[00:00:00] Speaker A: Foreign.
[00:00:14] Speaker B: Welcome to the latest episode of the Few and Far between podcast. I'm your host, Chris O'Brien. A lot has changed for small biotechs since the pandemic. The shift from rapid innovation and positive growth to to financial constraints and hyper competitive global markets has put more scrutiny on early stage companies looking to find their foothold in this dynamic industry. My next guest has established himself as a guiding voice in biopharma R and D strategy, defining how mission, vision and data analysis fit into the short term goals and long term results of today's biotechs. Frank David is the founder and Managing Director of pharmagellon Strategic Advisors to biotech, pharmaceutical pharma and medical device companies as well as professor of the Practice of Biotech at Tufts University. On today's episode, Frank and I will zoom in on tactics for de risking biotech, understanding how investors think about valuation and how to turn great science into clinically meaningful products. I'll also give Frank the titular magic wand and see what changes he would make in the current R and D landscape. It was great to have Frank on the show and be sure to check out the comments section for links to more information on R and D strategy. Okay, let's start the podcast.
Frank David, welcome to Few and Far Between.
[00:01:35] Speaker A: Thanks so much for having me.
[00:01:35] Speaker B: Okay, I've been really looking forward to this. Sometimes these conversations are more of me trying to extract somebody's life story, a set of experiences and we'll do a little of that. But in your case I have a lot of questions, so let's just jump in.
[00:01:48] Speaker A: Fire away.
[00:01:48] Speaker B: So let's talk a little bit about some of the trends in biopharma research that are showing shaping the industry today. How should companies adapt or respond to that? First, what are they? And then how should companies think about that?
[00:02:00] Speaker A: I guess to me the biggest thing that's happening certainly right now is not so much on the research side, it's more on the financial side. So gits are pretty constrained for early stage companies. So that's number one. Number two is that a lot of the bigger companies do have a bunch of resources. So you would think that they could swoop in and be the knight in shining armor and save the day for these little biotechs. But I think that they are generally being cautious about buying out risk. They have so much resources available, you know, not just the lily and novos of the world, but you know, a lot of companies are sitting on fairly healthy amounts of cash. They can afford to basically say, look, you know, we're interested Go do that last trial and then we'll pay a premium for the fact that you've de risked everything. And then I think the third thing that's going on on the financial side is this whole China situation, which is not an area I spend a lot of time thinking about. But I would say in simplistic terms it means that there are a lot of parts of the biotech process, especially in early stages, where there are going to be companies in China that can do it at a tenth the cost and maybe even 5x the speed. And I think that means that it's super hard to be competitive right now as a small company. You have to essentially figure out ways to go much further with less. And I'm not sure, especially coming off of the go go biotech times during COVID when there's just a lot of money flowed into the system, this is like the opposite of that. I think we're in for a long, potentially transformative period where kind of overfunded, early mega round companies that go off and hire 300 people, that might not be a thing.
[00:03:43] Speaker B: You said a lot there. So I want to go through them sort of one by one. So if you put yourself in the shoes of let's say a medium sized pharma or a mega pharma player that is holding a lot of cash, that maybe is looking at patent cliffs and has reason to move, but they're not moving. I think you're 100% right seeing folks not wanting to buy a lot of risk so they're willing to pay more for more certainty. Not a lot of deals are getting done, so that doesn't seem to be a winning strategy. What advice would you have for that corp dev leader who's trying to assess his or her options?
[00:04:12] Speaker A: Yeah, I think there is a short term problem that you're right about, which is that they would like to see these companies go further and de risk. The early stage companies, they'd like to see them go further and de risk but maybe they're not resourced appropriately to go further and de risk. I'm not really sure. In the short term maybe there's a deal structure way of getting around that. Maybe this is the time that they should be looking at innovative ways of financing to try to kind of help those companies get over the line without doing the big M and a deal and taking everything, taking everything onto their own balance sheet.
[00:04:44] Speaker B: Frank, does that mean like milestone based deals?
[00:04:46] Speaker A: I mean some other type of collaboration or sort of finding ways to sort of structure a deal? You Know, because from a small company point of view, look, their investors want an exit, right? And I think some of them are holding out thinking they're going to get an exit. And I think, you know, when you're in the later stages, there's not many milestones left, right?
[00:05:02] Speaker B: True.
[00:05:03] Speaker A: Run your phase three or don't run your phase three and get your data.
[00:05:06] Speaker B: Did it work or did it not work?
[00:05:08] Speaker A: Right. So I'm not sure that it's quite as simple as oh, do a milestone based deal. And actually for the early stage companies, again, I'm not saying that there's not going to be milestone based deals. There's going to be plenty of those being done. But you know, I think it's going to be in areas, I think the early deals that are going to get done are going to be in areas that are maybe a little bit more overall de risked. You know, areas that big companies understand well. Right. So you know, big companies that understand diabetes or and obesity, doing a deal with an early stage company with an oral in diabetes and obesity because they understand the area and they understand all those later risks better than a lot of other people. But then you end up in a bunch of other areas that are just inherently higher risk, either because there haven't been any drugs approved or it's a new technology or something like that. And there I'm not really sure I know what the answer is, but I think that exploring for a large company, you may not end up with much of anything if you wait around hoping that that company is going to not just do a phase three, but do the right phase three, or that that phase is going to actually go through phase 2B and go through commercialization, go through phase 3 rather and get all the way to the point that you can just pluck them off. So I don't know, maybe moving a little bit earlier. I always laugh when I see, you know, corporate update calls where somebody asks, you know, what are you looking for? And the head and the CEO or some other senior says, well, we're very interested in transformative late stage bolt on deals. And like, yeah, you and everybody else, basically.
[00:06:32] Speaker B: Yeah, exactly.
[00:06:33] Speaker A: Literally. That was true 20 years ago, it was true 10 years ago. It'll be true 10 years from now, it'll be true 20 years from now. And I think there will be, there are going to be opportunities to think differently about that because I just don't think there's going to be a lot of those companies aren't going to have a lot of other ways to get as far as they want to get.
