Invest Like the Best with Patrick O'Shaughnessy - Alex Telford - Unlocking Innovation in Pharma - [Invest Like the Best, EP.360]

Starting point is 00:00:02 Hello and welcome, everyone. I'm Patrick O'Shaughnessy, 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 join colossus.com. Patrick O'Shaughnessy 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.

Starting point is 00:00:45 Clients of positive sum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc. 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 in 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

Starting point is 00:01:19 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. 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? Yeah, so it starts with academic research. This is mostly funded by

Starting point is 00:02:06 government research and it 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. 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

Starting point is 00:02:54 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 volunteers and dosing. 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.

Starting point is 00:03:36 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. That long chain produces the things we use. But what was so interesting to me reading all your work was,

Starting point is 00:03:55 the rate of discovery and the rate of development seems to have changed a lot. You read 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. You said somewhere in there, like the average researcher will not work on anything

Starting point is 00:04:15 that ever get 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. 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 Apocotheries, which were selling extracts of plants like you might still get today, naturalistic medicine, and extracts of organs.

Starting point is 00:04:45 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 cymas grants, whatever. And then you have another set of companies that came out of the chemicals industry, so the dye industry. And around when our ability to understand chemistry and use chemistry to develop our own molecules was developing, so this was back in the 1900s, some of these dye manufacturers were figuring out ways to just adapt dye chemicals and use them for therapeutic purposes.

Starting point is 00:05:23 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 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 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

Starting point is 00:06:07 bacteria, and would purify them. And that worked for a while, 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 doctorate trained in medicine. And his dad was actually, 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 he answered a bit of a chiff on his shoulder, I think, from that. And he wanted to figure out, adapt, how can we actually improve on some of these compounds

Starting point is 00:06:50 that nature has given us? So he went to the US for a while. He realized, I think he went in 1948, there was some labs that were springing up that were trying to do more rational drug design, trade 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 to discover a new useful compounds. So you started with these functional

Starting point is 00:07:31 compounds that we found from nature. We had a lot of starting points, morphine, petaline, and atropine that could be tinkered around with to find useful things that are close to those natural compounds, but not exactly the same. There's subsubstably 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. So 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. And then you have in the 80s, the biotech industry arose.

Starting point is 00:08:14 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, monocular antibodies, there's all these like biotech products that are now somewhat still in ascendance. So you have sort of two curves overlapping. The full off of the kind of exhaustion of low-hanging fruits in traditional pharmaceuticals, and then the growth in what we can do with biotechnology tools,

Starting point is 00:08:48 like recombinant DNA and more recently CRISPR, things like that. 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? I love here you talk about biologics or anything else that you think of the major categories of discovery or new enabling technologies that will allow us to do more faster and maybe.

Starting point is 00:09:14 have another explosion like what we saw early in the industry's history. 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. Their 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 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

Starting point is 00:09:46 manufacture or 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, Kutruder, previously was Humira. They're both monoclonal antibodies. And now the processes to develop, produce, and sell those monoclonia antibodies at scale

Starting point is 00:10:06 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 and getting approved. So things like gene therapies, we have a few gene therapies that have been approved. So Zoldgensman is a treatment for spinal muscular atrophy. It's this really devastating infant neuromuscular disease.

Starting point is 00:10:31 The pace who have that disease would die before they're one year old in the most severe type. But now they can be seemingly almost cured with these gene therapies and other modern therapeutics. And that's one example of an early success story, but we haven't had to be a very success story. many other examples of gene therapies being commercially successful. But once you have one launch one success, you can then iterate and refine the processes for developing these drugs and you will have down the line future successes. So I think another 10 years, another 20 years, we'll see just the process for developing, manufacturing, gene therapy cells as Kings get worked

Starting point is 00:11:01 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. Immune cells extracts you 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, and it's reinfusing 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 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

Starting point is 00:11:42 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 equals cell gene, who are pioneers in this type of Cartyce cell therapy. 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

