:44:12 sort of
0:44:12 cultural
0:44:12 movement
0:44:13 is
0:44:14 of
0:44:14 you know
0:44:15 people
0:44:15 actually
0:44:16 doing
0:44:16 a lot
0:44:17 of
0:44:17 longitudinal
0:44:18 self
0:44:18 measurement
0:44:19 doing
0:44:19 blood
0:44:20 work
0:44:21 being
0:44:21 more
0:44:22 proactive
0:44:22 in their
0:44:23 care
0:44:25 and
0:44:25 then
0:44:25 I
0:44:25 think
0:44:26 you know
0:44:26 even
0:44:26 being
0:44:26 more
0:44:27 proactive
0:44:27 in
0:44:28 cancer
0:44:28 care
0:44:28 right
0:44:29 early
0:44:29 stage
0:44:30 screening
0:44:30 and having
0:44:31 types
0:44:31 of
0:44:31 medicines
0:44:31 that
0:44:31 have
0:44:32 the
0:44:32 right
0:44:32 risk
0:44:32 profile
0:44:32 to
0:44:33 actually
0:44:33 dose
0:44:33 people
0:44:34 with
0:44:34 if
0:44:34 you
0:44:34 are
0:44:35 able
0:44:35 to
0:44:35 detect
0:44:36 extremely
0:44:36 early
0:44:36 stage
0:44:37 cancer
0:44:38 right
0:44:38 now
0:44:38 it’s
0:44:38 just
0:44:39 an
0:44:39 ethical
0:44:40 question
0:44:40 if
0:44:40 you
0:44:40 know
0:44:40 for
0:44:41 our
0:44:42 fairly
0:44:42 barbaric
0:44:43 approaches
0:44:43 to
0:44:44 to
0:44:45 cancer
0:44:45 care
0:44:46 that’s
0:44:46 actually
0:44:46 a
0:44:47 meaningful
0:44:48 ROI
0:44:49 whereas
0:44:50 if
0:44:53 for
0:44:54 longevity
0:44:54 and
0:44:54 health
0:44:55 speaking
0:44:55 of
0:44:55 magic
0:44:56 wands
0:44:56 we
0:44:56 spent a lot
0:44:57 of time
0:44:57 talking about
0:44:58 the industry
0:44:59 and some
0:44:59 of the
0:45:00 challenges
0:45:02 that I think
0:45:02 the industry
0:45:02 faces in getting
0:45:03 drugs to
0:45:03 patients
0:45:04 if you had a
0:45:05 magic wand
0:45:07 and you
0:45:08 could change
0:45:09 something around
0:45:09 the regulatory
0:45:11 environment
0:45:12 you could change
0:45:12 something around
0:45:14 the sort of
0:45:15 laws of physics
0:45:16 of the industry
0:45:17 what would
0:45:17 those be
0:45:19 I think
0:45:21 sort of
0:45:22 two being
0:45:23 the cost
0:45:24 per patient
0:45:24 per trial
0:45:25 that should
0:45:25 almost be a
0:45:26 stat
0:45:26 that the FDA
0:45:27 cares about
0:45:28 right
0:45:28 so when
0:45:29 George Yonkopoulos
0:45:30 started Regeneron
0:45:31 it cost about
0:45:31 $10,000
0:45:33 per patient
0:45:33 in trial
0:45:34 that’s
0:45:34 ballooned
0:45:35 to $500,000
0:45:37 there is
0:45:37 no law
0:45:38 of physics
0:45:39 that requires
0:45:39 it to be
0:45:40 $500,000
0:45:41 in terms of
0:45:41 complexity
0:45:42 and cost
0:45:42 to dose
0:45:43 a patient
0:45:43 in a trial
0:45:45 if we want
0:45:45 to see
0:45:45 the next
0:45:46 Regeneron
0:45:47 we want
0:45:47 to
0:45:48 meaningfully
0:45:48 care
0:45:48 about
0:45:48 that
0:45:48 as
0:45:49 sort
0:45:49 of
0:45:49 a
0:45:49 KPI
0:45:49 for
0:45:50 regulation
0:45:50 for
0:45:51 industry
0:45:52 I also
0:45:53 think
0:45:53 like
0:45:53 we
0:45:53 talked
0:46:18 why
0:46:18 why
0:46:18 do
0:46:18 we
0:46:18 not
0:46:19 have
0:46:19 investigator
0:46:20 initiated
0:46:20 trials
0:46:20 for
0:46:20 cell
0:46:21 and
0:46:21 gene
0:46:21 therapy
0:46:22 in the US
0:46:23 if there is
0:46:24 this distinction
0:46:25 of them being
0:46:26 the engineering
0:46:26 state
0:46:27 and us being
0:46:27 the lawyer
0:46:28 state
0:46:28 we should
0:46:29 actually
0:46:29 sort of
0:46:29 say
0:46:29 let’s
0:46:31 win on
0:46:31 regulatory
0:46:32 innovation
0:46:32 let’s be
0:46:33 really creative
0:46:34 in terms of
0:46:34 the way
0:46:35 that we
0:46:35 actually regulate
0:46:36 this industry
0:46:36 we should
0:46:37 still be
0:46:38 the beacon
0:46:38 of where
0:46:39 people do
0:46:40 their clinical
0:46:40 development
0:46:41 and where
0:46:42 trials are
0:46:42 approved
0:46:43 well the
0:46:43 reality is
0:46:43 that
0:46:45 even if
0:46:45 you run
0:46:45 your
0:46:45 trials
0:46:46 in China
0:46:46 you still
0:46:46 have to
0:46:47 come back
0:46:47 to the US
0:46:48 even for
0:46:48 Chinese
0:46:49 companies
0:46:49 that run
0:46:49 their
0:46:50 trials
0:46:50 in China
0:46:51 they all
0:46:52 come back
0:46:52 to the US
0:46:53 because that’s
0:46:53 the biggest
0:46:53 market
0:46:54 and even
0:46:55 if
0:46:55 Chinese
0:46:56 population
0:46:56 will continue
