How do bio-pharma CFOs deliver accurate forecasts given the uncertainty ahead?
Pfizer’s former CFO, Frank D’Amelio, shares how he developed his playbook and what he sees ahead for peers working on what to share with boards and investors in coming months.
Chris Gale 00:02
Thanks, everybody, for joining us. And thank you, Frank, for making time to join us. I think most folks know who Frank is, so I feel a little ridiculous doing an intro. But I think it’s helpful to position what we want to talk about in the next 30 minutes. As I think most know, Frank’s the retired Chief Financial Officer and Executive Vice-President of Global Supply at Pfizer. You’re responsible for all corporate finance functions, including audit, controllers, tax, and treasury, as well as global supply, and you helped lead the acquisition and integration of Wyeth during the darkest days of the financial crisis. You led many other deals over the 15 years you were at Pfizer. You dealt every day with investors and board members who wanted to understand therapies, risks, prospects, and the process of greenlighting billions for R&D and bringing therapies to the market. You’re currently on the board of Humana, Zoetis, Hewlett Packard Enterprises, and Catalent. You are the CFO in residence for Deloitte. And you’re on the board of EntityRisk, which brings us to today’s webcast. I also want to introduce Neal Masia, CEO of EntityRisk and Adjunct Professor of Business and Economics at Columbia University. You served as chief economist and Vice President of Patient and Health Impact at Pfizer. And that’s, I presume, where both of you met, and you were a member of Pfizer’s Senior Leadership Council. I want to get into, Frank, that point about you on the board. What brought you to the board of EntityRisk? What did you see, both in the team and then externally?
Frank D’Amelio 02:06
Well, first, Neal. Neal and I worked together literally from my first day at Pfizer. When I started at Pfizer, Jeff Kindler was the CEO who recruited me to Pfizer who was on the board of EntityRisk. Neal worked very closely with Jeff. I got to know Neal through Jeff. And then Neal and I worked together, really, for 15 years, my entire time at Pfizer, much of which Neal worked in my finance organization, and at one point as Chief Economist and Assistant Treasurer. He had a number of jobs while he was at Pfizer where he and I worked together. So, one was getting the opportunity to work with Neal again, because I’m a big fan of Neal’s. If you think about what EntityRisk does, it’s a subject that Neal and I discussed pretty much throughout all my time at Pfizer: What’s the value of a medicine? What’s it worth? How’s it priced? How’s the market going to react to it? Whether it’s a big company or a small company, although Pfizer is obviously a very big company, looking at some of the other companies that we were potentially looking at from an M&A perspective. So, I think a combination of Neal, the folks who co-founded the company with Neal and then, obviously, what EntityRisk works on and how I think it can help the pharmaceutical industry, the biotech industry, etcetera.
Chris Gale 03:25
Externally, what’s going on now – I don’t know how much this might have influenced your thinking on EntityRisk – over the Inflation Reduction Act and some of the questions about how to properly evaluate therapies going forward, new therapies, especially.
Frank D’Amelio 03:42
The Inflation Reduction Act is worrisome to me, this whole thing about where they just picked the 10 medicines. (I think one of them was a Pfizer med, the one we did with Bristol [Meyers Squibb], it was Eliquis, If I recall correctly.) Then you’ve got nine years protection on [small-molecule drugs] and 12 on biologics. But to connect this to EntityRisk, once again, I think what it does – what’s a medicine worth, how do you price, what’s the reaction to the price going to be – I think it’s critically important now. I think all the Inflation Reduction Act does is literally raise the importance of getting those kinds of things right. Being able to really demonstrate the value of a drug, the worth of a drug, how that correlates to the price of a drug, and then, obviously, how that’s going to be received by society, and by all the various organizations and governments that pay for the medicine. I think all the IRA does is literally make something that’s already critically important, more important.
Chris Gale 04:46
As we were getting ready for the webcast, I think we talked about whether the value of a drug or therapy is the work and the research put into it or the value that it delivers to society. I don’t know, Neal, if you wanted to sort of step out on that point or Frank, if you’d like to speak on that.