Starting point is 00:12:18 next generation therapies like Cartes, 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 into patients in a timely fashion of manufacturing, which is very complicated. Another good example is radio pharmaceuticals. A lot of companies have been investing in this lately buying up biotics developing these drugs, and these are compounds where you have

Starting point is 00:13:01 a targeting element, let's say cancer, and then you have a radio nucleide, 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-flight to 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. Can you say a few words? There's four categories there that we'll focus on three that are terms that I think people

Starting point is 00:13:37 have heard, but they may not know exactly what it actually means. So the first is monocle 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? 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

Starting point is 00:14:03 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, 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 Humiro, which is until recently

Starting point is 00:14:51 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. So where the monoclonal comes in is that a while back there was some scientists

Starting point is 00:15:35 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 just scalable manufacturing process. You have these cells that are artificially immortalizing, that produce the antibody anyway, and they just produce tons of tons of this antibody product that you can just grow them up in a big bioreactor or a 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 gene to the body to compensate for effective genes. If you have something like this spinal muscular atrophy example, where patients who have that disease,

Starting point is 00:16:16 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, a 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, there's an industry, the dominant approach, is to use a virus essentially. So what Christopher is doing is making specific, let's say, cuts of the DNA to either eliminate

Starting point is 00:17:00 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, they're 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 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

Starting point is 00:17:45 towards removing those pathogenic cells. 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, your post and also just by this, I think it's Moore's Law backwards or E-Rom'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-Rom's Law more generally.

Starting point is 00:18:14 the things that are slowing down the iteration speed cycling of, can we make a discovery, we've got patients in need, how do we shorten the time between a discovery and an implementation? 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.

Starting point is 00:18:46 Because if you come down too hard on drugs and our 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. They may be acting completely good intentions, and they'll test according to the process the regulatory agency demands and bring the drug to market.

Starting point is 00:19:17 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 GLP1 drugs now. We have a number of other drugs that treat diabetes effectively. And they're very effective drugs. 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.

Starting point is 00:20:14 demonstrate that anything you bring to market is really safe. You have to jump to all these tubes. 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. Come back to the AIDS issue. When you had the AIDS epidemic, there were a number of drugs that seemed to be promising. The pharmaceutical companies were developing at speed, like they did with the AIDS epidemic. COVID, right? A lot of companies were developing with anti-COVID drugs and vaccines. And the AIDS patients were quite rightly saying that we're going to die anyway. So we have a death sentence. You should

Starting point is 00:20:55 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, so AZT and DDI to the drugs. These turned out to not be particularly effective drugs, but you could argue that potentially by starting on that process of defining a pathway for how you treat AIDS, getting some drugs on the market, getting some initial revenue, you then spurred the process to iterate and invent drugs that

Starting point is 00:21:40 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. 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 of care or there's not so many

Starting point is 00:22:20 patients. So you've seen a lot of this investment in cancers and rare genetic diseases. The requirement for evidence is lower and the need is greater. 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 speed running video games and relating that to the process of discovery. discovering things that might be useful in the farmer 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. Yeah, so I think if you look at how things get discovered in the pharma industry,

Starting point is 00:22:57 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 once is a good example where you had decades of the scientists at Novenortes 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 that artful tinkering.

Starting point is 00:23:37 because we just haven't proven to be very good at developing drugs to a pre-described positioning, if you like. They're really important and powerful drugs get discovered from this bottom-up process where you have scientists who are really passionate about an idea. They push through 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.

Starting point is 00:24:16 But you can't prospectively figure out what drugs are going 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 Novanortis was regarded as a pretty boring European pharma company, not very innovative, just doing insulin. But now they're, 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 that would come out of Novenortis. But Novanortis was able to produce JLPs because they had this really longstanding interest in a specific area. Edibolid diseases, they support the scientists there, and they've just been tinkering in this area for a long time.

