Scientist Stories: Andrew Lo, New Funding Models for Biomedical Innovation

Welcome back, everybody, to another webinar organized by Princeton for everyone worldwide. We're very happy to have Andy Loh with us from MIT. Hi, Andy. Hi. Andy will talk about new funding models for biomedical innovations, so we're very excited to learn more. And I think especially with the COVID crisis, this became very important and very prominent, so we will learn more. About this today. So let me give you a few opening remarks about innovation more generally. So as we all know, innovation ideas are non-rival goods. So you can just share it with many, many without costing anybody who shares it. That's very, very special about ideas and innovation. And Paul Romer always refers to this water bottle. The water bottle itself is a regular good,. But if you have an idea how to mix it with some certain salt and water and fight diarrhea with that, the idea how to fight some medical diseases, in particular here diarrhea, is actually free. You can just spread it around without costing anybody for spreading that. So that's one big aspect of ideas. The other one is, you know, R&D expenditures, they have positive externalities, and hence there's always some underinvestment. So what we want to do today, we want to understand this better. And there's of course an interplay between the innovator, the entrepreneur, the government, the state, and the venture capitalists or venture funding or the capitalist system. And there's an interplay between these three entities. I know this triangle I drew from Bill Janeway's book, which studies innovation in the economy in much more detail than what I can mention here briefly today.. So what's innovation? What's interaction with the government? And we will see, I will focus here a little bit on the government, and I think we'll focus much more on the venture capitalist financing aspects. So basic research, of course, is subsidized by the government because it has very long horizons and high-risk investments. You don't know the explicit outcome. But how can the government typically subsidize and help to get innovation going, given that there are positive externalities and there's underinvestment? One is just to subsidize the investment itself, the cost. The other one is just to buy the end product, to have some demand pull and buy that. And a third way is to grant patents and, you know, to give essentially temporary monopoly rights or allow for some price discrimination. But of course, you know, withholding monopolies to withhold certain products from the poor, and that seems a little bit immoral in terms of health considerations. So one has to probably be very careful with this aspect. And a fourth way is to provide a prize. You know, we had an earlier webinar last April or May where Michael Kramer was presenting, and we talked about the XPRIZE and PRIZE, where the government provides a prize for certain inventions. In general, I think it's very important that the government absorbs some of the risk and de-risks essentially innovation. And co-invests. And one way to co-invest essentially is to charge taxes. If you have low profits, you pay low taxes. If you have high profits, you have high taxes. So what are the two main innovation models? Perhaps we'll come to this. So the traditional innovation model, I would say, for continental Europe and a longer tradition, essentially of these large corporations. And within the large corporations, there's a lot of R&D activity. And that allows that the large corporations can try different ways and share the risk of different approaches. You know, whether one approach might work out, the other approach might not work out. But this actually was now supplanted by a startup model where actually most of the R&D is done by little startups, and then the large firms buy the successful startups. So it's more a takeover. So there will be takeovers of startups, and it was very prominent in the pharmaceutical industry in particular, where you buy startups and then the big companies have to distribution networks to market and then test the products and place it in the marketplace. And it's interesting to see how the world has changed towards the startup model. And of course, venture capital funding for the startups is very, very crucial for this as well. But in general, we would like to have a resilient society. So I'm always coming back to this resilience term and develop and be very agile, develop new medical innovations and vaccines in particular. And for this, we need a very good venture capitalist ecosystem and have optimal risk sharing. And here comes another aspect of our capitalist system, is, you know, to what extent do we welcome failure, and to what extent do bankruptcy lead to some stigma? And if you go back in the old principles of order liberalism, there's this liability principle which says, you know, Whenever you do something, you're liable for it. And there's, you know, 100% liable for it. You can't get out. There's no bankruptcy protection. And there's, of course, this limited liability principle. And depending how strict you put the limited liability aspects to it, you allow for more risk-taking and less risk-taking. And perhaps, you know, for these innovative aspects, you might promote risk-taking because of the social benefits. And, you know, that's where the bankruptcy code plays an important role. And as I mentioned, venture capitalism plays an important role because most of these innovations are real options. So the optimal risk sharing and the real option component is very important. And the venture capitalists also provide a lot of advice and expertise as they go across various different startups and bring expertise from one startup to the next startup. And that's an important role the venture capitalists do on top of funding the projects. So with these little introductory remarks, I will pass on the floor to Andy, who knows way more than I do in particular, and he will talk about the new funding models for biomedical innovations, focusing on biomedical aspects. And, you know, he's worked for many years in this space, and he also teaches a course on health economics and health finance at MIT. So drawing from all his insights, we get now a compressed view about his— what he's learned over the years. So thanks a lot, Andy. It's great to have you with us, and the floor is yours.