[00:06:49] Speaker B: I totally agree with that. And yeah, it is, it is a real eye roller. When you hear somebody say, what I'd really like is to spend not that much and get something great with really minimal risk. That'd be terrific.
[00:06:58] Speaker A: In my family, we call that the New York City real estate problem. I would like an apartment. I would like a big, sunny apartment in a doorman building with great amenities in an awesome neighborhood, et cetera, for virtually no rent. You know, the N minus one rule of real estate, right? Like, you're not going to get all of those things, you know, bring it back to the biotech side, you know, figuring out how can you help get over that, minus one thing.
[00:07:23] Speaker B: Do you think there are folks doing deals where they say, like, look, you need to raise X tens of millions of dollars to run your pivotal trial. We'll fund that, or we'll fund a lot of that in exchange for a discount on acquisition if it works, or things like that. In other words, are people buying an option on the future?
[00:07:40] Speaker A: Yeah, great question. I have not seen it from Strategics, but, I mean, the group Royalty Pharma, you know, has made good business of doing this from a pure financial point of view, right? Where they're like, look, you know, we'll put some money in and then we're going to get a percentage on the back end, you know, of whatever your revenues are worth down the road. I don't know them personally, I've never met them, but I followed that story for a while. I've been a little bit surprised that there aren't more players doing that because it seems like that could be a good solution right now, especially in this market.
[00:08:10] Speaker B: Right? You would think, right? Will you explain that model in a little more detail for folks who might not be familiar with them?
[00:08:15] Speaker A: I mean, there are different flavors of it, but the basic outline is that they'll invest upstream of a to help get a clinical trial done in exchange for royalty off of the back end. If a product eventually makes, you know, hence the name Royalty Pharma, you still own your company. It's a purely financial transaction. There's no strategic part they don't want to own. You know, they don't want to own the part of the company, they just want money off the back end.
[00:08:37] Speaker B: Yeah.
[00:08:37] Speaker A: And they've also done some interesting things. Way back in the day, they did a deal with a company called Sinise around an adaptive clinical trial where actually the way the trial was designed was there was going to be an interim readout where they were going to be able to tell whether they needed to add more sample size because the effect maybe wasn't as big as they thought. And they structured the deal that if it ended up in that promising zone, what they called it, that royalty pharma would put in the additional money in exchange for a little bit more, maybe a few more points of royalty. But they had it all structured around this. That's really interesting design. It was a super interesting. And again, it takes some sophistication. Right. Because the average financial investor, I think, doesn't really have that level of knowledge around trial design, but it's a super interesting model.
[00:09:24] Speaker B: Yeah, there's sort of a natural moat around those businesses. But plural, I think there are a few people that play. My sense is in that space now. I think royalty pharma pioneered it. And yeah, it does seem like I hadn't thought about that. Seems like it would be a good time to be hunting there. I want to come to the point you made about China. You were talking about, hey, maybe it costs less, but unfortunately it's also way, way faster. It's kind of a terrifying combination for us biotechs. And I know I've seen and heard some nervousness about that in the biopharma community in the US how big of a risk do you think that is and do you think there's an answer or is that just a big unknown right now?
[00:09:57] Speaker A: I think it's a super big risk. I think particularly a big risk when you think about the possibility that a second mover could easily leapfrog and become the first mover. Right. So you don't need to discover and validate the target.
[00:10:10] Speaker B: Yeah, great point.
[00:10:10] Speaker A: You just need to figure out a way to address the target and then get through that early R and D and those early trials faster. And, you know, I think we're seeing that in obesity, certainly, and I think we're going to see that in a lot of areas. And I think there's just the capabilities there. Again, it's not just the cost structure. It's just China is now reaping the benefits of the fact that so many big pharma companies set up R and D operations in places like Shanghai. So now there are people who have been doing big pharma R and D for 5, 10 years at a high level, and now they know how to do it. Now they can go and make it happen at a smaller company. So I think it's an enormous threat.
[00:10:45] Speaker B: So that kind of takes me then to, I guess maybe the Overall R and D process, the typical process for US Biopharma, if we were to redesign that, are there changes that you would make? We make you king for a day. You've got a magic wand. What would you do?
[00:10:59] Speaker A: I mean, the holy grail to me is still better predictability. So basically being able in the earliest stages to know what will and most importantly, what won't make it all the way in terms of both efficacy and safety. And yeah, that's a multifactorial problem. And a lot of people have worked on this and, you know, you've given me a magic wand as opposed to making me actually spec out how one would do this, which is great. So I'm loving the magic wand, but. But this is really the thing that I think you kind of need more of, which is probability of success.
Rates have not really budged much. Hard areas are still hard. And there are some of these clinical areas where you really just don't know the answer until phase three. And phase three is essentially a coin flip. And that's super scary place to be in as an industry and for individual companies.
[00:11:52] Speaker B: Yeah, I think that's absolutely right. We're seeing to your point about certainty. We're seeing more trial design that includes an interim look at the data, an effort to get to interim analysis. Do you think that's a factor just largely of this investing climate where people are nervous, or is that kind of the new normal?
[00:12:07] Speaker A: I think part of it is that large and small companies, I think, have gotten more comfortable with those types of trial designs. From what I understand, I'm not a biostatistician. I'm one degree of separation away from these discussions. But about 15 years ago or so, I was involved in a lot of these discussions of how to mix trial design with financial instruments. Sort of like the royalty pharma example that I just gave, the Synesis example. And at that time, what I was hearing from internal biostats groups and clinical development groups was that the biostats people loved these kinds of designs, but the rest of the organization just thought it was too complicated, it was too risky. They were worried about what fda, FDA would think, et cetera, et cetera. So they always just defaulted to kind of more standard approaches. I think enough time has passed and there's been enough experience and that experience has percolated out because people have switched jobs and moved around that there's a lot more acceptance of those types of trial designs, more novel approaches to. You said getting an early look, being able to these adaptive kinds of designs.