Starting point is 00:25:05 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. 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 Mario's 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 the castle

Starting point is 00:25:56 they were trying to jump up and finish a level much faster than previously possible. It's changed this whole way that speed running is approached for that game. Fee running itself is probably out of interest to that many people. But the general principle, I think, is interesting that you have this intuition that something is possible. With Jansen, when he was developing his drugs, he had an intuition that you could improve this compound pathodine 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,

Starting point is 00:26:29 to feed 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 that you might otherwise be. So one of the big problems with pharmaceuticals in 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,

Starting point is 00:26:57 you can get some feedback on that. You can then iterate it, later instantly encode effectively, and then show the next demo to customers, get more feedback. You can do these A, B, tests, massive scale. You can move super fast and learn very quickly.

Starting point is 00:27:10 But Varma is a very hard domain to learn quickly because everything takes so long. Clinical trials will take around 10 years to get everything to clinical trials. Even to do animal experiments, it takes weeks or months, do animal experiments in certain cases. Making all these chemicals takes a long time.

Starting point is 00:27:27 You're testing even in the very earliest stages. So you learn at a very slow rate about 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, like drug prices, just a huge healthcare spend, things like that. I don't say I figured to all this, obviously alpha fold within the mainst hearing consciousness. I think Jensen Wong, the CEO of NVIDIA was recently talking about how much explosion of activity there is using AI in the world of medicine and biology.

Starting point is 00:28:01 So talk about the role that this new set of tools might play in both discovery and speed, in tinkering and all these different ideas. 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

Starting point is 00:28:36 and the expanding toolbox that drug developers use to produce and test molecules. Alpha folds, 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 alpha fold. And a lot of people are saying, oh, alpha fold is going to revolution, is 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 alpha fold could be a helpful starting point for that specific circumstance. But actually, Alphald has problems with the very granular predictions of the

Starting point is 00:29:18 configuration of the sites where a drug 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 Darfurums. It's definitely speeded up parts of that process, but a very small part. And I think the way I is going to impact the farm industry looks similar to that.

Starting point is 00:29:48 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. 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.

Starting point is 00:30:16 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 AI2, 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 the otherwise might have been. 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?

Starting point is 00:30:59 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 involving them seems pretty impactful. If you were just a 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? What would you change that you think would most benefit patient outcomes or the overall system? I would make regulatory changes. I think I would want to formalize the idea that different

Starting point is 00:31:34 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 because 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. They maybe have too few patients, or they may have two few patients plus the

Starting point is 00:32:15 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 confidence. 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. Because you're just never going to get drugs for certain disease, it's just un-economical to achieve this vision of hyper-personalized medicine,

Starting point is 00:32:53 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 seem promising. But then the company's meant to do a confirmatory trial to determine, okay, actually we have a good

Starting point is 00:33:32 reason to believe this drug is going to work, 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 regulator should be much more stringent in forcing companies to collect that data and report it and takes the help of the market. So this combination of being more open to approving drugs based on a lower standard of 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 in the regulatory process. Surrogates as well, I think are really important.

Starting point is 00:34:09 So surrogates are measures like biomarkers that you can use as a proxy to tell whether a disease is being treated effectively or not. 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.

Starting point is 00:34:39 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, they otherwise would have been many really effective drugs because they've used these surrogate markers of efficacy like tumor shrinkage. 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 there's a tumor. gets smaller or not. If you're treating a disease that's eventually fatal, if you see that this

Starting point is 00:35:14 biological response happens early on, then that's very highly predictive of improves 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 into 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. You don't have to wait five, ten 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

Starting point is 00:35:52 out a surrogate for aging. 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 RCT? and learning. 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 you 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

Starting point is 00:36:37 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, then the doctors may be more inclined to give the drug to patients who are sicker, who really need it. And they're less inclined to give us 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 of doctors giving

Starting point is 00:37:15 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 and retrospective data that looks at vitamin D levels and outcomes is that you see people with low vitamin D levels have generally worse health outcomes. So they have high rate mortality, they have cardiovascular disease. I think it's probably been correlated

Starting point is 00:37:43 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

Starting point is 00:37:59 when they assign people randomly to groups and they give supplementation of vitamin D and then they measure outcomes. and 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. So how did that happen?

