Building Better Biotech Financial Models and Managing Valuation Risk with Frank David

Written By Paul Barnhurst

In this episode of Financial Modeler’s Corner, host Paul Barnhurst (aka the FP&A Guy) sits down with Frank David, an expert at the intersection of corporate strategy, clinical medicine, and biotech research. They discuss the unique challenges of financial modeling in the biopharma industry, the importance of understanding patient populations, and how drug development strategy influences financial decision-making.

Frank David is the Founder and Managing Director of Pharmagellan, a consulting firm specializing in biotech financial modeling and valuation. With a background as a physician scientist, Frank has extensive experience advising biopharma teams and investors. He also teaches at Tufts University and is the lead author of The Pharmagellan Guide to Biotech Forecasting and Valuation and The Pharmagellan Guide to Analyzing Biotech Clinical Trials.

Expect to Learn:

  • The unique challenges of financial modeling in biotech and pharma.

  • Why "small percent of a big market" is a flawed valuation approach.

  • How patient segmentation impacts drug development strategy.

  • The importance of risk-adjusted modeling and scenario planning.

  • How financial modeling supports strategic decision-making in biotech investments.


Here are a few quotes from the episode:

  • "Most early-stage biotech entrepreneurs don’t realize they need to think about who their patients actually are and what alternative treatments exist."

  • "In drug development, forecasting is about managing uncertainty, not predicting exact numbers."

  • "The clinical strategy and commercial strategy in biotech are so intertwined that modeling them separately can be misleading."


This episode explored the complexities of biotech financial modeling. Frank shared why traditional financial modeling approaches don’t always work in the biotech industry and how strategic decision-making relies heavily on understanding patient needs, regulatory pathways, and risk assessment.

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In today’s episode:
[01:14] - Introduction to the Episode

[02:02] - Meet Frank David
[04:17] - Financial Modeling Horror Story
[09:38] - Understanding Biotech Financial Models
[13:57] - Biotech Forecasting and Valuation
[18:15] - Where to Start When Analyzing a Drug Project
[24:52] - Risk-Adjusted Modeling in Biotech
[32:01] - Advice for Modelers in Biotech
[40:56] - Fun & Rapid-Fire Questions
[49:32] - How to Connect with Frank David


Full Show Transcript
[00:00:54] Host: Paul Barnhurst: Welcome to Financial Modelers Corner. I am your host, Paul Barnhurst, aka the FP&A guy. This is a podcast where we talk all about the art and science of financial modeling, with distinguished guests from around the globe. The Financial Modelers Corner podcast is brought to you by the Financial Modeling Institute. FMI offers the most respected accreditations in financial modeling. If you want to validate your financial modeling skills, take the Advanced Financial Modeler. I completed mine last year and it was a great experience. I'm thrilled to welcome on the show this week. Frank David, Frank, welcome to the show.


[00:01:38] Guest: Frank David: Thanks so much for having me.


[00:01:39] Host: Paul Barnhurst: Yeah, really excited to have you. I'll go ahead and give a little bit about your background, and then we'll jump into the questions here in our conversation. Just so my audience knows, you may find this background a little bit different from our typical guest, Frank laughing there is he never thought he'd be on a modeling podcast.


[00:01:56] Guest: Frank David: Not even for a second.


[00:01:57] Host: Paul Barnhurst: So Frank is an experienced consultant and former physician scientist who works at the intersection of corporate strategy, clinical medicine and basic research. The consulting firm he founded and leads for, Magellan helps biopharma teams and investors across all clinical areas integrate R&D strategy and business strategy. He is also the professor of the Practice of Biotechnology at Tufts University, where he teaches about drug development and does research at the intersection of drug development strategy, regulation, and policy. Frank is the lead author of two books, The Magellan Guide to Biotech Forecasting and Valuation and The Magellan Guide to Analyzing Biotech Clinical Trials. Do we get your bio right?


[00:02:56] Guest: Frank David: Sounds good to me.


[00:02:58] Host: Paul Barnhurst: Isn't it always weird to hear someone else read your bio?


[00:03:01] Guest: Frank David: Super weird, but that was great. Thanks.


[00:03:05] Host: Paul Barnhurst: Perfect. Well, you know, we're really excited to have you on the show. I know you and I spend quite a bit of time chatting before you came on, I think. What was your first thought when I sent you an email saying, hey, you want to come on a modeling podcast?


[00:03:17] Guest: Frank David: Yeah. I mean, I've always felt a little like I have imposter syndrome in the whole financial modeling community. So, you know, I'm happy to be helpful if people think that I have something to say that's helpful. Obviously, biotech and pharma financial modeling is sort of its own weird beast. So, for me, it's been super fun. And I'm really excited to the extent that there are people who maybe haven't thought that much about it, or maybe there are some parts that are relevant to the work they do. I'm really, really excited to be here. Yeah.


[00:03:52] Host: Paul Barnhurst: As I said before, I was fascinated. When we talk, I think people just enjoy learning about the industry because it's different than most modeling, even if they never do it. People who enjoy modeling enjoy learning. I think we'll really enjoy it. All right. This is the question. We start with every guest. Tell me about that worst financial model, that horror story that you've worked with.


