The Peter Attia Drive - #409 ‒ Inside modern drug development： the science, economics, and regulatory hurdles behind bringing new medicines to patients ｜ Lloyd Klickstein, M.D., Ph.D.

00:00:00 Hey, everyone. Welcome to The Drive Podcast. I'm your host, Peter Atiyah. This podcast,

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00:00:58 peteratiamd.com forward slash subscribe. My guest this week is Dr. Lloyd Clickstein, a physician,

00:01:07 scientist, rheumatologist, and drug developer who has spent more than two decades helping

00:01:11 discover and develop new medicines. After beginning his career as a physician scientist at Harvard

00:01:16 Medical School and the Brigham and Women's Hospital, Lloyd joined Novartis, where he helped

00:01:21 pioneer translational medicine and led the company's new indication discovery unit,

00:01:26 identifying entirely new disease and therapeutic opportunities. He later held leadership roles at

00:01:32 several biotechnology companies, including Versantis Bio, where he led the development

00:01:37 of BEMA before the company was acquired by Eli Lilly. Today, he serves as the CEO of Coslap

00:01:45 Therapeutics. I wanted to have Lloyd on because very few people have had a front row seat to

00:01:50 every stage of modern drug development, from identifying the unmet medical need, to the

00:01:55 discovery of new therapeutic targets, to navigating clinical trials, regulatory approval, and even

00:02:01 commercialization. While we use BEMA as a case study throughout this conversation, the real goal

00:02:07 is to pull back the curtain on how new medicines are actually created, why the process takes so

00:02:13 long and costs so much, and how scientists decide which areas are worth pursuing in the first place.

00:02:18 In the episode, we talk about how new drugs are discovered, developed, and ultimately brought to patients, the science and economics behind choosing which diseases and therapeutic targets to pursue, the differences between small molecules, biologics, gene therapy, and other drug platforms, why drug development takes so long, costs billions of dollars, and so often fails.

00:02:40 The story behind this one particular drug from its discovery at Novartis to its development as a therapy for muscle loss and obesity, what clinical trials, FDA approval, and patent protection actually involve, and how the next generation of obesity and muscle-preserving therapies may reshape the treatment of metabolic disease.

00:03:02 So without further delay, please enjoy my conversation with Dr. Lloyd Klickstein.

00:03:10 Lloyd, thanks so much for coming out to Austin. Great to see you again in person. It's been

00:03:16 probably six, seven, maybe eight years since we were last in person.

00:03:20 It might well be that long, yes.

00:03:22 So look, for folks who don't know you as well as some of us do, tell us just a little bit about

00:03:30 your background. You're a physician and a scientist, but talk a little bit about how

00:03:35 those two paths came together. Right. So my original plan when I finished college was to go

00:03:42 to medical school, following is basically the family business. But between college and medical

00:03:47 school, I worked in a laboratory at Brigham and Women's Hospital in Boston and found two things.

00:03:53 One, that I love doing science and two, that I was pretty good at it. And I ended up doing an MD

00:03:59 PhD degree. And that is the training, not required, but helpful for becoming a physician

00:04:09 scientist. And from there, I did medicine training. I was a rheumatology training,

00:04:16 and I practiced rheumatology for maybe 10 years and also had an NIH-funded research lab

00:04:23 doing very basic science on adhesion molecules in the immune system.

00:04:27 And that was also at the Brigham? Did you stay there after you finished your training?

00:04:31 I have a high activation energy for moving and doing other things. So I stayed there my whole academic career.

00:04:38 Yeah, that's right. Medical school, MD, PhD, the whole thing.

00:04:40 The only job I ever had until I left and went to industry, which was a little over 20 years ago.

00:04:48 and I went to Novartis Institutes, which was founded by Mark Fishman when he was recruited

00:04:55 by the then CEO of Novartis, Dan DeSella, to reimagine how research and early clinical

00:05:02 development are done in industry. And they basically brought the whole concept of translational

00:05:08 medicine to industry. Now, that term, I think, was coined at Oxford, somewhere in the UK,

00:05:15 for sure. And this was the first industry manifestation of it.

00:05:21 So Lloyd, there are a lot of specific drugs and indications, pathways and targets that I want to

00:05:28 talk about today. But I think before I go there, there's a black box that exists that is the world

00:05:36 you occupy that I really think it's unknown by the public, but I think it would be very

00:05:41 insightful if people understood it. And it is the process by which a drug is discovered.

00:05:47 The first person who I ever read articulating this was Steve Rosenberg, wrote one of my favorite

00:05:53 books about science called The Transformed Cell. This book is over 30 years old. I probably read

00:05:59 it cover to cover 10 times during the course of my life. But he starts with a question, which was,

00:06:05 hey, he's at a party one day and someone says, hey, how do you discover new drugs for cancer?

00:06:08 Do you just go into the kitchen sink and kind of grab things and experiment?

00:06:12 So, again, it's a very intelligent person can still have an enormous blind spot to what it is people like you do.

00:06:20 So take that in any way you want, but I think it might be a great foundation.

00:06:24 Let's start from a very broad area of medicine, not just focus on cancer, because cancer is a special case of a more general concept.

00:06:33 So I personally, and this may be different for different people, but I personally start

00:06:38 with the patients and the clinical indications.

00:06:41 And essentially, you're looking for what's not there.

00:06:44 So you're looking for a drug that doesn't exist or a therapy that doesn't exist but

00:06:50 is needed.

00:06:51 And then there are two broad categories.

00:06:53 There are incremental improvements, and then there are quantum steps in the concept of

00:06:57 drug discovery.

00:06:59 Incremental improvements are making a drug that you have to take less frequently, or

00:07:03 as we've seen a lot in the press recently, an oral drug instead of an injectable drug.

00:07:08 Or the same kind of drug, but works better.

00:07:12 And there have been great examples of that.

00:07:14 Think atorvastatin and rosuvastatin in a mevacorzocor world.

00:07:19 And those have been enormous commercial successes.

00:07:21 and there's a strong bias throughout the drug development infrastructure to do those kinds of

00:07:28 things because they're relatively low risk and there's not so much uncertainty. On the other

00:07:36 hand, to see indications that may not even have been described yet and don't have ICD-10 or 11

00:07:42 codes and have to deal with the whole complexity of making physicians and patients and payers and

00:07:51 everybody else aware of this and then creating a regulatory path for it, it's a heavier lift.

00:07:59 But I think that's where the biggest value comes to society and individuals and patients

00:08:05 in creating new therapies. And that's kind of what I do. And there's a lot of failure involved

00:08:12 in there. And the things that we can talk about are how do you get a new regulatory pathway

00:08:20 established. Been part of that once, soup to nuts. And then how do you sort of create new indications?

00:08:28 That's also a challenge. Maybe it would be informative to think about how my team and I

00:08:36 started something called the New Indication Discovery Unit at Novartis. So Mark Fishman,

00:08:42 who was the president of Novartis Research at the time, had a few of us come into his office and

00:08:47 said, we need to make the most needed medicines and find out what we're not doing that we should

00:08:54 be doing and get it started. And we had a budget for that. So super. As I mentioned, I start with

00:09:01 patients and indications and medical need. So starting there, we made a list of about 7,000

00:09:09 clinical indications that were unmet. All clinical indications. We just made a list of all the

00:09:15 clinical indications. It didn't have everything there. It only had the ones that people had

00:09:20 recognized at the time. But it gave us a framework in which we could start thinking.

00:09:26 And then it sounds like a lot, but surprisingly it isn't. It's maybe 10 or 20 pages of paper

00:09:32 at the time. And we grouped them into things that we were already working on. Things that were

00:09:40 rare genetic developmental things that would be difficult to approach. And the remainder fell

00:09:49 into maybe seven or eight different buckets. And then we started working on those. One of them was

00:09:54 healthy aging, and one of them was ENT, and one of them at the time was renal diseases.

00:10:00 And we should talk about that some more because that's the regulatory endpoint.

00:10:05 Novartis wasn't at the time working on liver diseases. One of them was those,

00:10:08 sort of fibrotic diseases in general. And there were some others. And we started our program and

00:10:15 it eventually grew to have dozens of projects and became a bit of a monster. And then we had to sort

00:10:21 of whittle it down a little. But some really interesting studies came out of that. One of

00:10:28 them, which I know we're going to get to later, is the Magromab for muscle diseases. It's a long

00:10:33 story there, but again, it came out of a medical need of frail elderly people. And I was very

00:10:40 mindful as a rheumatologist, I saw some of these people. If you, at the time, I haven't looked at

00:10:46 the data recently, but at the time, if a patient had to go to a nursing home, not wanted to go or

00:10:54 whatever, but had to go, the three-year mortality rate approached 90%. So being a frail elderly 0.96

00:11:01 person who has to go to a nursing home was worse than cancer. And there are a lot of serious 0.78

00:11:07 diseases in medicine that are worse than cancer in terms of clinical outcomes, but we don't treat

00:11:12 in the same way. And I think we should, but so we, we really embraced the concept of being a frail

00:11:18 elderly person and what could we do to number one, treat it, but then prevent it down the road. 0.99

00:11:24 Yeah. I didn't know it was that. I didn't know the three-year mortality for frailty 0.99

00:11:27 in that specific indication was so high, but that's a staggering statistic.

00:11:32 It was terrible. Again, it might be a little better now, but it's not good.

00:11:36 Yeah. Okay. So let's now talk about that next leap. So let's assume you or the scientists and

00:11:43 the team have decided we have an indication. There's a target we want to go for, an unmet

00:11:49 clinical need, and there's not an incremental opportunity, right? The example you gave is a

00:11:55 great one. I have six statins already. I'm going to come up with the seventh. Let's take that one

00:12:01 off the table. How do you begin the thinking around what we're going to do? And maybe, by the

00:12:09 way, for the listener, can you explain some of the different classes of molecules? I also think we

00:12:14 talk very quickly and casually about monoclonal antibodies, small molecules, biologics, but I

00:12:21 I think that that nomenclature might not be clear to everybody and maybe provide a little bit of

00:12:26 that as well. Sure. So let's start with what we call small molecules, which is industry jargon.

00:12:32 It basically means a chemical. Historically, the companies that became our biggest drug companies

00:12:41 started over a hundred years ago as dye companies because the chemistry is very similar for making

00:12:49 a dye is for making a drug. So these are just chemicals. The other class are biologicals,

00:12:57 which is pretty much everything that's not a chemical. Within that broader group of biologicals,

00:13:05 and actually there's a third group, let's say devices. So for biologicals, that could be

00:13:12 an antibody. It could be some other protein like a peptide or a soluble receptor. And

00:13:21 I think we're now creating a separate category of gene therapies,

00:13:26 which themselves can be complex based on how they're delivered or targeted.

00:13:31 And then let's talk about devices because that's a completely different animal and it's regulated

00:13:42 by its own group called CDRH in the FDA. And that could include things like a gadget you make in a

00:13:51 workshop. It could be an app. It could be something simple like a syringe that you use to inject a

00:14:01 drug or an auto-injector, which are very common nowadays, or some combination of those things.

00:14:06 And include all the way up to implantable?

00:14:09 Yes, absolutely.

00:14:10 And we don't have to go into it, but are there differences in the IP treatment of small

00:14:16 molecules and biologics? Are there longer patent lives or anything like that?

00:14:21 So patent law, I think, is the same for anything, but there are other regulatory conditions that

00:14:29 apply to one kind of treatment or another. For example, there are different exclusivity periods

00:14:36 for a small molecule versus a biologic, and they change periodically. And all of this is something

00:14:43 that's considered as you're working on how do you protect the drug you're making. This is a really

00:14:51 important point, and it's very topical now with the concern about expensive new drugs and how do

00:14:58 we make them available for people. And I don't think it's well understood or adequately understood

00:15:05 how patent law works about drugs.

00:15:08 Maybe we could take a few minutes and talk about that.

00:15:10 I'm going to preface my remarks by saying I'm not a lawyer.

00:15:14 I don't play one on TV, and I wouldn't if I were asked to.

00:15:19 But you've been to the rodeo many, many times.

00:15:22 Yes. 0.94

00:15:23 I was the clown.

00:15:26 So the way this works is if you think about, at a very high level,

00:15:31 Making a new drug that's used by many people takes longer, is more expensive, and takes as many people as building the biggest skyscraper in the world.

00:15:49 Think, you know, the Burj Khalifa.

00:15:53 Just think about that for a minute.

00:15:55 That's thousands of people, many years, more than a billion dollars.

00:16:01 And unlike a building which can ultimately pay for itself and pay all the bondholders

00:16:09 and provide a return to the investors over decades,

00:16:14 patent law gives a very limited term in which all of the investment and potential profit can be recovered.

00:16:23 And at the end of the drug's patent life, it is freely available for anybody to make for the rest of eternity.

00:16:33 That's the deal you make with getting a patent.

00:16:36 A patent is essentially a monopoly on being able to make, use, and sell the drug in exchange for telling everybody how to do it.

00:16:46 That's basically what a patent is.

00:16:48 Now, the term of the patent from the time you file it is 20 years.

00:16:53 and there are a few little things about extending it for a small further period based on how much

00:16:59 time it took to work on it. Practically, you get 10 to 15 years of exclusivity from the time it can

00:17:05 be launched. So a couple of points I'll add just for you to expand on if you like. Some people

00:17:11 might also be aware of the idea that not everything has to be patented. So for example, the classic

00:17:16 example is Coca-Cola. Had they patented the formula for Coca-Cola hundreds of years or I

00:17:22 I don't remember when Coke started, but call it 150 years ago or something like that.

00:17:26 We wouldn't be drinking the same thing today.

00:17:28 So they chose a different route, which is we are never going to make this secret public.

00:17:33 And in exchange for that, we will have no protection.

00:17:36 That's correct.

00:17:37 So you're describing a trade secret.

00:17:39 Correct.

00:17:39 That's another form of intellectual property.

00:17:42 Now, I assume that is not an option in pharma.

00:17:46 Well, surprisingly, it is.

00:17:48 Interesting.

00:17:48 There are some specific drugs where they were protected by trade secrets.

00:17:52 So a couple of my favorite examples are Armour Thyroid, which was a thyroid place.

00:17:59 Desiccated thyroid hormone, yeah.

00:18:01 Yep.

00:18:01 It was the thyroid hormone for people who needed replacement therapy, and it was before we could make it synthetically.

00:18:08 And the process by which that was made was kept secret.

00:18:12 Now, it didn't stop other people from trying, but they had to copy exactly the composition of all the peptides as well as the impurities in the final preparation in order to be able to use the clinical and filing package that Armort used at the FDA to get FDA approval.

00:18:32 Turned out that was technically really hard.

00:18:35 Another one I really like is Acthar Gel.

00:18:38 So this was purified from pig pituitaries, and it was ACTH, basically.

00:18:45 That's the ACTH, A-C-T-H-A-R, gel.

00:18:48 I used to love it.

00:18:49 I used it in the emergency room a lot because it was a rheumatology smart missile.

00:18:54 Patients could come in with acute gout and be miserable.

00:18:57 You give them one shot of that, and it gives them endogenous steroid taper over a period of several days.

00:19:04 It was great.

00:19:05 Totally unaware of that, I see.

00:19:07 Yeah. It was a funny story behind that one too. It was cheap and widely available

00:19:13 until the BSE scare, the bovine spongiform encephalopathy scare happened. And then because

00:19:20 it was purified from pig pituitaries, the company was worried that it was going to be,

00:19:25 there's going to be something similar in pigs. And we know pigs have endogenous viruses.

00:19:29 So it was pulled off the market. And then it was bought by a very small company

00:19:33 who eventually re-commercialized it for infantile seizures and they jacked the price up a hundred

00:19:41 or a thousand fold. And so it hasn't been available for rheumatologists to use since then.

