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

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1:23 To learn more, visit psum.vc. Mm. Our 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 twenty nineteen, keeping a pulse in the direction of innovation.

1:44 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. Alex, we're gonna 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.

2:19 incredible technologies for our health. And also the impediments to creating more of them in the future. Can you give me a little bit of a Your version of just a sweeping overview. Oh

2:30 The process and evolution of how we find Drugs Yeah, so it starts with academic research. This is mostly funded by government research and it occurs in universities.

2:41 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 things?

2:56 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 a disease

3:11 You then have to do a lot of work to convert it into a drug. So these initial hypotheses, these But target specific proteins, R and A, whatever. You can modulate to Affect the disease.

3:21 I then Taken up by Mostly. For profit. biotech companies.

3:27 Who then do all the hard work of turning this into a drug. Producing a molecule that can modulate these targets. Either inhibited or Increased activity. In a certain way.

3:37 And then Running everything through. First. these sort of in vitro systems uh Just basically sells in a petri dish.

3:45 Then after 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.

4:00 Phase two where you try to get a sense of initial efficacy of the drug. And then face three which confirms the efficacy and safety. To the extent we can, and then you have to go to the regulators. FTA em uh

4:12 The MDA. Until 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.

4:21 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 read about this guy, Paul Janssen, maybe you can describe who he was, but it seemed like in the early days of this research.

4:39 A single person or lab could produce dozens and dozens of things that get used for a long time and now Think you said somewhere in there, like the average researcher will not work on anything that ever gets the production and use in trials or practice. So give us a history of how efficient we are at finding new stuff. What the important timeline points are.

4:59 And not the mall. So if we go back to let's say the end of the nineteenth century. Where You have a really nascent pharmaceutical industry. So you have pharmaceutical company that came out of Apocryers, which were selling

5:12 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.

5:24 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 thibus grams, 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 I

5:40 our ability to understand chemistry and use chemistry to Develop our own molecules was developing because back in the nineteen hundreds. Some of these dye manufacturers were figuring out ways to Just adapt dichemicals and use them for therapeutic purposes. those companies eventually became more sophisticated at

5:58 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. Around the early twentieth century.

6:11 So Then you had Some point. The golden age of antibiotics between the two world wars. became the first great success story.

6:21 of the pharmaceutical industry. So moving away from Products like these natural extracts, it 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 for bacteria.

6:40 And would purify them. And That worked for a while. But limited to infectious diseases for the most part. And then in the sort of

6:48 You start getting People like Janssen So Yansen was a

6:55 Belgian. Doctor he trained in medicine. And his dad was actually one of these Importers of some of these natural products, so organ extracts. Jansen recalled when he went to school that

7:06 Is in medical school his contemporaries were making fun of him for Be part of a family who sold these sort of ineffective organ extracts. And so yeah, it's a bit of a chif over the shoulder, I think, from that. And he wanted to figure out. Adapt.

7:19 How can we actually Improve on Some of these compounds that nature has given us. So he went to the US for a while, he realized that he went for nineteen forty eight. There was some

7:28 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 and started his own lab in nineteen fifty two. And started trying to tinker with natural compounds.

7:44 And use some of the emerging tools of chemistry to just adapt. some of these existing compounds that had been you know discovered already. and find new uses for them. So this fifty is 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 new useful compounds. So you started with these functional compounds that will be found from nature.

8:05 We had a lot of starting points. Morphine, pecity, and atropine. That could be to get around with to find useful things that are close to those natural compounds. But not exactly the same, they're substantially different effects.

8:16 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 that were developed in that period of time. For the pharmaceutical industry.

8:30 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

8:42 What is easy to find. 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.

8:53 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.

9:02 And that's how you get a lot of those things like the humanized insulin. monocule antibody is There's all these like biotech products that are now somewhat still in descendants. So you have sort of two curves overlapping. The fall off of the kind of exhaustion of low hanging fruits in traditional pharmaceuticals and then the growth in what we can do.