[00:04:12] Guest: Frank David: Yeah. So for me, actually, when I think about horror stories in this area, it's actually not so much about a full model, but it's about sort of a pre model, shall we say. And I still remember being at some entrepreneur pitch session where people who were starting companies would kind of give a little bit of an intro to their idea and try to get people psyched up about it. Kind of one of these classic pitch day, demo day kinds of things in biotech. And I still remember there was someone who got up and they were developing some sort of new therapy in diabetes, and their idea was diabetes is a multi-billion dollar industry, and if we even get 2% of it, we'll be worth $5 billion. It was something. It was that kind of math, and it took all of my self-control to stay in my chair at that point. And it's the example I use with so many people in this area because, honestly, one of the whole points of building a model and thinking about it is really thinking about what the inputs are, and it just drives me bananas. When people have a story in biotech or pharma, which is this is a big market and we're going to get, you know, a small percent of a big number is still a big number, because that's just not how you think about the peak market opportunity in biotech or pharma. So, you know, I would say from there that honestly kind of encapsulates most of what drives me bananas in this area.


[00:05:46] Host: Paul Barnhurst: Yeah. And so what's kind of the takeaway when you see somebody do that saying, hey, we're going to grab 2% of the market and be worth 10 billion or 5 billion? Yeah. Whatever. Big number? Yeah.


[00:05:57] Guest: Frank David: So first of all, the actual answer used and usually ends up being you're actually going to get 0% of that market. Because you know, there's just no, it's not plausible to kind of have a hypothesis about what you're trying to build in biotech or pharma, where the answer is small percent times big number. That's just not a clinically relevant story. And I think one of the things that's always true in, in our industry, in the drug development industry, is you have to have some sort of hypothesis, who are these patients? What are the things they're being treated with now? What are you offering with your new thing? And there and from there, you get to a point of what percent of the patients you're going to get and what percent of the market you're going to get. So, you know, diabetes is not just one thing, right? There are people with long standing diabetes. There are people with relatively recent diabetes. There are people with other diseases like heart disease or obesity. There are people without those things. Old people, young people, people on different medications. People who are well managed on cheap drugs that already exist. People who are not well managed on those drugs.


[00:07:04] Guest: Frank David: And so, you know, for me, part of the whole point of the model of that part of the model in terms of the peak revenue piece of it, is really getting in the weeds and figuring out what you think you're actually making. And then on top of it, you also have the problems of depending on that population, that might be a huge population with like a really long and costly and risky trial associated with it. Right? In terms of the R&D costs and what it's going to take to get there, and maybe it's going to take you ten, 12 years from where you are now to actually make that happen. Maybe there's a huge amount of regulatory risk involved. On the other hand, maybe you're dealing with a super high need population and it's small and maybe there's a faster way to get there that is lower risk and costs a lot less. And I think, you know, for me, like that's actually why modeling is interesting in drug development, because it's actually so intimately tied to what your whole R&D strategy is and what you think you're making and how you're going to get to the finish line.


[00:08:08] Host: Paul Barnhurst: Yeah, I appreciate everything you said there. And I couldn't help but think when you said, hey, we're going to get 2% of a big number. There are a ton of planning software trying to help you model and plan for your company. Everybody wants to take from Excel. And it's like, well, you know, 70% of the market is on Excel. Yeah, but 50% of that is small enough. Or in a situation where they have no desire to change. So what's the real market size? You know, it's the same type of thing if, you know, let's just say, you know, the market for diabetes is $100 billion, right? Just make up a number. Yeah. If 50% of them are well served on the drugs they use, and as long as they take care of themselves, don't need anything else, okay? You're only going to get a very small percentage of anything on the market unless you can do it substantially cheaper or better, or what's the unique thing that's going to allow you to get into that market? You can't just say, hey, we're going to get 2% because we're researching this.


[00:09:03] Guest: Frank David: That's exactly right. That's exactly right. And I think a lot of a lot of early stage entrepreneurs in biotech, you know, they often come from academic backgrounds. And this is really their first foray into the business world. And they've never really thought about those kinds of business questions. You know, they've never thought about going out and doing market research, talking to doctors, talking to patients and figuring that stuff out, let alone all of the other things in terms of how you get there from an R&D perspective.


[00:09:30] Host: Paul Barnhurst: Yeah, they don't know how all the financials that go into it, building the model, the business plan, all those different things. So let's step back for a minute. Can you just tell our audience a little bit more about your background and what you do for a living? I know we talked a little bit in the bio, but if you could share a little bit about yourself and how you ended up where you're at. Sure.


[00:09:48] Guest: Frank David: So I started off as a physician scientist, as you said. So I did a bunch of school and then after I finished school, I did a residency in pathology. And with the idea all through that time, the idea was that I was going to be a professor at some medical school and run a lab and maybe do some clinical work on the side, but I was really interested in doing basic scientific research. And then about 5 or 6 years into that, I figured out that I actually wasn't as interested in making data as I was in understanding how data turned into drugs, and academia doesn't really do that part of it. I mean, academia is great on the basic science piece, but when it comes to actually making drugs, most of that work is done by either small biotechs or big pharma companies. So I had no idea what that world was like. But I started doing a bunch of reading and talking to people who worked in that world, and I was super interested in it. Turns out that transitioning from having been a hardcore academic into more of the business side of things is not exactly a seamless transition, but one of the one of the entry points that's very common is to do biotech consulting. And the reason is that consulting firms are kind of well set up to take pretty smart people who know some science and medicine and basically help them learn on the job, all of the business pieces by serving clients.