00:19:48 It's stupid, bad pharma trick. Yeah. Well, I'd like to actually talk about a few more examples 1.00

00:19:53 of that because there are several. Going back to the patent, in classic pharma, are patents

00:20:02 primarily issued only for decomposition of matter? Or are companies trying to get patents

00:20:10 for process manufacturing and other things? Or is the playbook that, hey, if there's a really

00:20:16 complicated process required to make this drug, I'm going to file my patent on composition of

00:20:21 matter. I'm going to keep the process as trade secret. So even when this thing runs off patent,

00:20:26 you might know what the finished product looks like. You'll never figure out how to make it.

00:20:30 So both of those things happen depending on the drug.

00:20:32 Exactly.

00:20:33 So one good example would be, let's say, AbbVie and Humira.

00:20:38 They patented every single little thing around that drug that they could.

00:20:42 It was a huge winner for them, and they wanted to keep it protected as long as they possibly could.

00:20:47 And they would stagger it.

00:20:48 So they would first patent composition of matter, wait 10 years, patent this step-in process, wait five years, and you just keep doing it, doing it,

00:20:57 and you sort of effectively extend the patent life of the molecule and process.

00:21:01 Exactly. And that can be abused, I think, personally.

00:21:07 Okay.

00:21:07 But exactly. They'll patent the drug. They'll patent the formulation. They'll patent the salts.

00:21:14 They'll patent the auto-injector. They'll patent, of course, the indication right at the beginning

00:21:19 to the extent that they can, which is sort of method of use. And they'll patent the dose.

00:21:25 They'll patent the route of administration, everything.

00:21:28 Yeah. Combining it with something else.

00:21:29 Okay. Going back-

00:21:30 And the method of manufacturing.

00:21:32 Yes. Going back to the clinical team, the scientific team that is beginning the exploration of what to do,

00:21:42 are you at the outset completely agnostic to whether you're looking for or entertaining small molecules or biologics as targets?

00:21:51 Do certain disease states lend themselves for you to go looking in one path versus the other?

00:21:57 I think there's two considerations.

00:21:58 One is that there are some conditions that might suggest one route or the other, but

00:22:06 there are also sometimes companies or infrastructure that would make it better to use one format

00:22:15 or the other.

00:22:15 I think big companies are format agnostic because all the big drug companies are now doing small molecules and biologics and many gene therapies and even some now cell therapies, which are sort of the frontier of complex medical therapeutics.

00:22:33 For example, if you're treating a childhood disease, then you need to make something that is oral and tastes good.

00:22:41 All the parents out there are going to remember, um, you know, the, the grape and the cherry

00:22:46 flavored Tylenol or Advil or grape Sceptra was, was a particular favorite of mine when I was

00:22:52 given my kids drugs. Yeah. So that's one example. Uh, others are, there are inhaled drugs for

00:22:59 specific lung conditions. And so some formats will, will make sense. And there's a medical

00:23:05 rationale for that. So then how do you begin the screening process? How do you begin to identify

00:23:10 molecules. And again, I think we should assume that our listeners who are otherwise well-informed

00:23:16 won't know the details of what an IND is, what we use phase one, two, three, four, and what has to

00:23:22 happen prior to the IND. Like, let's just start from the very beginning. Right. So with the

00:23:27 preamble that this would be about a year-long course. Yeah. If we did this as a seminar at

00:23:33 Harvard, this would take you a year. Yeah. But to cover it at a high level, starting from the idea

00:23:40 and the medical indication. And it might be useful to think about one specific example. And

00:23:46 we can, let's think about muscle weakness, which can be described as sarcopenia. And the definition

00:23:53 of that is still evolving, frankly. I've gone to some of these specialty meetings, like the

00:23:59 Cachexia Consensus Conference, and it's a topic of discussion every year. But I think one, we seem

00:24:06 to be converging on the concept of decreased muscle mass with impaired muscle function

00:24:11 as a good definition of sarcopenia, and whether the function is grip strength or gait speed

00:24:18 or stair climb, or people use different ones.

00:24:22 Okay, so we'll talk about that as a specific indication.

00:24:25 So impaired muscle function with low muscle volume.

00:24:30 Which to your point, by the way, nobody listening to this doesn't care about this.

00:24:33 So this is a very topical consideration.

00:24:35 and it's not esoteric.

00:24:36 It's something I've personally been working on

00:24:38 for 20 years, as you know.

00:24:40 So what should be the drug format?

00:24:43 Well, the patient population are likely to be older adults.

00:24:46 So it needs to be a drug format that'll be suitable to them.

00:24:51 And shouldn't underestimate the importance of this.

00:24:56 It's what's most likely to work.

00:24:59 Because in a drug development program,

00:25:01 especially with a new target and a new indication,

00:25:04 There are so many unknowns, and all of the risks multiply across a drug development program.

00:25:10 So if you have, say, 20 risks you're taking in a drug development program, whether it's target and format and bioavailability.

00:25:19 Toxicity, the whole thing.

00:25:20 Everything.

00:25:21 And all of them have maybe a 90% chance of success.

00:25:25 You multiply all that out 10 or 20 times, it's zero.

00:25:27 It's a huge failure.

00:25:28 Yes.

00:25:29 So you have to minimize risks at every step.

00:25:34 other than the ones that you sort of identify and accept, this is the big question that we don't

00:25:40 know and we have to answer early. So we want to- And you want to fail fast.

00:25:45 If you can, yeah, absolutely. The worst outcome in drug development is failing in phase three.

00:25:52 Oh, yeah.

00:25:53 Actually, that's probably not true. The worst outcome is succeeding in phase three and failing

00:25:56 commercially. But failing early is super important. Now, let me just ask a question

00:26:01 about this specific indication. So you've already made a decision, which is we're going after

00:26:07 sarcopenia, and you've decided to do that as opposed to say, we're going to go after muscular

00:26:13 dystrophy. So are you doing that because sarcopenia is a much, much, much bigger market,

00:26:19 which is the obvious choice? Or are you saying it's easier to get approval there, and then

00:26:25 ultimately we can also demonstrate that this will work in Duchesne's muscular dystrophy?

00:26:29 How would you think about those two ways to proceed?

00:26:33 So my personal bias is I have a limited amount of time on this earth to develop medicines and help people.

00:26:42 I want to help as many people as I can.

00:26:45 So I mostly do large indications, and that's my personal choice.

00:26:50 Now, in that situation, regulatory pathway might be harder for sarcopenia if it is not yet identified clearly as a disease.

00:26:57 No question.

00:26:58 Whereas Duchenne's muscular dystrophy, that's an orphan disease, you would probably get a quicker

00:27:03 regulatory pathway to approval. Yes. Yes. And so a different strategy might be we go after Duchenne's

00:27:11 because we can get there faster. And then once we have demonstrated a drug that works in that

00:27:16 category, we then chase the approval pathway to help as many people as possible. Would those just

00:27:21 be two different strategies? Yes. And frankly, I've done both depending on the circumstance and

00:27:27 mechanism of the drug and so forth. And frankly, I'm doing that right now with one of the two

00:27:33 companies that I work with. Okay. So let's go back to your example, though. Let's get back to

00:27:37 sarcopenia. The very first thing that I tried to do in sarcopenia was prevent falls. Now,

00:27:47 how do you measure a fall, Peter? Well, I was going to ask a harder question, which is,

00:27:53 which are the muscles that are most responsible for a fall. And maybe that's part of what you

00:27:58 could test. It turns out, I think it's very complicated, right? I've had a guest on this

00:28:02 podcast who presented data that suggested something that's seemingly as innocuous as

00:28:08 great toe strength is an enormous predictor of falls. Now there's a very objective way to measure

00:28:13 the force that a person can generate with their great toe. And as that force gets below certain

00:28:18 thresholds relative to their body weight, the probability of falls just starts to go straight

00:28:22 out. Balance, obviously. I would argue that they don't really know that. Okay. Because I asked the

00:28:30 question, what causes falls? There's about 11 different things that cause falls. And people

00:28:36 can think through themselves. There are the obvious ones like weakness and dizziness, which

00:28:41 dizziness itself is very complicated, but also vision, also attention. Lots of things can cause

00:28:49 falls. Yeah. I mean, I think another big one that we see clinically, Lloyd, is loss of reactivity.

00:28:56 So foot speed and reactivity. So you and I, if we went for a walk today around Lake Austin,

00:29:02 so there's a 10 mile beautiful loop around Lake Austin, the probability that on that 10 mile loop,

00:29:08 you and I wouldn't stumble once, at least once and miss our footing is zero. But I would venture

00:29:15 that neither of us would fall. And so the question is why, why wouldn't we fall despite being

00:29:21 challenged at a great level, you know, stepping on a twig, missing a branch or a root or something

00:29:27 like that. And the reason is we have the reactive speed of our feet to catch ourselves. And that to

00:29:33 me is one of the things that's missing. And that tends to come down to the type two, a muscle

00:29:37 fiber, right? Like that's a very explosive, but also proprioception. Yes, absolutely. Vision,

00:29:42 all the things that we're talking about. But maybe the broader point is this is multifaceted.

00:29:47 There is an atrophy that is beginning of various systems in the body, and it's creating this

00:29:53 perfect storm where when you watch an elderly person fall, you realize that that's a situation 0.98

00:30:00 where they would have saved that fall. It's not the insult that's the problem, whatever caused

00:30:05 the perturbation in the step. It's the inability to catch it. I think that's exactly right. So

00:30:10 let's get back to the issue of measuring falls. But that's hard. Yeah, but this becomes a very

00:30:16 hard problem. So I tried to do that because in a clinical environment, the only falls

00:30:24 that are ascertained are those that cause injury, right? From our perspective as physicians,

00:30:30 those are the patients we see. And patients are reticent about reporting falls because they know

00:30:37 they could potentially be taken out of their home environment if they were felt to be unsafe.

00:30:44 And frankly, I would do exactly the same thing if I were in that circumstance. I want to stay home.

00:30:49 So we developed a study to try to measure falls. And so we worked with a large company that

00:31:00 manufactures triaxial accelerometers. And we made a, we made a research device for this,

00:31:07 for this effort. And I designed a wonderful study. So the study was, we were first going to put the

00:31:13 device on bad ice skaters in Boston in the winter and videotape the rink. And so the videotape

00:31:21 results are the positive controls for actual falls. And then we would look at the device

00:31:27 telemetry and look at the sensitivity and specificity of the device.

00:31:31 It's worn on the ankle, the wrist, where would you wear it?

00:31:33 This was going to be a pendant.

00:31:35 Okay.

00:31:36 And then the second part of the study is if the first part worked and the device worked

00:31:40 on falls that were real, we were going to put it on elderly nursing home residents.

00:31:46 And there the positive control was little old lady found on the floor because the people

00:31:52 were old and frail and unable to get up themselves.

00:31:54 So when the nurses or the staff found them on the floor, that was a fall, however they got there.

00:32:00 And then we look at the device telemetry and see.

00:32:03 And sorry, was the purpose of that exercise, Lloyd, to see if the ice skating telemetry could predict a fall on ice?

00:32:10 And was it offering the same insight that you were seeing in the actual field with the elderly people?

00:32:15 Well, it's a little bit that falls are like the Supreme Court in pornography.

00:32:20 You know, you say you know it when you see it.

00:32:21 And so we wanted to see it on the ice skating rink.

00:32:24 And then look at the device telemetry and see, was it reporting falls when they actually happened?

00:32:29 Was it reporting falls when they didn't?

00:32:32 Was it missing things?

00:32:34 Basically to see if the device worked.

00:32:37 So I put that through the institutional processes for funding, and I was told, cut the first part out.

00:32:44 And so just put it on the older adults.

00:32:46 So I worked with a really good geriatrician named Lou Lipsitz, and we did that study.

00:32:52 And Kieran Dole was the clinical operations person, and it was really a good study execution

00:33:00 because working with patients and research subjects, participants in their 80s up to

00:33:06 a little over 100 is challenging.

00:33:09 And these were all individuals in one living environment or across multiple?

00:33:14 Yes, they were in one living environment.

00:33:15 We wanted to minimize the variables.

00:33:19 How many subjects were?

00:33:20 We had 60 subjects.

00:33:21 And you falled them for how many months?

00:33:22 We falled them for six months.

00:33:24 And these are people who had fallen at least once in the prior six months.

00:33:29 So we knew they're at a high risk for more falls.

00:33:32 And remember, this institution has a lot of protocols and procedures in place to try to prevent falls, and they're still falling now and then.

00:33:41 So Lloyd, 60 subjects followed by six months, how many falls did you capture?

00:33:45 Well, we ended up 117, I think, was the number of events that actually happened based on someone found on the floor.

00:33:53 However, the device was awful in that I think it had about, it detected 17% of the real falls.

00:34:04 And only 17% of the device actuations where it said somebody fell were false.

00:34:10 So it was just not useful.

00:34:13 And it was the best we could come up with.

00:34:16 And just to be clear, you weren't asking it to predict antecedent movement pattern of fall.

00:34:23 No, no.

00:34:23 You were just asking, once a person has fallen, do you know?

00:34:27 Yes.

00:34:28 Very simple.

00:34:29 I would have guessed that you would have been higher.

00:34:31 So did I.

00:34:32 But we were wrong.

00:34:33 This study is actually published now, but then we couldn't use it for measuring falls.

00:34:38 Yeah.

00:34:38 We tried one more thing. 0.99

00:34:40 There is a Massachusetts Institute of Technology professor named Dina Khatabi who was using Wi-Fi type devices to measure people's movements in their homes.

00:34:54 And it is a little scary.

00:34:55 It could measure wherever you were and whatever you were doing.

00:35:00 And so we thought that would be a great way to assess falls.

00:35:03 But ultimately there, I don't remember the issue, but we ended up not being able to use it.

00:35:08 So we had to stop our whole program from making drugs to prevent falls because we couldn't measure them.

00:35:15 Now, let me play devil's advocate for a moment. Couldn't you have just said, we want everyone involved in a study to report if they fell because it's going to help us get better data for the study. And I mean, given that you're, you're going to capture all the reported falls and really all you're trying to do is capture the signal of unreported falls.

00:35:40 But if you ask the people, say, look, we're not taking away your driver's license, you know, whatever it is that you're afraid of losing, we are just trying to report this as an AE or as an outcome.

00:35:50 I mean, it seems to me you'd have a much higher chance of capturing it than any sort of device at this point.

00:35:56 That would be true.

00:35:57 We were just worried about the data quality. 0.91

00:36:00 Because people, you know, remember, these are older adults who were frail.

00:36:05 and we were worried about the, you know, recall bias and we were worried about whether they would

00:36:12 have something to write it down with. And people generally weren't device savvy at the time. I

00:36:18 think that might be better now. Anyway, we elected not to do it. I have to think there's some AI way

00:36:21 to do it. Like there must be, I mean, were these accelerometers using any AI? This was a long time

00:36:27 ago. This was 10 years ago or more. So there was no AI in those days. Well, some people would take

00:36:33 issue with that. Not the way we see it today. Anyway, it was an interesting example of a lot

00:36:40 of ideas are conceptualized and we have to be able to objectively measure things ideally

00:36:46 in drug development. And we tried and we couldn't and we moved on. So this kind of brings us to

00:36:52 sarcopenia and muscle mass and strength, which was the genesis of Bimagramab and active and

00:36:59 receptor antagonists in general. Now, we knew at the time about myostatin. So Seijin Lee is sort of

00:37:08 the father of myostatin. He discovered the biology. And in rodents, myostatin is, you know,

00:37:18 kind of amazing. You can turn a mouse into an Arnold Schwarzenegger mouse by blocking myostatin.

00:37:25 this was in the mid nineties, right? I think so. Yeah. Yeah. I mean, I remember this was when I was

00:37:30 in medical school, I remember in 97 seeing the images of the mice, the chickens, the dogs,

00:37:39 cows. I mean, we, we couldn't get enough of these myostatin knockout animals. We thought it was the

00:37:45 greatest thing we'd ever seen as students. There were no people who had that though. No. So,

00:37:51 So at the time, the people who led the discovery project, so this is the laboratory research, were Chris Liu and his team and David Glass and his team.