9:19 with biotechnology tools like The component 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.

9:34 in small molecule pharma, what is the low hanging fruit of today? I love to hear 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 have another explosion like what we saw early in the industry. History. If you look at the history of technologies In biotech.

9:55 It takes a long time to Commercialise something. Going back to the idea. Two discovering targets in academia. As soon as you have an idea for a target, it takes something like twenty years for that to turn into a drug.

10:06 That's sort of time period twenty years plus minus ten. 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 manufactured, 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, Q True D.

10:31 Previously was humor, uh. They're both monoclonal antibodies. And now the processes To develop, produce. And

10:37 sell those monoclonopolis 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 gonna be really impactful. in the next ten, twenty years you need to look at

10:50 What is Just nascent today and getting approved. So things like gene therapies. We have a few gene therapies that have been approved. So Zul Genzo is a treatment for spinal muscular atrophy. It's this really devastating infant They're a muscular disease. Patients who have that disease would die before they're one year old in the most severe type.

11:08 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 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'll have down the line's future successes. So I think Another ten years, another twenty years we'll see.

11:29 just the process for developing manufacturing gene therapies, all those kings 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.

11:45 You mean so 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 reinfused into the body and then it goes and eliminates these pathogenic blood cancer cells. And that's an extremely complicated manufacturing process.

12:06 You're taking these cells out of human. You're flying them to a 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

12:19 if patients have very severe attacks of cancer. And if you don't get it fast enough. They're gonna die. There's a lot of kinks in those processes that need to be ironed out before These technologies become scalable beyond

12:30 These niche use cases. One interesting example is This company, Bristol Myers Square. So they bought a company called Cell Gene. Who are pioneers in this type of CART cell therapy. And they have

12:40 the same amount of employees working on delivering these compounds as they have treated patients to something like four thousand each. The high touch. process expertise and scale you need to deliver these super complex next generation therapies like CARTs, 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 skip it out.

13:03 So I think we're gonna see a lot more of these type of Process like Products that are way beyond just a standard pill. And it's much more

13:11 a whole complex Large The actual drug itself is a complex product. There's also a whole process around the drug. Uh delivering it to patients in a timely fashion. Uh

13:22 Manufacturing, which is very complicated. Another good example is radio pharmaceuticals. A lot of companies have been investing in this lately. Buying up biotechs developing these drugs. And these are

13:32 compounds where you have Targeting element. Let's say cancer. And then you have a radionucleiide, 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.

13:47 Because the half life of summit of these radioactive compounds is something like seven days. Need to manufacture and deliver it to the patient. Within a few days before they lose efficacy. So these Complex therapies are one

13:58 Way of farmers. Trying to build up motes. Two. Distribution uh products. Can you say a few words? There's four categories there that

14:07 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.

14:24 So I'll start with just antibody, right? So when you get infected with Some sort of virus or bacteria. You're Immune system will generate antibodies against The invading thread. And these antibodies are specific to

14:38 Thore proteins. Your body's always generating all the time all these antibodies. And when a virus infects you Then If you have an existing antivoid that binds that virus, it'll multiply within the body.

14:50 And then it will bind to viral copies and it will stop the infection by just finding and neutralizing these Viruses or bacteria. And that was Recognized as

15:00 Yeah. Antibodies combined proteins on These four microbes. It can also probably be useful for binding Other molecules

15:10 Or other proteins in the body that Are necessarily foreign, but may impact seeds 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

15:23 It's an antibody that binds to Uh protein cools. TNF Alpha. Which is an inflammatory protein. People who have arthritis and these inflammatory conditions.

15:36 will have an excess of this protein that just causes a immute 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.

15:50 of a certain protection. If we want to treat cancer. Cancers have a different composition of proteins on the surface than normal cells. You can But in antibodies that bind those cancer cells.

16:02 and kill them specifically. 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

16:15 A cancer cell. Essentially. And immortalize the cells that produce antibodies. That's how they make it into a scalable manufacturing process. You have

16:23 These Cells that we're going to do. are artificially immortalized. And they just produce Tons of tons of this antibody product that you could just grow up in a big bioreactor of that.