[00:11:11] Guest: Frank David: So I went into one of those firms. It was called Leerink Partners. It's still actually, the firm is a biotech investment bank, a healthcare investment bank. But at that time they had a small component that separate from the banking did straight up consulting. And I'll come back to that in a little bit because it does relate to today's topic. Anyway, went in there. I loved it, drank from the fire hose, learned really how the whole business works, and also realized that I really liked the client services piece of it also. So essentially, except for a very brief stint where I went in-house and did Centered strategy at a biopharma company. I've essentially been doing advisory, been on the advisory side and been doing R&D strategy consulting for lack of a better term. And what I have found so cool about it is that, I mean, I like the science, I like the clinical piece, but then there's all these interesting issues having to do with regulation. There's interesting issues in this industry having to do with the commercial aspects. And then there's the financial piece of it, both at the level of an individual drug candidate, but also at the level of an individual of a whole company.


[00:12:13] Guest: Frank David: Right. Whether it's a, you know, a small biotech that maybe has 1 or 2 things in its pipeline or you're talking about Pfizer. So it's been very fun for me to sort of put all those things together in interesting ways. And actually doing it at Leerink in particular for the first part of my career was very fun because we actually had a transactional consulting practice that I helped run where basically we would go out there as consultants together with the bankers to small companies, and we would say, look, you probably need some financing and you also need some strategy work. You don't necessarily know what you need in which order, but we'll figure that out for you. You know, we can kind of do it all. And that was super fun, actually, because that was really my first exposure at a more granular level to what you spend all of your time thinking about, you know, really more of the modeling piece of it. And then later on, you know, as I stayed at Leerink, I continued to do projects where part of a strategy project, we would often be building a model as part of that.


[00:13:08] Host: Paul Barnhurst: See, so you do have the modeling experience.


[00:13:10] Guest: Frank David: I have just enough. Just enough to be dangerous. I mean, I am not, it's funny. You know, I am very slow on Excel. You know, I am not technically kind of a wizard in terms of all of the capabilities, but you do end up learning a lot by doing the kind of work that I do about sort of how to turn the strategy concepts and the words on a page into numbers. And at the end of the day, like, that's what's fun and interesting about the intersection between what I do and what you do.


[00:13:42] Host: Paul Barnhurst: Sure. No, it makes a lot of sense. And so, you know, kind of speaking about that, you wrote a book called Biotech Forecasting and Valuation. How did that come about? What made you decide to write a book on that?


[00:13:53] Guest: Frank David: I had done projects when I was at Leerink and then afterwards when I formed my own firm for Magellan, where often you're trying to understand value of these things that are really early in the drug development process, you know, drug development, there's this whole stage where things are just in the lab, right, this whole preclinical stage, and then things go into the clinic. And usually even from the time that they start in the clinic, it can take five, seven, ten years to actually get to the market. There's a bunch of steps along the way. There's a ton of risk. Only about 10% of the things that actually get into the clinic and get tested in humans, make it out the other end and get approved by the FDA. And so when I do those when we were doing those projects at both of those places, one of the challenges is that you don't know very much about your drug candidate. At that point, you also are trying to project things that are pretty far in the future. One of the frustrations and challenges of doing this work in this industry, in the drug development industry, is you're often dealing with kind of what I call stub numbers, right? You're dealing with numbers that you think are good enough, but they're clearly not right in the way that, you know, if I were working on an on market drug, for example, and I'm trying to forecast what's going to happen next year or the year after, etc., you know, now I have historical data on the uptake. I know what the population is.


[00:15:15] Guest: Frank David: I know something about pricing. I know a lot of stuff. Right. So forecasting quarter to quarter for an on market drug is not going to call it easy, because there's a lot of subtlety and complexity there. As you know, you at least have more data that you can hang your hat on. Whereas, you know, for these early stage things, you're often in kind of a data free zone. It was very frustrating to me that there were no sort of there was no single source of truth for what to do when you're in that data free zone. So essentially, the book came about because I was basically trying to create something like that and then realized I was basically writing a book, which was going through all of the parts of a classic kind of single asset PNL and figuring out, okay, like, what are the things you need to think about? How do you think about pricing when you have when you're, you know, about to go into phase one trials in the clinic, how do you think about what the ramp rate should be once you launch your drug? You know, how long should it take to get to peak? How do you think about probability of success at all of these stages? And it turns out there's been some work by consulting firms and academics and other people to kind of get some good enough numbers on those that you can at least use as the middle of a sensitivity analysis. So really, the book was an attempt to really pull all of that together.


[00:16:29] Host: Paul Barnhurst: Yeah. And I think that's great. You pulled that together. Like I said, that's someone found that book and that's how you and I got connected. So reached out to me and they said, hey, you know, I'm starting to do these clinical type trial models and drug models had a biology background, but it got into financial modeling and was supporting different businesses. And he's like, I'd love to hear how somebody else thinks about this, because basically it's been tough, so to speak. And so if you could find someone and that's how it happened. So yeah, I'm sure he'll be thrilled to listen to this episode. He was quite excited when I told him yesterday. Great. And so, you know, I'm glad we connected on that. And you know something you said there, right? You have very little data. You mentioned, hey, if it's an off the shelf drug, it's a lot easier to forecast, right? Take aspirin or Tylenol. You got 30, 40 years of history. You can get relatively close, a pretty high degree of confidence of what your range is versus, like you said, a new drug that you're just starting phase one of some therapeutic or whatever it might be. And you're trying to figure out, okay, what could my revenue look like? What are my expenses going to be? What's the chances this is actually going to happen. And so obviously clinical drug studies as we've talked about it's a high risk business. I think you mentioned, you know, 10% that those that get to that clinical stage actually get approved by the FDA. And there's large cash outlays. This isn't cheap. It's not like you can whip up a prototype in the weekend and, you know, test not all right. There's a ton of paperwork and and work. So where do you typically start when analyzing a project. How do you how do you think about that knowing you know, the complexity and the risk that goes into this?