00:38:04 And that's where the original code of Bimagramab was BYM338.

00:38:09 And that's where the VAT came from.

00:38:11 I was part of that team on the clinical side.

00:38:16 And if they made the drug, how would we test it?

00:38:20 Do you want to explain how myostatin inhibition would lead to enormous muscles?

00:38:28 Yes.

00:38:28 So the broader question is what governs the size of your muscles?

00:38:34 And we know it's nutrition and we know it's use.

00:38:39 And then there are biochemical things that regulate it.

00:38:42 And myostatin is an inhibitor of muscle growth.

00:38:46 So if you inhibit the inhibitor or block myostatin, muscles get larger up to another point where there's something that regulates them, and we don't know what that is.

00:38:56 And that's most of the inhibitory effect biochemically in animal species.

00:39:02 In humans, it turns out that it's more complex.

00:39:05 It's myostatin plus activins, mostly active in A.

00:39:09 And this is the advantage of inhibiting myostatin and activin A together or blocking the receptor, which is what the Magrumab does.

00:39:22 What was the evolutionary, I mean, I'm asking you as though you were there during the design phase, but what is your best guess for what the evolutionary reason was to limit muscle growth? Was it simply a nutrient management system, which was like, hey, we grew up in a nutrient sparse environment. We can't just have muscles demanding all of this protein.

00:39:41 I would have to guess that based on everything else about people, there were times during our

00:39:47 evolution when resources were very scarce and we had to conserve. But I don't know. Your guess is

00:39:53 as good as well. But it is interesting because the other way that nature could have solved that

00:39:57 problem was just to make it completely supply limited and just say, yeah. But anyway, okay,

00:40:04 so we don't really have a great teleologic reason. That's kind of the way it works with

00:40:07 adipose tissue. Yeah, yeah, exactly. That's right. So we don't have a great explanation for why,

00:40:14 but regardless, in humans, myostatin plays a smaller role than it does in these less,

00:40:22 presumably slightly less complex mammals. And what is the actual mechanism by which

00:40:27 myostatin is inhibiting? Is it doing something in actin myosin filaments? What is it doing to

00:40:33 prevent hypertrophy complicated but to summarize it briefly the receptors are part of this larger

00:40:40 tgf beta super family of receptors and there's dozens of them there's type one type two type

00:40:46 threes and they all signal via mostly a common pathway of called smads which was named by those

00:40:56 whimsical drosophila geneticists it stands for similar to mothers against decapentaplegic

00:41:01 which people don't need to know. And those are transcription factors, and they

00:41:08 govern a whole lot of gene programs. Some of the more important ones are muscle. So muscle size

00:41:16 is regulated by nutritional availability and then muscle protein synthesis versus muscle protein

00:41:25 turnover. And the proteins that turn over muscle are MRF1 and MAFBOX or atrogen. And David Glass

00:41:36 was one of the discoverers of this pathway, who I mentioned earlier. And myostatin signaling via

00:41:44 the active in receptors suppresses the proteins that are involved in targeting muscle proteins

00:41:53 for degradation. So before we leave myostatin to talk about BEMA, do you want to say anything

00:42:00 about folistatin? And I don't even know if you're aware, but folistatin became a very popular

00:42:05 recreational sort of gray market agent that was sold for research purposes only in quotes.

00:42:13 And the marketing material suggested, look, if you take folistatin, folistatin inhibits myostatin,

00:42:20 you're going to get really big muscles. And so people were pumping themselves full of folistatin.

00:42:25 Actually, that's not really true. I think the folistatin was so expensive. They weren't doing

00:42:29 that. They were doing some attempt at folistatin gene therapy. So do you want to just explain

00:42:35 what folistatin is or why it may not be as holy grail as it was made out to be?

00:42:41 So folistatin and then there's folistatin-like proteins are endogenous inhibitors of this

00:42:46 pathway we've been talking about. And it does, if you do a gene therapy in rodents, result in larger

00:42:54 muscles. But it's a small protein with a relatively short half-life. And I don't think dosing it

00:43:00 systemically now and then would be successful. We did the math on this and you would need to

00:43:05 give it several times a day. And given the price of folistatin, you'd be spending above, I don't

00:43:14 You know, you'd be spending a million dollars a month on folistatin, but also it wasn't clear that

00:43:20 it would do anything in an adult. In other words, it seemed that there might've been a critical

00:43:24 window during which administration of folistatin or folistatin gene therapy would have an impact,

00:43:30 but it had to be pretty young. It'd be during the development of the muscle more so than a mature

00:43:35 phase of the muscle. I think it would work in adults if you could solve the half-life. And

00:43:41 I know there are companies working on this with FC fusion proteins and other half-life

00:43:46 extended versions.

00:43:47 That's a good point.

00:43:48 Can you tell people why an FC fusion, I mean, I think we have to talk about some of this

00:43:51 technical stuff, unfortunately.

00:43:53 I almost caught myself as I was asking the question, but explain what an FC fusion protein

00:43:57 is and why that might be able to keep it in place longer.

00:44:00 Sure.

00:44:00 You can edit this out later if it gets too technical.

00:44:03 I'll try to speak in plain English.

00:44:05 Well, yeah.

00:44:06 Just think about it through the lens of how you manipulate drugs.

00:44:09 Like, I think of this as part of the story.

00:44:11 Right.

00:44:12 So if I remember correctly, the technology that we use now for FC fusion proteins was

00:44:19 originally developed by Brian Seed at the Mass General.

00:44:23 And the very first drug that used it was Enbrel, which is etanercept, which is itself a really

00:44:31 interesting story because this is a TNF inhibitor that's now approved in rheumatoid arthritis

00:44:36 and psoriasis and some other things.

00:44:39 but it was first tested in sepsis. I didn't know that. And it made people worse. Well,

00:44:45 because I think physicians and scientists at the time knew that sepsis was an exuberant

00:44:51 inflammatory reaction to an infectious stimulus. And they thought by tamping down the inflammatory

00:44:58 component, you might be able to have better outcomes. And it turned out it made people worse.

00:45:02 But ultimately it was then tested in rheumatoid arthritis and it was amazing.

00:45:06 And you know the story after that.

00:45:09 But a company called Immunex at the time licensed the technology from Mass General to make a tannercept.

00:45:16 And essentially what it does, it does two things.

00:45:18 So the soluble receptor, which was the low affinity regulatory TNF receptor, I think, at the time.

00:45:27 Got to double check that.

00:45:28 I haven't thought about a tannercept in like 20 years.

00:45:30 had a relatively short half-life just as a protein injected all by itself. And doing the

00:45:39 recombinant DNA technology to attach it to this part of an antibody called the FC region

00:45:46 did two things. One is it extended the half-life of the protein in the circulation by allowing it

00:45:54 to recirculate the way some blood proteins are recirculated normally. So you're sort of

00:46:00 hijacking an endogenous mechanism to preserve proteins in the bloodstream and doing it the

00:46:05 same way. And the second thing it did is it put a hook on the protein to allow you to purify it

00:46:12 easily because the protein G and protein A columns were well-established at the time and easy to use

00:46:20 to purify proteins. So that's what it did for the drug developers. And since then, the technology,

00:46:27 this is a great example of going back to our patent discussion. Originally, that was patented

00:46:31 technology and only ImmuneX could use it or some other licensee of the Mass General. But the patents

00:46:37 expired and now the technology is freely available to the rest of the world for the rest of the

00:46:44 eternity. And many, many, many companies use this technology to make drugs and we're all better off

00:46:50 for it. Okay. So how did you guys discover BIMA? How did you create it? So again, the earliest

00:47:03 biology was done by Chris Liu's team in the Pathways group at Novartis Institutes. Jeff

00:47:10 Porter was the leader of that group. And the idea was we wanted to inhibit the receptors,

00:47:16 not go after all the possible ligands because we knew that myostatin wasn't the whole story in

00:47:22 humans. We didn't know which activins it was, thought it was probably activin A, but it could

00:47:27 have been others too. And that story and that thinking has panned out subsequently, as we can

00:47:31 get into later. And at the time, therapeutic antibodies were the best technology to do this.

00:47:40 Remember, the affinity of the ligands, myostatin and activins for the receptors was nanomolar,

00:47:50 low nanomolar, maybe high picomolar.

00:47:52 So we needed an inhibitor that could bind down in the low picomolar range in order to

00:47:59 effectively prevent ligands from binding.

00:48:02 Can't really do that easily with small molecules.

00:48:05 Okay, this is a great example of the importance of understanding that distinction.

00:48:10 Yes. I'm going to explain what you just said, and then I want to have you state that last point

00:48:14 again. So people, when they're talking about pharmacokinetics and they're talking about

00:48:20 affinity, you'd have to talk about a concentration. So what concentration of this hormone or this

00:48:26 ligand is necessary to get into this? And the, when you start talking about, you know, we can

00:48:33 talk about millimoles, micromoles, nanomoles, as we get smaller and smaller and smaller, as those

00:48:38 numbers get smaller and smaller and smaller, it means you don't need very much of the thing to get

00:48:42 in the receptor. And therefore, if you're trying to develop something to block that, it becomes a

00:48:48 harder problem because you better figure out a way to usurp this guy getting in and this guy gets in

00:48:55 very easily. Yes, exactly. Exactly. And you're saying to get a small molecule to have that degree

00:49:02 of sensitivity is very challenging.

00:49:05 Yes.

00:49:05 And therefore, something that's biologic makes more sense.

00:49:08 Yes.

00:49:08 And is that due to just the physics of the conformational fit?

00:49:14 Yeah, I think you could describe it that way.

00:49:15 It's essentially this, it's more complicated than this, but think about it as the surface

00:49:21 of interaction of the two molecules binding together.

00:49:26 Essentially, we need, if the ligand, myostatin and activin for the receptor is sticky, you

00:49:32 need an inhibitor that's even stickier. Stickier, yeah. And that would be very challenging to do

00:49:37 with small molecules. So in this case, we went with the biologic route, sort of ran a therapeutic

00:49:42 antibody project in collaboration with Morphosis, with whom we had a collaboration at the time,

00:49:50 and had a bunch of candidate antibodies, and then ran through the usual developability,

00:49:57 maturation, improvement of what we get in the screen until we have what we think could be

00:50:02 a therapeutic drug. How many molecules enter the top of that funnel? Oh, there might be thousands

00:50:08 of antibodies that get tested initially. This is done with something called phage display technology.

00:50:14 So I would say for the past 15 to 20 years, we don't make antibodies in mice anymore.

00:50:22 It's all done by recombinant DNA using these viruses. Okay. And I think back, I've been in

00:50:28 the business long enough. I've made antibodies by immunizing mice and fusing cells and growing

00:50:35 them up and putting them back into mice as a CITES tumors to get enough antibody to do experiments

00:50:42 with. I mean, it was terrible. It's much better now. So you're literally running a screen with

00:50:48 all of these antibodies and you're screening for two things, but well, basically one thing.

00:50:55 what is going to give me the lowest concentration that binds to this ligand?

00:50:59 That binds, sorry, to this receptor.

00:51:01 Yes.

00:51:01 So knowing that you have to be below a certain concentration.

00:51:05 We knew we needed really high affinity antibodies.

00:51:07 There were some things we didn't know.

00:51:10 So in the laboratory, most of the time you can measure a cell's surface receptor on the surface of the cell

00:51:17 pretty easily using things like flow cytometry.

00:51:21 The active and type 2 receptors are actually expressed at such low levels, you can't see

00:51:26 them by flow cytometry, unless you somehow very artificially manipulate the cell.

00:51:31 So we had to create a screening assay that was essentially a reporter assay.

00:51:37 So we couldn't measure the receptor on the surface of the cell.

00:51:40 We could have developed an assay to do that, but it would have been very laborious, radioactivity,

00:51:45 not necessary.

00:51:46 So we put in a reporter gene, which is, and basically made the cells glow with firefly

00:51:54 luciferase if the ligand bound myostatin or activin.

00:51:59 And then we were looking for decrease in the glowing with therapeutic intervention.

00:52:05 And we also didn't know whether we needed to do inhibit activin receptor type 2A or

00:52:11 B or both.

00:52:12 Most of the work in vitro suggested for muscle hypertrophy, most of it was driven by 2B, but we weren't sure.

00:52:23 And we ended up getting a 2B preferential drug, but it also hits 2A.

00:52:31 And this has nothing to do with muscle fiber type?

00:52:34 No, this works on all fiber types.

00:52:36 Oh, okay.

00:52:37 And then we looked for antibodies that worked in that cellular assay, where we wanted to prevent

00:52:45 cells from glowing when we added myostatin and activin. It had to work on both of them.

00:52:50 In other words, it shouldn't matter which ligand you put in. The antibodies should block

00:52:54 their activity. And the affinity of the antibodies had to be really good. So it had to be much

00:53:01 stickier than the ligands for the receptor. And so we had that. And then the real important

00:53:07 experiment is could we block the activity in an animal? But just that first step, Lloyd,

00:53:13 until you could identify candidates, how many months was that? Years. That was years. So again,

00:53:20 just going back to the analogy of building a skyscraper, that's the planning phase of the

00:53:26 skyscraper. That's the excavation of the hole. That's probably the laying the foundation. You

00:53:31 haven't actually put any of the big pillars up yet. It's the permitting. It's securing the

00:53:35 funding. It's the, it's sort of the site planning. It's all of that from your building perspective.

00:53:42 And by the way, I assume that the standard estimate 20 years ago was every approved drug

00:53:49 is approximately 10 years and a billion dollars. That's got to be pretty low today. What's the,

00:53:55 do you have a sense of what the more accurate dollar figure is? It's got to be more than a

00:53:59 billion today per approved drug. Oh yeah. There's a, there's a Tufts organization for the study of

00:54:05 drug development, and they think it's, I don't know, two, three, four billion, something like

00:54:10 that. Yeah. The part you're doing now is time-consuming. Luckily, it's not that expensive,

00:54:14 correct? Right. You're spending millions of dollars, but not necessarily, you know,

00:54:19 tens of millions at this point. Yes. Okay. So you finally identify a candidate or several

00:54:25 candidates that you now want to take to the next step, which is, hey, in vivo, does this thing work?

00:54:30 Yeah. So we would typically have two to 10 at this stage. And it's taken years because we have

00:54:39 to work through the biology and make sure that we understand what's going to happen.

00:54:45 Not because we want to save the cells in the Petri dish, but because

00:54:49 we don't want to start something unless we have confidence it'll succeed if we pass each step.

00:54:54 So we have to work through the biology. We have to make all the tools we need,

00:54:58 which are these glowing cells in response to myostatin, for example, and there's plenty of

00:55:04 other tools that we need too. We need to make the reagents. We need to make the myostatin and

00:55:09 the active NA and all the tools we need to do these experiments. So, and then run the experiments

00:55:16 and we have to make the antibodies and that takes quite a bit of time as well as

00:55:21 developability on the antibodies, which means we need to, at the very earliest stage, have

00:55:28 high confidence that we're going to make an antibody that we could give to people and it'll

00:55:32 be stable and it'll have predictable physical properties and it'll have a shelf life. And all

00:55:38 of these things are what we build into the antibodies at this very early stage. How

00:55:44 confident are you at that stage, Lloyd, that the antibody won't elicit an immune response in a

00:55:50 human? It's still one of the biggest unknowns. And the best way to do it is to use fully human

00:55:56 Humanized, yeah.

00:55:57 Not humanized, fully human.

00:55:59 Full, straight up human.

00:56:00 Yeah.

00:56:00 Can you explain to folks the distinction there?