0:46:57 to grow
0:46:57 US will
0:46:58 still
0:46:58 remain
0:46:59 the biggest
0:46:59 market
0:47:00 for
0:47:00 biotech
0:47:01 companies
0:47:01 to exit
0:47:01 at
0:47:02 there is
0:47:02 no
0:47:03 Chinese
0:47:03 pharma
0:47:04 that
0:47:04 people
0:47:04 are
0:47:05 selling
0:47:05 to
0:47:06 all
0:47:06 the
0:47:06 farmers
0:47:06 that
0:47:06 are
0:47:07 buying
0:47:07 Chinese
0:47:07 assets
0:47:07 are
0:47:08 US
0:47:08 farmers
0:47:09 European
0:47:09 farmers
0:47:09 that
0:47:10 would
0:47:10 then
0:47:11 go
0:47:11 and
0:47:11 run
0:47:11 those
0:47:11 trials
0:47:12 here
0:47:12 so
0:47:13 yeah
0:47:13 I don’t
0:47:13 think
0:47:13 we need
0:47:14 to
0:47:14 solve
0:47:14 that
0:47:15 question
0:47:15 because
0:47:16 eventually
0:47:17 everyone
0:47:17 will be
0:47:17 running
0:47:18 trials
0:47:18 here
0:47:18 either
0:47:18 way
0:47:21 my
0:47:21 magic
0:47:21 wand
0:47:22 I
0:47:23 would
0:47:24 cast a
0:47:24 spell
0:47:25 and
0:47:25 ask
0:47:25 for
0:47:26 something
0:47:26 that
0:47:27 would
0:47:28 be a
0:47:28 version
0:47:28 of
0:47:29 orphan
0:47:29 drug
0:47:30 designation
0:47:31 but
0:47:31 for
0:47:31 common
0:47:32 diseases
0:47:33 orphan
0:47:34 drug
0:47:34 act
0:47:35 I
0:47:35 think
0:47:35 was
0:47:35 enacted
0:47:36 around
0:47:37 1980s
0:47:38 before
0:47:38 then
0:47:38 we
0:47:39 had
0:47:39 less
0:47:39 than
0:47:40 40
0:47:41 approved
0:47:41 orphan
0:47:41 drugs
0:47:43 for
0:47:44 patients
0:47:44 that
0:47:45 have
0:47:45 a
0:47:46 population
0:47:46 of
0:47:46 less
0:47:46 than
0:47:47 10,000
0:47:47 patients
0:47:49 and
0:47:49 today
0:47:50 50%
0:47:50 of
0:47:51 drugs
0:47:51 approved
0:47:51 in
0:47:51 2024
0:47:52 were
0:47:53 all
0:47:53 orphan
0:47:53 drugs
0:47:54 so
0:47:54 orphan
0:47:55 drugs
0:47:55 for
0:47:56 very
0:47:56 small
0:47:57 population
0:47:58 and
0:47:58 I
0:47:58 think
0:47:59 we
0:47:59 are
0:48:01 at
0:48:01 the
0:48:02 stage
0:48:02 of
0:48:04 biotech
0:48:04 development
0:48:05 where
0:48:06 we
0:48:07 really
0:48:07 need
0:48:08 something
0:48:08 like
0:48:08 that
0:48:08 to
0:48:10 incentivize
0:48:10 development
0:48:11 of
0:48:12 drugs
0:48:12 for
0:48:13 age-related
0:48:14 diseases
0:48:14 or for
0:48:14 longevity
0:48:15 itself
0:48:17 right now
0:48:19 age-related
0:48:20 indications
0:48:20 have some
0:48:20 of the
0:48:21 highest
0:48:21 failure
0:48:22 rates
0:48:22 in terms
0:48:22 of
0:48:23 drug
0:48:23 development
0:48:24 because
0:48:24 there are
0:48:25 no
0:48:25 genetic
0:48:26 drug
0:48:27 variants
0:48:28 the process
0:48:29 is much
0:48:29 longer
0:48:30 the trials
0:48:30 are way
0:48:30 more
0:48:31 expensive
0:48:32 so
0:48:32 we
0:48:32 need
0:48:33 some
0:48:33 kinds
0:48:33 of
0:48:34 those
0:48:34 incentives
0:48:34 to
0:48:36 make
0:48:36 sure
0:48:37 that
0:48:37 more
0:48:37 biotech
0:48:38 companies
0:48:38 go
0:48:39 and
0:48:39 develop
0:48:39 drugs
0:48:39 for
0:48:40 cancer
0:48:41 where
0:48:41 phase
0:48:42 ones
0:48:43 kill
0:48:43 half
0:48:43 of
0:48:43 the
0:48:44 companies
0:48:44 at
0:48:45 early
0:48:45 stages
0:48:46 so
0:48:46 yeah
0:48:48 it’s
0:48:48 an
0:48:48 interesting
0:48:49 concept
0:48:49 that
0:48:49 an
0:48:49 orphan
0:48:51 drug
0:48:52 designation
0:48:52 for
0:48:53 more
0:48:53 chronic
0:48:53 disease
0:48:54 yeah
0:48:54 yeah
0:48:55 the
0:48:55 original
0:48:56 spirit
0:48:56 of the
0:48:56 orphan
0:48:56 drug
0:48:56 of
0:48:57 course
0:48:57 was
0:48:57 to
0:48:57 create
0:48:57 an
0:48:58 incentive
0:48:58 for
0:48:58 people
0:48:58 to
0:48:59 develop
0:49:00 drugs
0:49:00 that
0:49:00 face
0:49:00 small
0:49:01 populations
0:49:01 because
0:49:01 maybe
0:49:02 the
0:49:02 market
0:49:02 potential
0:49:02 is
0:49:03 there
0:49:03 but
0:49:04 in
0:49:04 this
0:49:04 case
0:49:04 for
0:49:05 chronic
0:49:05 diseases
0:49:05 the
0:49:05 market
0:49:05 potential
0:49:06 is
0:49:06 enormous
0:49:09 and so
0:49:10 in
0:49:11 your
0:49:11 mind
0:49:11 the
0:49:11 thing
0:49:11 that
0:49:11 needs
0:49:12 the
0:49:12 most
0:49:12 incentive
0:49:13 is
0:49:13 to
0:49:14 to
0:49:14 incent
0:49:15 companies
0:49:15 to
0:49:16 go
0:49:16 through
0:49:16 the
0:49:16 difficult
0:49:17 development
