Alex Telford - Unlocking Innovation in Pharma - [Invest Like the Best, EP.360]

My guest today is Alex Telford. Alex is the founder of Convoke, a software platform to help streamline drug development and commercialization. He also has been writing frequent blog posts on the biotech industry since 2019, keeping a pulse on the direction of innovation. He joined me today to talk about the history of the pharmaceutical industry and what’s becoming possible in medicine in the coming years. Alex helped break down the complexities of investing in new drug development, breakthroughs in gene therapy on the horizon, and the dance between timely progress and restrictive regulation. This industry has a lot to unpack, and he thoughtfully lays out the landscape. Please enjoy my conversation with Alex Telford. Listen to Founders Podcast For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Tegus, the modern research platform for leading investors. Stretch your research budget with flexible expert calls you can trust. At a fraction of the cost of traditional expert networks, Tegus customers pay only what an expert charges – with zero markups and no confusing call credits – netting an average 70% savings. Don’t want to conduct a full hour call? Tegus offers the ability to schedule 30-minutes, an offer you won’t find anywhere else. And they don’t stop there. With white-glove custom sourcing for every project and robust compliance measures, including a dedicated 50+ analyst team that vets every call transcript, Tegus ensures your privacy and protection. As the industry innovator for qualitative insights, Tegus helps you find the right experts you need at a quality and speed that can’t be matched. For a limited time, as a listener, you can trial Tegus for free by visiting tegus.co/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes. Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Show Notes: (00:00:00) Welcome to Invest Like the Best (00:04:01) First question - Exploring public perceptions of Big Pharma (00:04:56) Providing an overview of the drug discovery process (00:07:22) Assessing the efficiency of the pharmaceutical industry (00:12:13) Anticipating the future direction of drug development (00:16:46) Categorizing different types of pharmaceutical drugs (00:21:19) Strategies to reduce the time from discovery to implementation (00:25:55) Gaining insights from cross-disciplinary research (00:31:13) The emerging role of AI in pharmaceuticals (00:34:32) Analyzing potential regulatory shifts in the industry (00:39:13) Discussing the importance of Randomized Controlled Trials (RCTs) (00:43:29) Examining the impact of blockbuster drugs (00:47:47) Deliberating on the complexities of drug pricing (00:54:09) Highlighting exciting developments and concerns in pharma (00:59:55) The kindest thing anyone has ever done for him

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Patrick O'Shaughnessy?

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• 0:35Patrick O'Shaughnessy?

  Hello and welcome, everyone. I'm Patrick O'Neal and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, stories and strategies that will help you better invest both your time and your money. Invest Like the Best is part of the Colossus family of podcasts and you can access all our podcasts, including edited transcripts, show notes and other resources to keep learning at joincolossus.com.

• 1:00Speaker 01

  Patrick O'Neal is the CEO of Positive Sum. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Sum. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Positive Sum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc.

• 1:29Patrick O'Shaughnessy?

  My guest today is Alex Telford. Alex is the founder of Convoke, a software platform to help streamline drug development and commercialization. He has also been writing frequent blog posts on the biotech industry since 2019, keeping a pulse on the direction of innovation. He joined me today to talk about the history of the pharmaceutical industry and what's becoming possible in medicine in the coming years. Alex helped break down the complexities of investing in new drug development, breakthroughs in gene therapy on the horizon and the dance between timely progress and restrictive regulation. This industry has a ton to unpack and Alex thoughtfully lays out the landscape. Please enjoy my conversation with Alex Telford.

• 2:10Patrick O'Shaughnessy?

  Alex, we're going to spend a ton of time today talking about pharmaceuticals, not just the industry, some of the companies, but the process by which the world has created incredible technologies for our health and also the impediments to creating more of them in the future. Could you give your version of just a sweeping overview of the process and evolution of how we find drugs?

• 2:34Speaker 02

  Yeah, so it starts with academic research. This is mostly funded by government research and occurs in universities. So the NIH is the biggest funder of biomedical research in the world and they support scientists trying to understand the basic science of how diseases work. What are the mechanics of diabetes? What are the mechanics of heart disease? What are the different genes, proteins involved in those pathological processes? And to some extent, how could we modulate those processes to affect disease? But that's only really a small part of the story. So once you have an idea for how you might intervene in a disease, you then have to do a lot of work to convert it into a drug.

• 3:14Speaker 02

  So these initial hypotheses, these target-specific proteins, RNA, whatever, that you can modulate to affect the disease are then taken up by mostly for-profit biotech companies who then do all the hard work of turning this into a drug, producing a molecule that can modulate these targets, either inhibit it or increase the activity in a certain way, and then running everything through first these in vitro systems of just basically cells in a petri dish, then up to animal models, so testing them in mice, monkey models of disease potentially. Once it's safe enough, then you can go into human trials. You have a steadily escalating sequence of trials, starting from phase one, which is just basic safety trials in human

• 3:59Speaker 02

  volunteers and dosing to phase two, where you try to get a sense of initial efficacy of the drug, and then phase three, which confirms the efficacy and safety to the extent we can, and then you have to go to the regulators, FDA, EMA, the MDA. And they'll review the package of information and make a determination for whether or not the drug can be marketed for a certain specific use case, which is called an indication.

• 4:21Patrick O'Shaughnessy?

  That long chain produces the things we use. But what was so interesting to me, reading all your work, was the rate of discovery and the rate of development seems to have changed a lot. You write about this guy, Paul Jansen, maybe you can describe who he was, but it seemed like in the early days of this research, a single person or lab could produce dozens and dozens of things that get used for a long time. And now, I think you said somewhere in there, like, the average researcher will not work on anything that ever gets to production and use in trials or practice. So, give us a history of how efficient we are at finding new stuff and what the important timeline points are in that development.