00:56:02 Yeah, well, humanized is used to describe taking a mouse or other species antibody and replacing as much of it as you can with human sequences based on what we know about human antibody codon usage and amino acid preference and so forth.

00:56:20 whereas fully human means you're starting

00:56:23 with human genetic material antibodies.

00:56:28 But ultimately, it still has something in it that's foreign.

00:56:35 As little as possible. 0.97

00:56:36 Part of the sequence.

00:56:36 As little as possible.

00:56:37 There's going to be something new

00:56:38 because antibodies have this intrinsic ability

00:56:41 to recombine and create new sequences.

00:56:44 Yeah.

00:56:44 Now, in vivo, in people,

00:56:47 so if you make some brand new antibody

00:56:49 and it's not suitable for some reason, it gets selected out.

00:56:53 Whereas when we do this in vitro in a test tube, that doesn't happen.

00:56:58 We do the best we can and try to end up with sort of common codon usages,

00:57:03 common antibody sequences, even pair-wise and so forth.

00:57:07 But you don't really know until you put it into people.

00:57:12 There are some in silico screens you can do by looking at what peptides are likely to be generated

00:57:18 in a lysosome and are they going to have high affinity binding to an MHC molecule and so forth

00:57:24 and we do all that stuff. But you still don't know until you do it. Now it's an interesting

00:57:29 historical perspective because I'm getting old enough to be interested in history now.

00:57:35 The very first antibodies tested in humans were mouse antibodies. And there's still one that's

00:57:41 used to this day as a therapeutic. So it's OKT3. So this is an antibody used to prevent

00:57:47 transplant rejection. It's an anti-T cell antibody, human T cell antibody, and we still use it to this

00:57:55 day. And what is it targeting? Is it literally targeting CD3? Yep. Wow. So it's broad. Yes.

00:58:02 It's going after every T cell. Yes. Right. And there's rabbit antithymocyte globulin too that's

00:58:08 still used clinically. That's a blast from the past. It's still used. Yeah. And these were the

00:58:13 first antibodies used in people. And of course, uh, the other antibody, the other foreign antibodies

00:58:19 that are still used are antivenoms. Most of them are horse serum. As you can imagine, you only want

00:58:26 to get bitten by a snake once and need that because when you need it the second time, you have kind of

00:58:30 a ferocious serum sickness response. Great point. So you identify BEMA plus a few others and you

00:58:40 start now running these into the mice. Yes. Is there another chance that because you've gone to

00:58:47 all the trouble to make sure you have a human antibody, it won't work in the mice when it

00:58:53 otherwise would have worked in humans? We test that beforehand. Okay. Got it. So this is, this

00:58:59 is again part, there's an enormous amount of detail in drug development. And one of the things

00:59:08 is that your drug has to cross-react, I mean, work also in at least one of the two species

00:59:16 we're going to use later for toxicology. And it has to work in a species we're going to use for

00:59:21 pharmacology. If it doesn't, then you need to make surrogate drugs to do that. But in our case,

00:59:28 we made one that worked in rodents. Now, it turns out that bimagrimab is pretty immunogenic in mice.

00:59:38 but not in rats. Although- How common is that? It's kind of idiosyncratic.

00:59:45 So the construct we ended up using in mouse experiments was something called CDD866,

00:59:52 which was, you talked about humanizing antibodies. We murinized the Magromab so that we could use

01:00:00 it in mice. So you could go back and use it. Yeah. That makes sense. And to make a long story short,

01:00:05 we were able to give these antibodies to mice and see if it caused muscle hypertrophy or not.

01:00:10 And how much did it do so relative to the pure myostatin knockouts that were enormous?

01:00:16 Probably more so than the myostatin knockouts.

01:00:19 Wow.

01:00:20 It was really impressive.

01:00:22 Again, we're going to link in the show notes to what these images look like,

01:00:27 but it is truly a caricature.

01:00:30 It's impressive in the mice.

01:00:32 Look at also the whippet dogs, the Belgian blue cattle that are double muscled.

01:00:37 It's impressive.

01:00:39 Did you do anything else, Lloyd, at that time?

01:00:42 So when you demonstrate that BEMA is making bodybuilding mice, was there any assessment of muscle function?

01:00:51 Yes.

01:00:52 Okay.

01:00:53 And what did you find?

01:00:54 The mice were stronger and can run faster.

01:00:57 Okay.

01:00:57 But remember, they had perhaps a 30% increase in their muscle mass.

01:01:02 I mean, which is a huge, and people can look at the pictures online and see it's quite obvious.

01:01:11 It doesn't work this well in humans, because just skipping all the way ahead, and we'll come back,

01:01:17 humans get about 4% to 8% increase in muscle mass.

01:01:22 Most of the people we've tested have been older people, which is one caveat, whereas...

01:01:27 Did you do it in old mice?

01:01:29 Not in old mice.

01:01:31 I think David Glass did some experiments in older rats.

01:01:35 And it still worked, but not quite, not as well as in younger ones.

01:01:39 And remember, when people do these muscle experiments in rodents,

01:01:43 they almost always do males because it works better in males than females.

01:01:47 Cool thyself. 0.60

01:01:50 It would be interesting to know, as you ran it across a continuum of escalating age,

01:01:57 what accounts for the reduction in efficacy, right? Is it substrate limited? Is it a muscle

01:02:03 protein synthesis problem? I mean, what is, you know-

01:02:06 I don't think that's been studied formally by scientists. It might be known in the bodybuilding

01:02:13 community. And just a heads up to the listeners who might be bodybuilders, the first thing that

01:02:21 any company does when they're working on a drug that has the potential for abuse

01:02:25 is we work with WADA, the World Anti-Doping Agency,

01:02:29 to make sure that they can screen for these things.

01:02:32 So where in the pathway of BEMA did you begin notifying WADA

01:02:37 that we're working on this thing?

01:02:39 As soon as we had a therapeutic antibody, we started that process.

01:02:46 And basically, they've had an assay for more than 10 years.

01:02:53 Have they ever caught it?

01:02:54 Have they ever screened it?

01:02:54 I have no idea.

01:02:55 That's funny. Okay. So after you get the home run in mice, do you want to go into a primate?

01:03:03 Where do you typically go from mice?

01:03:06 So for therapeutic antibodies, often it includes a primate simply because we want to have one of the two toxicology species in whom the antibody has the expected pharmacology.

01:03:22 so we can look at sort of on-pathway and off-pathway toxicity of the therapeutic.

01:03:28 So we typically use non-human primates for this.

01:03:32 When you're at the mouse stage, Lloyd, how are you screening for tox besides the most obvious,

01:03:37 right? Obviously, mortality or something catastrophic is obvious. But for non-apparent

01:03:43 or non-mortality-based toxicity, what are you looking for? And is any toxicity at the mouse

01:03:48 level disqualifying to go forward? Or are you evaluating it case by case and saying, look,

01:03:52 okay, this ended up being pretty bad for the mice despite the efficacy. We don't think that's going

01:03:57 to be an issue, or we think we got the dose wrong, or do you basically keep going back and perfecting

01:04:02 it in the mice until you get the dose response right before you move up? Or do you just sometimes

01:04:08 say, no, we're going to go to the primate or whatever other model we're going to look at

01:04:11 and reassess tox as we get closer to our species of interest? Yeah. So with the caveat that I'm not

01:04:18 a toxicologist, the fundamental principles are you use a weight of evidence approach

01:04:24 based on all the data that accumulates. From a clinical perspective, we try to balance risk

01:04:32 and benefit with new medicines in general. So if it turned out that bimagrimab had some,

01:04:38 and bimagrimab has some tox that we'll get into, but if it were unsuitable for use in a big

01:04:44 population that we then think about higher medical need patients.

01:04:49 That's when you would go from maybe sarcopenia to Duchenne's, muscular dystrophy.

01:04:53 For example, yes.

01:04:53 If we, yep.

01:04:54 And where whatever the adverse effects are would be outmatched by the potential benefits.

01:05:01 Yep.

01:05:02 But secondly, we want to know precisely what the toxicology is.

01:05:08 And the two big things we look for are whether it's monitorable, whether it's reversible.

01:05:14 So if we have irreversible cardiac toxicity with a therapeutic, that's usually the end, for example, or neurologic. If it's serious organ toxicity.

01:05:26 But there's enough of a prodrome. So for example, if it's, well, you look at a drug like Lamisil, right? Something as supposedly benign as Lamisil. I mean, that can destroy your liver.

01:05:41 but there you can stop the drug. So that's your point. It's moderatable and reversible

01:05:46 if you stop it and you get a long enough warning. Because otherwise, if it would just

01:05:51 automatically destroy a person's liver at a frequency of one in a hundred people using it,

01:05:57 you can never justify it. So you're describing idiosyncratic liver toxicity,

01:06:01 which is the most common reason drugs get pulled off the market still. And I personally have killed

01:06:09 drug programs for that. And it's hard to, can't predict it preclinically. Yeah. Yeah. So, so let's

01:06:16 get back. You get BEMA into the primates. Yes. And how does it, how does it perform? 0.99

01:06:22 Well, in order to do toxicology studies, you have to know how much to give them and how long it's

01:06:29 going to last and what it's doing. So there are, there are preliminary studies in a small number

01:06:35 of animals. And so we did those, but we did them long enough so that we could see the muscle

01:06:39 hypertrophy of what were going to happen. And it did work, not as well as in the rodents,

01:06:45 but it did work. So it gave us confidence that we could move ahead with the rest of the activities.

01:06:51 Now, manufacturing enough antibodies for use in larger animals is time and expense. And so

01:06:59 all of that was going on in parallel. And each of these decisions, so you're doing this all

01:07:04 side of Novartis. Is there an, I see, is there an investment committee that basically revisits

01:07:10 every time there is a new, you know, allocation of capital to move from one thing to the other,

01:07:16 where everybody presents and how does that typically work in a large company?

01:07:19 All big drug companies work kind of the same way in that there are, there are typically two or

01:07:24 three or four, depending on the company, major checkpoints where all the data are assembled and,

01:07:30 made into slide decks and presented and feedbacks obtained and programs, courses adjusted. And so

01:07:39 that happens. And I think it's more frequent, but faster at small companies. But things are

01:07:46 constantly being reevaluated and reassessed. Yeah. And what's interesting for folks listening

01:07:51 to us is we're talking about this in the context of a large company that doesn't have to go out

01:07:56 raise capital every time it does this. But the exact same idea that you just described,

01:08:01 everything you just said could have been done by a startup, but now it would have a totally

01:08:05 different look and feel in that, Hey, we're going to go raise some seed funding to go test this

01:08:10 idea. Okay. Guess what? We were able to find the antibody. We're going to have to go raise another,

01:08:14 you know, $20 million and boom, boom. And now they'd be at the stage where they'd be probably

01:08:19 raising a series B or no, probably this would be a, still be an A I think as they go into the

01:08:25 primate. Whatever you call it. But give folks a sense of how much you'd have to raise for this

01:08:31 next stage, which is basically your pre-IND. So you have to manufacture toxicology and clinical

01:08:42 material. You have to run the IND enabling studies, which is toxicology and some other things.

01:08:49 and frankly, investors want value creation for their money and reasonably so. So I would think

01:09:00 for Bimagramab, if this were in a small company, the value creation step would be showing muscle

01:09:07 hypertrophy in the very first clinical study. So the funding that I would raise would be IND

01:09:13 enabling plus phase one plus phase one plus a runway to raise the next round yeah and you would

01:09:20 structure your phase one to demonstrate efficacy even though technically you only need to do talks

01:09:26 yes you would have it long enough big enough not not talks but safety that's right intolerability

01:09:31 yeah so okay and then just for using bema as an example how many dollars would that be from where

01:09:37 we are now to give you that runway into 2a in today's dollars probably 20 million dollars 0.99

01:09:43 Okay.

01:09:44 Yeah.

01:09:44 So Series A.

01:09:45 Thereabout.

01:09:45 Yeah.

01:09:46 Okay.

01:09:47 That's lower than I would have guessed, by the way.

01:09:50 Maybe a little more.

01:09:51 Okay.

01:09:51 It depends on where you do the manufacturing.

01:09:54 So for Novartis, this is nothing.

01:09:57 For a startup, this is everything.

01:09:59 You're betting the farm.

01:10:00 Well, not enough.

01:10:01 I don't want to make light of it inside of Novartis, but the point is Novartis doesn't

01:10:04 have to go back to the public market to say, I need to raise another $25 million to fund

01:10:07 this.

01:10:08 They're doing a lot of these in parallel.

01:10:09 Big companies, though, their resources are stretched, too. It's kind of funny thinking about it, looking from the outside, but having been inside a big company, people are competing for a fixed amount of research dollars, the people within the company, and resources are allocated based on company strategy.

01:10:30 and ironically sometimes there's more project capital available in a small company than in a

01:10:37 big company because the company the small company's got one or two or three right they're taking fewer

01:10:41 shots on gold all the money's going there yeah whereas there are hundreds in the big companies

01:10:46 so and i've seen it both ways i've seen some big company projects get high profile high importance

01:10:54 well-funded. So yeah, so we'll say $20 to $30 million maybe at this stage. And

01:11:04 the Magromab at that point made it through rodent and non-rodent toxicology studies and

01:11:12 what we call DMPK, which is distribution metabolism pharmacokinetics. It's

01:11:19 knowing that when you give a participant or a patient, a subject, a medicine, does it get into

01:11:26 their body? Does it go to where you want it to be? Does it do what you expect it to do?

01:11:32 You have to know all that stuff before you go into patients for the first time. And we assess that

01:11:37 in animals. You asked earlier, what do you actually do to measure the toxic effects of a medicine?

01:11:44 and animals receive courses of therapy and we do blood tests just like we do in people.

01:11:51 Sometimes we would do x-rays if it was warranted and then they get autopsied to look at all the

01:11:57 organs and look for microscopic changes that you might not perceive clinically. And we know,

01:12:03 we have to know all of that before we give people an experimental medicine for the first time.

01:12:08 So any red flags whatsoever as you, or anything that is of concern, not necessarily a red flag, but anything that's, that's still an unknown as you're going into the phase one?

01:12:22 Lots, lots of unknowns going in.

01:12:25 And there are always things of concern.

01:12:28 I've never seen a drug development program that couldn't be stopped for some reason.

01:12:33 and you have to balance the unknowns and the uncertainties and the risks with the potential

01:12:40 benefits and make a decision about whether you move forward or not. It's kind of a joke in the

01:12:48 industry that every really successful program has been almost killed or killed several times

01:12:55 before it eventually makes it out into humans and then eventually commercialization.

01:13:01 What's the approximate attrition from that first candidate drug discovery to the IND filing?

01:13:10 That's a winnowing down of what to one.

01:13:14 It's hard to put in an aggregate because it depends based on the format of the drug.

01:13:21 And then there's other factors like strategy and funding and everything else.

01:13:25 But for biologics like bimigromab and a therapeutic antibody, it's pretty low, actually.

01:13:34 Five to one? Six to one?

01:13:36 I would say maybe 30% of them actually could get into humans.

01:13:41 Okay. Yeah, more than I would have thought.

01:13:43 Yeah. It's simply because there are no off-target adverse effects with antibodies in general.

01:13:50 There are some specific counterexamples to that, but in general, an antibody is not like a small molecule that could have liver tox or some other tox that you can't predict for reasons that you don't understand.

01:14:03 That's a great point. Yeah. So maybe I'll restate that so folks get it because the antibody is so specific by definition, it can't bind to many other things.

01:14:14 And in fact, we screen to make sure it doesn't.

01:14:16 Yeah. Yeah. Whereas the chemical can do lots of things off target. You know, I had on recently,

01:14:22 we had a podcast talking about a CTEP inhibition and you know, the very first version of that drug

01:14:29 lowered LDL cholesterol, but raised blood pressure. And that was a completely off target

01:14:35 complication of the drug. Yeah. Exactly. So that doesn't generally happen with biologists.