Starting point is 00:38:20 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 stimuli, 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 push-ups.

Starting point is 00:38:46 They say if you can do less than 10 push-ups, then you're much more likely to die. Not literally the ability to do push-ups that determines how likely you are to die, is just that being able to do push-ups is a decent proxy marker for general health. So RCTs are just the most effective way we know of 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.

Starting point is 00:39:12 Can you imagine an alternatives, or do you think this will be and needs to be the method by which we learn truth and efficacy across medical interventions? 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 because even if you control for the sources of bias that you think are likely to exist,

Starting point is 00:39:39 there'll be hidden sources of bias that you just can't control for. So I think we want to keep RCTs as a conformatory 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 foreshamper 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 these patients would have progressed had they not had they not had the drug.

Starting point is 00:40:10 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 randomized trials. And then you have these instances of super rare diseases where you may have just a handful of patients in the U.S., 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.

Starting point is 00:40:38 You can't use RCTs. There's a lot of innovation in this digital twin idea and natural history cohorts. Obviously, a big driver of all this is profits. We talked about how people don't like pharma in part because it charged 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 U.S. has always been leader here. Can you talk about what you've learned about blockbuster drugs specifically? You can

Starting point is 00:41:07 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. I love to hear what you've learned there. Yeah, so a blockbuster drug is a drug that makes a billion dollars in annual revenue. So blockbusters are really, outside imports in the pharmaceutical industry because they account for a huge proportion of the overall revenue of the industry. 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

Starting point is 00:41:51 recoup the money who's invested in them and a few really huge winners that will go off and generate billions of 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 and having a mega blockbuster like Humair or Ktrudor, the COVID vaccines that generate tens of billions of dollars in revenue a year. Because if you look at all the drugs that get launched, it's sad that you have all this work that goes into developing a drug and it takes maybe 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

Starting point is 00:42:32 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 30 to 40% of the 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 170 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 a big, you have a venture capital, it distorts a lot of the incentives. So you see a similar thing in biotech investments that you do with VCs,

Starting point is 00:43:16 where big farmers 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 these small markets that patients really would like to have, 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 small genetic diseases, but it's just not worth Big Farmers' time or even Ballytech'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 to clinical trials, you maybe make a few tens of millions a year

Starting point is 00:43:53 and that's just absolutely not worth it because it will cost you 100, 200, 300 million just to get the drug through the whole development process. So there's a lot of, I think, alpha that could be unlocked in making the process much cheaper in how drugs are tested and validated because it would mean that it's actually worth developing all these drugs that are not worth developing. 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 is something that comes up all the time as predatory or strange or

Starting point is 00:44:27 U.S. system 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 do you change things? So I think drug pricing is really difficult. One thing I guess to say about the U.S. is the U.S. has by far the highest prices in the world. I think people recognize that. It's about twice higher as Europe on a net price basis. And the U.S. market for drug is something like 40% of the global market, and it's something like 60% by revenue of newer drugs.

Starting point is 00:45:03 So drugs launched in the past 10 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, but if you think the market will bear.

Starting point is 00:45:24 and in a reasonable world where all the incentives are aligned, 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 valuable drug based on metrics like how many quality-adjusted life years to this drug give us, what's the benefit of this drug over the existing standard of care, and how much better is it

Starting point is 00:45:55 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's satisfied everyone. If you look up the profit margins of drug makers,

Starting point is 00:46:06 they make something like 10 to 20% profit after you take out of their costs and the gross margins there's something like 80%. But really, that's a distorting picture of the actual profitability

Starting point is 00:46:17 of drug companies because there's a distortion between when R&D expenses are paid even 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.