16:33 And then After a certain level grow in and then you skim off the antibodies. Gene therapy is this idea that you Can Insert gene to the body to compensate for defective genes.

16:43 If you have something like the spinomuscular atrophy example, where patients who have that disease they lack a functional copy of a gene called SMN one. Which produces a protein that your motor neurons need to stay alive.

16:56 Essentially. And if you can Package up. that gene and deliver it into a cell. The cells that are missing this functional copy

17:04 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 with figuring out how to

17:18 Deliver gene to cells? And the approach that we've landed on now is an industry, the dominant approach. Is to use A virus, essentially. So

17:26 What Christopher's doing is Making specific Let's say cuts the DNA. Gene.

17:34 That you want to eliminate or potentially down a line to Insert. New functional copies of sheets. And then cell therapy is This idea that you can

17:44 Edit cells. Immune cells in the body. to program them in a way to do something that you others 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 Thorin cells and microbes.

17:57 That The body's a lot of systems to prevent Immune cells from killing. Your own cells. So what cell therapy is doing is

18:05 Taking out some of the mean cells. And Tweaking them so they'll bind and recognize Pathogenic cells, like cancer cells. That may look to the unedited immune system.

18:15 Similar to a normal cell. And then redirecting the immune system towards removing those pathogenic cells. Can you talk me a little bit about the Potential speed.

18:26 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 Rum's Law or something like that. Talk about the episode with the AIDS epidemic and the frustration.

18:41 with those that could have benefited from some drugs and just the slow process of being able to get them And Eram's Law more generally. 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?

18:58 The big tension in how you regulate drugs. Is How do you balance the need to developed goes 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.

19:12 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 I don't know manufacturers that make them jump through too many hoops. Then it's just gonna be uneconomical to develop drugs and there's gonna be a massive

19:28 Invisible graveyard. Uh. People who are. Could have been saved. Had the drug come to market faster.

19:33 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 A demands and bring the drug to market. And then it turns out later that it causes some harm.

19:52 That we could have foreseen if you 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.

20:03 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.

20:13 So if you have Diabetes or something. Then We have insulin, we have many varieties of insulin. We have

20:21 The GLP one drugs now, we have 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.

20:32 With great haste. To treat large number of patients. who are being well treated by current drugs. So Under that situation.

20:40 You probably as a regulator are inclined to But really high burdens on drug makers. So you have to absolutely demonstrate anything you bring to market is really safe. You have to jump through all these tubes, 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.

20:57 And Patients are dying or suffering Greatly from their condition. Then It makes sense to potentially relax the regulatory burdens.

21:06 Combat AIDS issue. When you had the AIDS epidemic. There were a number of drugs that seemed To be a Promising.

21:13 the pharmaceutical companies were Denoting at speed. Like they did with Covid, right? A lot of companies developing will do. Anti COVID drugs and vaccines. And the eighth patients

21:22 Where quite rightly saying that we're Gonna die anyway. So we have a Death sentence. You should let us Try these drugs.

21:29 And See if they're effective. Because the alternative is death. And the FDA Until that time.

21:37 didn't really have a process to deal with these differences. In knee 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.

21:52 ended up speeding a number of these compounds. Two development. So A Z T and D D I to the drugs. These turned out to Not be particularly effective drugs.

22:01 But you could argue that potentially By starting off that process. of defining a pathway for how you treat AIDS, getting some jobs on the market, getting some initial revenue. You then spurred the Process to iterate and invent drugs that are now very effective. So now there are very effective treatments for AIDS.

22:16 this initial speeding of the regulatory process during the eights. Epidemic. turned into what became the accelerated approval pathway. And That is for drugs of seem promising in disease of very high on net need.

22:28 Like A it was Like a lot of cancers are. Drugs couldn't get through regulators. With much less evidence than They can in other conditions like diabetes where the standards might be higher.