01:14:40 Got it. So that's why you have the higher throughput. Yeah. Okay. Let's skip. So you

01:14:45 file the IND and you're now ready to start a phase one. So an IND is requesting regulatory

01:14:51 permission to administer the drug to people. And you've already filed your patent at this point?

01:14:58 Yes. Yeah. Where in that process did you file it? Patents typically get filed.

01:15:04 Again, there's a... You want to do it as late as possible, but while still protecting.

01:15:09 Exactly. So typically around the point where you have a group of candidates from which your final drug will be selected, that's typically when we would do it.

01:15:21 Okay, wow.

01:15:22 Because you want the patent to last as long as possible, but once information about what you're doing is getting out, you want to have it protected.

01:15:30 Okay. So from the time you file the IND with the FDA until... And you have to show them

01:15:39 everything that we've talked about. Do you also have to, at the IND, show them that you can

01:15:44 manufacture in GMP? Yes. So the manufacturing is a core element of the OMN application that you do

01:15:51 for an IND. And this is US-specific nomenclature. IND stands for Investigational New Drug. In Europe,

01:16:00 It's called a clinical trial application, CTA.

01:16:05 There are other countries that have different nomenclature, and companies can do the first

01:16:10 in human study anywhere in the world that's got a proper regulatory environment and suitable

01:16:16 investigators and clinical sites with adequate quality and so forth.

01:16:22 But maybe we'll be U.S.-centric for this discussion.

01:16:25 Sure. Can you explain to folks what the hurdle is to GMP or good manufacturing processes and why

01:16:33 it's so important? And again, I call this out to listeners because we live in an era now where

01:16:39 these peptide therapeutics are very prevalent, these sort of gray market peptides. And there

01:16:45 are people out there that think, hey, I'm buying retitrutide. Yeah, no, they're not.

01:16:49 Yeah, exactly. Maybe use the GMP process as a way to explain why when you think you're buying retitrutide peptide for research purposes only, you are definitely not buying what Eli Lilly is going to eventually sell if they get FDA approval.

01:17:07 Right. So GMP stands for Good Manufacturing Process, and it's basically a commitment by the manufacturer to use high-quality standards with extensive documentation to be able to prove what they've made so that everybody can have confidence that this is a good quality material

01:17:35 and they know that what's on the label is what's in the bottle, basically.

01:17:38 And that there's nothing in the bottle that's not on the label.

01:17:41 Exactly.

01:17:42 It's purity, it's activity, it's contamination or lack thereof.

01:17:48 It's sterility, if you will.

01:17:50 It's all of those things.

01:17:53 It's that the material that's being purchased eventually commercially

01:17:58 is the same material as what was tested clinically.

01:18:02 and we can have confidence in it basically the factories are inspected and the factories that

01:18:08 make it have and typically there are many manufacturers involved when you buy a with

01:18:14 the exception of saying buying a bottle of terzapatide from eli lily but typically when

01:18:20 you buy a drug from somebody the drug's substance the chemical is manufactured by one company

01:18:28 And then it is formulated or put into a mixture that makes it predictably absorbed or administered, is done by another company, and then it's put into a package by a third company, and then it's distributed by a fourth company.

01:18:46 So there's a lot of people involved. And this whole manufacturing infrastructure and pipeline is well-controlled and well-documented. And you can buy online peptides that may be the same as redditrutide. They might not. You have no way of knowing.

01:19:03 Yeah. And in many ways, that's the premium you're paying when you're buying the drug from Novo Nordisk or Eli Lilly or Novartis or whatever. Part of the premium is it's very expensive to manufacture under GMP conditions. So it's a bit of a buyer beware when you decide not to.

01:19:21 I think it's a big mistake to buy these peptides from fly-by-night manufacturers.

01:19:28 Conceptually, it's no different than going in a drug user, going and buying some opioid from a street corner drug dealer.

01:19:36 You have no idea what's in that.

01:19:37 Could it have fentanyl in it?

01:19:38 Could it have car fentanyl in it, which is even worse than fentanyl?

01:19:42 Baking soda, you have no idea what's in there.

01:19:44 It's the same thing with these peptides.

01:19:45 You have no idea.

01:19:47 I think it's a mistake.

01:19:47 And plus, even if you were to have confidence that those peptides were what they're saying they were, the data to support what they do are almost non-existent.

01:19:59 I've been reading in the popular literature about this one that's, I think it's called BP-197.

01:20:04 BPC-157.

01:20:05 157, yeah.

01:20:06 All of the data for that peptide come from one investigator who's the only person published on it.

01:20:13 And remember, the fundamental tenet of science is if it's real, it's reproducible.

01:20:17 this has not been reproduced. What the hell is it? It's not encoded in the human genome,

01:20:24 so it's not a human peptide. And it has no known receptor.

01:20:30 Yeah. So we don't even know how it works. There's so many red flags for this.

01:20:35 No, it's the poster child for what I would argue is the absolute greatest grift of the entire

01:20:42 health and wellness industry. There's a lot of, I'm sorry if I'm insulting you, Peter,

01:20:47 There's a lot of grift in the health and wellness industry.

01:20:49 Oh, you're not insulting me.

01:20:51 But that's my point.

01:20:52 Despite how much grift there is in the health and wellness industry, I'm putting BPC-157 on the podium, at least.

01:20:59 I would, too.

01:21:00 Yeah.

01:21:01 I would, too.

01:21:02 Okay.

01:21:03 So we've made Bimagrimab, and it's made it through all of the IND-enabling study activities.

01:21:10 And we're ready to give it to people.

01:21:13 Who do we give it to?

01:21:14 And this depends on what we need to measure and what the expected safety and tolerability issues are in people.

01:21:23 Again, we talk about toxicology in animal species.

01:21:27 We talk about safety and tolerability in humans.

01:21:31 And again, most antibodies, including Bimagramab, won't have safety and tolerability issues that are off the pathway that it's working on.

01:21:41 and didn't really see any safety and any toxicology to speak of in the animals.

01:21:48 The only thing that I was a little worried about that we saw was in the rats, they had cardiac hypertrophy.

01:21:58 However, remember the animals had enormous change in their body size because of the muscle hypertrophy.

01:22:03 And if you normalize the heart size to the body size, it was normal.

01:22:09 So does that mean that you didn't know if the cardiac hypertrophy was in response to more resistance that the heart had to work against, or whether the antibody was working directly on the cardiac myocytes and increasing hypertrophy there as it was in the skeletal muscle or both?

01:22:28 Exactly. We didn't know. But we could make an argument that rats of the size that they became-

01:22:37 Should have bigger hearts. 0.77

01:22:38 Should have bigger hearts.

01:22:39 And that was the argument we made to regulators, and so we didn't think there was any specific cardiac toxicity.

01:22:45 And typically in toxicology studies, you have something we call a recovery period, where the drug is withdrawn and some of the animals are followed to look and see whether any toxic effects that did occur are reversible.

01:22:59 And in fact, when you stop giving the animals Vimagramab, the muscles got smaller and the heart got a little smaller.

01:23:06 How often was it dosed?

01:23:07 So the way you dose in the toxicology studies is you want the exposure, which means the amount of drug in the blood, to be ideally higher than what we ever expect to get in humans.

01:23:22 And then when you got to the humans?

01:23:24 So that was the preamble to the answer to your question, which was weekly.

01:23:29 So we gave the animals the drug weekly.

01:23:31 And the half-life of the drug presumably is short, but...

01:23:35 It's shorter in animals, but again, you drive the dosing in the toxicology studies to make

01:23:41 the amount of drug in their blood ideally higher than we will get in people so that

01:23:47 we have what we call a safety margin of exposure.

01:23:51 Now, how do you know at that point, Lloyd, if toxicology is driven by peak or trough?

01:23:55 Because some drugs are-

01:23:57 You don't.

01:23:58 You don't.

01:23:59 You make a best judgment, but-

01:24:01 Is there a general rule of thumb?

01:24:02 You measure both and you want both of them to be higher.

01:24:05 higher in the animals than what you get in people. Now, this is in general medicine

01:24:10 therapeutic indications. In some nasty oncology drugs, toxic effects and therapeutic effects are

01:24:17 at the same exposure or even lower sometimes. But the medical need is so great that you accept

01:24:23 the toxicity. Well, I was going to actually use that as an example. We sort of skipped ahead a

01:24:28 little bit on the phase one patient selection. We rushed through that. Or actually, we didn't

01:24:33 answer it. We went off topic. We're going to come back to which patients do you select for the phase

01:24:38 one? And that really depends on the drug. Because in an oncology drug, you're going to test it on

01:24:43 the most recalcitrant cancer patient, right? You're going to test it on a patient who's

01:24:49 progressed through every therapeutic. They have stage four version of whatever cancer you're

01:24:53 testing. And this is the Hail Mary. And you're not just testing for tolerance and side effects.

01:25:00 you're hoping to get a sliver of efficacy through dose escalation.

01:25:04 That's the most common scenario in Kassar.

01:25:06 But here, what are you doing? 0.85

01:25:07 Are you going out to the most frail, sarcopenic, elderly person,

01:25:11 or are you going to test it in?

01:25:12 So what I do personally,

01:25:15 and a very experienced drug developer named Bob Schmouter taught me this,

01:25:19 and I think he's right,

01:25:21 is ideally you'd like to test this, a new medicine,

01:25:27 in the cleanest population you possibly can where anything you measure is related to the drug and

01:25:35 not some underlying disease or other thing. However, we do not want to expose healthy

01:25:41 volunteers to risks if we possibly can. So we use our best clinical judgment to say that

01:25:50 I don't want to expose people to a risk greater than that of a lightning strike in a year.

01:25:57 Is that literally a probabilistic formula you use?

01:26:01 That's what I use.

01:26:02 Interesting.

01:26:02 Yeah.

01:26:02 So the risk of being struck by lightning in the U.S. in a year is about one in 100,000.

01:26:10 That's actually higher than I would have thought.

01:26:11 Me too.

01:26:12 That's a little scary.

01:26:13 But that's what it is.

01:26:14 Okay.

01:26:15 And so I don't want the risk of something bad happening to one of my volunteers to be greater than that.

01:26:24 And that's frankly how I explain it to him.

01:26:27 So if I can have some confidence that that's true, we will test drugs in healthy volunteers.

01:26:33 If we're worried about a toxicity or a risk, then we will go into people who have a potential benefit from the therapy so you can make a risk-benefit argument.

01:26:44 And this is all laid out in plain English in the consent forms.

01:26:48 I mean, so first of all, that's a great framework, Lloyd, which is if the risk of adverse event is greater than 1 in 100,000, we must move to a population that is going to potentially get benefits to justify it.

01:26:59 Yes, serious adverse events.

01:27:00 Yeah.

01:27:00 Is that a Lloydism or is that a truism across the entire industry?

01:27:04 Is that something the FDA would ask of every company?

01:27:07 It's a schmouterism, Bob, if you're listening, thank you.

01:27:11 But the FDA doesn't force that?

01:27:14 The FDA doesn't force that, but the principle is still there.

01:27:19 Okay.

01:27:19 But it's a great standard.

01:27:20 Yeah.

01:27:21 Again, and if you think about the industry as a whole, how do we do in bringing new medicines into healthy volunteer populations?

01:27:31 I've been in this business partially in academia, wholly in industry for maybe 30 years total where I've been watching this.

01:27:40 And about once every 10 years, we see something serious happen to healthy volunteers.

01:27:47 Once every 10 years in a study.

01:27:49 So that's pretty good.

01:27:52 And we learn something when those happen.

01:27:54 So we're talking about therapeutic antibodies.

01:27:57 The one that comes to my mind and maybe to others is the Tegeneron incident.

01:28:02 Say more about that.

01:28:02 I don't remember that.

01:28:03 This was a therapeutic antibody that was directed against CD28.

01:28:07 It was an agonist antibody.

01:28:08 Now, CD28 is an inhibitory receptor on T cells, and the idea was, I'm sorry, is an activating receptor on T cells.

01:28:16 And I forget the actual therapeutic indication they were going for, but they tested the antibody preclinically.

01:28:23 Everything was fine.

01:28:25 And then they started at a very low dose in humans, and what we think happened is they cross-linked the CD28 receptor,

01:28:32 and they had extremely strong T-cell activation and an acute cytokine release syndrome in healthy

01:28:40 volunteers, some of them died. I mean, it was terrible. I mean, why did more than one of them

01:28:46 die? In other words, why didn't they figure this out the very first time they administered this?

01:28:50 That's super important. So that study, which that happened, boy, more than 20 years ago,

01:28:58 That experience is why ever since we typically have sentinel patients in dosing cohorts when

01:29:05 we're bringing something brand new into people.

01:29:07 So they dosed, I think, six people at once with the active drug.

01:29:12 Oh my God.

01:29:13 We don't do that anymore.

01:29:14 Yeah, wow.

01:29:16 The other one, there was a example with a small molecule, Bial, I think was the example.

01:29:25 B-I-A-L, people can look it up.

01:29:27 But it's, it's extremely uncommon to have healthy volunteers have anything bad happen

01:29:32 to them in a, in a drug study.

01:29:34 Extremely uncommon.

01:29:35 If you think of the.

01:29:36 I remember there was one at Hopkins when I was there.

01:29:39 It was, it was an, oh no, no, no.

01:29:43 You know what it was?

01:29:43 I'm sorry. 0.99

01:29:44 That was not a, it was a woman that a healthy volunteer that underwent a bronchoscopy and

01:29:48 I think had a horrible bronchospasm.

01:29:51 So it was, if I'm remembering it correctly, it wasn't a drug that caused the issue, but

01:29:56 it was a horrible adverse event to procedures but anyways but she she died procedures can have

01:30:02 yeah adverse consequences which which are known and disclosed in the in the consent this gave me

01:30:07 a lot of when i was in medical school i was you know i was so broke and doing anything i could

01:30:16 to generate a buck i vol i was probably one of the most volunteered people for studies at stanford

01:30:23 and like if, if there was a study that paid a thousand dollars, it didn't matter what it asked

01:30:28 of me, I would do it. And I, I remember coming away from that. I mean, I had radial lines in my,

01:30:34 I had, you know, as you know, what a radial line is, but arterial lines into my radial arteries

01:30:39 that to this day, I still have scars over my wrists. Can you, can you imagine that I subjected

01:30:44 myself to that? That, that seems a little much. My, I think many of us as medical students

01:30:50 volunteered for this stuff. My personal favorite was, there was, there was a study called brain

01:30:56 electrical activity mapping that children's hospital was running when I was a medical student.

01:31:01 And essentially they, um, they attach electrodes to your head and then you go sleep in the lab

01:31:07 and they monitor that and video, video you while, while you're sleeping. They loved me as a subject

01:31:13 because I was bald as a medical student. It was really easy to put electrodes on and off.

01:31:17 I loved it because all I had to do is go in and go to sleep.

01:31:20 But there were some others like inhaling radioactive microspheres.

01:31:25 I used to donate plasma via plasmapheresis as often as I could when I was at the NIH.

01:31:30 And on one occasion, I was in there, and this was like a lymphocyte plasmapheresis.

01:31:35 It's a four-hour procedure.

01:31:37 And again, it probably paid 200 bucks, which seemed like-

01:31:41 It was a lot of money in those days.

01:31:42 When you're a medical student, that's infinite money.

01:31:45 and then at one point somehow the nurse stepped out and the lab locked and i was stuck in their

01:31:51 lock and they could not find a key so they couldn't get back in they were losing their

01:31:56 minds but i didn't know it i was just in there watching whatever movie was on the thing it

01:32:01 turned out to be like one of the most stressful moments in the in the nci history you know trying

01:32:07 to figure out a way to get a spare key to get into the lab and and think and i was completely

01:32:12 oblivious to it. Oh, yeah. Okay. So back to patient selection. So ultimately for BEMA,

01:32:19 ultimately for BEMA, a healthy volunteer study. You did go with healthy volunteers?