• 5:00Speaker 02

  So, if we go back to, let's say, the end of the 19th century, where you have a really nascent pharmaceutical industry. So, you have pharmaceutical companies that came out of epocetaries, which were selling extracts of plants, like you might still get today, naturalistic medicine, and extracts of organs. So, there was this idea that nature produces some bounty of molecules that are somewhat useful to treat health, but we don't really know what parts of the natural extracts actually treat health. So, you have some companies that are extracting just those natural products and selling them in extracts of cybus grans, whatever. And then you have another sort of set of companies that came out of the chemicals industry, so the dye industry. And around when our ability to understand chemistry

• 5:42Speaker 02

  and use chemistry to develop our own molecules was developing, so this is back in the 1900s, some of these dye manufacturers were figuring out ways to just adapt dye chemicals and use them for therapeutic purposes. Those companies eventually became more sophisticated at the types of chemical manipulations we could do, the type of extraction we could do, and purification of these natural materials. And that started to evolve into what became the pharmaceutical industry around the early 20th century. So, then you had, at some point, the golden age of antibiotics. Antibiotics between the two world wars became the first great success story of the pharmaceutical industry. So, moving away from products like

• 6:24Speaker 02

  these natural extracts that didn't really work in most cases, towards antibiotics, which had an obvious and noticeable effect on the ability to cure these infectious diseases. And a lot of these antibiotics were found through just experiments, go into the wild, find soil samples, find drugs in these soil samples from bacteria, and would purify them.

• 6:43Speaker 02

  That worked for a while, but limited to infectious diseases for the most part. And then in the sort of 40s and 50s, you start getting people like Janssen coming about. So Janssen was a Belgian doctor, he trained in medicine. And his dad was actually one of these importers of some of these natural products, so organ extracts. Janssen recalled when he went to school, that is a medical school, his contemporaries were making fun of him or being part of a family who sold these sort of ineffective organ extracts. And so Janssen had a bit of a chip on his shoulder, I think, from that. And he wanted to figure out, adapt, how can we actually improve on some of these compounds that nature has given us?

• 7:24Speaker 02

  So he went to the US for a while, he realized, I think he went from 1948, there was some labs that were springing up that were trying to do more rational drug design, create their own new drugs, rather than just take what existed already in nature. And he brought that idea back to Belgium, started his own lab in 1952, and started trying to tinker with natural compounds and use some of the emerging tools of chemistry to just adapt some of these existing compounds that had been discovered already and find new uses for them. So this 50s, there's a lot of opportunity in the sense that you had these newly evolving molecular tools and you had a lot of space to apply those tools, discover a new useful compounds. So you started with these functional compounds that we found from nature. We had a lot of starting points, morphine, pethy and atropine,

• 8:09Speaker 02

  that could be tinkered around with to find useful things that are close to those natural compounds, but not exactly the same, they're subtly different effects. And you had an emerging suite of molecular tools. And then you fast forward over time, the kind of low hanging fruit have, to some extent, been exhausted with a lot of those molecular tools were developed in that period of time. But the pharmaceutical industry, as it previously existed, has been declining in its efficacy of finding new products because you've exhausted the opportunities to develop new drugs, because we've tinkered around a lot of what is easy to find.

• 8:43Speaker 02

  And then you have in the eighties, the biotech industry arose. So just as you have this declining pharmaceutical industry, which is small molecules, medicinal chemistry, you have this rising biotech industry that's built on this idea that you can get bacteria to produce any kind of arbitrary protein by inserting it into its DNA. And that's how you get a lot of those things like the humanized insulin, monoclonal antibodies, there's always like biotech products that are now somewhat still in ascendance. So you have sort of two curves overlapping the fall off of the kind of exhausted low hanging fruits in traditional pharmaceuticals and then the growth in what we can do with biotechnology tools like recombinant DNA and more recently CRISPR, things like that.

• 9:24Patrick O'Shaughnessy?

  Yeah, it would be interesting to zoom all the way to now and talk a little bit about the best contenders for the next explosion, like what we saw in small molecule pharma. What is the low hanging fruit of today? Love here you talk about biologics or anything else that you think are the major categories of discovery or new enabling technologies that will allow us to do more faster and maybe have another explosion like what we saw early in the industry's history.

• 9:51Speaker 02

  If you look at the history of technologies in biotech, it takes a long time to commercialize something. Going back to the idea of people discovering targets in academia, as soon as you have an idea for a target, it takes something like 20 years for that to turn into a drug. That sort of time period, 20 years plus minus 10 is fairly consistent of how long it takes to translate this sort of idea of how you might treat a disease. And so much of what goes into commercializing and developing a drug is turning it from this idea into a product that is actually manufacturable, scalable, doesn't trigger all these unwanted side effects. And many of these things you don't know about until you've tried it out in humans. So monoclonals now are the top selling drug in the world, KEYTRUDA, previously was HUMIRA.

• 10:32Speaker 02

  They're both monoclonal antibodies and now the processes to develop, produce and sell those monoclonal antibodies at scale are pretty established, but it took a long time to get there. So I think if you want to answer the question, what is the next set of technologies that are going to be really impactful in the next 10, 20 years, you need to look at what is just nascent today. Getting approved. So things like gene therapies, we have a few gene therapies that have been approved. So Zolgens is a treatment for spinal muscular atrophy. It's this really devastating infant neuromuscular disease. The patients who have that disease would die before they are one year old in the most severe type. But now they can be seemingly almost cured with these gene therapies and other modern therapeutics.

• 11:13Speaker 02

  And that's one example of an early success story, but we haven't had many other examples of gene therapies being commercially successful. But once you have one launch on success, you can then iterate and refine the processes for developing these drugs. And you'll have down the lines, future successes. So I think another 10 years, another 20 years, we'll see just the process for developing, manufacturing, gene therapies, all those kinks get worked out. And they'll become applied to many more conditions at a larger scale. Also things like cell therapies. So there's some type of drug called CAR T cell, which is way more complicated than just a typical pill. It means they're extracted from your body, they're flown to a manufacturing site, they're gene edited to have this sequence protein inserted into their membranes that binds a specific type of protein found on cancer cells.

• 11:57Speaker 02

  And it's reinfused into the body and then it goes and eliminates these pathogenic blood cancer cells. And that's an extremely complicated manufacturing process. You're taking these cells out of a human, you're flying them to another country, probably. You're gene editing them and then you're flying them back and you have to do that in a very short amount of time. One, because the viability of the product and two, because these patients have very severe types of cancer. And if you don't get it fast enough, they're going to die. There's a lot of kinks in those processes that need to be ironed out before these technologies become scalable beyond the niche use cases. One interesting example is this company Bristol Myers Squibb. So they bought a company called Celgene, who are pioneers in this type of CAR T cell therapy.