[00:18:15] Guest: Frank David: Yeah, it's a great question. And again, it does come back somewhat to that diabetes example, which is for any of these types of projects, you sort of have to start with a target product profile of some sort. You have to imagine what is the thing that if everything goes well, that we're going to come out on the back end with and you may have 2 or 3 different hypotheses about that, and that's fine. And you would sort of test all of those. But if you want to build a model at some level, you have to have this idea of, here are the type of patients who we think would take this drug. Here's and at the time that we launch, here's what we think the competitive landscape is going to look like. Here's what their other choices will be. And then you can use that to start to get at how many of them are there. What share do we think we're going to get out of that market? What do we think are going to be the analogs? Is this a market where there are alternatives are generics so they only cost pennies a day or are all the alternatives still branded drugs. So then we're competed in a branded market and the pricing obviously would be totally different.


[00:19:16] Guest: Frank David: And then I think once you have that, then you can start to say, okay, if we are going to be successful and do all the things that we think we're going to do, what does that require of us in the clinical development stages? Right. So now you can start to map out what a trial strategy would be. Again broad strokes. You know, are we talking about? 100 patients. 1000 patients or 10,000 patients in a. In the last stage of trials based on that, we. And are we talking about a trial that's going to take six months or or six years to execute. And then you could start to use that and get a handle on the prelaunch part of this. What are the costs and risks and times. And then you start to fill in, you know, the left hand side of your model basically and figure out how that looks. So you know, I think most people if you're going to build a model, it kind of has to start with some sort of vision. Who are the patients who are going to take this and why? And then everything else kind of flows from that. Yeah.


[00:20:15] Host: Paul Barnhurst: And what I love about that is you're starting with the people that will use your product, right. The product being drugs. Sometimes I think we start with, hey, what's the revenue number? I need to hit a certain valuation okay. What are the expenses look like. Right. Models start with revenue. Even in this case, even though you're not going to get revenue for ten years. You still have to start by figuring out, okay, what do I think the revenue can look like? And then layer in all those expenses and say, okay, does this make sense? At the end of the day, given the risk that's involved here?


[00:20:47] Guest: Frank David: Yeah, I mean go ahead. No, I was just going to say I mean, you make an interesting point because again, one of the things that's super cool about drug development is that the path to get there, to actually get onto the market, often there are a lot of choices that you can make. Those choices end up being trade offs between cost, time and risk. You can map out a lot of different options once you've decided to go after, let's say, brittle diabetics who are poorly controlled and having a lot of bad outcomes, even on best available therapy. Depending on what you want to prove in those patients, you can go a lot of different routes to get there and you can start to have discussions. If you're in a company, separate if you're an investor, but if you're in a company building the model, then you can start to have discussions where you could play some of these strategies against each other and say, well, look, you know, there's one situation where we have a pretty high chance of success, but it's going to cost us a lot to get there and take a super long time. But we'll do all the de-risking along the way so that, you know, before we put down the big money on the giant trial, we're going to be pretty confident that, you know, as confident as we can that it's going to work. And then, you know, then there's the sort of on Earth two, we could also, you know, alternatively, we could do this other thing where we go super fast and we spend as little time as possible to get to the market, and that's great.


[00:22:12] Guest: Frank David: And if all goes well, we'll spend a lot less. And the NPV of this program will be a lot higher. But the problem there is that it's super risky. So these ended up being kind of the interesting conversations that teams have when they come up in sort of a project review committee in a big pharma company where, you know, you're really dealing with the aspirational kind of peak opportunity piece of it. Then you're dealing with the sort of risk adjusted value, and then you're also dealing with how much money do we have available right now to do whatever we think could come next, right. And all of those things sort of fight against each other in the room. I mean, the classic situation in a big company is you go in there and you have this beautiful presentation and you've mapped out a really nice path. And the next trial, the first trial in humans is going to cost $15 million. And management says it's very nice. We love everything about this. What can you do for me for 11 million though? And that ends up being then you have to go back and sort of figure out how to articulate what the trade offs are. Right? Because now we're doing something smaller and or riskier. In those first stages. And then, of course, as you know, that percolates all the way forward into the whole model.


[00:23:32] Host: Paul Barnhurst: FP&A guy here. And as you know, I am very passionate about financial modeling and the Financial Modeling Institute's mission. I have been a huge fan of the FMI for years, and I was super excited when they decided to sponsor the Financial Modelers Corner. I recently completed the Advanced Financial Modeler certification and loved the entire experience. It was top notch from start to finish. I am a better modeler today for having completed the certification. I strongly believe every modeler needs to demonstrate they are a qualified financial modeler, and one of the best ways to do that is through the FMI's program. Earning the accreditation will demonstrate to your current and future employers that you are serious about financial modeling. What are you waiting for? Visit www.fminstitute.com/podcast and Use Code Podcast to save 15% when you enroll in an accreditation today.


[00:24:38] Host: Paul Barnhurst: Yeah. So when you're building a model, let's talk about a single drug for a minute. Yeah. And then we'll talk a little bit more portfolio. I'm assuming every model is you're going to have multiple scenarios and you're doing some kind of risk adjusting to come up with an NPV at the end of the day saying, hey, scenario one has a 20% kind of chance. When you look at all the risk of actually getting to market or whatever, we've de-risked it with this revenue and this one has a 5% chance, but a much bigger revenue or lower expense or whatever, right? Bigger NPV. Is that basically the idea? Yeah. I mean.