0:49:17 process
0:49:18 because
0:49:18 the
0:49:18 failure
0:49:18 rates
0:49:18 high
0:49:19 yes
0:49:20 I
0:49:20 think
0:49:21 there
0:49:21 is
0:49:21 a
0:49:22 little
0:49:22 bit
0:49:22 of
0:49:23 disconnect
0:49:23 when
0:49:23 like
0:49:24 what
0:49:24 types
0:49:24 of
0:49:25 diseases
0:49:25 affect
0:49:25 humans
0:49:26 and
0:49:26 what
0:49:26 types
0:49:26 of
0:49:27 diseases
0:49:27 we
0:49:27 are
0:49:27 approved
0:49:28 for
0:49:28 if
0:49:29 50%
0:49:30 of
0:49:30 drugs
0:49:30 are
0:49:30 approved
0:49:31 for
0:49:31 rare
0:49:31 diseases
0:49:32 and
0:49:32 rare
0:49:32 diseases
0:49:33 affect
0:49:33 only
0:49:33 small
0:49:33 fraction
0:49:34 of
0:49:34 populations
0:49:36 I
0:49:36 think
0:49:37 that’s
0:49:37 important
0:49:38 but
0:49:39 how
0:49:39 about
0:49:40 every
0:49:40 disease
0:49:40 that
0:49:41 people
0:49:41 are
0:49:41 actually
0:49:41 done
0:49:42 for
0:49:42 from
0:49:43 and
0:49:44 I
0:49:44 think
0:49:44 the
0:49:44 fact
0:49:45 that
0:49:46 population
0:49:46 is
0:49:47 aging
0:49:47 should
0:49:47 be
0:49:47 a
0:49:48 big
0:49:48 push
0:49:48 to
0:49:48 do
0:49:49 something
0:49:49 like
0:49:49 that
0:49:49 because
0:49:50 age
0:49:50 population
0:49:51 is a
0:49:51 less
0:49:51 productive
0:49:52 population
0:49:52 and
0:49:53 that’s
0:49:53 where
0:49:53 us
0:49:54 society
0:49:54 now
0:49:55 okay
0:49:55 so
0:49:55 that
0:49:55 I
0:49:55 get
0:49:56 that
0:49:56 that’s
0:49:56 a
0:49:56 great
0:49:57 argument
0:49:57 that
0:49:58 if
0:49:58 you
0:49:58 actually
0:49:58 could
0:49:59 address
0:49:59 aging
0:49:59 in a
0:50:00 meaningful
0:50:00 way
0:50:01 you
0:50:01 could
0:50:01 have
0:50:01 massive
0:50:02 societal
0:50:02 benefit
0:50:02 and
0:50:03 so
0:50:03 therefore
0:50:03 you
0:50:03 have to
0:50:03 find
0:50:03 a way
0:50:04 to
0:50:04 incent
0:50:04 that
0:50:04 because
0:50:04 it’s
0:50:05 not
0:50:05 happening
0:50:05 today
0:50:07 how
0:50:07 about
0:50:08 your
0:50:08 magic
0:50:08 wand
0:50:09 I
0:50:10 think
0:50:10 look
0:50:10 I
0:50:10 think
0:50:10 if
0:50:10 I
0:50:10 had
0:50:10 a
0:50:11 magic
0:50:11 wand
0:50:11 it
0:50:11 would
0:50:11 be
0:50:11 a
0:50:12 combination
0:50:12 of
0:50:13 figuring
0:50:13 out
0:50:14 how
0:50:14 we
0:50:16 continue
0:50:16 to
0:50:17 incent
0:50:17 the
0:50:17 innovation
0:50:18 that
0:50:18 happens
0:50:18 here
0:50:18 to
0:50:18 stay
0:50:19 here
0:50:19 so
0:50:19 a
0:50:20 bit
0:50:20 about
0:50:20 what
0:50:20 Elliot
0:50:20 was
0:50:21 describing
0:50:22 where
0:50:22 you
0:50:22 know
0:50:22 what
0:50:22 we
0:50:23 know
0:50:24 is
0:50:24 we
0:50:24 have
0:50:24 this
0:50:25 wonderfully
0:50:25 effective
0:50:26 pipeline
0:50:26 where
0:50:26 a lot
0:50:26 of
0:50:27 innovation
0:50:27 happens
0:50:27 in
0:50:28 universities
0:50:28 and
0:50:29 happens
0:50:29 in
0:50:29 startups
0:50:29 that
0:50:29 get
0:50:30 funded
0:50:31 through
0:50:31 investors
0:50:31 like
0:50:32 us
0:50:33 and
0:50:34 you
0:50:34 get
0:50:35 a lot
0:50:35 of
0:50:36 incredible
0:50:36 novel
0:50:37 approaches
0:50:37 to
0:50:38 tackling
0:50:38 disease
0:50:38 that
0:50:39 comes
0:50:39 from
0:50:39 that
0:50:40 but
0:50:40 the
0:50:40 challenge
0:50:40 we
0:50:40 have
0:50:41 is
0:50:41 to
0:50:41 go
0:50:42 from
0:50:42 that
0:50:43 invention
0:50:43 to
0:50:44 an
0:50:44 actual
0:50:44 product
0:50:45 still
0:50:45 takes
0:50:45 a lot
0:50:46 of
0:50:46 time
0:50:46 and
0:50:46 money
0:50:48 and
0:50:48 there
0:50:48 are
0:50:49 a lot
0:50:49 of
0:50:49 hurdles
0:50:49 there
0:50:49 and
0:50:50 so
0:50:50 I
0:50:50 like
0:50:50 this
0:50:50 idea
0:50:51 of
0:50:51 being
0:50:51 able
0:50:51 to
0:50:51 say
0:50:52 why
0:50:52 don’t
0:50:53 we
0:50:53 see
0:50:53 what
0:50:54 works
0:50:54 in
0:50:54 the
0:50:55 rest
0:50:55 of
0:50:55 the
0:50:55 world
0:50:56 in
0:50:56 terms
0:50:56 of
0:50:56 being
0:50:56 able
0:50:57 to
0:50:57 run
0:50:57 that
0:50:58 relay
0:50:58 race
0:50:58 from
0:50:59 an
0:50:59 invention
0:51:00 to
0:51:00 an
0:51:01 approved
0:51:01 drug
0:51:01 to
0:51:01 run
0:51:01 that
0:51:02 relay
0:51:02 race
0:51:02 more
0:51:02 quickly
0:51:03 and
0:51:03 can
0:51:04 we
0:51:04 copy
0:51:05 those
0:51:07 processes
0:51:07 to