Starting point is 00:46:40 It's very much like a lottery model that is just the 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 super normal, profits. 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 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

Starting point is 00:47:20 to be worth investing in developing drugs. the incentive needs to be very strong. And because such a smaller 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, 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&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, it's true to be worthwhile you paying hundreds of millions of

Starting point is 00:48:02 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 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 incentive to even develop any drugs. 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. What about prevention? We've talked entirely about interventions where something bad happens in the body and we've developed ways of treating

Starting point is 00:48:47 that thing or making it better. What about similar research that could go into, what whether it's lifestyle or other things that we do before these bad things happen to us, they would prevent them from happening in the first place. Any thoughts on that side of the ledger? Yeah, I think prevention is difficult. People in the U.S. often say that the reason why the U.S. 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.

Starting point is 00:49:17 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 should be wanting to invest in those things. So is the problem really that there's a lack of supply preventative treatments or is it really that there's a lack of demand for preventive treatments. And I feel like the issue is probably more on the demand side where people, unfortunately,

Starting point is 00:49:56 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 a Zempic 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 Zemphic 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.

Starting point is 00:50:28 So you think about like the healthcare system. 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.

Starting point is 00:50:57 I 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 worry. I think what I'm most excited about is not a specific technology, but more this Cambrian explosion, if you like, of modalities and 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

Starting point is 00:51:27 of treating disease that are becoming established and have a lot of potential. So you have CRISPR, RNA interference, these carty 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 scaffold

Starting point is 00:51:55 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's already in some ways becoming a bit of an outdated technology. you have a lot of investment in what's next.

Starting point is 00:52:15 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

Starting point is 00:52:40 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 and technology our ability to control systems is really interesting. I'm interested in just general nearly new business models of the industry. So one trend you've seen over the past, I think it started probably in the 2000s, is this greater externalization research. So pharma companies, big pharma companies used to do a lot of research in-house. They've mostly externalized that to swallow biotechs that are venture capital funded. So the big pharma companies are really just like commercialization machines all that time. And they do maintain

Starting point is 00:53:21 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&D, externalized, to little biotechs. We have running clinical trials being externalized to these clinical research organizations 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 commercial analysis will be externalized in the future as well.

Starting point is 00:53:55 So you may end up with other companies just being a 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 it 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 tradeoffs there, but there's a lot of redundancy in the industry.

Starting point is 00:54:20 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. 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

Starting point is 00:55:03 that are very hard to copy. So even though once they do go generic, no one's able to copy me. So things like these radio, radio therapies, talk about Cartes, 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

Starting point is 00:55:18 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

Starting point is 00:55:33 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 there's a science-driven industry, it isn't an innovation-driven industry, the commercial parts of Big Pharma should be in service to the R&D portion of the organizations or the ecosystem.

Starting point is 00:55:53 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 innovation are down too much and maybe you get too many people in 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

Starting point is 00:56:23 an existing market, then you're not going to get the kind of meaningful breakthroughs like intruder, these immune oncology drugs, the GLP1 agonists that we're seeing now. So yeah, that's one thing I worry about. It's been 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 of it 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? I think this has a pretty obvious answer in my case.

Starting point is 00:56:54 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 now, while other people have done me many kindnesses, nothing really compares to my parents have done. Alex, thank you so much for your time. Thanks. If you enjoy this episode, check out join colossus.com. There you'll find every episode of this podcast complete with transcripts, show notes, and resources to keep learning. You can also sign up for our newsletter, Colossus Weekly, where we condense episodes to the big ideas, quotations, and more,

Starting point is 00:57:31 as well as share the best content we find on the internet every week.

Invest Like the Best with Patrick O'Shaughnessy - Alex Telford - Unlocking Innovation in Pharma - [Invest Like the Best, EP.360]

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