[00:25:11] Guest: Frank David: I think that there's maybe one version of that that is even simpler. That's kind of the even more standard, which is really like let's just put it out there. One main strategy for how we think we're going to go forward, and it's really around sort of testing some of the sensitivities around the key inputs, and understanding sort of especially for the inputs, where we have a lot less certainty around them. How anxious are we feeling about the fact that we don't have uncertainty? So I'll just give one example, which is, you know, when we talk about something like, again, let's stick with diabetes, for example. Sure. You know, in diabetes it's a pretty mature market. There's a lot of epidemiologic data and data from other drugs, etc., so we can be pretty sure how big the patient population is and how big all those subpopulations are. Just by, you know, looking in the literature, in the medical literature and looking at epidemiologic reports, etc.. But sometimes what you find in some of these rare diseases, for example, where there haven't been any drugs that have ever been marketed, there may actually be a lot of uncertainty around exactly how many patients there are. Maybe there are 10,000. Maybe there are 20,000, maybe there's only five.


[00:26:27] Guest: Frank David: We're not exactly sure. And part of it is that maybe patients have been sort of. Poorly identified and poorly diagnosed, in part because there hasn't been a good drug. But if you had a drug that was available for these patients, then kind of people, it would drive a lot of the diagnostic behavior. So, you know, sometimes you end up in a situation where there's even some error bars around the total size of the market, even if the total number of patients were available. And then, you know, that might drive some decisions. So for example, in some companies they'll say, well, look, we like this idea. We think you have a good idea. We think the science is good. We understand that we could get there. But in order to feel good about this, both in terms of the size of the prize and also in terms of how we're going to get there, maybe we need to do a little bit more digging and epidemiology work ourselves, and maybe fund a little bit of academic research, for example, just to figure out how many patients there are, you know, before we kind of go crazy here? So I think there are a lot of these situations where at early stages, the discussion is around sort of where do we have more or less certainty around the numbers? And then there's sort of the next stage I think is more like what you described, which is, well, we think there are, broadly speaking, kind of three options for how we can get to the goal line.


[00:27:50] Guest: Frank David: And now we want to sort of illustrate what the trade offs are and use a model to sort of show how, again, the risk and the time and the cost and the reward sort of play off against each other in some risk adjusted NPV model. And again, you can sort of imagine that, you know, sometimes companies want to just maximize the RNPV and that's great or the IRR of a project. And that's fabulous. But often that's not the only thing that they're trying to maximize. You know, sometimes they're trying to optimize around just sort of how much cash they have over the near term. And so that might, you know, make them either figure out, well, how much do we do? We love this. Enough to take money away from our other project, or if we can't take money away from our other project. Back to that other example, you know, what can we do for less and how does that impact the whole project?


[00:28:41] Host: Paul Barnhurst: Are we confident enough in this to try to go raise more capital?


[00:28:46] Guest: Frank David: Absolutely. So I mean, there's a whole fundraising aspect of this too, which I find very fun. You know, having been kind of peripherally in the finance world from having, you know, from where I worked before that, of course, there's the internal investment decisions. Right. So how much of our own money do we invest? Then there's the internal sort of fundraising decision, as you pointed out. And then there's the external perspective. Right. So if I'm a big pharma company and I'm evaluating a small biotech, someone's building a model there, both to figure out whether we should do the deal or not, but also sometimes around deal terms. Right. Because there's kind of an intimate relationship between where you think the risk gets discharged and how you might want to pay in a biotech deal. So very often these biotech pharma deals are what's called milestone driven. So, okay, like we'll pay you a bunch of money at the end, maybe we'll give you a royalty or whatever. But along the way, if you get past this certain super risky stage, then we'll pay you. We'll give you some cash, basically. So there's a very intimate relationship between what you believe to be true that you've put into your model and how that gets reflected in deal terms. And then, of course, there's the sort of pure financial investor side of things where people are just trying to figure out, you know, for public companies, for example, like, should I buy this? Should I buy stock in this company?


[00:30:11] Host: Paul Barnhurst: Yeah, I mean, it's a lot like, you know, venture capital in the sense I know it's not necessarily milestones but hit a certain amount of revenue. You do your next round of raise. All right. Now we can raise a little bit more money. Our valuation went up. Get to that next revenue number. Kind of like get to that next milestone, right? If I hit, you know, the last stage of the clinical trial and I just have FDA approval left and all the numbers look good, probably much easier for somebody to say, hey, I'll give you another 30 million or whatever the number is versus, hey, we have this idea. We don't even know the size of the population.


[00:30:46] Guest: Frank David: That's exactly right. That's exactly right. So there's a lot of, again, the cool thing, I think, you know, to the extent that I can sell any of the financial modelers who are listening on the idea of doing this specifically in biotech and pharma, it is a very cool area because I think that it is just so intimately related to the R&D strategy, commercial strategy, deal making, investment, all of it. It's just super neat.


[00:31:16] Host: Paul Barnhurst: Yeah. I can tell you really love the strategy and the whole business side of this, of bringing it all together. Like, that's a really fun project. Let's say I have someone out there listening. They're a modeler, you know, they're either wanting to do this type of work or they're doing a little bit of it first. Any advice on how they should manage assumptions, how to think about them? Because obviously assumptions play a huge part, right? You tweak a number a couple percent and it completely changes what comes out at the end. And that doesn't necessarily mean it's right or good or better. It just means you have a bigger number because you tweaked an assumption. So any advice on, you know, how they should kind of think about and make sure you're able to validate as best they can, knowing it's an assumption. It's never going to be perfect.