[00:13:10] Speaker B: Yeah, and I mean, of course that has to be done very thoughtfully and carefully. It's best if it's planned from the beginning. And it's something that you do in discussion with regulators. I am not a biostatistician or a regulator, but my sense is that that's not something that regulators are necessarily against. As long as you're careful to maintain the integrity of the trial data.
[00:13:26] Speaker A: No, not at all. And I think to the contrary, actually my kind of secondhand sense is that FDA is very encouraging when companies come to them with those types of ideas. Again, as long as they're doing it the right way and they're doing it from the beginning as opposed to bolting it on later. FDA loves those types of innovative trial designs.
[00:13:43] Speaker B: I think there's a funny trend in leaders inside of biotech startups. They're taking this enormous risk, risk on this asset. And I think oftentimes that can make them conservative about everything else. We want to take no additional chances. We don't want to do anything that's not bog standard the rest of the way. Not everybody, but certainly there's some caution about straying from the well beaten path.
[00:14:02] Speaker A: Yeah, I think that that's part of it. I think another part of it which I'm not as well versed in, but my understanding is the actual cost of just setting up and implementing one of those trials is in the near term more in some cases than it would be if you ran something more conventional. So if you're running very shoestring, you know, sometimes it look like a luxury that you just can't afford, even though it would be better strategically.
[00:14:25] Speaker B: Yeah. It's funny. I think that's right. Generally it's going to add cost and time. And of course, if you're in a race with other competitors, that's also an important factor. But it can get you an answer. It can get you to a yes faster, but it can get you to a no faster, which is the second.
[00:14:38] Speaker A: Best thing, I mean, for a large company for sure. Right. For a small company, I think there are some sort of misaligned incentives there.
[00:14:45] Speaker B: That's probably true. Yeah. Some incentive to just stay in the game, as it were.
[00:14:49] Speaker A: Absolutely.
[00:14:50] Speaker B: Let's talk a little bit about interpreting clinical trial. Are there common mistakes that you see either from the biotech companies themselves or from investors who are studying the market?
[00:14:59] Speaker A: I mean, I would say the general overarching answer to that is kind of the rose colored glasses problem. Right. Like really trying to see a glimmer of hope and Try to find a reason to move forward rather than looking for a reason to cut bait or being really objective. I mean, there is this sort of endowment effect among investors where they already hold onto it, or the company that's running it themselves, where they've already poured their blood, sweat and tears into it and they're very committed to the idea, but the science doesn't care. And if you do something and the answer is that it is now less likely to be effective and less likely to work than it was before you ran the experiment, then maybe it's time to cut and run. And I think that that's just still a problem in our industry.
[00:15:44] Speaker B: Do you think there's just a fundamental misalignment between management teams and investors on that sort of the problem that Adam Feirstein was talking about in a recent column about all these?
[00:15:54] Speaker A: Yeah, I mean, I thought Adam made a great point how these companies, when they have a failed trial, they should just return the money back to investors and just let them start all over again. Right. If they want to start it all again with the same team, great. If they want to form a different team. But this phenomenon where, you know, they either do the not dead yet, or they say, okay, we have a bunch of cash in the bank and we're going to go and try to buy some distressed asset or something like that. Who's to say that that asset and that team are actually the best way to deploy that capital? If I'm the person who put the capital in, I'm actually pretty pissed off off if someone did that because my money is fungible. So now you've told like, I liked your original idea. I put my $5 on Red 17, you know, because I thought Red 17 looked good.
[00:16:34] Speaker B: Looked pretty good.
[00:16:35] Speaker A: You told me that Red 17 didn't work, but now you're forcing me to put my five to take my $5 and put it on black six. I don't want to put it on black six. I want to have my $5 and decide what I want to put it on myself. So I actually, I'm very sympathetic to Adam's point that that's really a. Not to me, that's not being a good steward of other people's money.
[00:16:52] Speaker B: Yeah, I hear that for sure. I think the other thing that you would sort of expect situation a bunch of promising companies that don't have cash but have interesting pipeline and a bunch of people that have cash and no pipeline would be more mergers of these things. If they're not going to return cash to Shareholders. We haven't seen many of those. A few, but not a ton.
[00:17:09] Speaker A: Well, so I think maybe there are two problems with that. One problem is that second category of the promising pipeline, but no cash. Promising pipeline is in the eye of the beholder, especially at the time.
The way I look at it, because I'm a cynic, is, is all of those companies are failures until proven otherwise. Right. Because statistically they're all failures. Right. 90% of them, even if they make it in the clinic, 90% of them are going to die. So trying to find the best among those is often hard. I think the other problem you run into is something that former colleagues of mine used to call the tying two drunks together problem, that you've taken a company that didn't get a positive result for whatever reason. Maybe part of that was management. Maybe part of it was strategic decisions. Maybe whatever. Maybe some of it was just done bad luck. Now you have this other company that has promising science but not enough cash. Again, maybe there's problems there, too. Maybe it's not as promising as advertised. Maybe there were strategic missteps and now you're tying them together and you're asking me to think that that's a good investment. Maybe not.
[00:18:08] Speaker B: Yeah, a little bit, maybe of a tougher sell. Yet it doesn't seem that there's a rush to return cash to investors. Easier to understand, maybe in the public companies because nobody's actually got a position where they can force that. But why do you think it's so rare?
[00:18:21] Speaker A: I have no idea. Honestly. It's not an area that I've talked to enough people to understand. You should. You should have some VCs onto your.
[00:18:29] Speaker B: Yeah, that's coming up soon, listeners. Stay tuned. We're going to get a couple of investors on.
[00:18:32] Speaker A: Yeah, that's a great question to ask them. I have no idea. If I were a vc, that's what I would want.
[00:18:36] Speaker B: I'm saying give me back my money, I guess. Although there could also potentially there's also an incentive alignment issue there. Maybe I want to be able to tell a story to my limiteds about how there's potential here. I don't know. It'll be interesting to hear what a couple people have to say about that.