Okay, terrific. Thanks so much, Markus. Can everybody hear and see me okay? All right, great. So I want to start by thanking Markus and the Benheim Center for Finance at Princeton for organizing this great event and for giving me the pleasure and the honor of speaking to all of you today. And I want to thank all of you for joining from around the world. I hope you're all staying safe and healthy amidst this pandemic and very much appreciate your being part of this. So, um, as Marcus mentioned, um, I'm going to be focusing today on a topic involving healthcare finance and funding biomedical innovation. And I have to start with a disclaimer. And my disclaimer is that I am not a biomedical expert by any means. And in fact, I'm not even a healthcare economist. I'm, I'm a financial economist by training and trade, much like Marcus. And in fact, I was very interested to hear about his research on resilience because this is exactly the point of why we need a robust healthcare system. And it's not surprising that given Marcus's pathbreaking research on systemic risk, I think it naturally led him to start thinking about what it makes— what it takes to make an economy that is more robust, and resilience is an important aspect. I'll be talking about that in the very specific context of healthcare. So as I said, I'm not an expert in healthcare finance. Why, why did I start getting involved in this? And it really has to do with personal reasons. A number of years ago, friends and family were dealing with various kinds of cancer. And I have to say that I was really taken aback by that. I had really never dealt with these kinds of issues myself. Um, and, and even my close friends and family members were spared, until about 10 years ago, when in the space of 4 years, 6 people close to me, including my mother, all died of various kinds of cancer. And that was a pretty big wake-up call for me. During that process, I tried to do what I could to be sympathetic and helpful to, to my friends and family. And so I started reading about the way that cancer is treated and the business of cancer drug development. And something shocking emerged from that exercise, which is that finance plays a huge role, sometimes way too big a role, in how drugs get developed. And so that's what I want to talk to you about today, the, the rather strange conundrum that at the very same time that all of these incredible scientific breakthroughs are being made, there are nevertheless enormous bottlenecks in the system that could actually be addressed with better financing. So let me start by observing something important about where we are in the biomedical field, and that is the fact that right now we happen to be at an inflection point. And I want to describe to you what that means, what it means to be at an inflection point in biomedicine, through two examples. The first example has to do with these two kids, Caroline and Cole Carper. These kids were born with a relatively rare genetic disorder called Leber's congenital amaurosis. It's a genetic typo that ultimately causes their retinas to degrade shortly after birth. And so within a few months of birth, they become blind. And the amazing story about Caroline and Cole is that they participated in a clinical trial a few years ago with a company called Spark Therapeutics developed out of the University of Pennsylvania in Philadelphia. And in 2016, Spark received FDA approval for a gene therapy, a therapy that takes the correct version of the gene that was altered in Caroline and Cole's case. The correct version is inserted into a virus, and that virus is injected into the back of the eyes of these two young kids. And in a matter of days, they could see again. It's really quite extraordinary. Uh, for example, when, when Caroline was asked about what it was like, she said, I went outside when it was snowing and I was like, oh, I can see the snowflakes. It was really cool to actually see something that I've never seen in my life before. It's a one-time injection of this viral vector into their eyes, and now they can see. The second example I want to give you is a case study that I wrote about a company called Agilis Biotherapeutics. This is a company that was started up a few years ago, um, in here in Cambridge, Massachusetts. And it was a company that was designed to take advantage of some research that was done by a Taiwanese doctor by the name of Paul Hu. Dr. Hu studied a very rare condition known as L-amino acid decarboxylase deficiency. That's a mouthful. I realize it's a genetic typo that causes infants to not be able to produce Dopamine, a pretty important neurotransmitter. I'm sure you've all heard of the, the runner's high experience. I've never experienced it. Usually when I run, I just get exhausted, but people tell me that if you run long enough, you actually start feeling good. It's because dopamine begins to be released in your brain. Children with AADC deficiency cannot produce dopamine and This has some pretty significant effects because in addition to providing you positive reinforcement in various decision-making contexts, dopamine is critical for developing motor functions. And so infants that are AADC deficient, you don't actually know that there's anything wrong until after a few months when you realize that your baby is not lifting his head up, is not moving his arms, is not rolling over, is not sitting up, is not doing all of the things that a normal baby would do. So Dr. Hu realized that he could actually fix this genetic typo by doing the same kind of technique as we saw before with Spark. Take the correct version of the gene, insert it into a virus, take the virus, inject it into the brain, and that virus will cause the gene to be replaced and therefore begin to start producing dopamine. I'm going to show you a video clip of patient number 4 in the clinical trial for this particular therapeutic. And I think you'll see it's quite striking. The beginning of the video clip will show you the infant, who is actually not an infant when he was diagnosed, he's 2 years old. And we'll see the baseline before the treatment, and then we'll see what happens 1 year after this one-time injection. So here's the infant baseline. [FOREIGN LANGUAGE] And you can see that the infant can't raise his head, can't lift his arms without any kind of help, can't roll over, is basically confined to that kind of unfortunate circumstance. 