22:39 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 regulation'cause you don't need to do all these really huge phase three trials because there isn't a good standard care.

22:52 Well there's not so many patients. So you see a lot of this investment in Cancers and Regenetic diseases. The requirement for evidence is lower and the need is greater.

23:01 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 discovering Things that might be useful in the pharma world and the biotech world. Maybe flesh that idea out of just your interest in

23:21 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 farming industry. It's Often very serendipitous.

23:33 So you'll have these stories of People Tinkering away on some idea they think is promising for years and years. becomes a drug. So like the GLP ones is a good example.

23:44 Where you had Decades of The scientists at November Nordis. Pushing for this idea. Tinkering around the boundaries of

23:52 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

24:05 Try and Find ways of Promoting that artful Tinkering. 'Cause we just haven't proven to be very good at

24:14 Developing drugs. Two A pre described Positioning, if you like. The really important and powerful drugs get discovered from this bottom up process.

24:23 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

24:36 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 things. They'll say that. We knew it all along or something in retrospect.

24:50 You can't prospectively figure out. What drugs are gonna be really impactful a lot of the time. So I think it's quite dangerous in it. industry like pharma, which is so innovation

25:00 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.

25:10 I think Nova Nordis was regarded as a pretty boring European pharma company. Not very innovative. Just doing insulin. But now they're It's like the fifteenth biggest company in the world or something and

25:22 Everyone's excited about the GLPs. You would have necessarily predicted that would come out of Novan Nortis, but Nova Nordis was able to produced GLPs because they had this really long standing 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.

25:38 So to Come back to the Super Mario idea. That post is about This idea that One of the ways you might productively use

25:47 technologies like artificial intelligence and simulation tooling. Is to Have an intuition about What areas? Of science might be promising.

25:56 And then use automation to Do scalable tinkering around that nucleus of an idea. So the example in Insig Mario was that There is this Idea that you could do

26:08 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 Scatter Shot, which simulates millions and millions of Mario's jumping up this level and every possible configuration of jumps and starting positions. You managed to figure out there actually was a way to get up to the top of the castle they were trying to jump up and finish a level much faster than previously possible. It's

26:32 change this whole way that speed running is approached for that game. Free writing 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, which Anson when he was developing his drugs, he had an intuition that you could improve This compound.

26:49 Cetadine. Two Make a better drug. But we didn't exactly know. How to modulate it.

26:56 To make it the best drug it could be. He was just tinkering around. So if you could use technologies to automate a scientist Two Fear of the process. Of tinkering, trying out solutions, testing them.

27:07 Then you can tighten the feedback loop. Around the industry and maybe develop drugs a lot faster than you might otherwise be. So I think one of the big problems with pharmaceuticals is an industry. Is that

27:17 The iteration speed is super low compared to something like tech. Like in tech. Yeah. You have an idea for a product. You can spin up. A demo.

27:26 And A few days. And you can show it to some customers, you can get some feedback on that. You can then iterate it. Effectively.

27:36 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. But Pharma is a very hard domain. To learn quickly. Because everything takes so long. Clinical trials.

27:49 Well tape. Around ten years to get everything through clinical trials. Even though 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.

28:02 So You learn at a very slow rate. That what works. And if you could just find ways to Speed that up.

28:09 Cross the whole thing. Process. Then you'll do a a lot to I think. address some of these problems with the farm industry, like drug prices.

28:17 Just a huge healthcare spend, things like that. I'm saying I figure into all this. Obviously Alpha Hold within the mainstream consciousness. I think Jensen Wong with CO of NVIDIA was recently talking about How much explosion of activity there is using AI in the world of medicine and Biology.

28:33 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.

28:47 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.

29:00 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

29:12 Produce and test molecules. Alpha Volt's an example where Now you can predict the protein structure for a protein sequence, which is a problem that We didn't know how to do to a high

29:22 Degree. Until Before Alpha Fold. And a lot of people are saying, Oh, AlphaFold is gonna revolutionize drug discovery. But in reality.