• 12:39Speaker 02

  And they have the same amount of employees working on delivering these compounds as they have treated patients, so something like 4,000 each. The high-touch process expertise and scale you need to deliver these super complex next generation therapies like CAR Ts, like gene therapies that are somewhat personalized, is just way beyond traditional pharma where you make a pill in a factory and then you put the pill on the shelf and you just have a pharmacist give it out. So I think we're going to see a lot more of these type of process-like products that are way beyond just a standard pill. And it's much more a whole complex, large, the actual drug itself is a complex product. There's also a whole process around the drug of delivering it to patients in a timely fashion, manufacturing, which is very complicated.

• 13:24Speaker 02

  Another good example is radio pharmaceuticals. So a lot of companies have been investing in this lately, buying up biotics, developing these drugs. And these are compounds where you have a targeting element, let's say, cancer. And then you have a radionucleide which emits radiation. You infuse these drugs, it binds to the cancer, it emits radiation, the radiation kills the cancer cell in a targeted way. And these are really complicated to deliver because the half-life of some of these radioactive compounds is something like seven days. You need to manufacture and deliver it to the patient within a few days before they lose efficacy. So these complex therapies are one way of farmers trying to build up moats to distribution products.

• 14:04Patrick O'Shaughnessy?

  Can you say a few words, there's four categories there that we'll focus on three, that are terms that I think people have heard, but they may not know exactly what it actually means. So the first is monoclonal antibodies or biologics. The second is gene therapy, the third is cell therapy. Can you just describe what those mean and what they're doing as categories of drugs?

• 14:24Speaker 02

  So I'll start with this antibody, right? So when you get infected with some sort of virus or bacteria, your immune system will generate antibodies against the invading threat. And these antibodies are specific to foreign proteins. Your body's always generating all the time all these antibodies. And when a virus infects you, then if you have an existing antibody that binds that virus, it'll multiply within the body and then it will bind to viral copies and it will stop the infection by just binding and neutralizing these viruses or bacteria. And that was recognized as if antibodies can bind proteins on these foreign microbes,

• 15:06Speaker 02

  it can also probably be useful for binding other molecules or other proteins in the body that aren't necessarily foreign, but may impact disease in certain ways. So we can use it to knock out proteins that we would want to knock out to treat diseases. So one example is

• 15:22Speaker 02

  Humira, which is until recently the top selling drug in the world, something like 20 billion in revenue. It's an antibody that binds to a protein called TNF alpha, which is an inflammatory protein. People who have arthritis and these inflammatory conditions will have an excess of this protein that just causes a immune response that leads to inflammation and joint pain. If you can put in this antibody that binds this protein, takes it out, then you can treat some of these diseases that are pathologically too many of a certain protein. And if you want to treat cancer, cancers have a different composition of proteins on the surface than normal cells. You can put in antibodies that bind those cancer cells and kill them specifically.

• 16:03Speaker 02

  So where the monoclonal comes in is that a while back, there was some scientists figured out that you could take these immune cells that were producing antibodies and combine them with a cancer cell, essentially, and immortalize the cells that produces antibodies. That's how they make it a scalable manufacturing process. You have these cells that are artificially immortalized that produce the antibody anyway, and they just produce tons and tons of this antibody product that you can just grow them up in a big bioreactor vat. And then after a certain level, grow it and then you skim off the antibodies. Gene therapy is this idea that you can insert genes into the body to compensate for defective genes.

• 16:43Speaker 02

  If you have something like this spinal muscular atrophy example, where patients who have that disease, they lack a functional copy of a gene called SMN1, which produces a protein that your motor neurons need to stay alive, essentially. And if you can package up that gene and deliver it into a cell, the cells that are missing this functional copy will then produce the protein, you can restore that function. That seems like quite a promising approach, but then of course, the difficulty with fixing these mutations is actually getting the gene into the cells. So a lot of the innovation around gene therapy was figuring out how to deliver genes to cells. And the approach that we've landed on now is an industry, the dominant approach is to

• 17:23Speaker 02

  use a virus essentially. So what Christopher is doing is making specific, let's say, cuts of the DNA to either eliminate genes that you want to eliminate or potentially down the line to insert new functional copies of genes. And then cell therapy is this idea that you can edit cells, immune cells in the body, to program them in a way to do something that you want them to do, they might otherwise be inclined to do. So you take, immune cells are very good at killing other cells. They're very good at killing foreign cells and microbes, but the body has a lot of systems to prevent immune cells from killing your own cells. So what cell therapy is doing is taking out some of the

• 18:05Speaker 02

  immune cells and tweaking them so they'll bind and recognize pathogenic cells, like cancer cells, that may look to the unedited immune system, similar to a normal cell, and then redirecting the immune system towards removing those pathogenic cells.

• 18:22Patrick O'Shaughnessy?

  Can you talk me a little bit about the potential speed of all of this and the problems that slow things down? I was really struck by the AIDS example in your post and also just by this, I think it's Moore's Law backwards or E-Room's Law or something like that. Talk about that episode with the AIDS epidemic and the frustration with those that could have benefited from some drugs and just the slow process of being able to get them and E-Room's Law more generally. The things that are slowing down, the iteration, speed cycling of, okay, we make a discovery, we've got patients in need, how do we shorten the time between a discovery and an implementation?

• 18:58Speaker 02

  The big tension in how you regulate drugs is how do you balance the need to develop drugs quickly while also keeping patients who are participating in clinical trials safe and keeping people who take the drugs when it comes to market safe? So it's really difficult, I think, for regulators to find an appropriate balance. And it's almost an impossible problem because if you come down too hard on drugs and on manufacturers and make them jump through too many hoops, then it's just going to be uneconomical to develop drugs and there's going to be a massive invisible graveyard. Of people who could have been saved had the drug come to market faster, but it was blocked by various regulatory processes. But then if you're too lenient on drug makers, some of them will take advantage and will push drugs onto market that shouldn't have been there.

• 19:42Speaker 02

  They may be acting completely good intentions and they'll test according to the process that a regulatory agency demands and bring the drug to market. And it turns out later that it causes some harm that we could have foreseen if we had more rigorous testing. So there's no one size fits all solution to fix this tension. So you have to always make some compromises. And coming back to the issue about we're running out of low hanging fruit. One of the biggest problems is that we already have so many good drugs for many conditions. So if you have diabetes or something, then we have insulin, we have many varieties of insulin. We have the GLP one drugs now, we have a number of other drugs that treat diabetes effectively. And they're very effective drugs.