1 year later, This is the same child moving, crawling, can't quite stand yet because obviously he just learned how to engage in some of these motor functions recently. So you can see that he's still somewhat unsteady. But now take a look at what happens a year later. So this is now 2 years after the treatment, he's 4 years old, and he can stand. He can't quite walk normally yet, and we're not sure whether he'll ever walk normally because it's important to engage in these motor functions, you know, at the earliest stages of life, and he only got the ability at 2 years old. [SPEAKING CHINESE] But from his parents' perspective, this is an absolute miracle. That he's able to do this. The blind shall see and the lame shall walk. You know, that's a phrase out of religious text, but it's happening today thanks to these incredible therapeutics. Now, by the way, this little biotech company, the reason that I got in touch with them was because they were struggling for funding and read some of my research and wanted to get some help. And so I wrote the case study for them. And, uh, it turns out that after the particular phase 2 trial where they showed this video clip, um, this little biotech company was acquired by a much larger one for about $900 million. One therapy, 10 patients, $900 million. So this goes to Marcus's point. About how this ecosystem is incredibly important. The bigger fish will eat the smaller fish, and in this case, the smaller fish are perfectly happy to be eaten because it basically allows them to take these therapeutics and develop them and distribute them to a broader audience. So this is the backdrop for what I mean by biomedicine is at an inflection point. And a few years ago, my MIT colleagues Phil Sharp, Susan Hockfield, and Tyler Jacks published this report called Convergence, the convergence of the life sciences, physical sciences, and engineering. The point that all of these disparate fields are now coming together to create tremendous progress in the life sciences for medical interventions. And one way that experts in this field describe these inflection points is, is this omics revolution. Genomics, the study of the sequence of the human genome. Epigenomics, the study of the on-off switches that cause certain genes to be expressed and others to be suppressed. Transcriptomics, the study of how these gene sequences get translated into proteins. Proteomics, the study of the 20,000 to 25,000 different proteins that make up the human body. Metabolomics, the study of all the chemical reactions that occur to make life possible. And most recently, microbiomics, the study of the bacterial colonies that inhabit the body and provide us with all sorts of important functions. All of these omics have experienced tremendous advances over the last few years, with the exception of one. And that exception is economics. The fact that we need to figure out better ways of paying for all of these therapeutics That's where the bottleneck occurs. This is not to say that economists haven't made a lot of progress. We certainly have, but in terms of applying these ideas to the healthcare system, there's a lot that we can do. And that's what I want to talk about today. So in particular, I want to talk about what's called the valley of death. The idea that there is a certain set of bottlenecks, particularly in the early stages of drug discovery. When a drug is being developed in a laboratory, it turns out that there's a lot of risk, a lot that we don't know. But as it gets put into humans in clinical trials, Phase 1, Phase 2, Phase 3, as it progresses, it becomes de-risked, another term that the biopharma industry uses. And by the time it reaches Phase 2 or Phase 3, there's a lot of money that's around. Biopharma companies are able to fund those late-stage trials fairly easily, but the early stages of drug discovery is really the valley of death. That's what people call it in the industry. And the question that I wanted to ask as an economist is why, why is there this valley of death? I just assumed that if there was some real medical needs and some smart bios, biomedical experts that can figure out how to deal with it, that the money will just magically show up. And the fact is that it's because of these incredible breakthroughs, because of these breakthroughs, that sometimes funding is not available. And it has to do with increasing risk and uncertainty. You see, it's very counterintuitive, at least from my perspective. Typically in finance, when we learn more about an investment or a particular security, the more we learn, the smarter we get, the lower the risk. That's not true in biomedicine. The smarter we get about biomedicine, the more risky in some cases are the investments that are associated with it. Because as we learn about these new innovations, it gives all sorts of new opportunities for young upstarts to make obsolete the existing technologies that are generating revenues for companies and their investors. And if there's one thing I can tell you as a financial economist, It's that investors don't like risk. So I want to give you an example of that that I use with my first-year MBA students. Nothing to do with, with, with, uh, biomedicine or healthcare. It's about risk and reward. And the experiment that I do with my MBA students is to show them 4 financial investments. I don't tell them what they are or even over what time period they span. I just show them what happens if you put a dollar in each one of these investments. The green one turns a dollar into $2, but not particularly rewarding, not particularly risky. The red line turns a dollar into $5, more rewarding, but more volatile. The blue line is the most profitable, but also the most volatile. And the black line is somewhere in the middle. And I ask my students to choose one and only one of these investments. Uh, and so I'm going to ask you to do that now, if you don't mind, I'm going to take a poll. And ask all of you to choose one of these four investments for your retirement fund or your kids' college education fund. Maybe you're managing money for your parents or grandparents if they're older. Just pick one of these that you feel is the best trade-off between risk and reward. Clearly, there's no right or wrong answer. It's just a matter of what your preferences are, your risk preferences. And so there's a really interesting phenomenon that all of us exhibit in thinking about risk and reward. And this is the element that is pretty critical for understanding what's going on in the biomedical field. So I'm going to just give it a few more seconds and I'm going to close the poll. And I think you'll see an interesting pattern here that, that I've seen in virtually all of my classes that I've taught. There's actually only one exception in terms of the particular choices that people make, and I'll tell you about that in a few minutes. Okay, so most of you have responded, so I'm going to end the poll now, and let me just share the results, because what we see here is basically what we've seen in virtually every context that I've ever presented this problem, and that is that the by far the most popular choice is the black line.