Neal Masia 05:10
Well, I have worked for Frank for a long time, I know that he’d have no interest in a cost-plus type of thinking around what are you going to price a drug at. So, when companies call us, they’re asking us the same kind of questions, it’s true, that Frank and I used to bat around for many years, which is “What is the right price for this drug?’’ At the end of the day, if you had to boil everything down to a hard question that we can help answer, it’s that. It’s gotten harder to answer that question over the last five years, let’s say, in the US, because it’s clear that the methods they use in Europe to figure out what they think society ought to pay for a drug, some version of those methods is coming to the US. But the difficulty is nobody knows what version and when the IRA is a catalyst for that. And so, what we’ve seen is more and more questions about… what this drug is really going to earn in the market or, based on the value it produces for patients, how is that going to translate into a price and how that is going to translate into revenue. It’s just a hard thing to do because there are so many different frameworks in play for how that question is going to get answered. And so that was really what we saw as the opportunity and, I think, is a big challenge for forecasting, since you’re not sure what framework is going to get used. The different frameworks have different, very different answers. So, if Frank called me into his office today and said, “Give me a view on the revenue and the risks for this drug that we’re about to launch, or that we’re thinking of licensing or whatever we’re going to do. You’d have to have sort of a bunch of different hands. Like, on the one hand, on the other hand, and on the third hand. Being able to quantify all that has been a real analytical challenge for everybody in the industry. You just can’t do it by having a million consulting projects looking at each potential version of what a reimbursement system is going to be. Instead, we thought that lends itself to a software solution. And that’s basically what we’ve built.
Chris Gale 07:23
That’s great…I’m kind of setting the stage a little bit with the uncertainty and variability we have today. But Frank, as I said in the introduction, there you were, during the financial crisis. It’s not like recent years have been smooth sailing. That’s where I wanted to get into the playbook. What’s the playbook you’ve used for forecasting that you think your peers could take advantage of now? I know at least one of our attendees sent a message saying this is exceptionally timely right now. We’re going into the budgeting process; we’re trying to figure out the forecast exactly right now.
Frank D’Amelio 08:09
Yeah, so it’s the time of the year when people are putting together their operating plans for next year. A lot of companies do it for the next three years. Maybe the way I’ll talk about forecasting is like this: There’s only one thing I know with certainty on any forecast I’ve ever been given, literally the second it’s given to me: It’s wrong. Of all the years I’ve been in finance, I can’t literally think of any forecast where it was hit exactly the way it was put together. By definition, they’re wrong. But then the question becomes, you want it to be as right as it can be. And so how do you develop forecasts? What was the playbook that I tried to use? The answer is, the great thing about being the CFO of a company, if you’re organized correctly, you’re like an octopus. As the CFO, you have your corporate FP&A group, the Financial Planning and Analysis Group, that’s kind of the head of the octopus. And then you’ve got tentacles into every part of the organization. You got a VP at Finance and commercial, you’ve got one at manufacturing, you’ve got one at R&D. You’ve got one at the corporate centers. The beauty of being in the finance organization, and being the head of the octopus, is you want to have all those people working very closely with their operational leaders. And then having all those folks kind of put together an operating plan, that when I consolidated up to total Pfizer, I’ve got something that I think works really well. Typically, the tension in this is you get a bottom-up view, which is the view I just described. And then you get a top-down view in terms of where you’d like the company to be. Almost never do they always meet first shot. So, you’ve got to go through a couple of iterations to try to get it to a place where you think it needs to be. And then, philosophically, what I always tried to do with [a] forecast if I was talking externally, to investors, sell side, buy side, I’d always try to make sure we were under-committing and over-delivering. How do you do that? Once you lock and load on what the budget’s going to be, the operating plan inside the company, you always try to haircut that and create some cushion. If and when you deliver on your budget, and hopefully you do, and by the way our track record at Pfizer was we always did what we said we were going to do financially, that cushion just helps in terms of being able to achieve the under-commit, over-deliver. But net, the right people in the right positions financially, working with the right operational leaders. That’s not just the leader of the organization, but the right people within that organization, putting together a consolidated view that we can then bottom up, compare them to, from a top-down perspective where we want to be always in the context of creating shareholder value, and then coming up ultimately where the final number is going to be and then trying to create some cushioning within those numbers.
Chris Gale 10:53
I love the octopus analogy. And from what I’ve read, the head of the octopus apparently is somewhat disconnected from the arms. The arms apparently have their own brains and can kind of go off on their own. So, when all those different parts of the organization come up to where you are, how do you resolve it? I think you’ve spoken in the past about biases that kind of make their way up to you. How do you resolve those biases in order to do what you have described in front of the investors and the board?