01:32:24 Older volunteers. Okay. So people- What was your criteria specifically in terms of muscle mass?

01:32:31 So I actually didn't run these studies. The clinicians involved were Dan Rooks and Ronan

01:32:35 Rubinoff at the time. But the principle here is healthy volunteers doesn't necessarily mean

01:32:42 your 20 something year old with no problems. It means people without typically diagnosable

01:32:51 disease or concomitant medications that could confuse any, any assessments. In the case of

01:32:57 BEMA, because we were thinking older adults, these were older healthy volunteers and people

01:33:04 in whom we would be able to measure some of the effects of BEMA, we hope. And again, what you

01:33:10 measure in a healthy volunteer study depends on what the drug is expected to do and what adverse

01:33:16 effects you might expect. So there's some things you always do, like a set of standard blood tests.

01:33:22 But in the case of BEMA, we were assessing people's muscle mass.

01:33:26 Via DEXA?

01:33:27 And strength. This is almost archaeology at this point, thinking of what we measured, but

01:33:33 we would have measured muscle mass, and at different times we used MRI and we used DEXA.

01:33:38 I don't remember what that study had, but we would have measured muscle mass. We measure

01:33:44 soluble muscle proteins in the blood like CK and aldolase and LDH and so forth.

01:33:50 Did you see any adverse effects in the phase one?

01:33:54 Yes. So the three adverse effects that are evident with BEMA that we think are on target

01:34:00 that were assessed in that study, where muscle spasms were cramps.

01:34:09 Acne is rare in older adults, and it was rare in this study because those were older adults.

01:34:14 But skipping ahead, when we've tested younger people, acne is more common.

01:34:19 We don't understand why.

01:34:20 And then there are GI symptoms of diarrhea that happen.

01:34:24 They tend to be first-dose-related and less common subsequently,

01:34:28 but they're reproducible and we think they're real. And we saw that stuff.

01:34:32 And how many steps of dose escalation did you do in that study? By the way, if that's too much

01:34:37 detail to remember, don't worry about it. But I'm wondering if you remember how high you got

01:34:41 relative to what was an efficacious dose. So there's some principles here. I personally

01:34:48 like to dose as high as we can in the first in human study to understand if there is going to

01:34:53 be any safety and tolerability issues in people, while at the same time never exceeding the

01:35:00 exposures we've tested in animals. So the study designs include the opportunity to go as high as

01:35:06 we can. Typically in antibodies, that ends up being as high as is feasible. And there are a lot

01:35:13 of technical details here we don't need to get into, but when your antibodies are made from cell

01:35:19 culture, and they're highly purified, but there's still some measurable contaminants in them. And

01:35:25 the amount of contaminants, of course, they're also tested as part of the toxicology studies

01:35:31 because they're in the drug we give the animals. But we can't exceed the exposure to the contaminants

01:35:37 either in the clinical study. So sometimes it's the volume we can administer, the mass we can

01:35:43 administer and so forth. So I think the highest dose that we ended up doing in Bimagramab was

01:35:49 something like 50 to 100 milligrams per kilogram, but I don't remember.

01:35:55 And in that study in humans, you're administering once a month?

01:35:59 Initially, you'd administer once, then based on the emerging results for how long that lasts.

01:36:05 And we knew what exposures we needed to reach in order to get maximal efficacy based on the

01:36:11 culture data. There were a lot of cell culture experiments we did that we haven't talked about.

01:36:16 You can culture muscle cells in a dish, and we did that and looked at the ability of the drug

01:36:22 to cause hypertrophy of those cells. So we knew what exposures we needed to get to and how long

01:36:28 we wanted to do it. But again, we don't exceed the exposures that we get in animals. So I think

01:36:34 After the single-dose study, we probably did three doses, and that was it for the first

01:36:40 in-human study.

01:36:42 And typically, you need multiple doses in order to be able to see the technical term

01:36:48 is pharmacodynamic effect, so the effects on the body that the drug causes.

01:36:54 So the end point for the phase one, before you move to phase two, where you're really

01:36:59 going to actually look as your primary, and you're always looking, obviously, for safety,

01:37:04 but now you're really pivoting to efficacy being the thing that you're trying to chase.

01:37:08 What did you need to submit to the FDA to say, okay, have we checked our phase one box?

01:37:15 Typically, you're in reasonable communication with regulators, whether it's the FDA or whether

01:37:20 you're overseas elsewhere, and you provide them with a report.

01:37:24 And Novartis is a European company, right?

01:37:27 Yes.

01:37:27 Based in Switzerland.

01:37:28 But this work was being done in the U.S.?

01:37:30 I think we did do the first in-human study in the U.S., yeah.

01:37:33 Any reason for that? Are European and U.S. regulators so comparable on this point that

01:37:38 it's really just a question of where your teams are? So every country is a little different.

01:37:44 Europe is somewhat homogeneous, but not completely so. Every country is a little different.

01:37:50 And I think it's true both for large companies and small companies that you go wherever makes

01:37:55 the most sense. It's where you can, remember the three biggest challenges of any clinical study

01:38:02 are recruitment, recruitment, and recruitment. So you have to be able to get the subjects or

01:38:07 the patients or the participants. You need qualified, experienced, reliable clinical

01:38:13 investigators. You need a regulatory environment that's supportive for what you're trying to do.

01:38:20 And then you think about cost of the study. They're different in different countries.

01:38:24 And so you integrate all of that stuff. And that chooses where, at least personally,

01:38:29 where I would go to do a first in human study. Countries that are often used nowadays are

01:38:36 Germany, Australia is pretty popular, New Zealand. Australia, New Zealand's very popular now.

01:38:43 Things have really changed, I guess, maybe in the past year or two about China being really popular

01:38:48 because China has a regulatory environment that's become more favorable and they can do

01:38:57 investigator-initiated studies with less supporting data than we require for a typical

01:39:04 IND. So it can often be a faster way to test something. And of course, China has a lot of

01:39:10 patients. So that's something that's being done now too. I personally love doing studies in the

01:39:18 U.S. and Taiwan. Taiwan has, they have wonderful investigators. They speak English better than we

01:39:25 do. They have a very centralized clinical environment, so they have many patients at

01:39:29 a limited number of clinical sites. The regulatory environment is very similar to the U.S.

01:39:37 Australia and New Zealand is favorable because they have a, especially this is Australia now,

01:39:42 they have a different regulatory construct where safety is assessed by the ethics committee and

01:39:49 And drug quality is assessed by the regulators.

01:39:53 So they have a clinical trial notification process rather than an approval process.

01:39:59 And plus the exchange rate's favorable now, so too.

01:40:02 So if we get back to what is the trial going to cost, that's useful.

01:40:06 And how much reciprocity is there between agencies?

01:40:09 So if you, well, I should clarify the question.

01:40:13 You can conduct the trial in Australia, but under the auspices of the FDA where they're

01:40:18 issuing, or does it have to be in the U.S. if the FDA is overseeing? If the FDA is overseeing,

01:40:23 the study's done in the U.S. Okay. So if you do a study in Europe and get European approval,

01:40:28 or you do a study in Australia and get Australian approval, how much of an additional hurdle is

01:40:34 there for the FDA to typically approve a drug? So let's talk about running a study versus

01:40:41 marketing approval. Very different. So for running a study, if you're doing it in, say, Australia,

01:40:46 You apply to the Australian regulatory authorities and the ethics committee for the study, and they do the review and request modifications and eventually approve.

01:41:00 And then the studies run in Australia.

01:41:02 If you want to then do a study in the U.S., you have to apply for an IND, just as you would if you were doing it any other time.

01:41:10 But you include all the data that you got in Australia as well.

01:41:14 And if you were doing it the other way around, it would be the same thing.

01:41:17 And it's true for any two countries.

01:41:19 Meaning, if you had a drug that went all the way to the equivalent of a phase three ready

01:41:24 for approval in Australia, and you come back to the U.S. from scratch and say, we want

01:41:29 to be able to sell this drug in the United States, they're going to say, submit an IND.

01:41:33 The FDA.

01:41:33 I think so, yes.

01:41:34 Yeah, wow.

01:41:35 But how much do you get the shortcut?

01:41:37 Would they still make you do a phase one and a phase two, or would they let you go straight

01:41:41 to phase three?

01:41:41 I would think you could go right to a regulatory study. I mean, to a registration study. And in fact, this kind of thing is often done because typically you do the phase one study somewhere, but rarely in more than one, two, or three countries. And then you can use that data to go to many countries for a phase two and then use that data to go globally.

01:42:04 There are a few specific examples where you do have to run a phase one study before you go into that country.

01:42:11 Best examples, the best defined examples are Japan. 0.95

01:42:16 So to run a large study in Japan, you need to have run a phase one study in Japanese people.

01:42:23 And there is a formal regulatory definition of who is Japanese from the Japanese regulators.

01:42:29 and you have to provide that data before you can do a larger study in Japan.

01:42:35 They're called ethnic sensitivity studies.

01:42:38 And scientific rationale for this is that the genetic background of Japanese people

01:42:43 can be a little different.

01:42:45 Average body size is often different from people in the West

01:42:48 and you want to make sure that the dosing and exposure will be safe and tolerable.

01:42:53 But because the Japanese are so well organized and specific about what a Japanese person is, you can do these ethnic sensitivity studies in Hawaii, for example, or even California, or you can do them in Japan.

01:43:10 And I like to do them in Hawaii.

01:43:13 China generally requires an ethnic sensitivity study also for the same reasons.

01:43:18 And there are some specific examples of where there's toxicity of drugs in people on Chinese

01:43:24 ethnicity, but they have a less specific definition of who's Chinese. Easiest way to do it is in China. 0.98

01:43:34 All right. So let's go back to BIMA. 1.00

01:43:37 BIMA.

01:43:37 You go into phase two now. By the way, at some point, doesn't Novartis sell this asset?

01:43:43 Yes. So Novartis had strong confidence in Vimagramab. It was first in class, had really obvious biology in humans. And basically, Novartis ran maybe, I think, 16 phase two studies of one sort or another, or phase one, phase two study, different indications. Tried very hard.

01:44:11 So the drug reliably and predictably increases muscle size, but not performance assessments in a major way.

01:44:24 And I think that's because, remember in the rodents, in whom we saw both, size increase and performance increase, the mass increase was large, 20 to 30 percent or more.

01:44:35 In humans, it's 4 to 8 percent.

01:44:38 And 8 is the absolute max.

01:44:40 Were those differences based on dose or starting mass?

01:44:45 Biology. People are just not mice.

01:44:47 Oh, sorry. I mean, the difference between the four and the eight, how much of that is dose

01:44:52 dependent versus other demographic dependent on the pay? Like, you know, do you get more muscle

01:44:58 mass in younger people, more muscle mass in people starting with more muscle mass?

01:45:02 Yeah. There's a trend to more in males versus females, a trend to more in younger versus

01:45:09 older, but there's a lot of variability. Do we know if other variables such as resistance training,

01:45:17 nutrition, protein consumption would have augmented these findings? And how much were

01:45:22 those variables controlled in these studies? So we know some of that. We try to control as

01:45:25 much as we can. There was one study that has not been published in peer-reviewed form yet,

01:45:32 but there is an abstract for it. If people want to find it, they can look at it. So the BELIEVE

01:45:38 study of Bimagramab in obesity was just published a few months ago in Nature Medicine. If you look

01:45:44 in there, this nutrition study is referenced. We'll link to it in the show notes.

01:45:49 There was a study of Bimagramab in patients who were dosed at three different levels of

01:45:59 protein calorie nutrition. And the bottom line is the more protein, and it was the recommended

01:46:07 a daily amount, half of that or one and a half times that, I think. And basically within those

01:46:14 boundaries, the more protein you ate, the more muscle you built. Probably shouldn't surprise

01:46:19 anybody. The other really interesting finding- And by the way, twice the RDA is only 1.2 grams

01:46:24 per kilogram. Yeah. Maybe that's what we used. It was 1.2. Yeah. I would argue had you gone to 1.6

01:46:30 or 2, you probably would have seen more hypertrophy. It's not been tested. I think you're probably

01:46:34 be right, but it hasn't been tested. It's interesting. So it suggests that in humans,

01:46:39 you might've been substrate limited. Amino acid limited or protein synthesis limited.

01:46:45 It's a possibility. And not drug limited.

01:46:47 I'll tell you a funny story about that in just a minute. But just to finish that study,

01:46:51 the other very cool thing we found is that as you would expect, if you have

01:46:54 half the recommended daily amount of protein calorie nutrients, you lost muscle mass. But

01:47:01 Bimagrimab prevented that. So there was some biology working there for sure. No question.

01:47:07 No question. So the really interesting story is that when the Bimagrimab project, when it was

01:47:13 still in the research stage, moved from Chris Liu's lab to David Glass's department, which I was part

01:47:21 of as the clinical side of that, one of the things we really wanted to do was co-develop a nutritional

01:47:26 component to this therapy for the exact reasons that you brought up. And at the time, Novartis

01:47:32 had a nutrition arm. And so we were working with them to develop a specific nutritional supplement

01:47:41 for what became Vimagramab. At the time, it didn't even have a code yet. But then Novartis

01:47:46 sold their nutrition unit, I think to Nestle. And so got the rug pulled out from under us on that

01:47:52 side. And we were never able to fully pursue that. But in retrospect, I really wish we had.

01:47:58 Do you think this is a blind spot for big pharma? Just the role of nutrition and other behaviors

01:48:05 that can potentiate drugs? Big pharma tries to control it, but they don't see that as their

01:48:12 core mission. Yeah. But I'm saying a blind spot, I appreciate that they want to control it and that

01:48:17 makes sense, but I'm saying it's an opportunity lost. Here's a great example. Bima could have

01:48:26 been more of a hit if maybe, and maybe not, but a drug like that could have been a hit 0.87

01:48:33 had it been appreciated that, oh, by the way, you actually have to do something to reap the

01:48:38 benefits of this. We would have figured this out many years sooner if we had kept that nutrition

01:48:43 element. But again, one of the challenges of big companies is there's so many people involved. They

01:48:48 don't all know what the others are doing despite everyone's best efforts. It was a missed

01:48:54 opportunity. And so did Novartis then spin it out after the less than expected findings in humans?

01:49:02 Yeah. So what happened was we saw muscle mass get larger, but not stronger. And parenthetically,

01:49:09 that's the same as was seen with IGF-1 agonists and with androgen agonists. Remember, the SARMs 0.82

01:49:15 were extensively studied, is that you can make muscles larger, they don't get stronger in the

01:49:20 absence of resistance training. So it's not unique to the active and receptor antagonist pathway.

01:49:26 And in a meta-analysis of the Novartis studies, where they looked at muscle hypertrophy and

01:49:34 sarcopenia, the meta-analysis showed an increased six-minute walk distance,

01:49:42 nine meters, so a small effect. That's my least favorite test in the world.

01:49:49 It's surprisingly hard to standardize. Why wouldn't they just do something like,

01:49:54 you know, a wall sit or, you know, something that really tests strength?

01:50:00 Many other things were done. The timed up and go test, the short physical performance batteries. But six-minute walk was included in multiple studies, so you were able to do a meta-analysis of that.

01:50:12 got it so the an academic group did this and they published it and so the the four to i don't know

01:50:20 eight percent increase in muscle mass that these older adults got yielded a nine minute in nine

01:50:27 meter meter increased in six minute walk distance i'm not convinced that's going to help anybody not

01:50:33 fall no i don't think it will either and and and novartis didn't think so either is i guess i don't

01:50:38 I wasn't an insider at the time.

01:50:40 I don't know why they out-licensed it.

01:50:41 I was the recipient of that.

01:50:43 Yep.

01:50:43 We were on the outside pulling.