Why?

Because it seems like it's got the best trade-off between risk and reward, right? It's not the most rewarding, but it's a lot less volatile than the choices. Okay. So let me tell you now what you all pick. First of all, the time period runs from 1990 to 2008. So we're talking about close to a 20-year period. It's a long time. The green line, which only 3% of you chose, is US Treasury bills, the safest asset in the world, at least for the next few weeks. We'll see what happens with the budget discussions, and hopefully we'll be able to continue on with this. But not very rewarding. If you put your money in T-bills as of 2008, you would have earned pretty much nothing since then. Now, how about the red line? Some of you picked it. It wasn't terribly popular, but some of you picked the red line. The red line is the US stock market, the S&P 500. And if you had picked that, well, congratulations, you would have done just fine since 2008. The blue line, which actually a surprising number of you did choose, I'm actually quite impressed, that is the single pharmaceutical company Pfizer. Way more volatile, but way more rewarding. And if you had picked Pfizer, congratulations, you would have done even better. Than, than the rest. Now, what about the most popular choice? The black line? Well, the black line is the returns to a private investment called the Fairfield Century Fund. That was the feeder fund for the Bernie Madoff Ponzi scheme, which is why I had to stop this experiment in 2008. That's when the Ponzi scheme blew up. Now, I apologize. I know that you're very frustrated. Um, my students get annoyed as well. The purpose of this exercise is to illustrate why it is that this Ponzi scheme ultimately ended up suckering in so many people, $50 billion worth, by the way. And it's because it is human nature. We are all attracted to investments that have high yield and low volatility. And in finance, we have a term for that. It's called the Sharpe ratio. The Sharpe ratio is the excess return of an investment above and beyond T-bills per unit risk as measured by the standard deviation. So if you calculate the Sharpe ratio for Pfizer and for the S&P 500, it's about the same, you know, 0.4, 0.5. And on paper, the Sharpe ratio for the Madoff Ponzi scheme was an order of magnitude larger. That's why so many of us were drawn to this scam. It's because we're all looking for something that is high-returning, low volatility, and sometimes things that are too good to be true aren't true. It turns out that biomedicine has had a Sharpe ratio that has been going down over the course of the last decade. Down, not because the returns are any less. On the contrary, the returns are great. If you're successful, the problem is that the complexity of biomedicine has grown, and therefore it's much harder to tell now which is going to be a winner and which is going to be a loser. So let me give you a second pop quiz. The second pop quiz is a simple investment opportunity that I'd like you to just tell me whether you would or would not invest. Okay. So I'm going to put up a poll in a minute, but let me tell you what the characteristics are. So the investment is $200 million upfront. You need to give me $200 million, and there's a 10-year horizon before which you will see any kind of a return. Okay. And one more thing, the probability of any kind of a positive payout whatsoever is 5% with 95% probability. You're going to get nothing back after 10 years. So let me start the poll and let's see how many of you would be willing to invest in this. So let me pull up poll number 2.

And yeah. So, but then if it goes out well, Hamad, what's the return?

Uh, yeah, that's a great question, Marcus. I'm glad you asked. I was hoping that somebody would ask this. Um, and I will tell you in just a second. Let me just, uh, get this poll going. I launched the wrong poll.

I apologize.

Um, let's see, poll number 2. Okay, here we go. So yeah, let me tell you, um, it turns out, oh, for some reason it's still giving me poll number 1. Let me see. Let me end this poll and try it again. Apologies.

Um, share results. And, uh, I can launch it.

Yeah, if you can launch poll number 2, that'd be great. Yeah, great. Thank you. So, um, Usually most of my students don't even ask me the question, what do I get if I succeed? Because they don't need to know. All they need to know is that it's $200 million, 10 years, 95% failure rate. And their answer is exactly what we're getting from the participants, which is no thank you, not interested. Now it turns out that what I'm showing you is the back-of-the-envelope calculations for what it takes to develop a single anti-cancer compound. And so if you actually do the math and you are successful in developing an anti-cancer compound, and by the way, the success rate, uh, is in fact only 5%. Historically, it's a little bit lower than that. If you are successful though, you're going to get revenues on the order of about $2 billion a year from years 11 to 20. Why is that? It's because typically a biomedical patent lasts for 20 years. If it takes 10 years to do clinical trials, you've got another 10 years to be able to benefit from that. And if you're successful, you can generate $2 billion a year in years 11, 12, 13, all the way through 20. And then your, your drug goes off patent. So that amortizes to about $12.3 billion in year 10 if you're successful, and zero if you are not. And so what that amounts to is a return of about 12% on average, the expected return during those first 10 years annualized. And then a standard deviation of 423.5%. In other words, a Sharpe ratio of about zero. It's, it's really, really small because the risk is so high. And so I'm going to end the poll now, and we can see that, um, most of you are not interested, and you're not interested because This is way too risky for the typical individual. Now, I'm gratified that some of you said yes, and I'm hopeful that we'll see more of that. But it turns out that there's an even more powerful way to be able to get this.

Can I ask you a medical question? Yes. So with the vaccine, everybody was expecting it took us about 10 years to develop a new vaccine as well, but now we could do it in 1 year. Do you expect we can— new technology will bring the 10-year horizon down to?

