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

There are a ton of planning software trying to help you model and plan for your company. 70% of the market is on Excel. What's the real market size?

The actual answer usually ends up being you're actually going to get 0% of that market because Frank David, an experienced consultant and former physician scientist who works at the intersection of corporate strategy, clinical medicine and basic research. He's the founder of Phar Magellan, helping biopharma teams integrate R and D and business strategy.

So what's kind of a 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, 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.

What do the expenses look like?

If you're in a company building the model, then you could start to have discussions where you could play some of these strategies against each other. We have a pretty high chance of success, but it's going to cost us a lot to get there.

Any advice on how they should kind of think about and make sure able to validate as best they can?

Small changes in particular inputs are just causing things to skew wildly. That's a whole other level of business risk because.

Welcome to Financial Modeler's Corner where we discuss the art and science of financial modeling with your host, Paul Barnhurst. Financial Modelers Corner is sponsored by Financial Modeling Institute. Welcome to Financial Modelers Corner. I am your host, Paul Barnhurst, AKA the FPA 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.

Thanks so much for having me.

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. We just so my audience knows, you may find this background a little different from our typical guest. Frank's laughing there as he never thought he'd be on a modeling podcast, not.

Even for a second.

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 in Leeds. Pharmagellon 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 pharmagellon Guide to Biotech Forecasting and Valuation and the pharmagel and Guide to Analyzing Biotech Clinical Trials. Did we get your bio right?

Sounds good to me.

Isn't it always weird to hear someone else read your bio?

Super weird, but that was great. Thanks.

Perfect. Well, you know, we're really excited to have you on the show. I know you and I spent quite a bit of time chatting before you came on, I think. What was your first thought when I sent an email saying, hey, you want to come on a modeling podcast? Yeah.

I mean, I've always felt a little like I have imposter syndrome in the whole financial modeling community. 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 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. 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.

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 multibillion 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 actually I use with so many people in this area because like 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 like that honestly kind of encapsulates most of what drives me bananas in this area.

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.

So first of all, the, the actual answer use 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 the, what, 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. 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 and D costs and, and what it's going to take to get there. And maybe it's going to take you 10, 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 and D strategy is and what you think you're making and how you're going to get to the finish line.

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. 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, 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 of 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.

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 and D perspective.

Yeah, they, they don't know all the financials that go into 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 hear a little bit about yourself and how you ended up where you're at.

Sure. So I started off as a physician scientist, as you said. So I did a bunch of school and I 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 five or six years into that, I figured out that I actually wasn't as interested in making data as I was in understanding how data turn 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. So I went into one of those firms, it was called the Rank Partners. It's still actually the firm is, that is a. The firm is a biotech investment bank, a health care 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 strategy at a biopharma company, I've essentially been doing advice, been on the advisory side and been doing, you know, R and 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. Right. Whether it's a, you know, a small biotech that maybe has one or two 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 leering 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 Lyran, I continue to do projects where part of a strategy project, we would often be building a model as part of that.

See, so you do have the modeling experience.

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. 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. That's what's fun and interesting about the intersection between what I do and what you do.

Sure. No make 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?

I had done projects when I was at Lear Inc. And then afterwards when I formed my own firm, pharmagellon, 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 pre clinical stage and then things go into the clinic. And usually even from the time that they start in the clinic, it can take 5, 7, 10 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, 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 though, when we were doing those projects at both of those places, you know, 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 frustrations and challenges of doing this work in this industry, in the drug development industry, is you're often dealing with kind of stub, 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, the year after, et cetera, you know, now I have historical data on the uptake, I know what the population is, 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, P and L 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.

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. Someone reached out to me and they said, hey, you know, I'm starting to do these clinical type trial models and drug models. He 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 that's how it happened. So, yeah, he'll be thrilled to listen to this episode. He was quite excited when I told him yesterday. Great. And so, yeah, 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 on 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 I 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 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, not at all.

Not at all.

Right. There's a ton of paperwork 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?

Yeah, it's a great question. And again, it does come down 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 two or three 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 their alternatives are generics so they only cost pennies a day, or are all the alternative still branded drugs? So then we're competed in a branded market and the pricing obviously would be totally different. 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, 1,000 patients or 10,000 patients in a pivot in the last stage of trials based on that? And are we talking about a trial that's going to take six months or six years to execute? And then you could start to use that and get a handle on the pre launch part of this. What are the costs and risks and times? And then you start to fill in the left hand side of your model basically and figure out how that looks. So 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. And what I love about that is you're starting with the people that will use your product. Right. Product being drugs. Sometimes I think we start with, hey, what's the revenue number? I need to hit a certain valuation. Okay, what do the expenses look like? Right. Models start with revenue. Even in this case, even though you're not going to get revenue for 10 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 in here? Yeah, 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, you know, 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, you know, separate, if you're an investor, but if you're in a company building the model, then you could start to have discussions where you could play some of these strategies against each other 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 2, 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. 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 end 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, 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.

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Yeah, so when you're building a model, let's, let's talk 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 de risk it with this revenue, 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 I think that the, 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 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, 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 diabetes again, let's stick with diabetes, for example. You know, diabetes. It's a pretty mature market. There's a lot of epidemiologic Data and data from other drugs, et cetera. 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, et cetera. 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 maybe 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. 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 sometimes you end up in a situation where there's even some error bars around the total size of the market, even if, you know, the total number of patients were available. And then 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, 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 search, for example, just to figure out how many patients there are 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 gold line. 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, 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?