Hi, this is Chris O'Brien, host of Few and Far Between Conversations from the Front Line of Drug Development. We'll be right back with this episode in a moment. I personally want to thank you all for listening to our podcast now in our fifth season. It continues to be an amazing opportunity to Speak with some of the top thought leaders in the drug development industry. If you're enjoying this episode, please leave us a review on Apple Podcasts. It really helps people discover the pod. And don't forget to subscribe to Few and Far between so that you never miss an episode. One last request, know someone with a great story you'd like to hear me interview. Reach out to
[email protected] thank you. And now back to the podcast.
All right, moving along. If a small biotech has one shot to impress investors, what are the key data points that you're looking for? What do you think is critical? Maybe give us a little sense of how you think people ought to be thinking about pitching.
[00:19:50] Speaker A: I can speak to how I look at these things when I'm asked to by investor clients or by strategic clients. You know, I would say one of the most important things I look for is a pretty sophisticated vision and understanding of the vision in terms of where this could go as an actual drug. I think there are a lot of situations where early stage companies either don't have a lot of clinical know how in house or they've got very limited clinical exposure and they've gotten people to tell them what they would like to hear.
[00:20:21] Speaker B: Yeah.
[00:20:22] Speaker A: And the problem is that on the other side of the table you have financial investors or strategic investors, investors who don't love your thing as much as you love your thing.
[00:20:30] Speaker B: Yeah, it's not their thing.
[00:20:31] Speaker A: Yeah. They're being much more clear eyed and rational about it. And I think one of the things that's really credibility destroying in those kinds of situations is not having a realistic view of how you're going to fit in and being overly hyperbolic, thinking that you have to make sure we're going.
[00:20:47] Speaker B: To take the market, we're going to.
[00:20:48] Speaker A: Take the market, we're going to win it all. We're going to be best in all of these different dimensions and all the patients are going to come and switch to us. Those stories just again, you could sort of, you can hear and see the eye rolls.
When those stories start to happen.
[00:21:03] Speaker B: Tell us a little bit more then double click on that. When the presenter is telling you the story, they understand standard of care, they understand the competitive environment, they're thoughtful about some of the challenges with current standard. Is that right? Tell us a little bit more.
[00:21:17] Speaker A: Usually I think that the problem is the devil ends up being in the details. Right. So I still remember it's a story I tell all the time. But there was this diabetes pitch that I saw many years ago where. And I don't even remember the specifics of the science, but the part that I remember is they said, look, you know, diabetes is a really big business, big area. We have an awesome new thing. We think we'll capture 5% of the diabetes market. 5% of 20 billion is, you know, billion dollars. You know, show me my money, write me a check. And then that's just a relatively unsophisticated view of it just shows a lack of understanding of how doctors treat patients, how doctors and patients select therapies, about what the nature of unmet needs are and sort of what you're actually trying to do with a new medicine. And that can be super hard in early stages. And I think there is a good argument for not being too tied to a particular vision. Mike Ria at Idea of Pharma in the UK has written and spoken a lot about this idea of like, that a target product profile at very early stages can sometimes lock companies in, lock the thinking in before you actually have enough information. And I'm very sympathetic to that. But at the same time, I think telling at least one or two plausible stories with the right amount of clinical detail saying, look, we don't know everything yet, but here are a couple ways we think this could unfold. And telling that in a way that actually shows some, not just scientific, but also clinical and commercial and business sophistication, I think goes a long way, especially in our industry where whether you're talking to financial, financial investors or strategic investors, if you're an early stage company, you're usually talking to people who know stuff. Right?
[00:22:57] Speaker B: That's right.
[00:22:58] Speaker A: You're not talking to some generalist who last week they were meeting with a construction firm and today they're meeting with you. Right. That's just not. It's not generally what's happening. You're meeting with people at Atlas or.
[00:23:08] Speaker B: Third Rock or, you know, pretty sophisticated. Yeah. Somewhere between pretty and extremely. Yeah, that's right, exactly.
[00:23:14] Speaker A: People who, people who know stuff.
[00:23:15] Speaker B: And I guess, I mean, I think what you're saying, if I boil it down, is common with a thesis that includes the patient perspective or the prescribing physician perspective, the thesis may adjust. But don't tell me about market without at least having thought about where you're going to fit into that.
[00:23:30] Speaker A: Yeah. Or maybe you have two or three different ideas. Right. Maybe you say, look, we're not exactly sure what the profile of this is going to be, but based on what we know and what we understand about the Clinical environment and where the clinical environment is heading. We think there are three ways this could play out. And actually we win in all three of these. We win in slightly different. There's slightly different flavors of winning. We do well in all three of these different areas. And we're going to sort of show you that. Now, part of our question, and maybe that's also tied to the use of investment proceeds in the early stages. Maybe part of what you're now proposing is, look, you give us $20 million and part of what we're going to do is figure out which of these lanes we're in. Right. We're going to do the experiments to figure out are we better, are we safer, are we more convenient? We don't know. We're fine with any of those.
[00:24:13] Speaker B: We better pick at least one.
[00:24:15] Speaker A: Yeah. And you're going to give us money and we're going to focus on the experiments. Help us figure that out.
[00:24:19] Speaker B: So something that you've said before is that this is not a science experiment. Will you talk a little bit about that and about how that fits into the idea of a model for where this company's going to go or this asset will go?
[00:24:28] Speaker A: Yeah, I mean, I think that a lot of early stage companies believe that just making data is a good thing. And sometimes that's because it's come out of an academic group and the science is super interesting. Sometimes the science is very unclear and there's an argument, maybe we could defer it to another day, but there's maybe an argument that some companies get founded too early, maybe it's not time, maybe the science isn't mature enough to actually figure out in the context of a privately held company as opposed to an academia, maybe the science is just too immature and it's not the right time to start a company. But I think at some point when you're going out and talking to investors, your job is to turn dollars into value generating information. And value generating is the key piece of the puzzle there. Right. Like not all pieces of data that you can make at early stages necessarily change the value of your thing. You have to be very disciplined about figuring out, fine, we're going to do this experiment and now let's sort of map it out. If it comes out this way, that takes us down this certain road and tells us ABCD comes out this other way. It's like E, F, G, H. To me. One of the examples that I always think about is I remember being in medical school and learning about how to think about diagnostic tests. Right. So the crappy doctors will just order a bunch of of tests.