0:51:07 make
0:51:08 sure
0:51:08 that
0:51:08 our
0:51:08 lap
0:51:09 time
0:51:09 is
0:51:10 at
0:51:10 least
0:51:10 as
0:51:10 fast
0:51:11 as
0:51:11 the
0:51:11 rest
0:51:11 of
0:51:11 the
0:51:11 world
0:51:12 lap
0:51:12 time
0:51:13 because
0:51:13 if
0:51:13 we
0:51:13 do
0:51:13 that
0:51:13 I
0:51:14 think
0:51:14 we
0:51:14 will
0:51:15 find
0:51:15 that
0:51:16 we
0:51:16 can
0:51:17 maintain
0:51:17 a lot
0:51:17 of
0:51:17 innovation
0:51:18 here
0:51:18 and
0:51:18 one
0:51:19 thing
0:51:19 that
0:51:19 is
0:51:19 promising
0:51:19 is
0:51:20 at
0:51:20 least
0:51:20 if
0:51:20 we
0:51:20 look
0:51:20 at
0:51:21 where
0:51:22 things
0:51:22 stand
0:51:22 today
0:51:23 the
0:51:24 regulatory
0:51:24 agencies
0:51:25 the FDA
0:51:26 is
0:51:26 at
0:51:26 least
0:51:26 signaling
0:51:27 that
0:51:27 they
0:51:27 want
0:51:27 to
0:51:27 find
0:51:28 ways
0:51:28 to
0:51:29 really
0:51:30 innovate
0:51:30 to
0:51:31 modernize
0:51:31 I
0:51:31 think
0:51:32 that’s
0:51:32 very
0:51:32 promising
0:51:33 I
0:51:33 think
0:51:34 when
0:51:34 it
0:51:34 comes
0:51:34 to
0:51:35 some
0:51:35 of
0:51:35 the
0:51:35 other
0:51:35 challenges
0:51:36 we
0:51:36 have
0:51:36 geopolitically
0:51:37 whether
0:51:37 it’s
0:51:37 with
0:51:37 China
0:51:37 or
0:51:37 just
0:51:38 in
0:51:38 the
0:51:38 rest
0:51:38 of
0:51:38 the
0:51:38 world
0:51:39 what
0:51:40 a lot
0:51:40 of
0:51:41 the
0:51:42 administration
0:51:43 is
0:51:43 pointing
0:51:43 to
0:51:43 is
0:51:44 saying
0:51:44 how
0:51:44 do
0:51:44 we
0:51:45 incent
0:51:45 innovation
0:51:46 to
0:51:46 stay
0:51:47 here
0:51:47 how
0:51:48 do
0:51:48 we
0:51:48 incent
0:51:49 the
0:51:49 supply
0:51:50 chain
0:51:50 to
0:51:50 stay
0:51:50 here
0:51:50 or
0:51:51 to
0:51:51 re-on
0:51:51 shore
0:51:52 how
0:51:53 can
0:51:53 we
0:51:53 do
0:51:53 this
0:52:00 things
0:52:00 that
0:52:00 are
0:52:00 working
0:52:01 elsewhere
0:52:01 and
0:52:02 bring
0:52:02 them
0:52:02 here
0:52:02 to
0:52:02 get
0:52:02 us
0:52:03 back
0:52:03 up
0:52:03 to
0:52:03 speed
0:52:04 in
0:52:04 terms
0:52:04 of
0:52:05 being
0:52:05 able
0:52:05 to
0:52:07 run
0:52:07 the
0:52:07 race
0:52:07 as
0:52:08 quickly
0:52:08 as
0:52:08 other
0:52:08 countries
0:52:08 can
0:52:09 I
0:52:09 think
0:52:09 that
0:52:09 would
0:52:09 be
0:52:10 extraordinarily
0:52:10 promising
0:52:11 for
0:52:11 the
0:52:12 industry
0:52:12 for
0:52:13 society
0:52:13 and
0:52:14 arguably
0:52:14 for
0:52:14 the
0:52:15 world
0:52:15 so
0:52:16 that
0:52:16 is
0:52:16 my
0:52:17 reason
0:52:17 to
0:52:17 be
0:52:17 optimistic
0:52:17 and
0:52:18 that
0:52:18 would
0:52:18 be
0:52:18 what
0:52:18 I
0:52:18 would
0:52:18 use
0:52:18 my
0:52:19 magic
0:52:19 wand
0:52:19 for
0:52:20 is
0:52:20 how
0:52:20 to
0:52:20 figure
0:52:21 out
0:52:21 how
0:52:21 we
0:52:21 can
0:52:21 make
0:52:21 all
0:52:21 this
0:52:22 innovation
0:52:22 get
0:52:22 to
0:52:22 where
0:52:23 it
0:52:23 needs
0:52:23 to
0:52:23 go
0:52:24 so
0:52:24 the
0:52:25 GOP
0:52:25 ones
0:52:26 as
0:52:27 a
0:52:27 drug
0:52:27 class
0:52:27 are
0:52:27 this
0:52:28 incredible
0:52:28 example
0:52:29 I’ve
0:52:29 heard it
0:52:29 be
0:52:30 described
0:52:30 as
0:52:31 the
0:52:31 most
0:52:31 important
0:52:32 consumer
0:52:32 product
0:52:32 that
0:52:33 we’ve
0:52:33 seen
0:52:33 in the
0:52:33 last
0:52:33 several
0:52:34 decades
0:52:34 for
0:52:35 obvious
0:52:35 reasons
0:52:35 the
0:52:36 impact
0:52:36 it’s
0:52:36 having
0:52:36 on
0:52:37 societal
0:52:37 health
0:52:38 what
0:52:38 makes
0:52:39 a
0:52:39 drug
0:52:39 blockbuster
0:52:40 in
0:52:40 your
0:52:40 mind
0:52:41 like
0:52:41 why
0:52:41 is
0:52:41 it
0:52:42 some
0:52:42 drugs
0:52:42 are
0:52:42 so
0:52:43 incredibly
0:52:43 successful
0:52:44 why
0:52:44 don’t
0:52:44 we
0:52:44 see
0:52:44 more
0:52:45 of
0:52:45 them
0:52:46 yeah
0:52:46 I
0:52:46 feel
0:52:46 like
0:52:47 people
0:52:47 tend
0:52:48 to
0:52:48 generate
0:52:49 certain
0:52:50 wisdoms
0:52:50 around
0:52:51 what
0:52:51 it
0:52:52 takes
0:52:52 to
0:52:52 develop
0:52:52 a
0:52:52 successful
0:52:53 drug
0:52:53 sometimes
0:52:54 it’s
0:52:54 oh
0:52:54 you
0:52:54 have
0:52:54 to
0:52:55 be
0:52:55 first