Are we confident enough in this to try to go raise more capital?

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, 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's 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. 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 for public companies, for example, should I buy stock in this company?

Yeah, I mean, it's a lot like, 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.

Sure.

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.

Exactly.

Versus, hey, we have this idea we don't even know the size of the population.

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 and D strategy, commercial strategy, deal making, investment, all of it. It, it's just, it's super neat.

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, 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. 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.

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 change, 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 that tells me, like, in the work that I do, it tells me we have to have a much deeper strategic conversation around how much inherent risk there is in this program. Because forgetting about the technical risk of can we make a drug 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 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, 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, what are the inputs, where they have big strategic implications and, and we need to figure out sort of 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 RNPV 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 a rare disease, for example, to help foot some of the bills. 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, that's 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.

Yeah, I mean it's definitely true in a lot of industries for sure. You know, leads me to a question. So let's say we have a financial modeler and you know, typically often, you know, modelers, FP and a or other areas will say, hey, learn the business as best you can. Obviously this is a very technical business. You're, 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 chance of success to make sure they're building a good model? You mentioned the assumptions, but yeah, just learn a little bit about the drugs or how do you think about that?

No, that is a super idea. And honestly like I have tons of clients who come at, who've come into the industry from the 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, 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 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, 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 technical and strategic questions. 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, 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 ideas in different areas. So, you know, I think that it is great in, in our, 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, et cetera, 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 kind of stuff that's already out there. Yeah.

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. Yeah, I gotta say, like even outside of the financial piece, I mean the, 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, an RNPV 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 also recognizing that, that if you're in that situation, like you, 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, there's, there's asymmetry on both sides. Sure.

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, you, you know, you do risk adjusted NPV and you got portfolio and, and it's high risk. So many of the principles are the same across modeling. Right. A good model is the same in that you want structure, you want inputs, you want assumptions you can validate, you want it to be clean, simple. At the end of the day, it's all about trying to make a decision. Yeah.

And I will say, actually, that thing that you just said are about the cleanliness and simplicity. Well, especially because when you're in these really early stages, you know, it's, it's, first of all, it's not worth anyone's effort to build all the bells and whistles into a model. And second of 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.

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.

Yeah.

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.

Yeah.

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.

Exactly. That's exactly right.

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, in general.

Wow. You know, again, I am not sort of a, a spreadsheet hobbyist, per se. Right. So I'm not sort of. So whatever.

Yep.

Whatever example I would have would be, 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 a fairly large pharma company. And the amazing thing we realized was essentially the market was valuing almost all of their portfolio, their R and 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, it. 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 just kind of super crazy to realize that Wall street was basically not ascribing any value to the stuff that all of us thought had all of this value and opportunity that investors just didn't really care there.

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?

You know, I have a general rule that I do not understand the stock market well enough to, to play that game. I like to just get paid in cold hard cash. I am not, I am not a stock market investor kind of guy, but, but I do watch those, I do watch those things and I am interested in seeing how they, 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. A ton of time. So I get super nervous.

Very much a different exercise when you're trying to value Bear.

Yeah, totally.

Decide if this one drug will get to market and be successful.

Absolutely right. Absolutely right.

Yeah. I mean, very, very different levels of complexity in different ways.

Totally, totally.

All right, so we're gonna 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. I have four questions for you. Simple. You can't say it depends. You just kind of pick an answer.

Okay.

And then if there's, if you want to elaborate at the end on one of you can. So what do you prefer Google Sheets or Excel for building models?

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 that works.

Will Excel ever die?

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.

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.

Okay, I like that answer too. I like that.

Yeah. So will AI build the models for us in the future?

Future, yeah. I think that's great. I mean, I. Whether you call it AI or you just call it sort of a souped up version of, of, of something like Excel, I do think that there'll probably be tools that make it a lot easier for normies to, to make functional models. And I actually think that would be super exciting. I mean, one of the things again in, in the biotech and pharma, I wish that the R and 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, et cetera. If they could come, if they could just sort of, you know, mess around a little bit on their own, I think we'd all do better to work for.

Sure. I, I agree with you. All right, so one more. Do you believe financial models are the number one corporate decision making Tool.

That, 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 said, what people who come from the R and 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.

Favorite answer I've received of all time was no, it's politics.

Okay. That's good too. That's. There's a lot of politics.

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 things to get to know you a little bit better. If you could switch jobs with anyone in the world for one week, who are you going to switch jobs with and why?

I am so boring. Because honestly, like the jobs that I'm interested in are ones that 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 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.

Yeah, you're in the right industry, it's obvious. Yeah. All right. What is your all time favorite movie?

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. It's just a great, fun piece of cinema. Love it.

One of my favorite, I watched it more than once as a kid is it's a Mad Mad, Mad Mad world. I love that one.

Solid, solid.

A lot of 11 was kind of built off that type with the actors and. Yeah, yeah, great. Favorite hobby or passion? What do you like to do in your spare time?

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, I do a lot of music. Super fun, love it.

My wife's a huge music person. Me pretty much tone deaf. And you don't want it. My daughter asked me not to sing when I started. Like, dad, please stop.

So, you know, as 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 gonna be terrible at something and that's fine.

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.

For sure. For sure.

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 can 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?

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.

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?

Yeah, I mean, I'm, I. Pharmagellon.com is the website. My email, you know, there's an email address on there. I'm Frank at pharmagellon. That's how you found me. 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's the best way to find me.

Perfect. 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.

Oh, thank you, Paul. This was super fun. I really appreciate it.

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