29:31 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 Fall could be a helpful starting point for that specific Circumstance.

29:44 But actually Alphafold has problems with the very granular Predictions of the configuration of the sites where drugs might bind. So it's not completely accurate. So we've more accurate models. For it to be really useful for drug discovery.

29:59 And To improve Alpha Fold. We need more data. It's just take a long time to collect all this data. So Alpha Ford is trained on

30:06 decades of protein structures that have been collected painstakingly by researchers growing crystals. In Darth Rooms. It's definitely speeded up. Parts of that process. But a very small part. And I think the way I add an impact.

30:19 The farm industry? Look similar to that. A lot of little tools that are gonna be Very useful and aggregate, but they're gonna 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

30:33 Drug discovery is rate limited by Our ability to design molecules. And designed your drugs? And that's not really true. our ability is much more rate limited by

30:43 The downstream clinical development. And testing these drugs and gathering information. We need to actually Justify approving these drugs for sale and marking them. Well I'm optimistic about

30:55 over long term using things like A I two Predicts. What drugs might be. Effectives.

31:02 To do drug discovery and drug candidate selection. I'm much more optimistic about using AI in the short term too. help drugs get to market faster than they otherwise might have been. So things like Can we use

31:13 A I to Help prepare regulatory documents quicker. Can we use AI to do things like Helps. Companies.

31:22 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.

31:35 Time? So Just better tooling. to identify patients who might be a good fit for a trial. And it probably them seems pretty impactful.

31:44 If you were just the czar of this entire universe and godlike powers Why? Two or three changes would you make? regulatory technology wise, industry structure, anything that you could change.

31:56 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 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

32:15 Should have quite a high standard. of evidence. So you should have to enroll very large numbers of patients. And Test mortality.

32:23 '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.

32:33 With a weaker standard 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

32:44 Tell 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 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 gonna be able to determine this in a trial. We have to based on some principled reasoned

33:06 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.

33:18 What's on the market? 'Cause you're just never gonna get drugs for certain diseases. It's just uneconomical to achieve this vision of type of personalized medicine. Where you're Developing a drug for Everyone's individual condition in biology without some ability to

33:32 make inferences about what is likely to work based on early evidence and genetics and reasoning about the mechanism of action. So I think the second thing I would do is just Be much more vigorous about Data collection, post marketing.

33:44 So you have a lot of drugs that Go on to the market. And then They're approved and then we're not really Following up.

33:50 sufficiently to be able to tell if they really work. So there's been some instances of drugs that have got this accelerators approval. On some Fairly flimsy evidence. But they seem promising. But then the company's meant to do a confirmatory trial.

34:02 to determine okay, actually we have a good reason to believe this drug is gonna work. But We don't actually know that. We've collect this evidence over Five, ten years or whatever. The companies drag their heels on doing the confirmatory study.

34:15 I think the regulators should be much more stringent in forcing companies to collect that data. And report it and takes to 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 trail.

34:32 Coupled with more rigorous data collection and then taking things off the market when they're not working. I think that'll help increase the sort of rate of learning in the regulatory process. Sorgoods as well, I think, are really important. So surrogates are measures like biomarkers that

34:45 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.

35:01 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.

35:11 So it's actually you're doing net harm with that drug. The idea of a surrogate is it? You have A measure Like tumor shrinkage. That predicts benefit down the line.

35:21 And that helps you develop a drug much faster. A lot of drugs have been brought to market much faster in cancer than they otherwise would have been many very effective drugs because they've used these surrogate markers of efficacy like tumor shrinkage. But it's harder. Other conditions like more complex chronic conditions.

35:38 Where you don't have An objective measure that's as simple as does a tuber get smaller or not? If you're treating a Disease that's Eventually fatal.

35:46 If you see that this biological response happens early on, then that's very highly predictive of improved survival down the line. And just finding more of these surrogates is a really I think a 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.

36:04 the the regulators could 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 could decide whether not to continue or discontinue based on that. You don't have to wait.