• 20:27Speaker 02

  So it's hard to make the case that you need to bring another drug to market with great haste to treat large number of patients who are being well treated by current drugs. So under that situation, you probably as a regulator are inclined to put really high burdens on drug makers. So you have to absolutely demonstrate that anything you bring to market is really safe. You have to jump through all these hoops, you do mortality studies, all these really onerous requirements. On the other hand, if you have a disease where there's no effective standard of care and patients are dying or suffering greatly from their condition, then it makes sense to potentially relax the regulatory burdens. Coming back to the AIDS issue, when you had the AIDS epidemic, there were a number of drugs that seemed to be promising.

• 21:12Speaker 02

  The pharmaceutical companies were developing at speed, like they did with COVID, right? A lot of companies were developing all their anti-COVID drugs and vaccines and the AIDS patients. Were quite rightly saying that we're gonna die anyway, so we have a death sentence. You should let us try these drugs and see if they're effective because the alternative is death. And the FDA until that time didn't really have a process to deal with these differences in need between conditions. So they would treat diabetes similar to how they would treat AIDS. So after a lot of protesting by the AIDS community and a lot of patient advocacy, the FDA ended up speeding a number of these compounds through development.

• 21:55Speaker 02

  So AZT and DDI to the drugs. These turned out to not be particularly effective drugs, but you could argue that potentially by starting off that process of defining a pathway for how you treat AIDS, getting some drugs on the market, getting some initial revenue, it then spurred the process to iterate and invent drugs that are now very effective. So now there are very effective treatments for AIDS. This initial speeding of the regulatory process during the AIDS epidemic turned into what became the accelerated approval pathway. And that is for drugs of seem promising in disease of very high unmet need like AIDS was, like a lot of cancers are, drugs can get through regulators with much less evidence than they can in other conditions like diabetes where the standards might be higher.

• 22:38Speaker 02

  So now what you've seen in the industry is regulatory arbitrage, where a lot of companies are investing in things that are easier to get past the regulators. You don't need to do all these really huge phase three trials because there isn't a good standard care or there's not so many patients. So you see a lot of this investment in cancers and rare genetic diseases. The requirement for evidence is lower and the need is greater.

• 23:01Patrick O'Shaughnessy?

  The things that you write a lot about is just the discovery process itself. And there's a very clever video game analogy to Super Mario and what we can learn about speedrunning video games and relating that to the process of discovering things that might be useful in the pharma world and the biotech world. Maybe flesh that idea out of just your interest in the process of discovery itself and what we can learn from other domains like games.

• 23:25Speaker 02

  Yeah, so I think if you look at how things get discovered in the pharma industry, it's often very serendipitous. So you'll have these stories of people tinkering away on some idea they think is promising for years and years, and then eventually that becomes a drug. So like the GLP-1s is a good example, where you had decades of the scientists at Novo Nordisk pushing for this idea, tinkering around the boundaries of GLPs and trying to figure out how to actually take this idea from just a concept to an actual drug. So I think one way to help make the industry more effective is to try and find ways of promoting

• 24:08Speaker 02

  that artful tinkering, because we just haven't proven to be very good at developing drugs to a pre-described positioning, if you like. The really important impactful drugs get discovered from this bottom-up process, where you have scientists who are really passionate about an idea. They push through with their management, saying, "You should stop this. It's clearly not working." And they try to find ways to work on this without management finding out. They try to get resources wherever they can and champion, despite lots of opposition. And then eventually, it turns into this drug that becomes a mega blockbuster and helps a huge amount of patients. And then the management thinks, they'll say that we knew it all along or something in retrospect. But you can't prospectively figure out what drugs are going

• 24:52Speaker 02

  to be really impactful a lot of the time. So I think it's quite dangerous in an industry like pharma, which is so innovation-driven and science-driven, to impose these top-down, pre-described notions of what people should invest in. You're very likely to be wrong. Until a few years ago, I think Novo Nordisk was regarded as a pretty boring European pharma company, not very innovative, just doing insulin. But now they're

• 25:19Speaker 02

  It's like the 15th biggest company in the world or something and everyone's excited about the GLPs. You would have necessarily predicted that would come out of Novo Nordisk, but Novo Nordisk was able to produce GLPs because they had this really longstanding interest in a specific area. Edible diseases, they support the scientists there and they've just been tinkering in this area for a long time. So to come back to the Super Mario idea, that post is about this idea that one of the ways you might productively use technologies like artificial intelligence and simulation tooling is to have an intuition about what areas of science might be promising? And then use automation to do scalable tinkering around that nucleus of an idea.

• 26:03Speaker 02

  So the example in Super Mario was that there is this idea that you could do a specific type of jump to get through a level in Super Mario much faster than people have been able to do so far, but no one knew it was possible. So until a software developer developed this tool called Scattershot, which simulates millions and millions of Marios jumping up this level in every possible configuration of jumps and starting positions. He managed to figure out there actually was a way to get up to the top of Castle they were trying to jump up and finish a level much faster than previously possible and it's changed this whole way that speedrunning is approached for that game. Speedrunning itself is probably not of interest to that many people, but the general principle I think is interesting that you have this intuition that something is possible. But Janssen, when he was developing his drugs, he had an intuition that you could improve

• 26:48Speaker 02

  this compound, Pethidine, to make a better drug. But he didn't exactly know how to modulate it to make it the best drug it could be. He was just tinkering around. So if you could use technologies to automate a scientist's to speed up the process of tinkering, trying out solutions, testing them, then you can tighten the feedback loop around the industry and maybe develop drugs a lot faster than you might otherwise be. So one of the big problems with pharmaceuticals as an industry is that the iteration speed is super low compared to something like tech. Like in tech, if you have an idea for a product, you can spin up a demo in a few days and you can show it to some customers, you can get some feedback on that.

• 27:32Speaker 02

  You can then iterate it, leer instantly in code effectively. And then show the next demo to customers, get more feedback, you do these A B tests massive scale, you can move super fast and learn very quickly. But pharma is a very hard domain to learn quickly because everything takes so long. Clinical trials will take around 10 years to get everything through clinical trials. Even do animal experiments, it takes weeks or months to do animal experiments, certain cases. Making all these chemicals takes a long time, you're testing even in the very earliest stages. So you learn at a very slow rate that what works. And if you could just find ways to speed that up across the whole process, then you'll do a lot to, I think, address some of these problems with the pharma industry,

• 28:15Speaker 02

  like drug prices, just a huge healthcare spend, things like that.

• 28:20Patrick O'Shaughnessy?