0:52:55 in
0:52:56 class
0:52:56 or
0:52:56 first
0:52:57 to
0:52:57 market
0:52:57 but
0:52:57 I
0:52:58 think
0:52:59 successful
0:52:59 drugs
0:53:00 are
0:53:00 kind of
0:53:00 like
0:53:00 the
0:53:01 opposite
0:53:01 of
0:53:01 that
0:53:02 Tolstoy
0:53:02 wisdom
0:53:02 in
0:53:03 that
0:53:03 each
0:53:04 happy
0:53:04 family
0:53:04 is
0:53:04 happy
0:53:05 in
0:53:05 its
0:53:05 own
0:53:05 way
0:53:07 and
0:53:07 in
0:53:08 case
0:53:08 of
0:53:09 JLP1
0:53:09 they
0:53:10 weren’t
0:53:10 the
0:53:10 first
0:53:11 smeglutide
0:53:11 and
0:53:11 trisepotide
0:53:11 weren’t
0:53:12 the
0:53:12 first
0:53:12 JLP1
0:53:13 to
0:53:13 be
0:53:13 developed
0:53:14 or
0:53:14 be
0:53:14 approved
0:53:15 just
0:53:16 that
0:53:16 in
0:53:16 this
0:53:17 case
0:53:17 Lily
0:53:18 and
0:53:18 nobody
0:53:18 took
0:53:18 on
0:53:19 a
0:53:19 very
0:53:19 contrarian
0:53:20 bet
0:53:20 that
0:53:21 obesity
0:53:21 is
0:53:21 actually
0:53:21 a
0:53:22 real
0:53:22 market
0:53:22 which
0:53:23 now
0:53:23 seems
0:53:24 obvious
0:53:24 but
0:53:24 10
0:53:25 years
0:53:25 ago
0:53:25 if
0:53:25 you
0:53:26 were
0:53:26 a
0:53:26 company
0:53:26 trying
0:53:26 to
0:53:27 raise
0:53:27 for
0:53:28 obesity
0:53:28 as
0:53:28 a
0:53:29 disease
0:53:29 you
0:53:30 probably
0:53:30 having
0:53:30 much
0:53:31 success
0:53:31 I
0:53:32 think
0:53:32 many
0:53:39 people
0:53:39 with
0:53:40 obesity
0:53:40 would
0:53:40 be
0:53:41 injecting
0:53:41 themselves
0:53:42 with
0:53:42 drugs
0:53:42 I
0:53:42 think
0:53:43 Pfizer
0:53:43 terminated
0:53:44 their
0:53:44 JLP1
0:53:44 program
0:53:45 because
0:53:45 internally
0:53:46 they
0:53:46 decided
0:53:46 that
0:53:47 actually
0:53:49 chronic
0:53:49 disease
0:53:50 injectables
0:53:50 I
0:53:50 don’t
0:53:50 think
0:53:51 patients
0:53:51 want
0:53:51 that
0:53:53 Chimera
0:53:53 was
0:53:53 also
0:53:54 not
0:53:54 the
0:53:54 first
0:53:54 TNF
0:53:55 alpha
0:53:56 antibody
0:53:56 out
0:53:57 there
0:53:58 it
0:53:58 was
0:53:59 third
0:53:59 to
0:53:59 market
0:54:00 third
0:54:00 TNF
0:54:01 alpha
0:54:01 antibody
0:54:01 to be
0:54:02 approved
0:54:02 but
0:54:02 it
0:54:02 was
0:54:02 the
0:54:03 first
0:54:03 human
0:54:04 monoclonal
0:54:04 antibody
0:54:04 one of
0:54:05 the
0:54:05 first
0:54:05 ones
0:54:05 to be
0:54:06 approved
0:54:08 antibodies
0:54:08 from
0:54:09 mice
0:54:09 so
0:54:09 I
0:54:09 think
0:54:10 in
0:54:10 both
0:54:10 of
0:54:10 those
0:54:11 cases
0:54:13 it
0:54:14 wasn’t
0:54:14 a
0:54:15 biological
0:54:15 take
0:54:16 that
0:54:16 was
0:54:16 unique
0:54:16 the
0:54:17 targets
0:54:17 were
0:54:17 pretty
0:54:18 consensus
0:54:18 and
0:54:18 I
0:54:18 think
0:54:19 biology
0:54:19 is
0:54:19 like
0:54:20 one
0:54:20 of
0:54:20 the
0:54:20 areas
0:54:20 where
0:54:21 you
0:54:21 really
0:54:21 don’t
0:54:21 want
0:54:21 to
0:54:21 be
0:54:22 contrarian
0:54:22 you
0:54:22 don’t
0:54:22 want
0:54:23 to
0:54:23 be
0:54:23 the
0:54:23 only
0:54:23 company
0:54:24 pursuing
0:54:24 some
0:54:24 obscure
0:54:25 mechanism
0:54:26 usually
0:54:26 you
0:54:26 want
0:54:26 to
0:54:27 have
0:54:27 some
0:54:27 literature
0:54:28 validation
0:54:28 but
0:54:28 in
0:54:28 both
0:54:28 of
0:54:29 these
0:54:29 cases
0:54:29 it
0:54:29 was
0:54:30 either
0:54:31 a
0:54:31 big
0:54:32 modality
0:54:33 differentiator
0:54:33 in
0:54:33 case
0:54:34 of
0:54:34 Humira
0:54:35 or
0:54:35 big
0:54:36 contrarian
0:54:37 take
0:54:37 on
0:54:37 what
0:54:38 indication
0:54:38 to
0:54:38 pursue
0:54:39 and
0:54:39 I
0:54:39 think
0:54:39 aging
0:54:40 might
0:54:40 be
0:54:40 a
0:54:40 contrarian
0:54:41 indication
0:54:41 to
0:54:41 pursue
0:54:41 for
0:54:42 some
0:54:43 I
0:54:43 think
0:54:43 muscle
0:54:44 is
0:54:44 in
0:54:44 a
0:54:44 similar
0:54:45 space
0:54:45 right
0:54:45 now
0:54:46 where
0:54:46 for
0:54:47 a
0:54:47 while
0:54:49 people
0:54:49 weren’t
0:54:50 treating
0:54:50 sarcopenia
0:54:50 as a
0:54:51 real
0:54:51 indication
0:54:51 because
0:54:52 it’s
0:54:53 muscle
0:54:53 loss
0:54:53 in
0:54:54 elderly
0:54:54 but
0:54:55 there
0:54:55 is
0:54:56 now
0:54:56 a
0:54:56 similar
0:54:56 race
0:54:57 to
0:54:57 JLP
0:54:57 wants
0:54:57 to
0:54:57 go