36:16 Five, ten years to see if someone survives or not. So something like aging. We're never gonna get Drugs too. Along people's lives.

36:24 Until To figure 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, R C T is an incredibly important. Process.

36:36 That's helped us learn a lot. What, if any, limitations are there? Your mind to R C Ts and Learning. So the big limitation to RCTs is

36:46 They take time, they're complex to run. And then people don't want to be in the control room, essentially. So you have to expose patients to something that they don't want to take really'cause you're entering an R C T for a drug You want the active drug. But maybe take a step back.

36:59 So The idea behind an R C T is that You Have Patience.

37:05 Or people who are gonna 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.

37:17 There's gonna 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.

37:27 May Be more inclined to give the drug to patients who are sicker. Who really need it. And they're less inclined to give this experimental drug to patients who seem like they're gonna recover anyway.

37:36 So if you just Do that non random allocation. You're gonna 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 the drug to People who are less likely to do it.

37:50 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.

37:59 In Data and retrospective. Data. That looks at Vitamin D levels and outcomes.

38:06 is that you see people with low vitamin D levels. Have Generally worse health outcomes. So they have irritate metality, they have cardiovascular disease. I think it's probably been correlated with Every bad thing you can probably get.

38:18 And There's a natural Inference there that We should just supplement vitamin D. To the people who are low levels. And then

38:25 they'll have less rates of all these bad diseases. So then people do the trials. And They do a randomized controlled trial when they decide to go randomly to groups.

38:34 And they can Supplementation 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

38:44 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? One.

38:53 you find out that something like vitamin D is actually a marker for poor health in general. So vitamin D is produced Fire body. In response to sunlight. And if you

39:04 Are ill. Or poor helpster money. You're less likely to go outside. Or You may be less mobile in general.

39:11 So you make it less exposure to sun. And then you'll see correlation between just general poor health. And vitamin D. It's like with push ups. I they say You can do less than ten push ups then You're much more likely to die. No, literally the ability to do push ups that determines how likely you are to die is just that

39:27 Being able to do push ups is a decent proxy marker. for general health. So R C T s are just the most effective way we know. Of eliminating all these biases.

39:36 That you don't know exist. From the start. 'cause otherwise it's just everything you try. Gets really confounded. You don't even know how it's confounded.

39:45 Can you imagine an alternative or do you think this will be and needs to be the method by which we learn Truth and advocacy. Cross medical interventions. So I think R C Ts are always

39:56 Going to be the gold standard? And I think when we can do R C Ts reasonably, we should do them. But they just want to be a little bit more. Eliminate so many of these spice problems that you just aren't aware of. That's because 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.

40:14 So I think we want to keep R C T s as a conformatory method. But there are Other tools. In these instances where R C Ts aren't feasible that have a lot of promise. So things like

40:24 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

40:35 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. So there's been a few approvals.

40:44 That have used some of these matched natural history data as a as an alternative cohort. And those have been in cases where there's been too few patients to really do Large scale randomized trials. And then you have these instances of super rare diseases.

40:56 Where you may have Just a handful of patients. In the US, for instance. It's literally impossible to run an R C T and have any reasonable statistical outcome. You need to use things like

41:07 Digital twins. Or surrogates. single arm trials, you can't use R C T. There's a lot of innovation in this digital twin idea and natural history cohorts. Obviously a big driver of all this is profits, so

41:19 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

41:30 Profits and revenue drive. 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 many power law here that

41:47 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. Yeah, so a blockbuster drug is a drug that makes A billion dollars in annual revenue.

42:03 So blockbusters are really have an outside importance in the pharmaceutical industry because they account for A huge proportion of The overall revenue of the industry. The way the economics operate. It's pretty analogous to

42:17 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's invested in them. And a few really huge winners that will go off and generate.