  I was AI figuring into all this, obviously AlphaFold within the mainstream consciousness. I think Jensen Huang, the CEO of Nvidia, was recently talking about how much explosion of activity there is using AI in the world of medicine and biology. So you talk about the role that this new set of tools might play in both discovery and speed and tinkering and all these different ideas.

• 28:43Speaker 02

  I think I'm short term pessimistic, long term optimistic when it comes to AI for drug discovery. And I'm much more optimistic in the short term about using AI to automate some of the processes along with drug development, not necessarily the designing of drugs itself, but all the steps to bring a drug to market. So if we start with drug discovery, like finding a drug, I think it's just very difficult to see how AI is anything more than just another tool in the expanding toolbox that drug developers use to produce and test molecules. AlphaFold's an example where now you can predict the protein structure from a protein sequence, which is a problem that we didn't know how to do to a high degree of accuracy until before AlphaFold.

• 29:25Speaker 02

  And a lot of people are saying, oh, AlphaFold is going to revolutionize drug discovery. But in reality, it's just one little piece of the hugely complex puzzle, where sometimes you'll have situations where you don't know the structure of a protein you're trying to make a drug against. And AlphaFold could be a helpful starting point for that specific circumstance. But actually, AlphaFold has problems with the very granular predictions of

• 29:51Speaker 02

  The configuration of sites where drugs might bind, so it's not completely accurate. So we need more accurate models for it to be really useful for drug discovery. And to improve AlphaFold, we need more data. It's just take a long time to collect all this data. So AlphaFold is trained on decades of protein structures that have been collected painstakingly by researchers growing crystals in dark rooms. It's definitely speeded up parts of that process, but a very small part. And I think the way AI is going to impact the pharma industry looks similar to that. A lot of little tools that are going to be very useful and aggregate, but they're going to take a long time to deploy and figure out how best to use them and work them into the process.

• 30:30Speaker 02

  One thing I should also mention is that there's this idea that drug discovery is rate limited by our ability to design molecules and design new drugs. And that's not really true. Our ability is much more rate limited by the downstream clinical development and testing these drugs and gathering information. We need to actually justify approving these drugs for sale and marketing them. While I'm optimistic about over the long term, using things like AI to predict what drugs might be effective to do drug discovery and drug candidate selection. I'm much more optimistic about using AI in the short term to help drugs get to market faster than they otherwise might have been.

• 31:11Speaker 02

  So things like, can we use AI to help prepare regulatory documents quicker? Can we use AI to do things like help companies figure out what the markets for their drugs are and prioritize what opportunities they should invest in? How can we use AI to help identify patients who might be a good fit to enroll in a certain trial? So things like trial enrollment just takes way too much time. So just better tooling to identify patients who might be a good fit for a trial and enrolling them seems pretty impactful.

• 31:44Patrick O'Shaughnessy?

  If you were just the czar of this entire universe and godlike powers, what two or three changes would you make? Regulatory, technology-wise, industry structure, anything that you could change? What would you change that you think would most benefit patient outcomes or the overall system?

• 32:01Speaker 02

  I would make regulatory changes. I think I would want to formalize the idea that different diseases require different standards of evidence. And then pre-publishing the degree of evidence that we required for each different disease. So something like diabetes should have quite a high standard of evidence. So you should have to enroll very large numbers of patients and test mortality. Cause we have extremely good drugs, different diseases where our standard of care is not as effective. We should formalize this idea of smaller trials, maybe approved drugs with a weaker standard of evidence. And we are seeing some moves towards that with rare diseases. So you have a lot of these conditions that are too small to really be able to tell.

• 32:44Speaker 02

  They maybe have too few patients, but they may have too few patients plus the disease evolves over too long of a time. For you to reasonably able to figure out in a standard clinical trial, whether or not these drugs are really working to any high level of competence. So I think in those conditions, you'll have to be able to say, look, we're just not going to be able to determine this in a trial. We have to, based on some principled reasoned evidence we've collected about how the drug works in animals, and maybe some initial human testing and some preliminary biomarker evidence. We can then approve this drug and roll it out and then collect information more rigorously. What's on the market? Cause you're just never going to get drugs for certain diseases. It's just uneconomical to achieve this vision of hyper personalized medicine,

• 33:25Speaker 02

  where you're developing a drug for everyone's individual condition and biology, without some ability to make inferences about what is likely to work based on early evidence and genetics and reasoning about the mechanisms of action. So I think the second thing I would do is just be much more rigorous about data collection, post-marketing. So you have a lot of drugs that go onto the market and then they're approved. And then we're not really following up sufficiently to be able to tell if they really work. So there's been some instances of drugs that have got this accelerated approval on some fairly flimsy evidence, but they've seemed promising. But then the company's meant to do a confirmatory trial to determine, okay, actually we have a good reason to believe this drug is going to work.

• 34:06Speaker 02

  But we don't actually know that until we've collected this evidence over five, 10 years or whatever. The companies drag their heels on doing the confirmatory study. I think the regulators should be much more stringent in forcing companies to collect that data and report it and takes the half the market. So this combination of being more open to approving drugs based on lower standard evidence when there's no good treatments and when it's just unfeasible to do a standard clinical trial, coupled with more rigorous data collection and then taking things off the market when they're not working. I think that will help increase the sort of rate of learning of the regulatory process. Surrogates as well, I think, are really important. So surrogates are measures like biomarkers that you can use as a proxy to tell whether a disease is being treated effectively or not.

• 34:50Speaker 02

  So for cancer, what you really care about when someone has cancer is stopping them from dying. But you can get a proxy for that by looking at the size of the tumor. And it's not always a perfect correlation. You shrink the tumor, most of the time, that's a good thing. Sometimes you shrink the tumor, but it doesn't actually help the patient survive longer in the end. So you're exposing them to toxicity and you're not helping them survive. So it's actually you're doing net harm with that drug. The idea of a surrogate is that you have a measure like tumor shrinkage that predicts benefit down the line and that helps you develop a drug much faster. A lot of drugs have been brought to market much faster in cancer than otherwise would have been many really effective drugs because they've used these surrogate markers of efficacy like tumor shrinkage.