Absolutely. No, no, no, not on the contrary. It is absolutely happening. So in the area of vaccines, that's one example, but there are many other examples in cancer, in immunotherapy treatments for various different diseases, in the gene therapies that I just showed you at the very beginning. That's what I mean by the inflection point. The economics are changing as the science is changing and the combination of having better science and better financing could actually make some tremendous, tremendous impact on healthcare. So it's exactly right. So let me first then, now that we acknowledge that the science is progressing, let me tell you how finance can actually help. In this case, it turns out that instead of doing one project at a time, imagine if we did 150 of them all at the same time. Now, I realize that that's a tall order because we're going to need 150 times $200 million or $30 billion of capital. And where are we going to get $30 billion? Well, as an economist, I have a very simple answer. The answer is assume we have $30 billion. Now, I'll come back to it, tell you how we get that in a minute, but let's suppose that we do. It turns out that if we have $30 million, the economics change entirely. Imagine that the 150 projects are statistically independent. I'll come back to that assumption a little bit later on, but if they are independent and identically distributed, or IID, then the expected return stays the same of the portfolio, but the risk goes down at the rate of the square root of N, which means that the volatility is now 35% to give us a Sharpe ratio of 0.34. You see, that's an incredible reduction in risk.

[Speaker:STIG_BRODERSEN] So, Andy, can I ask you, if I go back to these two models of funding, where I say traditionally it was these big companies doing many, many projects and many vaccines while we moved actually to the startup model, So this would be a strong argument to have these big companies, you know, having much lower funding costs and proceeding and developing 150 drugs simultaneously. Why did we see then this startup move to the startup model? Is there some other force which makes the startup model way more successful?

There is, absolutely. That's a fantastic question. The answer is the nature of innovation itself. When you are a big pharma company, it does not necessarily pay for you to take risks and innovate. Why? Because you have much higher Sharpe ratio activities to get involved in, namely marketing, distributing, and licensing drugs. Drug discovery, the early stage R&D, that is something that is best left for startups. And so the question is, is there a way that we can have our cake and eat it too. Can we figure out a way to finance startups and take that risk without having to become a big pharma company, but at the same time, still lowering the cost of capital? And that's where finance comes in. So can we really raise the $30 billion? Now, clearly pharma companies already have that kind of money. If you look at Pfizer, GSK, Novartis, They have enormous amounts of corporate cash, but their incentives are different than smaller biotech companies, and I'll, I'll come to that in a minute. So can we really raise the $30 billion? Well, the answer is it depends. It depends on the risk-reward profile. And so let me assume that we are statistically independent, and let me assume that we have 150 projects. And let me assume that we can actually finance a portion of this particular fund using bonds, using debt financing. Now, how much debt can I issue? Well, the answer is it depends on what the yields are and what the risk profile is of your portfolio. So let's talk about risk. I've got 150 projects. They're all independently and identically distributed with a 5% probability of success.. But if I'm successful in any one of them, I make $12.3 billion in year 10. So what's the probability that I have at least 3 successes out of 150 shots on goal, to use a hockey or a soccer term? Well, let's do the math. We know that this is a binomial distribution because each one of these shots on goal is a Bernoulli trial. The probability of at least 3 hits out of 150 independent tries, each with a probability of success of 5%, happens to be 98.18%. And so if I issue debt of 3 times $12.3 billion, or $36.9 billion, If I issue debt with the face value of slightly less than $36.9 billion, what's my default rate? The default rate is 1 minus the probability that I'll have at least 3 hits, and that is a little less than 2%. A default rate of a little less than 2% means that I've got a single-A rating. And as of a few days ago, the single-A yield was given by 1.64% according to the Bank of America Merrill Lynch Index. And at that yield, I can actually raise more than $30 billion to finance this portfolio. Okay, Marcus, can you launch the third poll? The question I want to ask is now, how many of you would be willing to invest in this particular opportunity? If I've got 150 projects, all independently identically distributed, 5% chance of success, what is the likelihood that you will be willing to invest in this? I have troubles launching the poll. Yeah, let me, let me see if I can do it from here. Poll number 1, 2, 3, uh, and launch poll. Okay. So how many of you will be willing to invest in this? And I think you're going to see a pretty interesting difference from poll number 2. The fact is that using financial engineering, we have been able to change the risk-reward trade-off. And that has dramatic effects on the supply of funds. So not surprisingly, right now I'm looking at the results and about 92% of you polled have said yes, you'd be willing to invest in this. Whereas the one-off, the one-off investment, less than a quarter of you would be willing to take that investment. And With all of the other tricks of the financial trade, things like securitization, credit default swaps, government guarantees, we can do even better. Now, you might— so these are the results, not surprisingly. And I think you would understand now what the power of financial engineering is. Now you might be reacting to this by saying, wait, wait, wait a minute, isn't this what got us into all the trouble with the financial crisis of 2008? And the answer is yes, exactly. These techniques played a prominent role in the financial crisis, not because they don't work, but because they work way too well. These are incredibly powerful financial tools, and if you apply them to focused objectives, you can make enormous progress, but you have to be careful. And the fact that interest rates are at an all-time low, this is a single-A rated yield over the course of the last several decades, it means that there's tremendous resources waiting to be tapped, but you need to be able to use that energy in a controlled and responsible way.