[00:25:46] Speaker B: A bunch of tests? Yeah.
[00:25:47] Speaker A: And they're sort of like, I don't know what's just LFTs and this and blah blah, blah, blah, blah. And you know, without sort of thinking through what are you going to do with the information when it actually comes out. And the real art and skill of managing patients is actually figuring out what are the right experiments to do, the right tests to do in that case where the information is actually going to lead you down a path and help you then set oh, now I know the patient doesn't have ABCD and they do possibly have efgh. And now that's informed this other suite of tests.
[00:26:20] Speaker B: So is that a fair test for somebody who's considering starting a company? Don't pull out of academia until you have a theory for value creation. Like a pretty specific, hey, we're going to raise this money and it's going to enable us to test these things and find a path or validate this thing that we believe, whatever it might be, but not we're still in the like, hey, this is pretty cool. And could be a lot of different things, like maybe don't rush.
[00:26:41] Speaker A: I mean, I would say yes with an asterisk. And the asterisk is, you know, given the uncertainty around academic funding, I'm not really sure anything I would have thought about that two weeks ago necessarily holds true. But two weeks ago or a month ago, I think I would, that was the answer. Look, stay in academia where you can continue to just figure some stuff out and you really want to go and take other people's money at the point that you know you can generate value, that every dollar can generate value. And until you know that every dollar can generate value, you want to spend as few dollars as possible and bring in as little outside money that is expecting return as possible. And again, at least a month ago I would have said that the NIH is a good way to use money where you're not on hook for generating value.
[00:27:26] Speaker B: Yeah. Yeah. Okay, so I think then our rubric gets modified and it sort of says like if there's NIH funding kind of at scale, then take your time until you at least have a not take your time but wait until you have a thesis for value creation if there is no NIH funding new paradigm and go ahead, do your thing.
[00:27:43] Speaker A: I haven't thought through that very well.
[00:27:44] Speaker B: But I think you're in good company on that one, my friend. It's a confusing time. Okay, so that's super helpful on modeling. Let's flip more to your bailiwick and talk about biotech valuation. So this is where you spend a lot of your time. What should biotech companies understand about the way in which investors think about valuation?
[00:28:04] Speaker A: So, yeah, I think there are a few points that I would make there. One is that if we're talking specifically about early stage companies, kind of preclinical companies, there's an ongoing debate about how much time to spend doing modeling. And certainly when I talk to VCs, a lot of them will not build models and they'll figure out the valuation looking at comps and other types of things, which is great. But I still maintain there is some value to building the model, even if you know it's going to be wrong. And the reason why there's that classic, classic all models are wrong, but some are useful, quote. And I would actually modify that and say all models are wrong and most of them are useful. And the reason they're useful is they force you to actually codify your assumptions and also create a structure of the model which reflects your view of the world. Right. So when you think about that sort of patient funnel, quote, unquote. Right. Where you start with. Again, let's go back to our diabetes example. We start with all the diabetes patients up at the top of the funnel and then at the bottom of the funnel, there's some number that we think are going to be eligible for our drug. There are a lot of different ways to slice and dice, right?
[00:29:10] Speaker B: Yes.
[00:29:11] Speaker A: And I think a model can be very useful for at least forcing you to think through a couple of different ways that make sense to slice and dice the market and figure out where are the areas where you know stuff, where are the areas where maybe you don't know stuff and maybe you would like to learn some more things, structure.
[00:29:26] Speaker B: Your thinking kind of. Is that the assumption? The process of the assumption?
[00:29:29] Speaker A: Sure. So some examples that kind come up a lot, I would say. Two things that I find are very commonly come up in this early stage modeling scenario. One of them has to do with making us a therapy that you think is going to be sort of a set for treatment refractory patients. Right. So look, I understand. Let's take depression, for example. Right. So, you know, we haven't cracked major depression, but on the other hand, there are a lot of really good drugs, many of which are generic for major depression. A lot of patients do pretty well on them. Okay. So if you have a theory of the case, so to speak, which is that we're not necessarily thinking that we're going to be first line for all patients because I can go and get generic fluoxetine for pennies, you know, every month.
[00:30:06] Speaker B: Lots of people have a good experience.
[00:30:07] Speaker A: Yeah. But I think that maybe the patients who have their depression has not responded to fluoxetine and two other things. So then how many of those patients are there? And it might actually illustrate to you. Hmm, I'm actually not sure how many patients there are. And actually what you find a lot of times in these kinds of problems is that the academic literature, it can often be tricky to figure out those kinds of quant answers that are interesting to investors and company builders, but are not as interesting to academic investigators. And it might actually make you think, okay, what are the different ways? What are the data sources? How would I find an answer to this question? How can I at least get an approximation that's good enough and then how can I fill that with better information later? So that's one example that comes up a lot. I would say a related situation is when you're sort of thinking about one of these diseases where they're sort of mild, moderate and severe. Severe. Right. And you're thinking, okay, like I'm pretty sure that the severe patients are the ones that would be the most applicable for mine. There's treatments for mild and moderate, but severe, you know, they don't have great treatment options, then the outcomes are really bad and that's where we're going to position ourselves. And same sort of thing. Often when you go into the academic literature, you find that academic papers have not necessarily sub segmented it in the way that you think is relevant. And then you have to think, okay, like maybe this is a place where out of my friends and family money, maybe I need to spend $20,000 and do a little bit of market research and try to understand a little bit about how big each of these buckets are when they're defined a certain way. And think about, and get some help thinking about, how do people actually define that severe population? And that that would lead me through my model. And again, that's important, not so much in terms of what the end result of your model is. You know, what is your risk adjusted npv? It's more like these are the questions you're going to need to answer even to have any semblance of a clinical strategy, and someone's going to ask you what your clinical strategy is. So I think the model is useful just for tightening up the thinking. Not so much as a math exercise it's not the Excel part of it, per se.