• 35:31Speaker 02

  But it's harder to apply this principle in other conditions, like more complex chronic conditions, where you don't have an objective measure that's as simple as does a tumor get smaller or not. If you're treating a disease that's eventually fatal, if you see that this biological response happens early on, then that's very highly predictive with improved survival down the line. And just finding more of these surrogates is a really, I think, valuable activity that something like the NIH and government agencies should invest a lot more money in developing. Because as soon as you have an established surrogate that the regulators can use, you really speed up how quickly you can iterate on developing drugs. You can take it to the clinic, you can see if it works in the surrogate, and then you can decide whether or not to continue or discontinue based on that.

• 36:15Speaker 02

  You don't have to wait five, 10 years to see if someone survives or not. So something like aging, we're never going to get drugs to prolong people's lives until to figure out a surrogate for aging.

• 36:27Patrick O'Shaughnessy?

  Can you talk about RCTs, randomized controlled trials, and something like vitamin D? I love the idea that this tool, RCTs, is an incredibly important process that's helped us learn a lot. What, if any, limitations are there in your mind to RCTs and learning?

• 36:43Speaker 02

  So the big limitation to RCTs is they take time, they're complex to run, and then people don't want to be in the control group, essentially. So you have to expose patients to something that they don't want to take, really, because you're entering an RCT for a drug, you want the active drug. But maybe to take a step back, so the idea behind an RCT is that you have patients or people who are going to take some intervention, and you want to figure out, does this intervention really work or not? And you have to find some way of assigning the intervention randomly, because if you rely on anything other than randomness, there's going to be some bias that creeps into the study. Let's say you have this new drug, and you take it to doctors, and you say, "I want to test this new drug."

• 37:25Speaker 02

  Then the doctors may be more inclined to give the drug to patients who are sicker, who really need it, and they are less inclined to give the experimental drug to patients who seem like they're going to recover anyway. So if you just do that non-random allocation, you're going to see that people who take the drug die at a much higher rate or have a much worse illness than people who don't take the drug, because you have this selection bias that doctors giving the drug to people who are less likely to have a good outcome. So you need to find a way to eliminate those biases, and you can do that with just total randomization. And so coming back to the vitamin D example, one thing in data, in retrospective data, that looks at vitamin D levels and outcomes

• 38:06Speaker 02

  is that you see people with low vitamin D levels have generally worse health outcomes. So they have higher mortality, they have cardiovascular disease. I think it's probably been correlated with every bad thing you can probably get. And there's a natural inference there that we should just supplement vitamin D to the people who are low levels, and then they'll have less rates of all these bad diseases. So then people do the trials, and they do a randomized controlled trial where they assign people randomly to groups, and they give them supplementation of vitamin D, and then they measure outcomes on all these things like mortality and health. And then what they find is that actually vitamin D has very minimal or no effect on most of these outcomes, even though some of the data from retrospective analysis looks pretty strong.

• 38:51Speaker 02

  So how did that happen? What you find out is something like vitamin D is actually a marker for poor health in general. So vitamin D is produced by your body in response to sunlight. And if you are ill or poor health thermally, you're less likely to go outside, or you may be less mobile in general, so you may get less exposure to sun. And then you'll see correlation between just general poor health and vitamin D. It's like with pushups, they say if you can do less than 10 pushups, then you're much more likely to die. Not literally the ability to do pushups determines how likely you are to die, it's just that being able to do pushups is a decent proxy marker for general health. So RCTs are just the most effective way we know of

• 39:34Speaker 02

  eliminating all these biases that you don't know exist from the start. Because otherwise, it's just everything you try gets really confounded. You don't even know how it's confounded.

• 39:45Patrick O'Shaughnessy?

  Can you imagine an alternative? Or do you think this will be and needs to be the method by which we learn truth and efficacy across medical interventions?

• 39:54Speaker 02

  So I think RCTs are always going to be the gold standard. And I think when we can do RCTs reasonably, we should do them. Because they just eliminate so many of these bias problems that you just aren't aware of. And that's the biggest, even if you control for the sources of bias that you think are likely to exist, there'll be hidden sources of bias that you just can't control for. So I think we want to keep RCTs as a confirmatory method. But there are other tools in these instances where RCTs aren't feasible that have a lot of promise. So things like if you do a single arm study, which is just one treatment group, and you compare the progression of a disease with a matched digital twin of these patients, you can train with information from natural history data. We can make a good assumption about the rate at which

• 40:38Speaker 02

  these patients would have progressed had they not, had they not had the drugs. So there's been a few approvals that have used some of these matched natural history data as an alternative cohort. And those have been in cases where there's been too few patients to really do largely a randomized trials. And then you have these instances of super rare diseases where you may have just a handful of patients in the US, for instance. It's literally impossible to run an RCT and have any reasonable statistical outcome. You need to use things like digital twins or surrogates, single arm trials. You can't use RCTs. There's a lot of innovation in this digital twin idea and natural history cohorts.

• 41:16Patrick O'Shaughnessy?

  Obviously, a big driver of all this is profits. So we talked about how people don't like pharma in part because they charge so much for really valuable drugs and they make big profits. And that bothers people as it relates to their health. But profits and revenue drives the motive for discovery and the US has always been a leader here. Can you talk about what you've learned about blockbuster drugs specifically? You can define what you mean by a blockbuster drug, but it seems like the biggest drugs that represent there's sort of a mini power law here that the top handful of drugs represent a huge percent of the entire industry's revenue. So talk about the business side here, the distribution of revenue, the role of blockbuster drugs, the good, the bad, the ugly. Love to hear what you've learned there.

• 41:58Speaker 02

  Yeah, so a blockbuster drug is a drug that makes a billion dollars in annual revenue. So blockbusters are really, you have an outside imports in the pharmaceutical industry because they account for a huge proportion of the overall revenue of the industry. Well, the way the economics operate is pretty analogous to venture capital and these long tail models where you have many losers who don't make much money or don't recoup the money that was invested in them. And a few really huge winners that will go off and generate billions and billions and billions of dollars and pay for all the failures many times over. So the industry is in many ways worth investing in for investors and for companies because there's always this potential of hitting it really big

• 42:43Speaker 02

  and having a mega blockbuster like Humira or Keytruda or the COVID vaccines that generate tens of billions of dollars in revenue a year. Because if you look at all the drugs that are getting launched, it's sad that you have all this work that goes into developing a drug and it takes me 10 years to get it to market or 12 years to get it to market. And it launches and then it flops. Fifty-five percent of drugs that launch, that get through the process, they don't even recoup enough money to pay the average development cost of a drug. And then you have a very small number of drugs, blockbusters, that thirty to forty percent of revenue of the whole industry is made by these blockbuster drugs, which are quite a small fraction of all drugs. When I look at the numbers, something like