Frank D’Amelio 11:27
So, obviously, the biases come from organizations, and those organizations are made up of people, colleagues, and employees. You want to work those discrepancies thoroughly with those people. … I was in the Pfizer CFO role for 15 years; you get to know pretty much everybody in the company. What I would always do is develop baseball cards on people, at least in my mind, in terms of this is a person that gives me a forecast. Typically, they’re a little bit optimistic about revenue, but they’re always a little bit better than they say they’re going to be on cost, for example. You factor that kind of baseball card into how you’re having those discussions with people. And, you know, you can literally see it. A lot of times you’re doing an M&A deal, if you look at trending M&A deals. This is across many of the companies I’ve worked with over the years. If you say where’s the optimism typically on an M&A deal, it’s on the top line. So, on line one, on revenue. And if we’re doing a deal, on cost synergies, we almost typically did better than we said, we were going to do now typically compensate a little bit for potentially missing on revenue. To me, one, you want to be reading with and talking with, negotiating with, the right people. But as you’re having those negotiations, you want to keep their baseball cards in perspective as you’re having the conversations. Just one other thing on this, Chris. The other thing I would do to create accountability is particularly in M&A deals, whenever you’re doing an M&A deal, you’re presenting the deal to the board. As the CFO, you’re presenting the financials to the board. There are typically, call it a dozen, slides that you use, but there’s that one P&L [profit and loss] slide that’s the critical slide that has the future projections. It’s what you’re using to hit it with a cost-to-capital discount rate with a terminal value growth rate to get a net present value. That that key P&L slide on deals, I would laminate. If it was a deal with a company with revenues, I’d literally laminate the slide, give it to the operational leader and I’d call my VP of finance, because I wanted to make sure they knew, “Hey, in a year, we’re coming back and looking at the laminated slide to see how we’re doing versus what we said we were going to do.’’ Obviously, you take currency out of it. You keep the currency constant. So, it’s a pure operation, you know, review on what the against what the projections were.
Chris Gale 13:51
So, it sounds like behavioral science in some ways, where it is the math, but it is also understanding where the math is coming from – the baseball cards. You’re saying, after 15 years, you kind of know the track record of those individuals. And then it’s understanding where the numbers come from, really people-based, it sounds like.
Frank D’Amelio 14:17
Well, yeah, I mean, I think it’s clearly people based. I call it track-record-based. You used the term just now. You mentioned behavioral science. I think one of the major roles of a CFO is to drive the right behavior in a company, to really get all your employees, connect them, to explain to them how achieving their budgets impacts our guidance, to explain to them how achieving their budgets would be doing better than budgets, drive shareholder value, how it connects to a stock price, you know, getting everybody to really get incredibly focused on how to best allocate capital, how you want to spend company money, exactly as if it’s your own personal money. I think driving those kinds of behaviors is one of the critical roles that a CFO plays in a company. You want to drive the right behavior.
Chris Gale 15:10
So, if I’m joining this call from a smaller biopharma company, maybe a startup if I hear the 15 years, should I be worried? Neal, I think you’ve had conversations with smaller outfits. If I don’t have the 15 years to build up those baseball cards, what can I do to make sure the sort of stats on those baseball cards are reliable? How can I replicate what Frank has achieved over his career?
Neal Masia 15:44
Yeah, great question, Chris. We work with companies big and small, and the octopus analogy definitely breaks down. It’s more like, at the small company, you’re an octopus with no arms. You’re just a head with a sucker, you know, trying to figure everything out. We’ll talk to companies that just have a Chief Commercial Officer, for example, [or] might have a part-time CFO – very limited resources. And what are they trying to do? Sometimes, they’re trying to figure out the price for, let’s say, a rare drug or a cancer drug they’re getting ready to launch. They have some resources, and they want to try to replicate what a big company can do, but do it in a way that’s efficient, and where they get some objective advice. One of the challenges, in terms of the number of folks who have been involved with launches, and who really have expert views on what’s the reimbursement environment going to look like in five or ten years? It’s a hard problem. The people that are in those jobs tend to stay in those jobs at big companies. So, there is this well of expertise, academically, where people have an understanding of where the reimbursement environments are going to go. So, when we talk to small companies, they’re trying to get a view of all that stuff in a very efficient way. There’s a role for calling up people that used to work at a payer, and asking them, “Hey, what do you think we’ll get? Here’s our label, what do you think?’’ But it’s not objective and it’s not consistent. And so, they want to know where do I fit in the pantheon of other launches? How can I articulate the value of a medicine? What are my vulnerabilities? Where do I need more evidence? What piece of evidence do I think people assume I have that I don’t? Those are all things where working with the right outside folks can be very, very high-leverage for smaller companies, whether they’re getting ready to launch a drug, or even earlier, as they’re thinking of maybe out-licensing a drug. When Frank and his team come calling from the potential places, they have their way of looking at it, which is very comprehensive. As the person selling it, sending it out into the world and trying to get a good deal on a license, you know, [anticipating] what they’re going to be looking for in terms of value and proof points, is very, very helpful. It’s something that the smaller companies are hungry for and can get with the right partners.