[00:32:04] Guest: Frank David: Yeah. I mean I'm not sure if that I doubt that what I'm about to say is biopharma specific. I think it's probably much more general. But, you know, I get super anxious in models where very small changes, absolute changes to a particular input are causing big swings like that gives me heartburn every day of the week, every week of the year. So, you know, I think if you know that that kind of thing is true, then, you know, I think that tells me, like in the work that I do, it tells me we have to have like a much deeper strategic conversation around, you know, how much inherent risk there is in this program, because forgetting about the technical risk of, can we make a drug, you know, if we have a, if we have just inherent risk because small changes in particular inputs are just causing things to skew wildly? That's a whole other level of business risk and financial risk that we're undertaking. So, you know, I would say that that's a key piece of the operation for me. And then the other piece, assuming that that's not terrible, you know, the other piece to me is just really trying to go back and forth and figure out what are the parts of the model that really are driving the bus.


[00:33:23] Guest: Frank David: You know, what are the inputs where they have big strategic implications. And, we need to figure out what to do about that particular part. So again, sometimes, you'll find in a model that there's very little you can do under the base case assumptions to make it look attractive from an NPV point of view. And that becomes an interesting strategic discussion. If you're a small company, maybe you start looking for non-dilutive sources of funding. You know, maybe you need some nonprofit disease advocacy organization, you know, if you're working in a rare disease, for example, to help foot some of the bill. Or maybe you need maybe this is something where more of the work has to be done in academia, outside of a corporate structure, before it gets taken over by a company. So, I mean, I think those are just some examples of where, you know, using the model really as a guide to what does it mean in terms of the overall drug development process? I think is really is key in this industry in particular. And again, I suspect it's the same in a lot of the areas you work in.


[00:34:32] Host: Paul Barnhurst: Yeah, I mean, it's definitely true in a lot of industries for sure. You know, it leads me to a question. So let's say we have a financial modeler and, you know, typically often, you know, modelers FP&A or other areas will say, hey, learn the business as best you can. Obviously, this is a very technical business. You're limited on how much you can learn as general as a financial modeler of the real science. So what are things modelers can do to help, you know, improve the chances of success to make sure they're building a good model? You mentioned the assumptions, but yeah. Could you learn a little bit about the drugs or how do you think about that?


[00:35:07] Guest: Frank David: Yeah, no, that is a super idea. And honestly, like I have tons of clients who have come into the industry from more of the quote unquote, business side of the House. Right? So they don't have MDS or PhDs, you know. They have, you know, either bachelor's degrees or sometimes MBAs. Right. And they just come in with totally different experiences. So, you know, I would say, you know, there are one of the things that's great about this industry is there are a lot of resources available. I actually wrote a thing on LinkedIn where I collated a bunch of resources, sort of how to learn about biotech and pharma. If you don't know very much about the industry. And a bunch of people have written really great stuff there, which kind of I think is good for giving sort of non-technical experts a good sense of not only what all the parts are and what order they go in, but also like what people are worried about or what they're thinking about at each stage along the way. So, you know, to me, that is key to the operation, especially if you're working sort of solo. Right? I think if you're already in a company that is working in this area and you're the finance person and just want to get better at it. You know, to me, there's no substitute for just being a fly on the wall in as many of the meetings as you can that are wrestling with, you know, technical and strategic questions.


[00:36:34] Guest: Frank David: Because even if you don't understand everything, common things are common, right? So you'll start to hear people talking about, oh, well, if we can get accelerated approval for this drug, then that would change, blah blah blah blah blah. And you're like, hmm, maybe I should learn something about what accelerated approval is and how that works, and what kind of drugs get accelerated approval. Or people start talking about sort of safety risks of a particular class of drugs, then you start, then that kind of directs your reading and thinking in those different, different areas. So, you know, I think that it is great in biopharma that in companies usually these are kind of multifunctional project teams. So there'll be people who think about the modeling. There'll be people who think about, you know, commercial strategy, regulatory, clinical, preclinical, etc. you know, all in the room together talking about these things. So, you know, there's no substitute for that type of experience. But certainly you can get up to speed. Certainly there are a lot of ways to sort of get up to speed using the kind of stuff that's already out there. Yeah.


[00:37:40] Host: Paul Barnhurst: You know, at the end of the day, as you say, that, you know, even though it's technical, it still comes back to learning as much as you can about the business. You may never understand all the technical, but the more you learn, the better prepared you are. I mean, I gotta say that.


[00:37:53] Guest: Frank David: Yeah, I gotta say like, even outside of the financial piece, I mean, the basic science people have the same problem thinking about clinical trials, for example. I mean, they don't really you know, that's not their expertise. And the clinical trials people don't know very much about commercialization. They don't know very much about the model. Right. It's always funny to me to have clients who are more on the technical side, and then they're getting up in front of kind of governance meetings. And the governance people want to see an NPV model, and they literally do not have any idea what those four letters stand for, right? They just they've never thought about sort of the time value of money and what risk adjustment means in the context of a model and how all of this works. And often people, you know, over the course of a small number of years when they're in a situation where they're in those meetings, they really appreciate a lot more about how the ideas turn into numbers, and it ends up being super valuable for them, too. So I think recognizing that if you're in that situation, like you may not know that much as the modeling person about drug development, but rest assured, the drug development people know nothing about what you do. So, you know, there's asymmetry on both sides. Sure.