• 43:24Speaker 02

  one hundred and seventy blockbuster drugs that were actively generating revenue. And then the whole long tail of other drugs is making very small sums relative to blockbusters. So because the industry operates like a lottery type model where you just want to really hit it big, you have venture capital, it distorts a lot of the incentives. So you see a similar thing in biotech investments that you do with VCs where big pharma is only really interested in developing a drug if it can potentially become one of these blockbusters. Because anything less than that is just unlikely to recoup the investment. So you get a lot of the small markets that patients really would like to have drugs for. They could be really beneficial and we know that we could potentially develop a drug against this disease. We have a good understanding of the mechanism, like many

• 44:09Speaker 02

  small genetic diseases, but it's just not worth big pharma's time or even biotech's time to invest in developing these drugs because the economics don't work out. Even if you get through the whole process of developing this drug and it works and you get through clinical trials, you maybe make a few tens of millions a year and that's just absolutely not worth it because it will cost you hundred, two hundred, three hundred million just to get the drug through the whole development process. So there's a lot of, I think, alpha that could be locked in making the process much cheaper in how drugs are tested and validated because it would mean that it's actually worth developing all of these drugs that are not worth developing.

• 44:47Patrick O'Shaughnessy?

  What do you think about drug pricing as a key variable in all of this big equation? Blockbuster drugs are driven by a price times an amount. What drug pricing something that comes up all the time as predatory or strange or US system such that seems to subsidize a lot of the rest of the world. We make an outsized percent of the discoveries and you want to be compensated for that. It seems like a very complicated equation. So what, if anything, have you learned about drug pricing that you feel is different than the norm or how would you change things?

• 45:17Speaker 02

  So I think drug pricing is really difficult. One thing I guess to say about the US is the US has by far the highest prices in the world. I think people recognize that it's about twice as high as Europe on a net price basis. And the US market for drugs is something like forty percent of the global market. And it's something like sixty percent by revenue of newer drugs. So drugs launched in the past ten years or so. So the US, it accounts for outside share of the revenue that drug companies make. And part of that is because the US system is, for the most part, some of this is changing, but it is mostly a free pricing system where you can charge whatever you want. So when you're pricing a drug for the US market, you'll often, as a company, just try to charge whatever you think you could get away with, whatever you think the market will bear. And in a reasonable world where all the incentives are aligned,

• 46:00Speaker 02

  what the market will bear is close to the actual value of that drug. In healthcare, you have all these strange incentives in markets that don't function properly. But in the rest of the world, you have systems where you have governments doing the negotiation on behalf of the population. So they'll make assessments of the value of a drug based on metrics like how many quality-adjusted life years does this drug give us? What's the benefit of this drug over the existing standard of care? And how much better is it? And how much of a price premium can we give it on the existing drugs? So both systems have flaws. I don't think it's possible to find a perfect system that satisfies everyone. If you look at the profit margins of drug makers, they make something like ten to twenty percent profit after you take out their costs. And the gross margin is something like eighty percent.

• 46:44Speaker 02

  But really, that's a distorted picture of the actual profitability of drug companies. Because there's distortions between when R and D expenses are paid and when the drug revenue comes in. So it's not necessarily a good picture of how much a drug company is getting for their investment. And if you look at how much drug companies are getting for their investment, actually, most of them are pretty near zero or very low return on invested capital. The industry as a whole is not that great of a business. It's very much like a lottery model that is just a type of lottery that's attractive to people with biomedical PhDs, where most people are not making that much money. But a small number of drugs and companies are making supernormal profits.

• 47:26Speaker 02

  And you can point to the examples of people who have very high prices and making supernormal profits, like Humira, 20 billion seems like an incredible amount of money for a drug to make. And I think if you just look at that isolated example, you can say, it just seems unreasonable that anyone is making 20 billion off this drug per year. It's way more than what someone should reasonably get for producing this drug. But you have to think more about the system of incentives. You want to be worth investing in developing drugs. And so the incentive needs to be very strong. And because such a small number of drugs produce so much of the revenue, it's like a real Pareto distribution in revenue. You need these lottery winners to make it worthwhile investing in the system,

• 48:10Speaker 02

  which is an unfortunate reality that because drug development is so inefficient, you need high prices and you need these super blockbusters to make the economics work out. When you're doing R and D, you're paying money that you have now for revenues in 10 years. So from a temporally discounted point of view, you need to think you're going to get a really huge amount of money in the future to be worthwhile you paying hundreds of millions of dollars in the next few years. I think it's a really difficult problem. If you put pressure too much on drug prices, you can very easily remove the incentives for people to invest in the industry. Quite a fragile ecosystem that's built up. Once you start cutting down the tall poppies, you actually are removing a lot of the incentives to even develop any drugs.

• 48:55Speaker 02

  And it's just that you can do outsized damage by cutting down even just a small number of these huge lottery winners, if you like. You have to accept a bit of a trade off where you are trading off innovation for how much you're spending on drugs.

• 49:10Patrick O'Shaughnessy?

  What about prevention? We've talked entirely about interventions where something bad happens in the body and we've developed ways of treating that thing or making it better. What about similar research that could go into, whether it's lifestyle or other things that we do before these bad things happen to us? That would prevent them from happening in the first place? Any thoughts on that side of the ledger?

• 49:36Speaker 02

  Yeah, I think prevention is difficult. People in the US often say that the reason why the US doesn't invest in prevention is because a lot of people are on these employee insurance plans and there's a lot of insurance plan turnover. So any insurance company that invests in prevention for its members is unlikely to reap the benefits of those investments when they switch to another employer and get a different plan. But the problem is that if you look at other countries that do have these nationalized healthcare systems, like the NHS, other European countries, they actually don't invest in that much in prevention either, even though they should in theory because it's a whole nationalized health system. They shouldn't be wanting to invest in those things. So is the problem really that there's a lack of supply of preventative treatments?

• 50:20Speaker 02

  Or is it really there's a lack of demand for preventative treatments? And I feel like the issue is probably more on the demand side where people unfortunately don't have a strong demand for preventative medicine and they don't take up preventative medicine in many cases when it's offered to them. So it's the whole thing with the argument about Ozempic and why don't people just practice diet and exercise? That's a more sustainable way of dealing with obesity. The reality is that people just don't do that. So they would rather just take Ozempic when they are obese and have that treat their obesity. So I think there's a whole consumer attitude shift that needs to happen before preventative medicine ever becomes really established. It needs to be demand driven. So you think about like the healthcare system.