0:54:58 and
0:54:58 develop
0:54:58 drugs
0:54:59 for
0:54:59 muscle
0:55:01 and
0:55:02 we’ll
0:55:02 see
0:55:03 it
0:55:03 succeeds
0:55:04 okay
0:55:04 so
0:55:04 you
0:55:05 with
0:55:05 all
0:55:06 the
0:55:06 time
0:55:06 you’ve
0:55:06 spent
0:55:07 studying
0:55:07 and
0:55:07 focusing
0:55:07 on
0:55:08 the
0:55:08 industry
0:55:08 have
0:55:09 decided
0:55:09 to
0:55:10 jump
0:55:10 into
0:55:10 a
0:55:10 startup
0:55:11 still
0:55:12 stealth
0:55:12 as I
0:55:12 understand
0:55:12 it
0:55:12 but
0:55:13 to
0:55:13 jump
0:55:13 into
0:55:13 a
0:55:13 startup
0:55:14 that’s
0:55:14 going
0:55:14 to
0:55:14 tackle
0:55:15 aging
0:55:17 what
0:55:17 are
0:55:17 you
0:55:17 thinking
0:55:18 yeah
0:55:19 I
0:55:19 think
0:55:20 our
0:55:20 take
0:55:20 is
0:55:21 more
0:55:21 of
0:55:21 a
0:55:21 modality
0:55:22 take
0:55:22 I
0:55:23 every
0:55:23 time
0:55:24 I
0:55:24 think
0:55:24 about
0:55:25 where
0:55:25 the
0:55:25 biggest
0:55:26 breakthroughs
0:55:26 and
0:55:27 excesses
0:55:27 in
0:55:27 biology
0:55:28 came
0:55:28 from
0:55:30 it
0:55:30 was
0:55:30 always
0:55:31 served
0:55:31 from
0:55:32 some
0:55:32 type
0:55:32 of
0:55:33 technology
0:55:33 or
0:55:33 process
0:55:34 or
0:55:34 technique
0:55:34 and
0:55:35 rarely
0:55:35 from
0:55:36 discovering
0:55:36 a
0:55:36 new
0:55:36 target
0:55:37 so
0:55:37 we
0:55:38 are
0:55:38 developing
0:55:38 a
0:55:38 new
0:55:39 modality
0:55:39 that
0:55:39 should
0:55:39 make
0:55:40 it
0:55:40 easier
0:55:40 to
0:55:41 tackle
0:55:41 aging
0:55:43 if
0:55:44 you
0:55:44 look
0:55:44 back
0:55:45 at
0:55:45 something
0:55:45 like
0:55:46 human
0:55:46 genome
0:55:46 project
0:55:47 it
0:55:47 took
0:55:47 us
0:55:47 several
0:55:48 decades
0:55:48 and
0:55:49 several
0:55:49 billions
0:55:49 dollars
0:55:50 to
0:55:50 sequence
0:55:50 one
0:55:50 human
0:55:51 genome
0:55:51 and
0:55:52 once
0:55:52 we
0:55:52 discovered
0:55:53 better
0:55:53 sequencing
0:55:54 approaches
0:55:54 we
0:55:54 can
0:55:55 now
0:55:55 do
0:55:55 it
0:55:55 daily
0:55:56 for
0:55:56 a
0:55:56 few
0:55:56 hundred
0:55:57 bucks
0:55:57 I
0:55:57 think
0:55:58 aging
0:55:58 is
0:55:59 in
0:55:59 a
0:55:59 somewhat
0:56:00 similar
0:56:00 space
0:56:00 in
0:56:01 that
0:56:01 it’s
0:56:01 a
0:56:01 massive
0:56:02 multifactorial
0:56:02 disease
0:56:03 and
0:56:03 if
0:56:03 we
0:56:04 rely
0:56:04 on
0:56:05 existing
0:56:05 modality
0:56:06 approaches
0:56:06 it
0:56:06 would
0:56:06 just
0:56:07 be
0:56:07 an
0:56:08 uphill
0:56:11 that
0:56:12 allow
0:56:12 us
0:56:13 to
0:56:13 go
0:56:13 and
0:56:13 target
0:56:14 this
0:56:14 complexity
0:56:15 with
0:56:16 so much
0:56:17 additional
0:56:17 engineering
0:56:18 every time
0:56:19 we want
0:56:19 to start
0:56:19 a new
0:56:20 program
0:56:20 I think
0:56:21 that would
0:56:21 be
0:56:21 sort of
0:56:22 a big
0:56:22 catalyst
0:56:23 for
0:56:24 success
0:56:25 okay
0:56:25 so
0:56:25 we
0:56:27 started
0:56:27 this
0:56:27 conversation
0:56:28 talking
0:56:28 about
0:56:28 where
0:56:28 the
0:56:29 trillion
0:56:29 dollar
0:56:30 biotech
0:56:30 companies
0:56:30 are
0:56:32 you
0:56:32 guys
0:56:32 you
0:56:32 are
0:56:32 both
0:56:33 students
0:56:33 of
0:56:34 history
0:56:34 of
0:56:34 this
0:56:35 industry
0:56:36 where
0:56:37 do
0:56:37 you
0:56:37 see
0:56:37 the
0:56:38 next
0:56:39 wave
0:56:39 of
0:56:41 iconic
0:56:41 biotech
0:56:42 companies
0:56:42 coming
0:56:43 from
0:56:43 where
0:56:43 would
0:56:44 you
0:56:44 see
0:56:44 them
0:56:44 coming
0:56:44 from
0:56:44 where
0:56:45 is
0:56:45 the
0:56:45 next
0:56:45 you
0:56:46 obviously
0:56:46 the
0:56:46 industry
0:56:47 started
0:56:47 with
0:56:47 the
0:56:47 genentex
0:56:48 and the
0:56:49 biogens
0:56:49 of the
0:56:49 world
0:56:50 then we
0:56:50 eventually
0:56:51 got the
0:56:51 vertexes
0:56:52 and the
0:56:52 regenerons
0:56:52 of the
0:56:53 world
0:56:54 where
0:56:54 do
0:56:54 you
0:56:54 see
0:56:54 the
0:56:54 next
0:56:55 wave
0:56:55 of
0:56:56 iconic
0:56:57 biotech
0:56:57 companies
0:56:57 emerging
0:57:00 I’m a big
0:57:00 believer
0:57:01 in
0:57:01 modalities
0:57:02 also
0:57:02 I think
0:57:03 that
0:57:04 if you
0:57:04 look at
0:57:04 the