[00:32:00] Speaker B: Yeah. So that's the big miss, right? Is that people who, or at least I think often it's people who don't want to build a model say, it's too early for me to get to a meaningful number or numbers at the end that are going to be helpful. And your point is? Doesn't really matter. Helps me to figure out what I know, don't know, and what my theory of the case is for this particular asset.
[00:32:19] Speaker A: And also I love that for sure that's true on the population identification. You know, sort of figuring out what the addressable population is. And then I think as you go through and think about building out a valuation model where you're assigning levels of risk to different phases, again, you don't know what your clinical trial is going to look like exactly, but it is worth thinking through where you think are the biggest buckets of risk here. And what are the kinds of things going back to what we were talking about before. Are there things you can do even early that might help you figure out if the thing you're working on is on the higher side of the risk band or the lower side of the risk band? What are those things? Sometimes there's no substitute for just getting out there in the clinic and doing it, and that's fine. Sometimes there are preclinical experiments or translational types of experiments that can be very useful. Sometimes there's other type of work. And maybe you would then realize, oh, actually maybe I should prioritize some of those things, because if I could actually prove X is true, that when I treat patients with my thing that interleukin, whatever goes up. And because one of the big questions here is whether the whole mechanistic hypothesis is true. And if I could do a little, a small sort of human pharmacology experiment in phase one and sort of prove to myself that the mechanism is as advertised, that it's doing the thing it's supposed to do, that that would be enormously de risking because nobody really believes, nobody knows enough to know whether our mechanism is believable or not. And again, that's part of, of sometimes by building a model, you can get to that when you realize, oh, like I'm not sure that these probability of success numbers that I just grab out of kind of wherever my book or at other places, I feel like those are too high because my thing looks scarier than that. And then you figure out ways to make your thing less scary.
[00:33:53] Speaker B: Yeah, that's a Great way to frame it. Parenthetically, we've had on the podcast a couple of people have companies that do market research answer some of these questions. We just had Eileen from Brass Tech Insights on that's oncology focused and really looking at, at competing assets that might be working their way into or through the clinic. We've had Danielle Drayton from Reach Market Research, who does this kind of work, trying to understand patient population's unmet need, all that stuff. So there are experts who can help you. Okay, now we're going to jump to the question that brought me to you in the first place, the way in which investors in the media overhype clinical results tell us a little bit about what the problem is there and some of the red flags that you see.
[00:34:27] Speaker A: I would say the biggest issue that comes up is, is a spin issue, usually. Right. It's around taking data that are complicated and maybe a little bit messy and not perfect and trying to put lipstick on the pig, as they say, and make it seem sort of better than what it is and feeling like there's some sort of, that it's some sort of failing if you actually show that you are aware of the challenges and of the data. So I think that's probably, that's maybe the overarching issue that I see in large companies and again, sometimes in small companies, rather big companies do it too occasionally, but I think it's more prevalent in small companies. I think sometimes it comes from a not terrible place and sometimes it comes from a bad place.
[00:35:12] Speaker B: Say more about that.
[00:35:13] Speaker A: Well, I just think that, you know, there are clearly situations in which by any objective measure, the trial didn't work. And you should have said the trial didn't work.
[00:35:22] Speaker B: Yeah, yeah.
[00:35:23] Speaker A: And instead companies title their press release, making it sort of seem like it's positive, as if they're going to pull the wool over somebody's eyes.
[00:35:30] Speaker B: Yeah.
[00:35:30] Speaker A: And now I'm sorry, Adam Feuerstein is too smart for that. And most serious investors are too smart for that. And, and so you end up in a situation where you just look silly.
[00:35:40] Speaker B: Is this, is this sort of like, exciting tertiary endpoint kind of stuff, or is it like, what are the things that you hear people say? Is it stuff like that?
[00:35:46] Speaker A: It's around different endpoints. It's around, it's around subgroup analyses, right?
[00:35:51] Speaker B: Yeah, yeah, for sure.
[00:35:53] Speaker A: Not in the whole population, but we found people who, you know, were born on a Tuesday seem to do better than anyone else else. You know, it's kind of silly. Usually it's more plausible Than it's more on its face plausible. But when you actually scratch beneath the surface, you realize that they just sort of did a bunch of analyses afterwards and didn't cherry picking the day and they cherry pick. And again, there's no problem with doing that as hypothesis generation. But the right way to report that is to say, look, our trial failed. However, we learned some promising things from doing other analyses that are just hypothesis generation. But you know, we think that actually maybe patients who look like ABCD did show a response and we're going to try to figure out if that's real or not and that then kind of litigate that. Right. Like go and test that. Go and find out.
[00:36:34] Speaker B: Go and find out. Yeah. Okay, that's great. So is there some advice in there? It sounds like there might be some advice in there for companies that are faced with data that are where they didn't hit their primary endpoint.
[00:36:43] Speaker A: Yeah, don't play games. As my daughter would say, it's not that deep. Okay. This is not, this is not complicated.
[00:36:49] Speaker B: Right.
[00:36:50] Speaker A: Treat people like the smart and thoughtful. You would like to attract smart and thoughtful investors, either strategic or financial investors. Treat them like the smart and thoughtful people they are. Don't play games.
[00:37:02] Speaker B: Very, very helpful. All right, we're going to switch now to the world of advice, so look forward a little bit. I know you spend a fair amount of time mentoring students at Tufts and I'm sure elsewhere. As you look forward in this, let's call it mildly confusing world that we're all living in right now, what do you say to people who are interested in careers in the life sciences?