[00:39:09] Host: Paul Barnhurst: And you got a subject matter expert there. So ask them questions. Take advantage of them. You don't have to figure it all out on your own. Yeah. I mean, you know, at the end of the day, even though this is different and you, you know, you do risk adjusted NPV and you got portfolio and it's high risk. So many of the principles are the same across modeling right. A good model is the same thing that you want structure. You want inputs. You want assumptions you can validate. You want it to be clean, simple. You know at the end of the day it's all about trying to make a decision.


[00:39:40] Guest: Frank David: Yeah. And I will say actually, that thing that you just said, ah, about the cleanliness and simplicity, you know, I well, especially because, you know, when you're in these really early stages, you know, first of all, it's not worth anyone's effort to build all the bells and whistles into a model and all like, you just don't know, as we were talking about, you just don't know that much. So I think the model can be even a very simple model can be super useful at that stage is just to educate decision makers in terms of, you know, look, we're building, we understand this is a toy model like this is not telling us to the penny what the heck's going on? But it can help illustrate a lot of really interesting principles about where this program could go or should go.


[00:40:23] Host: Paul Barnhurst: Well, you know, a lot of times back of the envelope is just as accurate or more accurate than the model that tries to account for everything, right? For sure, you want to keep it simple and find that right balance. Because as I like to say, you're never going to be right anyway.


[00:40:36] Guest: Frank David: Yeah for.


[00:40:37] Host: Paul Barnhurst: Sure. Good decision making. Yeah. And more often than not, you want to be right or you got a problem in the sense of you need to be successful a few times or you're going to be out of business. But as I, you know, as I've always said, if I could forecast with a high degree of accuracy, I'd be sitting on the beach somewhere because I would have made a fortune in the stock market.


[00:40:56] Guest: Frank David: Exactly. That's exactly right.


[00:40:59] Host: Paul Barnhurst: So all right, so we're going to move on to kind of some fun sections we have here. First I want to ask a question. What's the most unique thing you have ever seen or built with the spreadsheet. So this doesn't have to be a financial model just in general.


[00:41:13] Guest: Frank David: Wow. You know, again, I am not sort of a spreadsheet hobbyist, per se, right? So I'm not sort of. So that's why whatever you come up with. Yeah. Whatever example I would have would be more work based. But I would say that from, again, sticking to the area that I know best, I was with a group of people and we were actually trying to do a bottoms up model of, you know, a fairly large pharma company. And, the amazing thing we realized was essentially the market was valuing almost all of their portfolio, their R&D stage portfolio at zero. So if you actually if you modeled out their whole commercial portfolio and the stuff that was maybe a year or two away from getting to the market, you could basically get to the entire market cap of the company Without adding in anything earlier. So that was again, it was fun for me because I'm just interested and kind of super crazy to realize that Wall Street was basically not ascribing any value to the stuff that all of us, you know, thought had all of this value and opportunity that investors just didn't really care.


[00:42:33] Host: Paul Barnhurst: So I'm curious, did you buy any of the stock thinking, hey, if they hit on any of these, it has to go up?


[00:42:37] Guest: Frank David: You know, I have a general rule that I do not understand the stock market well enough to play that game. I like to just get paid in cold, hard cash. I am not a stock market investor kind of guy, but I do watch those. I do watch those things. And I am interested in seeing how they work. And also, in all seriousness, you know, doing this type of modeling that I've been talking about, if you're doing for a single asset or you're doing it for like a very small biotech. That's one thing when you start building it up to bigger companies. You know, it really starts to break apart a little bit. And that's not a place that I've spent a ton of time. So I get super nervous.


[00:43:22] Host: Paul Barnhurst: Definitely a different, very much a different exercise when you're trying to value bear.


[00:43:27] Guest: Frank David: Yeah, totally.


[00:43:28] Host: Paul Barnhurst: Decide if this one drug will get to market and be successful.


[00:43:32] Guest: Frank David: Absolutely right.


[00:43:33] Host: Paul Barnhurst: Absolutely right. Yeah. I mean, very, very different levels of complexity in different ways.


[00:43:39] Guest: Frank David: Totally, totally.


[00:43:40] Host: Paul Barnhurst: All right. So we're going to move into our rapid fire section. This is going to be an abbreviated because normally people questions like circular references yes or no VBA and models. And you're shaking your head like please don't please don't. I have four questions for you. Keep them simple. You can't say it depends. You just kind of pick an answer. Okay. And then if there's some if you want to elaborate at the end on one of them, you can. So what do you prefer: Google Sheets or Excel for building models?


[00:44:07] Guest: Frank David: Excel. I'm an old guy. I started doing this before Google Sheets even existed, and I'm just too old to learn anything new.


[00:44:14] Host: Paul Barnhurst: That works. Will excel ever die?


[00:44:17] Guest: Frank David: No. Thousand percent? No. I mean, it's just, again, there's enough old guys like me around and, you know, the new people, whatever they use, they'll I think I think Excel will still be around for a while.


[00:44:32] Host: Paul Barnhurst: It definitely has staying power. It's not going anywhere, at least in the near term. I've had a few people answer yes, but just don't be in my lifetime, please.


[00:44:40] Guest: Frank David: Okay, I like that answer too, I like that. Yeah.


[00:44:42] Host: Paul Barnhurst: So will I build the models for us in the future?


[00:44:46] Guest: Frank David: Yeah, I think that's great. I mean, whether you call it AI or you just call it sort of a souped up version of, or of something like Excel. I do think that there'll probably be tools that make it a lot easier for normies to make functional models. And I actually think that would be super exciting. I mean, one of the things, again, in the biotech and pharma, I wish that the R&D people could build some of their own models and start playing around with it just by themselves. I think it would be a really interesting discussion to have with the finance people, with the modeling people, etc. if they could come, if they could just sort of, you know, mess around a little bit on their own. I think we would all do better at work.