Chris Gale 18:30
So Frank, is that a control for, maybe, you’re building experience with a team? Maybe the team is sort of small, it’s that outside expertise? In fact, would Neal play that role sometimes in some of your decision-making in terms of an internal sort of objective point of view? Or how would you approach this?
Frank D’Amelio 18:56
I thought Neal answered the question perfectly in terms of smaller biotech. He’s right. My answer was really kind of more for the big companies. But in terms of Neal’s role, at least one of his roles when he was at Pfizer working with me, is when we were looking at deals, I’d always ask [him] to kind of give me a perspective on the deal with a devil’s advocate view … Give me all the potential risks. What’s the probability of those risks? I’d always want that devil’s advocate view. Back to Neal’s answer on the smaller companies, you’d have a look at big pharma companies, and obviously, I know Pfizer pretty well. They have their own large market access groups. But I still think EntityRisk can play a very effective complementary role to those groups in terms of helping them answer some of the questions we’ve been talking about on the call. And then if you’re dealing with the smaller biotechs, where what they … only have the head of the octopus, they don’t have any of the arms, I think, once again, EntityRisk can be incredibly helpful to those firms. That firm may not have a history, but EntityRisk has a whole history of a whole bunch of other like companies that they can bring to bear to help that company with, what’s the medicine going to be worth, and how should it be fairly priced. Those kinds of things.
Chris Gale 20:15
For anybody who wants to ask questions, there’s a Q&A section. I will keep my eye out for that. When we were talking earlier, before the webcast, I was thinking about the baseball card analogy, and bringing in independent experts. It reminded me a little bit of Michael Lewis’s writing in Moneyball and in The Blind Side. The movies focus on Billy Beane, they focus on Michael Oher. But if you read the books, Lewis focuses on Bill James, who put together baseball stats that helped managers make decisions about players. There was a counterpart [Tom Lemming] on the football side, that started putting together stats. So, I’m wondering, just like Billy Beane had the scouts to help him assess various players, is there a role for algorithms at this point to help with decisions, to sort of strengthen those baseball cards, strengthen the decision-making? Over time, what software has been able to do has been increasing its power. But where does it stand today? From your perspective, either of you, or both of you?
Frank D’Amelio 21:39
Do you want to take this?
Neal Masia 21:41
Yeah, sure. I think it gets back to the question, Ruth, it’s a good question, Chris…. In healthcare, at every level, there’s an infinite amount of data. And the question is really, what do you do with it? You can go down the road of data mining, and people do that kind of work. And then they do it, they call it AI. Or maybe they call it machine learning. What are they trying to do? They’re trying to look for patterns in the data. You know, as a bunch of economists at our company, we’re sort of skeptical of exercises like that, because you’re basically focused on the past. But the world has changed. The past may or may not be relevant to predictions about the future, because the world is really changing right now. This is a huge period of change in the drug industry. So, what we’re focused on, and I think some others are focused on, is more of a bottom-up approach, a model where we have an understanding of the discrete elements of how a market pays for a specific set of attributes. Is it life extension? Is it caregiver support? Is it the future value of other things? Is it a different value, depending on how sick you are? These are things that the old models really didn’t do. There are new models, some of which our team has invented, others that are out in the wild, where you can use software to match the elements and the properties of your potential drug with how society values those. Part of that can be an argument to just illustrate the value. And then part of it can be an argument about creating evidence that aligns with the elements that maximize that. And you know, those are two really good use cases for the kind of software you’re talking about. It requires an algorithmic approach because there are just so many different dimensions of the problem. But it is a tractable problem, and one that we’ve spent a lot of time trying to solve.
Frank D’Amelio 24:03
The only thing I would add is that, by doing all of those things, it’s not only just about, I’ll call it, the value of the medicine. It’s how it’s to be priced. There are huge potential cost savings that can be derived from using these algorithms effectively as well, in terms of where to allocate capital, and where not to allocate capital. So that can be very helpful in terms of some of what EntityRisk can bring forward to companies.