Now I can ask you a question. So we would like to know how— I made a comment essentially. There's of course market risk and then there's idiosyncratic risk, and this medical risk seems to be totally idiosyncratic risk. Can you elaborate on that a little bit? Do you see something that the big— you cannot carry it through to the end because of some market disruptions? Is this the only market risk or what is—

I love that question. Can you hold on for just about 5 minutes? Because I'm going to get directly to that. But yes, that's an excellent question. And it's one that I struggled with. So we're going to get right to that. But before I do, I want to make the point about diversification, which is related to that question. And that is the assumption that I made that 150 projects are independently and identically distributed. That is a tall order, right? That's a very, very restrictive assumption. And we know from the financial crisis that all of these mortgage-backed securities, they failed. The subprime market failed because these defaults were highly correlated. So correlation matters a lot, and I can show you why. In the case of no correlation, the probability of at least 3 successes out of 150 tries, each of them with a 5% probability of success, is what I just described to you, 99— 98.18%. That's what this graph shows. But now what happens if there's a 10% pairwise correlation in terms of the success and failure of these compounds? Well, then the probability of at least 3 successes goes down to about 89%. If the correlation between successes is 40%, now the probability of at least 3 successes is down to about 55%. And if the pairwise correlation is 80%, then you're now at a 23% probability of at least 3 hits. And at that level, nobody is gonna wanna buy your debt. You cannot finance by debt. So the key is correlation and diversification. When I described this to one of my pharma colleagues, one of my colleagues that is in the pharma industry, he said, you know what, this is exactly what I've been telling my colleagues but the way that I put it is using third grade soccer as the analogy. And I said, what do you mean? I don't play soccer, my kids didn't play soccer. And he said, well, if you've ever coached third grade soccer, which is what I do for my daughter's team, this is what third grade soccer looks like. All the kids crowding around the same ball. And what I tell them is, no, no, no, spread out, spread out, go to where the ball will be, not where the ball is. And when you compare third grade soccer to World Cup soccer, you see the difference. The fact is that the biopharma industry does tend to crowd around the same set of ideas. There's a reason for that. They're not dumb. Obviously the reason that they do so is because they want to make use of the scientific knowledge that they have. And there's benefits to being able to work together collaboratively on the same set of ideas. But what makes sense from a scientific point of view does not necessarily make sense from an investor's point of view. And so diversification is key. If we can better diversify, we can lower the cost of capital. And now I'm going to get to the question that Marcus just asked about diversification and the cost of capital. So, and this goes to the last poll that I wanted to issue, and that is, We know from models like the capital asset pricing model that the cost of capital, the required rate of return, is directly related to how much risk you're bearing. But as the person in the chat asked about systematic or idiosyncratic risk, it matters a lot as to what kind of risk we're talking about. Because what matters for the cost of capital is systematic risk. It's beta. It's the correlation with the market portfolio, right? So the higher the correlation, the higher the cost of capital. And you would think with biomedicine, the correlation of a drug to the S&P 500 should be virtually zero, right? Because it's scientific risk. So I want to ask you one last question, which is to tell me what you think the systematic component of risk is in the biopharma industry. And for the sake of argument, I'm going to use 1 as the benchmark for beta, right? So if you've got a beta of 1, you are as risky as the S&P 500, and therefore you should carry a cost of capital or a risk premium comparable to the market portfolio. Historically, the risk premium has been about 8.3%. Most people think going forward it should be closer to a 6% cost of capital. If you add a Treasury bill yield of 2% for a 10-year yield, you're getting an 8% cost of capital, right? So the poll that I want to ask you is to tell me, what do you think the beta is of the pharma industry, which is focused on late-stage drug development and marketing, distributing, and licensing, or the early-stage biotech industry, the startups that are focusing on the really innovative, the really risky, kind of science and medicine. So let me launch this poll and let's get your thoughts, and I'll tell you what my thinking is, you know, once we do that. Oh, sorry, I didn't share the results from before. I apologize. But I think you knew what the results would be. Okay, so let me stop that and then launch the last poll. So the last poll is a two-part question. Uh, it's basically asking you to pick what the beta is of the pharma industry in the first part of the question. And then tell me, what do you think the beta is of the biotech industry in the second part of the question? Because the biotech industry is really where that kind of scientific risk is the greatest. The pharma industry is really focusing on the marketing, distributing, and licensing of drugs. That's much more late stage. And the Sharpe ratio actually tends to be pretty high. The question is, what about the biotech industry? What do you see as the risks and therefore the cost of capital in the biotech industry? And after I get your answer, I'm going to show you the results. And I think you'll be surprised. At least I was really surprised by the results. Now, just to let you know as a backdrop, most people think that healthcare is a really great diversifier, right? It's a really great investment because The risks don't seem to be correlated with anything that we would care about in the world of finance. So let me stop the poll here and share the results. And I think you'll see what we were talking about. So for the pharma industry, the majority of you felt that the beta was somewhere between 0.5 and 1, and between 1 and 1.5. Those are the two most popular responses. The biotech industry, most of you felt that the beta was smaller, right? The majority of you said that the beta is between -0.5 and +0.5. So let me stop sharing the results and tell you what the answer is. So with regard to the pharma industry, you are absolutely correct. That when you look at the beta of the pharma industry, for the most part, it's been below 1. And the reason is that the price elasticity of demand is generally pretty low for drugs, right? You need your medication regardless of whether the economy is in a boom or a bust. And so pharma companies generally have had a pretty reliable risk premium and rate of return. And therefore, the cost of capital for pharma is on the order of 8% to 10%. Now let me show you the shocking result. This is the beta for the biotech industry. And while now it is slightly below 1, in fact, historically, when you look at all the biotech companies in the entire industry, it is way higher than 1. On average, the typical biotech company has a beta of about 1.5 to 2. So I was shocked by this result because it's exactly as the person who asked the question said. The risk of these biomedical experiments have nothing to do with the S&P going up or down. Why should they have a higher beta? And it took me a while to figure out the answer. And the answer, once I figured it out, so obvious I felt quite embarrassed. The answer is that a biotech company faces two kinds of risk. The first kind of risk is scientific, and the scientific risk is indeed totally uncorrelated with financial considerations. However, the second kind of risk is financial risk, because biotech companies have to pay for stuff. And so if you are in the midst of a very important experiment and you run out of money, Well, guess what? That experiment is not going to get done. And so what we're seeing in the biotech industry is that financing risk often trumps scientific risk in terms of its cost of capital. And effectively what biotech risk is, is rollover risk. The fact that at some point, if you run out of cash and you need to refinance, If markets are in a recession and you can't refinance, you're out of luck. And unlike other kinds of industries and assets, a biomedical asset is really, really driven by the underlying people that are involved. And so if you've got expertise to develop a drug and you run into a cash flow problem and people start to leave, the value of that asset gets destroyed. Unlike a parking lot, if you buy a parking lot and you go bankrupt and you can't afford to develop it, somebody else will take the parking lot and develop it. There's very little loss in value. Tremendous loss in value when it comes to complicated intellectual property in the life sciences.