01:50:45 But the very last study Novartis did was a study in type 2 diabetics

01:50:50 because we had had data that hemoglobin A1Cs decreased in patients given bimagromab.

01:51:00 And that study, which ran for 48 weeks,

01:51:04 So it was 10 mgs per kg monthly for 12 doses.

01:51:10 Okay.

01:51:11 So a lower dose than you were giving for hypertrophy?

01:51:14 No, this maximized the-

01:51:16 Oh, why did I think you said earlier 50 mgs per kg?

01:51:19 In the phase one, we went up higher.

01:51:21 You went up that high.

01:51:22 Got it.

01:51:22 We went up as high as we could because we wanted to know what would happen if people were overdosed later.

01:51:26 Got it.

01:51:26 Okay.

01:51:27 And the answer was nothing.

01:51:28 Okay.

01:51:29 So at 10 mgs per kg monthly over 48 weeks?

01:51:32 Yeah.

01:51:32 It was a maximal dose.

01:51:34 in terms of effect size. And they saw the expected muscle mass increase.

01:51:42 Interestingly, they saw a substantial fat mass decrease. And hemoglobin A1c in these type 2

01:51:50 diabetics decreased by about 0.7% or 0.8%, absolute, which is a pretty good effect.

01:51:55 Yeah. And do you think that that was on account of just more insulin sensitivity,

01:52:00 or was it a larger reservoir for glucose disposal?

01:52:04 Both of those things, I think.

01:52:07 And these patients didn't have to do anything else.

01:52:10 It wasn't like, in addition to that,

01:52:12 they changed the way they ate or they exercised more.

01:52:15 You gave them a drug that added muscle mass,

01:52:17 took off fat mass, and lowered A1C by 0.7%.

01:52:20 Yep, and they had standardized dietary advice to...

01:52:25 Yeah, both groups.

01:52:26 The 500...

01:52:27 So who...

01:52:27 Did Novartis run that study?

01:52:28 Novartis did the whole study.

01:52:30 And then they made a strategic decision that the effect size wasn't big enough.

01:52:35 I mean, actually, I don't know what their strategic decision was, but they decided...

01:52:38 The output was we're going to spin it out.

01:52:40 Yep.

01:52:40 The output was to spin it out.

01:52:42 And at the time, I was working with Joe Jimenez and Mark Fishman and Praveena Kandula at Adidam

01:52:49 Bio as an advisor, and we really wanted Bimagrumab.

01:52:56 What year is this approximately?

01:52:57 That it spun out was 2021.

01:53:03 Okay.

01:53:04 I think it was 2021.

01:53:06 Yeah.

01:53:07 Discussions had been ongoing in 2020, but I think it finally happened in 2021.

01:53:11 Okay.

01:53:11 So you guys acquired the asset, obviously, for a lot less than you could have produced it.

01:53:16 Yeah.

01:53:17 Still wasn't cheap because it was a phase two ready program.

01:53:21 Yeah.

01:53:22 But we acquired it.

01:53:24 Did you guys raise money for that acquisition?

01:53:25 No, they did and did it all themselves, to their credit.

01:53:31 And the plan was we were going to develop it in older adults with low muscle mass and impaired muscle function,

01:53:41 because we thought, who were also obese, because we thought this was the patient population most likely to benefit,

01:53:47 Losing fat and building muscle and maintaining muscle in the context of weight loss, we thought

01:53:55 would be super important for those people. And remember, all of this happened in the context of

01:54:02 nobody being interested in obesity. Everybody thought it was a wasteland for drug development.

01:54:07 Every drug that had been developed in obesity had failed commercially. I mean, there are some that

01:54:12 it had been registered, right? But by 21, you're saying pre-21? This was before NOVA's semaglutide

01:54:20 data came out. Yeah. Okay. That's right. Yep. And I'll tell you- Because that was 21, wasn't it?

01:54:26 Later in 21. Okay. So it's November of 21. Yeah. I think February 21, we started Versanus Bio,

01:54:32 which is the company that licensed Bimagrimab from Novartis. So at that point,

01:54:38 So I was the founding CEO. I was working with Elon Zipkin, and we went out to raise money from investors because now, all right, great, we had this asset. We needed to run a big phase two study, and we went out to raise money. I think we talked to 53 investors. Almost none were interested.

01:54:58 Because you told them indication, sarcopenia still.

01:55:02 Well, it was sarcopenic obesity.

01:55:04 Yep.

01:55:04 and obesity was just not a successful area for drug development so people weren't interested

01:55:12 how much did you need to raise we ended up how much did we need versus how much we got

01:55:17 are different issues yeah i know that's why i asked but we ended up raising 70 million

01:55:22 and what was your way if you could have had your wish list what would you have raised

01:55:26 About 100.

01:55:27 Okay.

01:55:28 However, Atlas Venture and Medici liked the story.

01:55:33 And I had worked with Atlas before.

01:55:36 And Michael Gladstone was the partner.

01:55:39 And it was Vani Marigi and Nick at Medici, Nick Williams.

01:55:46 And we then built an investment syndicate and they funded the company.

01:55:52 And then everything changed when Novo's data came out with semaglutide, which was amazing.

01:56:00 It was the first really effective obesity medical therapeutic.

01:56:05 But then when you're doing drug development, you skate to where the puck is going to be,

01:56:10 as Jay Bradner's favorite saying.

01:56:12 But the puck was going someplace else now.

01:56:16 Semaglutide was going to become the standard of Clare or some incretin agonist.

01:56:20 We knew it.

01:56:20 And so what we did is we quickly repositioned the company to think about what is bimagrimab

01:56:30 going to do on top of that, because that's going to be the standard of care.

01:56:34 So I quickly ran a bunch of mouse studies, and the efficacy was additive when you took

01:56:43 bimagrimab with semaglutide or terzepatide or liraglutide.

01:56:48 did them all. And the efficacy was sort of unprecedented. Never seen anything.

01:56:53 For both fat loss and obviously for preservation of lean mass.

01:56:56 Exactly. For weight loss, especially fat loss and preservation of lean mass. It was amazing.

01:57:04 So the opportunity became much larger. And I was part of the board. The board then brought in

01:57:12 a super experienced CEO, this was Mark Brzezansky, to lead the company then because we had a really

01:57:20 big opportunity and we knew it. And we brought in a CMO, Ken Addy, because I had been serving

01:57:26 as the CMO also. And then I stepped into president and CSO role just in terms of company organization.

01:57:33 But we all kept working on the program. We ultimately ran what became the Believe Study.

01:57:39 And the story here, we spent a lot of time thinking about what we would name our studies.

01:57:43 The plan was believe was going to be phase two, become was going to be phase three, and behold was going to be post-registration studies.

01:57:53 And then you actually have to come up with what those things stand for, knowing only what the B stands for when you start.

01:57:58 I mean, this is so funny how drug name studies work.

01:58:00 Yeah, you know how it goes.

01:58:01 I know the drill.

01:58:02 But they all became with B for B-Magrumab.

01:58:05 Yeah.

01:58:05 Now, you haven't asked me where Bimagramab came from.

01:58:09 So this is another interesting, wonky drug development thing.

01:58:14 So the generic name is called the INN name for, I think, International Nomenclature.

01:58:20 I'm not sure what it's an acronym for.

01:58:22 Which is why it ends in MAB, obviously.

01:58:24 So the suffix of a drug generic name is pre-specified based on the class.

01:58:30 If you're the first in class, pick a new one.

01:58:33 but the company gets to recommend the prefix and sometimes the infix. So Bima is the Indian god 0.81

01:58:46 who's as strong as 10,000 elephants. And that's why Bimagrimab is Bimagrimab. 0.88

01:58:52 And then ends in Mab, monoclonal antibody.

01:58:55 The Grumab is a monoclonal antibody suffix.

01:58:58 So any other drug that comes along in that class, what would be the nomenclature naming options?

01:59:04 Antibodies are just complicated. There's a lot of different criteria for naming antibodies and

01:59:09 you have options for infixes and suffixes. And what was the GLP-1 before liraglutide?

01:59:17 There's exenotide.

01:59:18 Exenotide, right. So the tide became the thing that everybody had to link to going forward?

01:59:24 Tide is peptide.

01:59:26 So were they forced into liraglutide, semaglutide, terzepatide?

01:59:33 The Tide is used for that class, but there are other peptides that end in Tide.

01:59:37 Yeah.

01:59:38 Okay.

01:59:39 So you guys ran Believe.

01:59:41 Yes.

01:59:41 So Believe, it was originally planned to be a 24-week study in patients with sarcopenic

01:59:50 obesity.

01:59:51 That was what we were going to do.

01:59:52 But then when Novo's data came out and we got super excited about obesity, we said, oh, my God, we've got to do a bigger study.

01:59:59 We're going to do it in combination with semaglutide.

02:00:02 And this was during the pandemic.

02:00:04 So there was a lot of complexity and supply chain disruptions.

02:00:09 And remember, with semaglutide, it was a proprietary drug of Novo.

02:00:14 We couldn't get the drug substance.

02:00:17 so we had to use the commercial presentation of semaglutide, which was expensive, and it's an

02:00:25 auto-injector. We couldn't make a placebo for that, so the study design included semaglutide

02:00:31 as open label, but we did placebo-controlled Bimagromab because we had control of that.

02:00:39 We used Bimagromab intravenously just because it was the fastest, most straightforward way to get

02:00:45 into the clinic, not public, was that we were working hard on an auto-injector and we would

02:00:51 have been ready in the next study for an auto-injector, but it was intravenous for

02:00:56 bimagromab. And then what combination should we use? We ended up doing something called a

02:01:03 full factorial design. So we did all possible combinations of low-dose semaglutide, high-dose

02:01:11 semaglutide, low-dose spemagromab, high-dose spemagromab, and placebo. So it's a nine-arm

02:01:17 study. Why? Because we didn't know in humans what would happen with those different combinations.

02:01:24 We didn't know if there would be adverse effects of the drug combinations or not.

02:01:28 They did have a couple of adverse effects in common, diarrhea, for example. And this is in

02:01:35 part why I did that pharmacology study in rodents, because if there was anything unexpected that

02:01:39 what happened with the drug combination. I wanted to know about it. Technically,

02:01:43 actually, we, and we had to do this because we weren't using the semaglutide in its

02:01:50 intended population. I think it, it wasn't registered yet, I guess was the issue.

02:01:56 We had the data, but it wasn't registered. So it wasn't indicated in obesity.

02:02:00 So if you're using it. And so, yeah, you were using Ozempic, not Wagovi. You were still using.

02:02:04 We used them both. Oh, you did.

02:02:06 Whatever we could get. Remember they were in short supply.

02:02:08 And to the regulators' credit, they recognized all of this and were willing to allow us to substitute interchangeably Ozepic and Mugovie.

02:02:22 So what were the findings of this six-month, nine-arm study?

02:02:27 So it was originally going to be six months, but we changed it from the original plan with

02:02:33 Bimagramab alone to the combination, and it eventually became 72 weeks of treatment,

02:02:39 48 weeks was the primary endpoint, and then we had a six-month follow-up period. So 104 weeks

02:02:46 total study, two years. Did you have to raise more money? We did. Yeah. I was going to say,

02:02:52 that's a hard study to do for 70 million bucks. Yes, we did. And it was 500 people

02:02:57 enrolled roughly 507 was the exact number so

02:03:02 we found so i guess number one and one of the things i'm kind of proud of is i got the doses

02:03:11 right because you wanted to see a partial response with the low dose a full response with the high

02:03:18 dose for both of the drugs and see those kinds of dose responses in the combination arms remember

02:03:24 there's four combination arms, right? There's low-dose bema, high-dose bema, low-dose sema,

02:03:29 high-dose sema, four combinations, and then placebo. And we saw the dose effects in all

02:03:34 the arms. So that was good. And the primary endpoint was body weight. It wasn't what I

02:03:40 wanted for a primary endpoint. I wanted waist circumference.

02:03:44 Why couldn't you get DEXA? Too expensive.

02:03:47 And we thought that since the registration decision is made on the basis of body weight loss, we wanted that as the primary endpoint.

02:03:57 We included DEXA in every single patient.

02:04:00 Yeah, it's crazy to me that you would be held to the standard of weight loss when in reality a better outcome might be less weight loss.

02:04:11 That is true.

02:04:11 If you're preserving muscle.

02:04:13 We were acutely aware of that.

02:04:14 Yeah, that's awful.

02:04:15 But it is not what the field was thinking at the time.

02:04:19 No, I know, but it's just, I mean, it's good biology abuts regulatory simplicity for Lutman.

02:04:27 Yeah, yeah.

02:04:27 I personally wanted waist circumference, and I wrote a long white paper about this because waist circumference is more closely linked to important clinical outcomes than is BMI or body.

02:04:40 Yeah, for sure.

02:04:42 Yeah.

02:04:42 So body mass index is, if you're following longitudinally, is essentially the same as body weight because height doesn't change over the short term, just for listeners.

02:04:54 So we ended up using body weight as the primary endpoint.

02:04:58 And it wasn't just regulatory intransigence.

02:05:01 It was also what do investors and potential acquirers think.

02:05:06 Everybody cares about what the approval endpoint is going to be.

02:05:09 We wanted that to be the primary endpoint.

02:05:12 but we measured all these other things. And to sort of zip ahead to the end, in the high-dose

02:05:18 combination group, the body weight loss at 72 weeks was 22, 23% of starting body weight.

02:05:25 High, high.

02:05:26 Yeah.

02:05:26 The double positive.

02:05:27 The high, high combination.

02:05:28 And what was the high?

02:05:29 But the fat loss was 45.7% of starting body fat. Now, that's what you get with bariatric surgery.

02:05:38 So this, to me, this is the first medical therapy that gives fat loss equivalent to or superior than bariatric surgery.

02:05:50 That's amazing.

02:05:51 Did you do any functional testing in that study?

02:05:54 We did.

02:05:55 We did.

02:05:56 And we even did a preliminary observational study in overweight adults at different age cohorts.

02:06:04 And we tested a few different things.

02:06:06 We tested essentially timed up and go for short physical performance battery.

02:06:12 We tested the 30-second chair stand test, which is my personal favorite.

02:06:18 And we tested grip strength.

02:06:19 But ultimately, we went with grip strength in the BELIEVE study because it was the one most closely linked to clinical outcomes.

02:06:26 And did you see an improvement in strength?

02:06:28 Small, but also it was a variable assessment.

02:06:32 Okay.

02:06:32 it's and it's in the published study that came out a few months ago

02:06:36 now novo bought this asset from you guys right no lily did oh lily did yeah okay so we were

02:06:45 super excited about the study it was ongoing we were enthusiastic

02:06:49 we closed a series b in two tranches and we called the first tranche

02:06:58 And then the company got bought by Eli Lilly.

02:07:01 And so they have Bimagrimab now.

02:07:04 What are they doing with it?

02:07:05 You have to ask Lilly.

02:07:07 But they made some noise last fall that they were pausing the program or-

02:07:12 No, they just, they paused one study, but they still have other studies in clinicaltrials.gov.

02:07:18 Okay.

02:07:19 But you got to ask them.

02:07:20 I see.

02:07:21 So publicly, the only thing we know is they're still doing something with it, presumably

02:07:27 testing it with terzepatide, I'm guessing, or retitrutide.

02:07:30 The study that's in clinicaltrials.gov is a complex combination study with terzepatide.

02:07:36 I do think it's fair to mention the one adverse outcome that happened in the BELIEVE study

02:07:42 that we weren't really expecting, which is an increase in LDL.

02:07:46 Yeah.

02:07:46 How much I remember that.

02:07:48 Now, I thought that was actually something that Lily saw, but that was in your study.

02:07:53 Yeah.

02:07:54 And how much of an increase was it?

02:07:55 It was about 20%.

02:07:56 Why do you think that was biologically?

02:08:00 It's a direct effect of the drug in the liver.