Chris Gale 24:34
Can you say more about that? Because forecasting leads to capital allocation, does it not?
Frank D’Amelio 24:40
There’s no question. If you think about it, you do an operating plan. You form it, you finalize an operating plan. That operating plan, in many ways, is kind of the compass for how capital is going to be allocated. And, you know, the thing about pharma – we spend a lot of money on R&D. It’s a high-risk business. [You] never know how successful R&D is going to be. It’s literally a high-risk business. You sit in meetings, and you’ll get “this has a PTR [price to retailer] probability of technical and regulatory success of 75”, which is a good number. But when all is said and done, it’s either going to be one hundred percent or zero; it’s either going to work or it’s not going to work, right? So, forecasting is critical. You get it as right as you can. Obviously, that’s going to be a compass for how you allocate capital. But once again, the modeling and the algorithms that Neal just discussed can very much help with how to put those plans in place. And, quite frankly, where to invest and where not to invest. That can have massive, positive implications for cost savings and quite frankly, the effectiveness of how you allocate capital and then the returns you get on that capital.
Chris Gale 25:55
Excellent. I mentioned that Q&A. We’ve got three questions. Let me start with the most recent: “I’m curious about your perspective on how you think pricing will evolve for gene therapies. We keep seeing higher and higher list prices. In some cases, therapies are not really reaching patients because of the friction those high prices create in the purchase, payment, and reimbursement process. How can biotechs price these drugs so that there’s a win for payers, patients, providers, and pharma?’’
Neal Masia 26:26
I can give my answer, Frank, you can give yours. Thanks for the question, Catherine, great to see that you’re on. I think that for these very high-priced therapies, millions of dollars of therapies, it’s going to fundamentally come down to a different model. You know, I don’t think the high-price discount-to-payers model is going to work, especially for these one-time treatments that are supposed to have multiyear, maybe permanent, benefits. The companies that sell those products are going to have to go at risk for some of those payments. And there have been some examples of that. But I think it’s going to have to be much more meaningful. In fact, when we started the company, that was the first problem that we thought was important to solve – to design a contract that’s going to be the win-win win that you’re looking for. Drug companies – a like if I would have brought a contract like that, or Dave would have done that to Frank – would have said, “Well, I just need to know, how much risk am I sitting on. I’m happy to guarantee the payments if that’s what I have to do to get market access. I’m going to get $3 million for this drug. You’re telling me I have to guarantee it for seven years. Okay, if that’s what I have to do, that’s what I have to do. But how much risk am I sitting on every quarter?’’ That’s the kind of question that you need a health outcomes model to settle. So, that’s where we’ve moved. I do think that the existence of those kinds of tools will eventually enable – and the pressure that comes from not one or two, but 10 or 20, expensive gene therapies coming along in the next couple of years. That convergence of tools and opportunity is going to lead to what I think will be a proliferation of these risk-based contracts. I don’t know if it’s in the next year, two or three years. But companies need to be prepared for that and have the set of opportunities in front of them, so they know exactly how much risk they are taking. When can I recognize the revenue and all these sorts of tactical finance questions that will feed into Frank’s forecast? If you don’t have a health outcomes model underneath all that, that connects it all together, it’s not very convincing to frame.
Frank D’Amelio 28:34
What I would add, to see if this syncs with how you think about it: I don’t like the lump sum upfront. I think it’s going to need to be some multiple installment payments. And then with some sort of – I’ll call it value-based – payments based on some measurement. If it’s hemophilia, and you’re trying to cure hemophilia, maybe at the end of every year you get paid X amount of money after the hemophiliac takes a test that shows that the hemophilia has been cured, has met the test that the drug said it was going to do. And then based on that, you get paid, and you recognize revenue. But I think it’s going to be some sort of an installment approach with some sort of value-based payment. I think the other nice thing about that – it’s more palatable to the insurance companies because people will bounce around and change insurers year to year, particularly folks on Medicare and Medicaid. So, I think it’s some installment plan that’s value based. That’s where I think it’s going to settle out, which I think is consistent with what you were saying.
Chris Gale 29:35
I have taken us over time. So, we’re going to follow up afterward for everybody online. But we want to respect your time. Frank and Neal, thank you so much. This has been a fast conversation. Maybe we will have a chance to do it again, but really appreciate everybody for joining us
Frank D’Amelio 30:01
Thanks, Chris. Thanks for your time, everybody.