But why is it structured this way? Why can't you issue more long-term debt or equity instruments and have to rely on rollover?

Sorry. That's exactly what I'm suggesting.

Yes.

So the fact is that the way that biotech has been financed historically is by venture capital. Venture capital is a phenomenal business model for financing an app, a piece of software, something where the proof of concept is a few hundred thousand to a couple of million, and it'll take 18 to 24 months. That's why Silicon Valley has become such a dominant force They've got tremendous ability to finance these companies. A biotech company requires tens of millions of dollars before you get to a proof of concept. It requires 5 to 10 years, not 24 months. And the failure rate, particularly in oncology, is much, much higher than in social media or software. And so the, the business model of venture capital is the wrong model for financing biomedical innovation. We need a different business model. We need longer-term financing, financing that matches the horizon of the underlying asset. And that's where securitization and permanent capital and using these kinds of holding company structures come into play. Now, I'm sure that there are many other questions that are in the chat window. Do we really need $30 billion? What's the market failure? Why hasn't this been done already? Isn't pharma already doing it? If not, shouldn't the government do it? So on and so forth. And the short answer to all of these frequently asked questions is, I have no idea. I'm just a financial economist. What do I know about these things? But I started asking people who should know. I asked, for example, pharma executives, and their view is that, well, we don't know. We use these financing methods because that's what's been done in the industry. That's how the biotech industry developed. It was really through venture capital. So then I started asking scientists and clinicians and their view is, I have no idea. I just do the science and the medicine. I'm not interested in the finance. And the long answer to the question about all of these different issues is what I've been working on with collaborators over the last 10 years, a variety of papers that I've written. You can check out my website if you're interested in them, but the long answer is that I think it is possible to do this, and there are a number of ways that it's being done now. So, you know, in the last few minutes, let me start wrapping up by telling you about how this actually is being done. So first of all, in terms of the amount of capital that we need, the answer of $30 billion really depends upon the particular projects you're working on. The cost per shot, the probability of success, duration of the trials, so on and so forth. All of that is what leads to how much money you need. And in a paper that I recently posted on SSRN with source code that allows you to run the simulations yourself, you can actually determine how much capital you need depending on the particular medical area that you're focusing on. But the key issue to all of this is that, that finance experts and biomedical experts need to collaborate because No one group knows the answer to all of these questions. You need to work together to figure out all of these different parameters to plug them into the software. And that's really where I think the, the value can be created by building bridges between these two fields. I'll give you one example and then wrap up. The one example where this method has actually been used has to do with orphan diseases, rare diseases. Kind of like the diseases that I described at the very beginning of the talk. Things like hemophilia, cystic fibrosis, ALS, Gaucher syndrome, all of these are examples of a rare disease which, according to the 1983 Orphan Drug Act in the United States, are diseases that affect 200,000 patients or less. So it turns out that because of these diseases, uh, the, the each, each of them are relatively small. But the fact that there are so many of them, over 30 million Americans suffering from over 7,000 rare diseases, this is actually a really important area. And due to a variety of policy as well as regulatory and economic and scientific issues, it turns out that you don't need $30 billion to deal with this. You need maybe $400 to $500 million. You don't need 150 projects. You can actually get diversification with only 10 or 20. And why is that? It's because the assumption of lack of correlation that I made in the previous example, that actually holds pretty closely in the case of rare diseases. And it does so because these rare diseases are so different and the therapeutics are so different in most cases, not in all cases, but in most cases they're so different that the success or failure of one of them generally has nothing to do with another one of them. And so the pairwise uncorrelated assumption that I made earlier on, that actually applies most closely in the case of orphan diseases. And so when I published a paper in 2015 and ran a simulation of a rare disease portfolio of 20 assets that cost about $500 or $600 million, We were getting rates of return of on the order of 22% for the equity tranche and the comparable returns for the appropriate risk-adjusted ratings for the debt tranches. So this illustrates that in the case of rare diseases, you can actually do really well with a relatively small portfolio. And it's really