[00:37:20] Speaker A: Look, I still think that this is a great industry. It's going to remain a great industry. The capacity to do great good by turning science into clinically meaningful products is enormous and we've barely scratched the surface. I think that for the people who are 20ish right now, I don't think the wheels are going to fall off the bus in the industry over their professional lifetimes. I mean, anything's possible. But you know, that would really be. If you want me to put a percentage chance on Pfizer declares bankruptcy in 30 years. I would put a pretty low, low percentage chance on that. Right. If you want to tell me if you want a percentage chance on will the entire venture capital ecosystem in biotech go away in the next 30 years? Similarly, I'm struggling to see it not taking that back. That said, I think there are certain areas that are under threat. Right. So 10 years ago or 15 years ago, I think if you were a chemist, a synthetic organic chemist, people would be advising you to be very cautious and strategic about how to build a career in biopharma. Because at that time companies were already starting to cut back dramatically on how many chemists they really needed in house and really trying to get an understanding of what are the skills that are required to be successful in a job like that. And they may not be the same as the skills that got the current bosses into their jobs. I think that's true in a lot of areas. I just saw this article on the regulatory side. How Novo. I think it was novo. They were preparing regulatory documents using AI and they did like 30 FTEs worth of work. It would have taken 30, 30 person weeks of work. And they did in like half an hour with one person. Basically I'm messing up the.
[00:39:06] Speaker B: Messing up the. That's sort of the.
[00:39:08] Speaker A: Conceptually, you know, there are some of these things that are, that are changing very rapidly. That being said, I think there's still going to be a place for two types of people. There's going to be a place for people who have very focused technical expertise. Right. And I don't mean technical, just on the R and D side. I mean it could be technical in terms of the technical details of regulatory, commercial, et cetera, et cetera, like finance, et cetera. And then there's going to be place for people who understand the strategy piece and can sort of figure out how to navigate between all of these different choices. I think those are some of the things that are going to be last to the robots will take those jobs last. And I think that there'll still be opportunities there, I think for younger people. Getting exposure in a big company remains advice that I tend to give people. I like small companies as much as the next person. I just think that you get, you see more volume of material and you interact with more and more experienced people in big companies than you do at small companies.
[00:40:07] Speaker B: What is the definition of a big company? There is that 200 people or 2,000 people, 20,000 people?
[00:40:12] Speaker A: I think it's in the sort of vertex and bigger kind of stage. It's sort of mid to large biotech pharma. You know, big biotech mid to large pharma, like a place where there's half. There's a real pipe pipeline, there are real professionals, there are people who have been doing it and just seen a lot of stuff.
[00:40:29] Speaker B: Deep expertise across all the functions.
[00:40:31] Speaker A: It's just. Yeah, and it's just this is a mentorship business still in a lot of ways like you're learning, you learn how to do this no matter whether you're in commercial or regulatory or clinical or basic science. I think a lot of the parts of this job are still a mentorship and this industry are still mentorship based. So I think you want to go someplace where the mentors quality of mentors is high and the number, the number and quality of mentors. Again, at small company, you'll often have high quality mentors, but maybe you only have one or two of them. You know, you go to Pfizer and now you're on a bunch of different project teams or you're in a bunch of different meetings. And now either directly or indirectly, you have access to 50 mentors in any particular area and you see different ways of doing things and then you start to develop your own taste and your own, you know, your own judgment.
[00:41:12] Speaker B: That's terrific. So get that experience, get started. And then should people also think about smaller or do you think that smaller is.
[00:41:19] Speaker A: How do you think about that in this job market? You, you should get employed. Yeah.
[00:41:22] Speaker B: Go where you can.
[00:41:23] Speaker A: Yeah. So, I mean, I think it's just a tough market right now. I do think, paradoxically, I think there might be maybe some more opportunities in early stage things, especially if you're willing to take a little bit less cash and take a little bit more equity. Maybe doing some stuff with startups and much smaller companies could be a good move right now. Because bigger companies there seems to be. I'm not sure whether it's that they're contracting in terms of early stage hiring or that they're just the volume of applicants, applicants has exploded. But anecdotally what I hear from students is that those jobs are super competitive. So I think, look, just get what you can, obviously. But if you are lucky enough to have a choice, I think going to a bigger organization is great. I've been advising students to go to CROs, honestly.
[00:42:03] Speaker B: Thank you, Frank. We'll tip you later.
[00:42:04] Speaker A: Yes, I will cash my check later. But in all seriousness, you know, I think a lot of them don't really know that that's a thing. I had a student who was interested in regulatory, who applied to a bunch of industry jobs. Nobody was really hiring at her level. She was coming out of master's, but she got hired at iqvia. I think it's going to be a great experience for her because again, one of the great things about consulting is you do a lot of reps, you see a lot of volume and you see a lot of stuff from different companies and you also build a ton of relationships. So sure, it can be grueling at the beginning. I think quality of life wise, pound for pound, working on the services side tends to be just a harder quality of life than working internally at a company. But if you're young and you can take some lumps in the early phase of your career, I tend to advise people to do that because it's a good place to just do a lot of reps, get a curve for sure.
[00:42:47] Speaker B: All right, so speaking of that then, in closing, Frank, David, where can people get in touch with you, follow you, learn about the stuff that you're doing?
[00:42:55] Speaker A: I put a bunch of stuff on the farmagellon website. You can go there, you can follow me on LinkedIn. I will say that I have a general rule on LinkedIn which is if people just send me an invitation without anything else, I ignore those. I am an old school, I'm an old soul. So I, I kind of like to actually have talked to someone for half.
[00:43:14] Speaker B: An hour before considering them a contact.
[00:43:16] Speaker A: Before actually establishing some contact. That said, I mean, I respond to emails. My email is freely findable on the pharmagellon website. So if you have a question or want to chat with me, drop me a line.
[00:43:26] Speaker B: Frank, David, what a pleasure. I know we committed to sort of land this plane in around 45 minutes. We're around there now. I could easily spend another hour talking to you. Thanks so much for coming on few and far between.
[00:43:36] Speaker A: It was a pleasure. Thanks so much.
[00:43:40] Speaker B: All right, welcome, producer Adam, what did you make of that one?
[00:43:43] Speaker C: Thanks, Chris. You know, some great info from Frank and a lot to digest. I think I'd like to take questions today based around Frank's aphorisms. Thought they were really good.
[00:43:54] Speaker B: King of aphorisms. I agree.