[00:45:30] Host: Paul Barnhurst: For sure. I agree with you. All right. So one more. Do you believe financial models are the number one corporate decision making tool?


[00:45:39] Guest: Frank David: But wow, that's a pretty strong statement. So I'd have to say no because I don't think anything is the number one corporate decision making tool. But, you know, I think, look, I at the end of the day, particularly in my line of work what people who come from the R&D side always forget is that at the end of the day, we're here to make money. Like you have to have a viable business. And the way you prove that you have some hope of building a viable business is by building a model. So in that sense, it may not be the most important thing, but it's clearly a big piece of the puzzle of how you're going to make money for sure.


[00:46:21] Host: Paul Barnhurst: Favorite answer I've received of all time was no, it's politics.


[00:46:25] Guest: Frank David: Okay, that's good too. There's a lot of politics.


[00:46:28] Host: Paul Barnhurst: Yeah, you could relate to that one. All right. We're going to move on to a get to know you section. This is just some fun questions to get to know you a little bit better. Yeah. If you could switch jobs with anyone in the world for one week, who are you going to switch jobs with and why?


[00:46:43] Guest: Frank David: I am so boring because honestly, like, the jobs that I'm interested in are ones that are still fundamentally are related to this industry. I would love to work at FDA for a week long enough to basically figure out what the heck goes on there, but not long enough to actually have any responsibility. So like framing it as a week, like, I don't want to manage people I don't want to actually do any of the hard work, but I just want to be there and just sort of see how the sausage gets made on the regulatory side. It's one of those black boxes that I would just love to peek into.


[00:47:15] Host: Paul Barnhurst: Yeah, you're in the right industry. It's obvious. Yeah. For sure. All right. What is your all time favorite movie?


[00:47:23] Guest: Frank David: I would have to say Ocean's 11. I am like a sucker for heist movies. I think it is one of the great ones. It is just so fun. I have watched it so many times. Alone with my wife, with my kids. It's just a great, fun piece of cinema. Love it.


[00:47:42] Host: Paul Barnhurst: One of my favorite. I watched it more than once as a kid. It's a mad, mad, mad, mad world. I love that one.


[00:47:48] Guest: Frank David: Solid, solid. A lot.


[00:47:50] Host: Paul Barnhurst: Of Ocean's 11 was kind of built off that type, but with the actors and yeah.


[00:47:54] Guest: Frank David: Yeah, great fun.


[00:47:56] Host: Paul Barnhurst: Favorite hobby or passion? What do you like to do in your spare time?


[00:48:00] Guest: Frank David: I'm a big music guy, so I play clarinet, classical clarinet, I play a little jazz sax. And over Covid I started learning jazz drums. So I do a lot of music. It's super fun, I love it.


[00:48:14] Host: Paul Barnhurst: My wife's a huge music person. I am pretty much tone deaf. And you don't want it. My daughter asked me not to sing when I start singing. But dad, please stop.


[00:48:23] Guest: Frank David: So you know, as I, my wife went to some seminar, one of these business school kind of seminars where they said in order to be best in class at something, you have to be worst in class at something else. So I think it's just good to lean in and acknowledge the fact that you're going to be terrible at something. And that's fine.


[00:48:41] Host: Paul Barnhurst: You know, it's reality and sometimes you just have to let it go and focus on your strengths. There's a lot of value in that for sure.


[00:48:47] Guest: Frank David: For sure.


[00:48:47] Host: Paul Barnhurst: All right. So we're in the wrap up stage. Just two quick questions and we'll let you go here. Last thing, if you could offer one last piece of advice or say something to someone who's building models in the biotech space looking, you know, for any recommendations or thoughts, what would you give them?


[00:49:05] Guest: Frank David: Yeah, I think coming back to some of the stuff we've been talking about before, it's really to learn as much as possible about the clinical side. You know, if you're building a model for a particular drug, like, really try to understand who are these doctors, who are these patients? What are they thinking? You know, how are they deciding what to do? Because I think if you understand that a lot of other things sort of lead into that. But it's the hardest part to get, but it's worth the additional investment of time.


[00:49:36] Host: Paul Barnhurst: So invest the time to learn. And last question. If our audience would like to learn more about you or potentially get in touch with you, what's the best way for them to do that?


[00:49:44] Guest: Frank David: Yeah, I mean, Pharmagellan.comm, is the website my email you know, there's an email address on there. I'm frank@pharmagellan.com.


[00:49:53] Host: Paul Barnhurst: I found you that way.


[00:49:54] Guest: Frank David: That's how you found me. And so, yeah, I respond, you know, I respond as much as I can to emails there. I would say I'm still an old school guy. Email is. Email is the best way to find me.


[00:50:05] Host: Paul Barnhurst: Perfect. Well, we'll definitely put that in the show notes. So anyone who wants to learn more, they can go there and find you. Really appreciate you carving out a few minutes for me today, Frank, and getting to chat with you. It was a real pleasure. So thanks for being on the show.


[00:50:18] Guest: Frank David: Thank you Paul. This was super fun. I really appreciate it.


[00:50:21] Host: Paul Barnhurst: Financial Modelers Corner was brought to you by the Financial Modeling Institute. This year I completed the Advanced Financial Modeler certification and it made me a better financial modeling. What are you waiting for? Visit FMI at www.fminstitute.com/podcast and use Code Podcast to save 15% when you enroll in one of the accreditations today.

Paul Barnhurst
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