0:57:05 history
0:57:06 of
0:57:06 the
0:57:06 industry
0:57:07 there
0:57:07 is
0:57:07 an
0:57:07 enormous
0:57:08 amount
0:57:11 types
0:57:11 of
0:57:12 medicines
0:57:13 so I’m
0:57:13 really
0:57:14 excited
0:57:14 for the
0:57:14 fact
0:57:15 that
0:57:15 you
0:57:15 have
0:57:15 all
0:57:15 these
0:57:16 new
0:57:17 generative
0:57:17 design
0:57:18 tools
0:57:19 and
0:57:20 sequencing
0:57:20 technologies
0:57:22 and delivery
0:57:22 tools
0:57:23 that can
0:57:23 all start
0:57:24 to be
0:57:24 stitched
0:57:24 together
0:57:25 into
0:57:25 sort of
0:57:25 a
0:57:26 composite
0:57:26 specific
0:57:27 product
0:57:27 so
0:57:28 there
0:57:28 are
0:57:28 these
0:57:28 sort
0:57:28 of
0:57:28 waves
0:57:29 within
0:57:29 technology
0:57:30 right
0:57:31 where
0:57:32 there’s
0:57:33 specific
0:57:34 problems
0:57:34 that are
0:57:34 solved
0:57:35 and
0:57:36 you
0:57:37 start
0:57:37 to
0:57:37 bundle
0:57:38 a bunch
0:57:38 of
0:57:38 different
0:57:38 components
0:57:39 together
0:57:39 and
0:57:40 then
0:57:40 there’s
0:57:41 new
0:57:41 ideas
0:57:41 that
0:57:41 come
0:57:42 and
0:57:42 it
0:57:42 sort
0:57:42 of
0:57:43 unbundles
0:57:43 the
0:57:44 stack
0:57:44 and
0:57:44 this
0:57:44 sort
0:57:44 of
0:57:45 happens
0:57:45 across
0:57:46 software
0:57:46 different
0:57:47 markets
0:57:48 I think
0:57:49 that in
0:57:49 biotech
0:57:49 we’re in
0:57:49 this
0:57:49 sort
0:57:50 of
0:57:50 moment
0:57:50 where
0:57:50 there’s
0:57:51 a lot
0:57:51 of
0:57:51 opportunity
0:57:52 in
0:57:52 rebundling
0:57:53 the
0:57:53 types
0:57:53 of
0:57:54 platforms
0:57:54 that I
0:57:54 see
0:57:55 that I’m
0:57:55 super
0:57:55 excited
0:57:56 about
0:57:56 are
0:57:57 this
0:57:57 composite
0:57:58 of
0:57:59 incredible
0:57:59 synthetic
0:58:00 biology
0:58:00 and
0:58:00 genomics
0:58:01 tools
0:58:02 plus
0:58:03 modeling
0:58:04 you know
0:58:05 plus
0:58:05 other
0:58:06 tools
0:58:07 on top
0:58:07 of
0:58:07 them
0:58:08 that
0:58:08 just
0:58:09 unlock
0:58:09 things
0:58:09 that
0:58:09 otherwise
0:58:10 weren’t
0:58:10 possible
0:58:11 and
0:58:11 so
0:58:11 I’m
0:58:11 really
0:58:12 excited
0:58:12 about
0:58:12 that
0:58:12 sort
0:58:12 of
0:58:13 opportunity
0:58:13 to
0:58:13 make
0:58:13 things
0:58:13 that
0:58:14 are
0:58:14 net
0:58:14 new
0:58:14 in
0:58:14 the
0:58:15 industry
0:58:15 and
0:58:15 I
0:58:24 one
0:58:24 of
0:58:25 those
0:58:25 options
0:58:26 right
0:58:27 okay
0:58:27 I
0:58:27 mean
0:58:29 recombinant
0:58:29 DNA
0:58:30 gifted
0:58:30 us
0:58:31 the
0:58:31 first
0:58:32 off-the-shelf
0:58:32 insulin
0:58:34 mRNA
0:58:35 vaccines
0:58:36 gifted
0:58:36 us
0:58:36 vaccines
0:58:37 that
0:58:37 we
0:58:37 can
0:58:38 synthesize
0:58:38 in
0:58:38 less
0:58:38 than
0:58:39 months
0:58:39 produced
0:58:39 from
0:58:40 the
0:58:40 genomic
0:58:41 sequence
0:58:41 of
0:58:41 the
0:58:41 virus
0:58:44 first
0:58:45 human
0:58:46 monoclonal
0:58:46 antibodies
0:58:47 gifted
0:58:47 us
0:58:47 a wave
0:58:48 of
0:58:48 cancer
0:58:49 precision
0:58:49 medicine
0:58:49 that
0:58:50 we
0:58:50 have
0:58:50 now
0:58:51 and
0:58:51 I
0:58:51 think
0:58:52 something
0:58:52 similar
0:58:53 has to
0:58:53 happen
0:58:53 for
0:58:54 chronic
0:58:55 multifactorial
0:58:55 diseases
0:58:56 I
0:58:56 think
0:58:57 we
0:58:57 are
0:58:57 starting
0:58:57 to
0:58:58 see
0:58:58 light
0:58:58 at
0:58:58 the
0:58:58 end
0:58:58 of
0:58:58 the
0:58:59 tunnel
0:58:59 with
0:58:59 gene
0:59:00 editing
0:59:02 more
0:59:03 tailored
0:59:03 targeting
0:59:04 approaches
0:59:04 where
0:59:06 no longer
0:59:07 do our
0:59:07 LNPs
0:59:08 just go
0:59:08 to the
0:59:08 liver
0:59:08 we can
0:59:09 now
0:59:09 target
0:59:10 HCCs
0:59:10 we can
0:59:11 target
0:59:11 kidney
0:59:12 we can
0:59:12 target
0:59:13 potentially
0:59:13 brain
0:59:15 and
0:59:15 yeah
0:59:16 I’m
0:59:16 bullish
0:59:16 on
0:59:16 new
0:59:17 modalities
0:59:17 there
0:59:18 is
0:59:18 something
0:59:18 really
0:59:18 interesting
0:59:19 in
0:59:19 this
0:59:20 argument
0:59:20 right
0:59:20 now
0:59:20 within
0:59:21 biotech
0:59:21 if there
0:59:22 are
0:59:23 hyperskillers
0:59:23 that
0:59:24 emerge
0:59:25 there’s
0:59:25 this