02:08:03 So interesting.

02:08:04 Again, you wouldn't expect this off target, would you?

02:08:07 This is on target, because remember, there's active in receptors everywhere.

02:08:12 Oh, I didn't realize that.

02:08:13 Yeah.

02:08:13 Ah. So it's doing something to interfere with LDL clearance, presumably.

02:08:21 I guess, but I don't know what the biology is. It hasn't been studied to my...

02:08:25 Or no, maybe not. I mean, what would be a more... I mean, that would be studyable, right? Is it

02:08:29 impeding LDL clearance or is it increasing LDL synthesis?

02:08:32 I know who knows the answer to this. So Chris Liu, when he left Novartis,

02:08:37 eventually founded a biotech called Lakena in China. And he's made therapeutic antibodies to

02:08:44 active and receptor type 2A, type 2B, and the combination, and he studied them. So he knows

02:08:50 the answer to this, and I assume he'll publish it at some point. And Lloyd, were there any adverse

02:08:55 effects on glucose in the other direction? Did glucose ever go up? No. Okay. And did you continue

02:09:01 in the belief to see glucose go down the way you did in the diabetic studies? Yes. Independent of

02:09:06 what you would have seen from SEMA, I mean. Yes. So we have all of that data and it's published

02:09:11 in the Nature Medicine paper. At EASD in September, which is a European meeting,

02:09:17 we're going to publish the results of the six-month off-drug results. So that's-

02:09:24 That's off both drugs.

02:09:26 Yes. And so the real issue is what's going to happen when you withdraw the drugs?

02:09:31 and we know what happens when you withdraw semaglutide. Everything goes back towards

02:09:37 where it was. It doesn't quite get there. And we're going to find out with bimagrumab.

02:09:42 I expect some things are going to reverse. We know that muscle mass with every muscle anabolic

02:09:49 agent reverts towards baseline when you withdraw the therapy. I expect that's going to happen

02:09:53 in the humans. It happens in the rodents. In BELIEVE, we deliberately included patients

02:09:59 with metabolic syndrome. So these are people who are pre-diabetic. So we can measure diabetic

02:10:06 endpoints in these people. And it's going to be super interesting to see what happens there.

02:10:11 Personally, if we had kept Vimagrimab in Bersanus and had been a standalone entity,

02:10:17 we would be well advanced into phase three by now. And the reason is because I believe,

02:10:23 even with those LDL effects, which are not favorable, LDL predicts adverse cardiovascular

02:10:29 endpoints. But I believe that- Because you can monitor it and you can treat it.

02:10:33 Yes. As an aside, since my personal interest is making drugs to prevent the most common

02:10:43 causes of morbidity and mortality in older adults, side effect of that is healthy longevity.

02:10:50 That's what I do. I am not making cardiovascular drugs, even though it is the number one cause of

02:10:56 morbidity and mortality in adults in the U.S. and in many developed countries around the world.

02:11:01 The reason is we've already got a lot of good drugs. We're just not using them for primary

02:11:05 prevention, which we need to be doing more of. So with that aside, I would be well advanced in

02:11:12 developing Vimagrimab in phase three, but I think the paradigm for managing obesity is going to be

02:11:20 induction and maintenance of remission, probably combination and injectable therapies,

02:11:26 to get people to move them categorically from obese to non-obese. And then they need something

02:11:34 for maintenance, which might be something like Orforglopron or some oral GLP-1 agonist to

02:11:40 maintain appetite and satiety. And you don't think just a lower dose of the injectable could do?

02:11:46 Absolutely, it could. Yeah. You're just saying economically,

02:11:48 it might be easier to make it orally or something. Exactly. Yeah. Exactly.

02:11:51 Okay. I want to pivot and talk about one other thing, which is also an area where you know a lot about it, which is selective mTOR inhibition. We're not going to spend as much time on it, of course, but again, talk to me about where your head is at these days on that pathway in general.

02:12:10 Do you believe that there are, that this is geoprotective in humans? I mean, it's been well

02:12:18 established how geoprotective this is in mice. Almost assuredly, I think it'll end up being

02:12:24 geoprotective in dogs. So it might be safe to say that inhibiting mTOR in everything from yeast to

02:12:32 dogs and maybe even primates extends life. We don't have a clue if it's going to in humans.

02:12:37 we'll never probably get to directly test it. There are really good, compelling arguments on

02:12:43 both sides of why it may or may not be the case in humans, including the longevity quotient

02:12:49 argument and things like that. What are your thoughts? I think it probably will. It's highly

02:12:55 conserved biology across evolution. So I think so. Reductively, if you envision mTORC1 as a master

02:13:05 regulator of sensing, sort of integrating nutritional inputs and then deciding to grow

02:13:11 or not grow. And not grow means circling the wagon, upregulating autophagy and recycling

02:13:17 pathways. I think it probably would. I think the effect size is going to be modest.

02:13:23 Is it just as it has been preclinically? And I think it's TORC1. I haven't seen a lot of new data

02:13:30 on that. Well, there was that study somewhat recently suggesting that rapamycin impaired,

02:13:38 I don't know if it was impairing MPS or some other metric of physical performance or something.

02:13:44 Obviously, you're familiar with the agents you've tested. We're still at sort of the infancy of

02:13:52 these drugs, right? What do you think is standing in the way of more drug development on more and

02:13:57 more selective, potentially higher efficacy, but potentially lower side effect burden versions of

02:14:03 drugs that can inhibit mTOR complex 1? The selectivity is the big challenge,

02:14:11 because with rapalogs, as you know, their TORC 1 is selective, but there's a downregulation of

02:14:20 TORQ2 with sustained exposure. And I don't know that we, you know, so in RestoreBio, we tried to

02:14:28 manage that by a combination of a catalytic and an allosteric inhibitor, which seemed to do it.

02:14:34 And I know there are other companies that are working on other ways to get TORQ1 selective

02:14:38 inhibition. And I think that's what we need is a real TORQ1 selective inhibitor. And then we can

02:14:43 test the biology. And do you think that just intermittent dosing of Everolimus or Sirolimus

02:14:49 gets that? Maybe. Again, it's hard to tell in healthy people because, you know, in cancer,

02:14:58 when you study mTOR inhibitors, the cancers have a highly upregulated pathway and it's easy to see

02:15:04 the biology. You can't really see the active biology in humans measuring blood. It is not

02:15:09 necessarily the tissue you want anyway. When we do this in rodents, we measure their liver activity.

02:15:14 And I think we mentioned, you and I discussed this before, that in young rodents with fasting, they down-regulate mTOR, as you would expect.

02:15:24 In old rodents, they didn't.

02:15:27 So it makes me call into question the whole concept of intermittent fasting in older people, because I don't know if it'll do the same thing.

02:15:36 Yeah.

02:15:37 Again, imminently testable.

02:15:40 Nobody's lining up for liver biopsies, though.

02:15:42 No.

02:15:42 And we can't get that with MRS or anything else?

02:15:47 I don't think so.

02:15:48 It's just...

02:15:48 Yeah.

02:15:49 Again, the price of admission is so great on that.

02:15:53 Okay.

02:15:53 Final question slash topic.

02:15:56 As you think about drug discovery over the next decade, I'm not going to ask you the

02:16:01 question everybody's thinking is, how is AI going to help?

02:16:03 We'll punt that for now.

02:16:04 Thank you.

02:16:05 Yeah.

02:16:05 What are you most optimistic about in terms of pathway, disease?

02:16:14 Where are you most excited?

02:16:16 Where do you think we're going to be in 10 years where there's been a step function change?

02:16:20 I think we're starting to wake up to the concept of real medicine, real medical prevention.

02:16:26 I mean, this is something I've been saying for years.

02:16:29 you've talked about it a lot, is that we need to get away from being a sick care system to a

02:16:35 health care system. And the way you do that is with preventive medicine. And the way to implement

02:16:40 it is you need better primary care and you need codes for preventive visits. Because right now

02:16:48 if I wanted to see a patient for prevention of cancer, for example, so my new company is

02:16:54 cancer prevention. There's not codes for that. So you can't bill for it. So there's, there's a lot

02:17:02 of institutional hurdles that we need to get through. But I think people are waking up to

02:17:10 the concept of, I want to stay healthy rather than get sick and get treated.

02:17:14 Say a bit more about your current company and how, how could one develop a drug for cancer

02:17:19 prevention. Yeah. So this is conceptually difficult to wrap your head around. But again,

02:17:26 where do new drugs come from? They come from reading the literature and thinking, which is

02:17:31 sort of what I did after Versanus ended for me. It's still ongoing in Lilly. And there are some

02:17:39 papers published over the past five to 10 years about drugs that cause cancer. So if a drug causes

02:17:48 cancer, it must be most likely inhibiting a cancer-protective pathway. Most drugs are

02:17:54 inhibitors of things. The prototype for this is serafinib, which is a multi-kinase inhibitor

02:18:00 that's used to treat renal cell carcinoma and hepatocellular carcinoma primarily. If you give

02:18:06 that drug to people, about 10% of the older patients get skin cancers. Why is that?

02:18:13 And are these melanomas or are these squamous or basal cells?

02:18:17 The cancers they get seem to be the prevalence that's reflected in the normal population.

02:18:23 So almost everything that's ascertained is basal cell and squamous cell.

02:18:27 And more recently, we understand the pathway biology of that.

02:18:32 Serafinib is a multi-kinase inhibitor.

02:18:34 It inhibits a lot of kinases.

02:18:37 But one of the ones that it inhibits is the sensing kinase that triggers something called

02:18:42 ribotoxic stress. And this is a pathway that causes cell death. That pathway, if you turn it

02:18:49 on irreversibly and covalently, is the target of some of the nastiest toxins that you know about,

02:18:56 like diphtheria toxin, sarsin, ricin. It's a very, very potent pathway. My innovation is putting

02:19:05 together different parts of the literature. I came up with a way to turn it on in a gentle

02:19:12 in controlled fashion. Remember, it's on constitutively in people because if you turn

02:19:17 it off with these multi-kinase inhibitors, you get cancer. So the hypothesis of the company is

02:19:24 if we turn that pathway on a little more, we'll prevent cancers. Not all of them, but maybe 50%

02:19:31 is what I'm hoping. And since skin cancer is almost as common as all other cancers put together,

02:19:37 We got to start there. But in a phase two study of older adults and older adults in this context

02:19:45 means 50 and up. Sorry, Peter. I'm in that category squarely. Don't worry. I have been for a while

02:19:51 and who have had at least five skin cancers in the past. Those people have a 50% chance of having

02:19:57 another skin cancer within a year. So if we recruit a cohort of 100 or 120 of those, we would

02:20:03 be able to test the low dose, high dose, placebo, and actually measure cancer prevention in a phase

02:20:10 two study. Now, is there a risk that it will only work in preventing squamous cell and basal cell

02:20:16 carcinoma, but will not progress an epithelial tumor? Or prevent, I'm sorry. That's possible

02:20:23 because the only data we have are for skin cancer. But even if it only prevented skin cancer,

02:20:28 That's a really big medical need, but I think it will work on multiple cancers, but it's

02:20:34 going to be almost impossible to test that before approval, just because cancer incidence

02:20:41 is a really rare event.

02:20:42 Yeah.

02:20:43 Yeah.

02:20:43 I guess the next thing that would be an interesting question, Lloyd, would be you take a bunch

02:20:50 of patients who have successfully undergone adjuvant therapy for a stage three epithelial

02:20:57 cancer. So I would think colon cancer or breast cancer. They're NED, no evidence of disease for

02:21:06 the listener, but there's a 50% chance they're going to have a recurrence. You know, you stratify

02:21:13 it in a way that you, you basically find people who have a very high risk of a cancer recurrence

02:21:17 and then you, you treat them. I think that's one way to do the other study.

02:21:22 What I've done in COSLAPE therapeutics is a collaboration with the Broad Institute where we took our tool compound.

02:21:31 Now, we don't quite have a development candidate yet, but we took the tool compound, which is good enough, and run it through their panel of a thousand cancer cell lines to see what tumors are sensitive to it.

02:21:44 So this would be a treatment mode rather than a prevention mode.

02:21:48 And you think it could have efficacy in treatment as well?

02:21:52 That's the question we were asking.

02:21:54 Okay.

02:21:54 And melanomas emerged as by far the most sensitive tumor.

02:21:59 Now, I'm not sure why, because my hypothesis, the thing about skin cancer is it's got a heavy mutational burden because of all the UV exposure.

02:22:08 Yep.

02:22:09 It's the highest mutational burden organ we have in normal people.

02:22:14 And I thought that was going to be it.

02:22:16 And there was a correlation between mutational burden of the cell lines and susceptibility

02:22:22 to this mechanism, but it wasn't great enough to explain the tumor susceptibility.

02:22:27 So it's something else.

02:22:27 That would be good news.

02:22:29 Yeah.

02:22:30 It's there, but it's not good enough.

02:22:32 So I don't know why melanomas are so sensitive, but they're enormously sensitive.

02:22:37 So I'm very confident this will be a therapeutic for melanoma as well.

02:22:41 And it augurs well to the idea of preventing melanoma, which is also testable and has been

02:22:48 proven with a therapeutic intervention in a wonderful study conducted in Australia.

02:22:53 The intervention was intensive sunscreen use compared to usual practice.

02:22:58 So it's going to be testable in a large phase three study, but not before that.

02:23:04 So, yeah.

02:23:06 So that's what I'm doing.

02:23:08 And I think preventing cancers...

02:23:09 That's a very interesting idea.

02:23:10 I mean, talk about a whole new playing field, right?

02:23:15 Yep.

02:23:16 Pharmacologically.

02:23:16 Yep.

02:23:17 Nobody's made a drug for this mechanism.

02:23:19 Because we usually think avoiding cancer or preventing cancer comes down to avoiding carcinogens.

02:23:26 Which we should definitely do.

02:23:28 Yes, yes, absolutely.

02:23:28 So don't drink alcohol much.

02:23:30 Or don't smoke.

02:23:32 Don't smoke.

02:23:33 Be as insulin sensitive as possible.

02:23:35 Yes.

02:23:35 Yeah, all of these things.

02:23:37 All of those things.

02:23:37 Lose weight if you're overweight.

02:23:38 We know that successfully treating obesity prevents a bunch of cancers, and we know that

02:23:44 from the Swedish obesity study, which is an observational cohort of Swedish patients who've

02:23:52 undergone bariatric surgery, and these people are being followed for decades.

02:23:58 It's doing all the good things you'd expect of successfully managing obesity.

02:24:04 Well, Lloyd, this has been great.

02:24:06 this has been kind of a wonderful education on drug discovery using, I think, a very interesting

02:24:13 drug in BEMA as a case study for the complexity and the nuance of the process. And by the way,

02:24:20 I don't think I realized that the BEMA story is still ongoing. So that's great. So we're going

02:24:24 to continue to follow this biology and it'll be interesting to see where Eli Lilly goes with this

02:24:29 drug. But it sounds like based on what's showing up on clinicaltrials.gov, they're following in

02:24:34 your footsteps in that they're probably testing this in parallel with the newer generation GLP-1

02:24:40 agonists and the... As best I can tell, that's what they're doing. And it's not now just Eli Lilly

02:24:44 because many other companies, you know, who have seen the Believe data because we've been presenting

02:24:50 it at national meetings and international meetings for that matter. There's a lot of

02:24:57 other pathway inhibitors that are under development. Yeah. Well, really appreciate

02:25:03 your time, Lloyd. It's been a pleasure to chat again. Thank you. Thank you. Thank you for

02:25:09 listening to this week's episode of The Drive. Head over to peteratiamd.com forward slash show

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The Peter Attia Drive - September 28, 2026

#409 ‒ Inside modern drug development： the science, economics, and regulatory hurdles behind bringing new medicines to patients ｜ Lloyd Klickstein, M.D., Ph.D.

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