• 51:03Speaker 02

  Who is really the end consumer of pharmaceuticals and healthcare system? I think you can make the argument that it's actually healthy people who are enrolling in the plans and are paying the majority of the money that goes into the system. And they want that system to represent value for money for when they do get sick. So you have to have the people who are healthy have a greater demand for products of prevention that then incentivize companies to invest in them, insurance plans and health systems to actually provide these services.

• 51:30Patrick O'Shaughnessy?

  I'd love you to talk about two extremes. The things that have you in this entire world the most excited for the future and the things that have you the most worried.

• 51:39Speaker 02

  I think what I'm most excited about is not a specific technology, but more this Cambrian explosion, if you like, of modalities in biotech. So for a while, I think back maybe 10, 20 years ago, there were that many different types of different treatment classes. And since the rise of biotech, you have all these different ways of treating disease that are becoming established and.

• 52:04Speaker 02

  Have a lot of potential. So you have CRISPR RNA interference, these CAR T cells we talked about, base editing, different variations of CRISPR. Our ability to control our biology is getting much better than it was even just a few decades ago. So this increase in our ability to control our own biology and intervene in biological systems and very precisely use these molecular scaffolds to push a system into a desirable state is really interesting. And just this flywheel of improving on the modalities that we do have. So you look at something like CRISPR, we just got our first CRISPR approved therapy in the UK and the US. But CRISPR is already in some ways becoming a bit of an outdated technology. You have a lot of investment in what's next.

• 52:48Speaker 02

  So instead of making cuts, you're going to have these base editors that are changing specific letters in the DNA sequence. And that's a more effective way of precisely treating certain genetic diseases than making cuts that CRISPR cuts. And then maybe after these base editors, you have prime editing. It's even more versatile in the type of edits it can make and you have different types of editing. So just a lot of improvements in tooling for how we intervene in biological systems to treat diseases. And seemingly like an increase in the rate at which we're developing these tools and applying them to the clinic to solve biological problems and to treat diseases. So like general explosion in technology, our ability to control systems is really interesting. I'm interested in just general nearly new business models of the industry.

• 53:31Speaker 02

  So one trend you've seen over the past, I think it started probably in the 2000s, is this greater externalization of research. So pharma companies, these pharma companies used to do a lot of research in-house. They've mostly externalized that to smaller biotechs that are venture capital funded. So the big pharma companies are really just like commercialization machines all the time. And they do maintain some research labs, but the purpose of the research labs, it in many cases, just to validate external opportunities and test them in-house and have the expertise to actually meaningfully evaluate these external opportunities they're going to bring in. Maybe you'll see greater and greater externalization of more and more functions. Now we have innovation R and D externalized to little biotechs. We have running clinical trials being externalized to these clinical research organizations.

• 54:17Speaker 02

  That specialize in that, you can argue whether that's been a good thing or not, but it's just part of the externalization trend. And then you have more and more just pieces being externalized. So if you're commercial analysis will be externalized in the future as well. So you may end up with our company just being very specific IP holding companies, plus just managing the finances of actually selling and distributing these drugs. Every little piece of the ecosystem is focused on some specific component of the process. And maybe that will help to be more efficient. So I'm interested in that. I think you can argue that it may not be helpful. Externalization hasn't been completely helpful. There's been some trade offs there. But there's a lot of redundancy in the industry. And if you're in a biotech who's getting ready to launch, people having to build up redundant capacity to run and execute trials in an efficient way.

• 55:00Speaker 02

  And then what I'm worried about is there's a genuine worry that it's not going to be worthwhile for a lot of companies to continue to invest in developing new drugs. So I'm worried that if we don't get the balance rewards innovation and incentives to innovation, we'll end up with a system where companies will decide, okay, I'm not actually incentivized to really invest in doing meaningful fundamental research and spending 30 years tinkering on some opportunity that may eventually bear fruit. I'm actually just going to market the drugs I already have that are old. I'm going to invest in drugs that are very hard to copy. So even though, once they do go generic, no one's going to be able to copy me. So things like these radio, radio treatments talking about CAR Ts,

• 55:45Speaker 02

  they have a whole process associated with them. So they're quite hard to copy. And then you're just going to sit on existing treatments and try to milk them as much as possible and use tricks and techniques to extend the patent life and make it harder to copy and try and extract as much value for as long as possible. And maybe not invest in this fundamental research that really drives meaningful products that has this element of unpredictability to it. One thing I think is quite dangerous in the pharma industry is just this over reliance on an accounting type mindset, rather than a recognition that this is a science driven industry, this is an innovation driven industry. The commercial parts of Big Pharma should be in service to the R and D portion of the organizations or the ecosystem.

• 56:26Speaker 02

  And you should really be hiring good people with good intuitions about what's worth developing and letting those scientists tinker for as long as they need to on some of these really challenging ideas at the forefront of what is possible and give them enough time, 10 or 20 years, for these ideas to mature into actual products. And then you can commercialize them. And if the returns to innovation are down too much, and maybe you get too many managerial positions who are focused on the accounting aspect of it, they just want to milk existing products and copy the big ones that we know have an existing market, then you're not going to get the kind of meaningful breakthroughs like KEYTRUDA, these immuno-oncology drugs, the GLP-1 agonists that we're seeing now. So yeah, that's the only thing I worry about.

• 57:05Patrick O'Shaughnessy?

  Just a totally fascinating overview of one of the most interesting parts of the world and of the business world of just the technology and innovation world. I've so appreciated everything you've written and sharing so much with us today. In these interviews, I always ask the same traditional closing question. What is the kindest thing that anyone's ever done for you?

• 57:23Speaker 02

  I think this has a pretty obvious answer in my case. And many people have done many kind things for me, right? But I think I just have to go with probably what is the most common answer, which is my parents have always been an unending well of support and they bailed me out of many low points in my life. So I'm very thankful for that. And while other people have done me many kindnesses, nothing really compares to what my parents have done.

• 57:45Patrick O'Shaughnessy?

  Alex, thank you so much for your time.

• 57:47Speaker 02

  Thanks.

• 57:49Patrick O'Shaughnessy?

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