0:59:25 question
0:59:26 there’s
0:59:26 a lot
0:59:27 of
0:59:27 AI
0:59:27 and
0:59:27 biology
0:59:28 companies
0:59:28 that are
0:59:29 raising
0:59:29 a lot
0:59:29 of
0:59:30 capital
0:59:30 and some
0:59:31 just have
0:59:31 no aims
0:59:32 to make
0:59:32 drugs
0:59:33 and
0:59:33 and
0:59:34 there’s
0:59:34 this
0:59:35 continual
0:59:35 again
0:59:35 for
0:59:36 sort
0:59:36 of
0:59:36 like
0:59:36 the
0:59:37 background
0:59:37 cynicism
0:59:37 or
0:59:38 discussions
0:59:38 in the
0:59:38 industry
0:59:39 a lot
0:59:39 of
0:59:40 people
0:59:40 are
0:59:40 asking
0:59:40 like
0:59:41 what
0:59:41 is
0:59:41 that
0:59:42 all
0:59:42 about
0:59:43 I
0:59:44 think
0:59:44 that
0:59:44 there’s
0:59:44 this
0:59:45 really
0:59:45 interesting
0:59:46 component
0:59:46 where
0:59:46 we
0:59:46 have
0:59:46 to
0:59:47 believe
0:59:47 in
0:59:47 net
0:59:48 new
0:59:48 market
0:59:49 creation
0:59:49 right
0:59:50 so
0:59:50 at
0:59:50 one
0:59:50 point
0:59:51 Illumina
0:59:51 sold
0:59:52 exactly
0:59:53 0.0
0:59:53 dollars
0:59:54 of
0:59:54 next
0:59:54 generation
0:59:55 sequencing
0:59:55 technology
0:59:56 to the
0:59:56 industry
0:59:57 right
0:59:57 that
0:59:58 turned
0:59:58 into
0:59:58 over
0:59:59 10
0:59:59 billion
0:59:59 dollars
1:00:00 of
1:00:00 sales
1:00:00 and
1:00:00 you
1:00:01 know
1:00:01 independent
1:00:02 large
1:00:02 listed
1:00:03 companies
1:00:03 where
1:00:04 their
1:00:05 cost
1:00:05 of
1:00:05 goods
1:00:05 sold
1:00:06 were
1:00:07 primarily
1:00:07 going
1:00:07 to
1:00:08 Illumina
1:00:08 right
1:00:09 and
1:00:09 I
1:00:10 think
1:00:10 there’s
1:00:10 this
1:00:10 interesting
1:00:11 question
1:00:11 right
1:00:11 where
1:00:12 the
1:00:12 largest
1:00:12 company
1:00:13 in the
1:00:13 world
1:00:14 NVIDIA
1:00:15 is an
1:00:15 infrastructure
1:00:16 company
1:00:17 is it
1:00:18 possible
1:00:19 for there
1:00:19 to
1:00:19 emerge
1:00:20 a
1:00:20 really
1:00:21 large
1:00:21 and
1:00:21 sort
1:00:22 of
1:00:22 fundamental
1:00:23 infrastructure
1:00:23 company
1:00:24 in
1:00:24 biotech
1:00:25 and so
1:00:25 I
1:00:25 think
1:00:25 there’s
1:00:25 sort
1:00:26 of
1:00:26 questions
1:00:28 going
1:00:28 where
1:00:28 others
1:00:29 can’t
1:00:29 and
1:00:29 making
1:00:30 something
1:00:30 that
1:00:30 people
1:00:31 can’t
1:00:31 make
1:00:32 or
1:00:33 making
1:00:34 the
1:00:34 sort
1:00:34 of
1:00:35 final
1:00:35 arc
1:00:35 of
1:00:36 commoditization
1:00:37 and
1:00:37 building
1:00:38 these
1:00:38 sort
1:00:38 of
1:00:39 consolidated
1:00:40 platforms
1:00:40 that
1:00:40 do
1:00:41 all
1:00:41 of
1:00:42 discovery
1:00:42 for
1:00:42 sort
1:00:43 of
1:00:43 small
1:00:43 molecules
1:00:44 and
1:00:44 antibodies
1:00:46 as
1:00:46 this
1:00:46 technology
1:00:47 matures
1:00:47 and so
1:00:47 it’s
1:00:47 kind
1:00:57 what
1:00:57 would
1:00:57 be
1:00:58 your
1:00:58 bet
1:00:59 well
1:00:59 my
1:00:59 bet
1:00:59 is
1:01:00 we’d
1:01:00 make
1:01:00 the
1:01:01 orthogonal
1:01:01 bets
1:01:01 I
1:01:01 agree
1:01:02 we’re
1:01:02 big
1:01:02 believers
1:01:03 in new
1:01:03 modalities
1:01:04 and we’re
1:01:04 big
1:01:04 believers
1:01:05 in there’s
1:01:05 going to be
1:01:05 modern
1:01:06 infrastructure
1:01:07 that drives
1:01:08 that underpins
1:01:08 the ability
1:01:08 to make
1:01:09 modern
1:01:09 drugs
1:01:10 and so
1:01:11 I think
1:01:11 there’s
1:01:11 a ton
1:01:12 of value
1:01:12 creation
1:01:12 on both
1:01:13 of those
1:01:13 axes
1:01:14 and we’re
1:01:14 very
1:01:15 very optimistic
1:01:15 about
1:01:15 that
1:01:15 future
1:01:17 long
1:01:17 live
1:01:17 biotech
1:01:18 long
1:01:18 live
1:01:19 biotech
1:01:22 thanks for
1:01:23 listening to
1:01:23 this episode
1:01:23 of the
1:01:24 a16z
1:01:24 podcast
1:01:25 if you
1:01:26 liked this
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1:01:43 thanks again
1:01:44 for listening
1:01:44 and I’ll see
1:01:45 you in the
1:01:45 next
1:01:45 episode
1:01:47 as a
1:01:47 reminder
1:01:48 the content
1:01:48 here is for
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1:02:11 forward
1:02:11 slash
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1:02:42 and I’ll see you next time on the next one.