42:29 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

42:38 For investors and for companies. Because there's always this potential. Of hitting it really big and Having a mega bluff buster like Humira or Ktruder are the Covid vaccines that generate tens of billions of dollars in revenue a year. Because if you look at

42:51 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 Ten years to get it to market or twelve years to get it to market. And

42:59 It launches and then it flops. Fifty five percent of drugs that launch that get through that 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.

43:12 Blockbusters. That thirty to forty percent of the revenue. of the whole industry is made by these blockbuster drugs. Which are quite a small fraction.

43:21 Of all drugs. When I look at the numbers. Something like one hundred and seventy. Blockbuster drugs. That's

43:27 For actively generating revenue. And then the whole long tail. Is making very small sums relative to blockbusters. So because

43:36 The industry operates like a lottery. type model where you just want to really hit it big, you have adventure capital. It distorts a lot of the incentives. So You see a similar thing.

43:46 In biotech investments that you do. with V Cs where Big farm is only really interested in developing a drug if it can potentially become one of these blockbusters. Anything else than that is just unlikely to recoup the investment. So you can load it small markets.

44:00 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. Like we have a good understanding of the mechanism. Like many small genetic diseases. But it's just not worse. big farmers time or even ByTech's time to invest in developing these drugs'cause the economics don't work out.

44:17 Even if you get through the whole process of developing this drug. And it works. And you can do 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

44:28 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

44:36 are locked in making the process much cheaper. In how drugs are tested and validated. Because it would be a good thing. Mean that It's actually worth developing all these drugs. They're not

44:46 Or it's developing. What do you think about drug pricing as a key variable in all 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

45:01 US system subside seems to subsidize a lot of the rest of the world. We make an outsized percent of the discoveries and You wanna 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

45:14 different than the norm, or how would you change things? So I think drug pricing is It's 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

45:29 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 counts for outside share of the revenue that drug companies make.

45:41 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.

45:56 But it would be the marketable bear. And in a reasonable world where all the incentives are aligned. What the market will bear is close to Uh actual value of that drug.

46:05 In healthcare you have all these strange incentives and markets that don't function properly. But in the rest of the world you have Systems where You have Governments. Doing the negotiation.

46:15 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

46:32 Flaws. I don't think it's possible to find a perfect system that satisfied everyone. If you look at the profit bars of the drug makers. They make something like Ten to twenty percent profit after you take out their costs and the gross margin are something like eighty percent. But

46:45 Really That's a distorted picture of The actual Profitability. of drug companies'cause there's distortions between

46:54 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's getting for their investment. And if you look at how much drug companies are getting for their investment

47:05 Actually most of them are pretty near zero or very low return on investing capital. The industry as a whole is not that great of a business. It's very much like a lottery bottle. The it's just type of lottery that's

47:16 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

47:25 Super normal profits. And you can point to the examples of people who have very high prices and making super normal profits, like Humira Twenty billion seems like an incredible amount of money. For a truck to make.

47:37 And I think if you just look at that isolated example, you can say Just seems unreasonable that anyone is making twenty billion off this drug. Per year. It's way more than What someone should reasonab for

47:47 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

47:59 produce so much of the revenue. It's like a real Pareto distribution. In revenue. You need these Lottery.

48:07 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

48:17 These super blockbusters. to make the economics work out. When you're doing an R and D. You're paying money that you have now. For

48:25 revenues in ten years, so From a temporally discounted Point of view. You need to think you're gonna get a really huge amount of money. In the future should be worthwhile you paying.

48:34 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 puppies, You actually are removing a lot of the incentives.

48:53 To even develop any drugs. And I just think you can do outsize damage. Buy Cutting down. Even just a small number.

49:01 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

49:09 How much you're spending on drugs. What about prevention? Entirely about Interventions. Where

49:16 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

49:27 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. Yeah, I think prevention is difficult. People in the US often

49:40 Say that the reason why the US doesn't invest in prevention It's'cause 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.

49:54 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

50:09 Either, even though they should in theory ex the whole nationalise health system. They shouldn't be wanting to invest in those things. So is the problem really that There's a lack of supply. Um

50:19 Preventative treatments, 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.