[00:43:56] Speaker C: Let's start with the tying two drunks together problem. A worst case scenario for de risking by bringing bad pipelines together with bad science. How can you avoid this misstep?
[00:44:07] Speaker B: Yeah, it's a phenomenally evocative image, isn't it? Two drunk guys tied together does not result in one more productive human who can move forward. And we have seen that happen sometimes, right? Sometimes it's about connecting a company with capital and no pipeline with a company with a pipeline and no capital. That's a little bit cleaner. I think it's actually quite tricky if you're going to merge two pipelines together for two struggling companies. If one company's doing well and wants to enhance its pipeline by bringing in some new asset or some new Capability, Fair enough. But part of it is about, I think, a little bit of humility about how much chaos, how much drunkenness you can handle in the merger that you're trying to make happen. And I think that's really what Frank was cautioning us on, is to be very careful and thoughtful about whether combining these two things is likely to result in something that's better or frankly, just more confusion.
[00:44:57] Speaker C: Very good point. The next one he talked about was the New York City real estate problem. Very much trying to get the best of everything for very little investment. How do you remind today's biotech companies that these prior merger and acquisition tactics are just not working well anymore?
[00:45:14] Speaker B: Yeah, I mean, I think, look, we'd all like to get the perfect thing for. And part of being perfect is it being affordable. I would say this is aimed at as much at Big Pharma Corp. Dev teams that are very cautious about bidding on assets that are earlier in the process. I think we'd all like to see a little more risk taking from Big Pharma to help some of these biotechs exit, and frankly, so that Big Pharma can hopefully be better prepared for the patent cliffs that they're all that get a little closer every day. These large companies, companies that are revenue generating, are facing real declines in revenue as some of these drugs come off patent. That is the aforementioned patent cliff. So sometimes you got to either pay more, which they do seem a little bit willing to do, or you have to buy something that's a little more speculative. And that's where there's been a lot of hesitation. So I think I would point that one more in that direction.
[00:46:01] Speaker C: Gotcha. So this one is really more of a statement. Don't play games. What is your take on. And I know you have a good take on this, Chris, what is your take on spinning clinical trial results that are not as successful as they seem?
[00:46:14] Speaker B: Yeah, I mean, I think that the takeaway from that is that the. The investing community and the industry journalists don't assume that people are idiots. That, you know, people are smart, they're thoughtful, and they have the scientific chops to analyze your results. So trying to pretend that bad data are not bad is not really a winning strategy, and I think damages the credibility of companies that do that. So when Frank says, don't play games, he's saying, okay, put a positive spin on your results. Sure. But don't try to pretend that something that's not good is actually good. And I strongly agree with that.
[00:46:49] Speaker C: It's a very good point. So the next one I wanted to bring up is, I think my favorite one that he discussed is all models are wrong and most of them are useful. There is just so much you can learn about a company from early stage models. What's your take on that?
[00:47:03] Speaker B: Yeah, I think that's a great one that applies beyond this industry. I remember a professor of mine in business school saying something similar to this. Professor Powell at Tuck used to drum this into our heads, that the point wasn't that the model was necessarily going to be all that predictive, because the more variables that are involved, the less predictive a model can be. But by defining your assumptions, by being really clear about what you believe, first of all. And secondly, by looking at what the impact of all these different variables potentially can be, because not all factors are of equal import, you get a real sense of, of what the potential outcomes can be and you get clearer and cleaner in your ability to tell your story and think about what's coming. So I strongly, strongly agree with what he said. All of them are wrong. None of us can say, you know, that the crystal ball that we have will tell us what the world's going to look like, especially several years down the road, which is often the case for biotech models. You're predicting a future that's not predictable. Right. But it's still very, very valuable to do this. And I think one his points is to start doing it relatively early because it will help you to sort of refine your targets, understand your risks, and get smarter about the way you talk about what you're doing.
[00:48:10] Speaker C: It sounds like a necessary step in the evolution of a company or an idea that they have.
[00:48:14] Speaker B: Yeah, I think that's absolutely right, Adam. And not just in biotech. I think it's something all businesses should do. It's tempting not to do too much of that in the early stages because there are so many unknowns. But I think Frank's point here, she's a really good one, is that simply by defining all of your assumptions, you do get smarter about what the risks and opportunities are.
[00:48:33] Speaker C: So the last one I have for you is the phrase that he used. This is not a science experiment.
Yeah, very interesting point. How do you get companies to evolve from an academic perspective to value creation?
[00:48:48] Speaker B: Yeah, I mean, I think this is a real challenge for CEOs and sometimes for chief medical officers inside of biotech is to reorient people away from a hey, it would be interesting to find out mindset, which is a pretty consistent one in academia. You know, you're rewarded for asking lots of interesting questions and trying to come up with ways to validate those questions or come up with answers. But in a company, you should really be focused on what's going to advance this asset towards ultimately a a product that could be beneficial for humans. And anything other than that, while it may be interesting, is probably a distraction. And I think that's really what he was saying. So don't treat this as a science experiment. It's a company. It's a company with limited resources and a ticking clock, as all companies have. And so you should be working really hard to focus, really. I guess you should be really focused. I think everybody's working hard. I don't have any doubts about that. But you should be really focused on things that are gonna advance the defined objectives of the company.
[00:49:42] Speaker C: Agreed? Agreed. Definitely. Again, this was a great episode. I hope everyone who's listening thing really enjoys it.
[00:49:48] Speaker B: Tons of fun. Frank King of Aphorisms, I think we're going to call it.
[00:49:53] Speaker C: That's great.
[00:49:53] Speaker B: Thanks, Adam.
Thank you for listening to the latest episode of Few and Far Between Conversations from the front lines of drug development. Our podcast is now available on Apple Podcasts and other streaming services. Please take a moment and leave us a user review and rating today. It really helps people discover the podcast and we read all the comments. Those comments help us make Few and Far between better and better. Also, be sure to subscribe to Few and Far between so you don't miss a single episode. Got an idea for a future episode? Email us at fewandfarbetween.com or contact us on our website at bio rossi.com I'm your host, Chris O'Brien. See you next time.