50:31 For a 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

50:38 The argument about And Why don't people just practice diet and exercise? That's a more sustainable way of dealing with obesity. The reality is that the people just don't do that.

50:48 So they would rather Just take Zenpik. When they are obese and have that treat their obesity. So I think this whole consumer attitude shift that needs to happen before preventative medicine ever becomes really established. It needs to be demand driven.

51:00 So you think about like the healthcare system. Who is really the end consumer of pharmaceuticals and healthcare systems. 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

51:14 They want that system to represent value for money for when they do get sick. So you have to And have the people who are healthy have a greater demand for Products of

51:24 prevention that then incentivize companies to invest in them, insurance plans and health systems to actually provide these services. 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 worry. I think what I'm most excited about is not a specific technology.

51:43 But More This Cambrian explosion, if you like, of modalities in biotech. So

51:50 For a while. I think back Maybe ten. Twenty years ago. There were that many different types of different treatment classes.

51:58 And since the rise of biotech, you have All these different ways of treating disease that are becoming established and 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.

52:14 Our biology? It's 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 his molecular scaffolds to push a system into a desirable state.

52:31 It's really interesting. And just This Flywheel of improving on the the modalities that we do have.

52:38 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. So instead of making cuts, you're gonna have these base editors that are changing specific letters in the DNA sequence. And that's a more effective way of precisely treating cergenetic diseases than making cuts, the CRISPR cuts.

53:00 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.

53:13 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. Second world explosion and technology our boss to control systems is really interesting. I'm interested in Just general nearly new business models.

53:30 Of the industry. So One trend you've seen. Over the past I think it started probably in the two thousands.

53:37 Is this greater externalization? uh research. So pharma companies They've mostly externalized that to smaller biotechs that are venture capital funded.

53:49 So the big pharma companies are really just like commercialization machines a lot of the time. But I do maintain some research labs, but the purpose of the research labs is 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 gonna bring in. Maybe you'll see greater and greater externalization of more and more functions. Now we have an innovation R and D externalized to little biotechs. We have running clinical trials being externalized to these clinical research organizations.

54:17 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 your commercial analysis will be externalized in the future as well. So you may end up with specific IP holding companies.

54:33 Class. 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.

54:44 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

54:54 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

55:05 No. 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 towards innovation and incentives to innovation. We'll end up with a system where

55:18 companies will decide, okay, I'm not actually incentivized to really Invest in doing Meaningful fundamental research and spending thirty years tinkering on some opportunity that may eventually Bear fruit.

55:30 I'm actually just going to Market the drugs I already have that are old. I'm gonna invest in drugs that are very hard. To copy. So even though once they do go generic.

55:40 No one's gonna be able to copy me. So things like these radio Right of their choice talk about car T's. They have a whole process associated with them, so they're quite hard to copy. And then you're just gonna sit on Existing treatments.

55:51 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.

56:06 this element of unpredictability to it. 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.

56:19 the commercial parts of big pharma should be in service to the R and D portion of the organizations or the ecosystem. And You should really be Like hiring good people with good intuitions about what's worth developing.

56:32 And letting the scientists tinker for as long as I need to on some of these really challenging Ideas at the forefront of what is possible. And give them enough time ten or twenty 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 for the managerial positions who are focused on the accounting aspect of it.

56:52 They just wanna milk existing products and copy the big one instead. we don't have an existing market, then you're not going to get a kind of meaningful breakthroughs Like Petruda, these immediately drugs, the GLP one agonists that we're seeing now. So yeah that's one thing I worry about. Just a totally fascinating overview of

57:09 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 here 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. And many people have done many kind things for me, right? But I think I just have to go with

57:30 Probably what is the most common answer, which is My parents have always been in 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 what my parents have done.

57:45 Alex, thank you so much for your time. Thanks. Mm. If you enjoyed this episode, check out joincolossis.com. There you'll find every episode of this podcast complete with transcripts, show notes, and resources to keep learning.

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