because of that lack of correlation. Now, if you want to know how unusual that lack of correlation is, ask yourself the following question. Can you name 5 financial investments that are mutually uncorrelated? I don't believe you can. You could probably name 2, maybe 3, but you can't name 5. I can construct a portfolio of 20 very attractive rare disease therapeutics that are pairwise uncorrelated,. And if that's the case, that means that you can increase the Sharpe ratio of this portfolio at the rate of the square root of n, where n is the number of projects. Now, when I published this paper, I was called by one of my former students, a fellow by the name of Neil Kumar, who was a chemical engineering student at MIT and read the paper and said, you know, gee, this is really interesting. Would you mind rerunning your simulations But not with your assumptions, which you got from the industry literature. Use my assumptions instead. So I said to Neil, sure, I'd be happy to do that. And, um, over the course of 4 or 5 months, we ran more and more simulations. Each time the results kept getting better and better because he was using, you know, his industry knowledge to make the appropriate assumptions about probability of success and so on. And what we did— at the end of that process, what we concluded was that this was actually feasible. So he came into my office one day and said, I just want to let you know, I quit my job yesterday. I'm going to do this. And I have to tell you that scared the hell out of me. I've never had that effect on any of my students. I said to Neil, are you sure you don't want me to run a few more simulations before you make that jump? He had a small child, and so I was really nervous for him. He said, no, no, no, no, I'm really excited about this. And so he started a company called BridgeBio and, uh, I was a small seed investor in the company. Uh, I wish I had invested a lot more now in retrospect. Um, but very shortly after the friends and family kind of funding, we were able to raise money from a venture capitalist, actually a private equity investor, KKR, that usually doesn't invest in these kinds of companies because they don't invest in other people's funds. Because they are a fund, but BridgeBio was not a fund. It's an operating company that had subsidiaries that develop various different therapeutics. So it was a portfolio, a different business model, a portfolio, but with a company structure that allowed the underlying individual companies to innovate. And over the course of the next few years, we were able to raise more funding. This is Neil here, Neil Kumar. Um, uh, over the course of about 4 years, the company was able to raise about $700 million, uh, did an IPO in 2019, the largest IPO at the time. And, um, uh, just last week announced yet another fundraising in addition to the IPO, raising convertible bonds. So now debt financing, uh, on the order of about $650 million. And as of a couple of days ago, the company's market cap was about $7.5 billion. 5 years, $7.5 billion market cap. But this is not what the company is most proud of. The company is most proud of the fact that they put together a portfolio of about 20 projects, of which 4 of them are now in phase 3 trials. And we expect one of these to be approved by the end of this year and another one next year. So therapies that would not have been developed except for the kind of funding that was, uh, raised by this company. Um, and, uh, there are now more and more companies that are using these new business models, increasing the scale because that's what's needed here. It's the right scale. And we don't yet have any securitized debt for biomedical portfolios, but I'm hoping that sometime in 2021, we're going to see the first bio bonds issued. So let me wrap up by, by saying that, you know, all of this, all of these ideas and all of these methodologies was made very personal to me a few years ago when I ran into a colleague of mine in the biology department by the name of Harvey Lodish. When I heard about Harvey's story, I decided right then and there that I want to be Harvey Lodish. And let me tell you why. So in 1983, Harvey was an assistant professor of biology at MIT, and he was asked by a venture capitalist to work with him to develop a treatment for a rare condition called Gaucher syndrome. That's also a single gene mutation, a typo that prevents your body from producing some important enzymes. And without those enzymes, fatty acids build up in your bone marrow, in your spleen and other organs. And by the time you become a teenager, um, you're dead. It's a, it's a very debilitating and deadly disease for a portion of Gaucher patients. And so Harvey developed a method for replacing the enzyme that's missing in your body. And in 1991, the drug Ceredase was approved. And since that time, that drug and, and various improvements have saved the lives of at this point hundreds of thousands of patients around the world. The company ultimately did very well. It was bought out in, I think, 2010 for about $20 billion. You may have heard of that company that Harvey helped to start.

It's called Genzyme.

And Sanofi bought them for about $20 billion. But that's not why I want to be Harvey Lodish, although that's not a bad reason. I want to be Harvey because of what happened in 2002. In that year, Harvey's daughter was pregnant with her second child, Harvey and his wife's second grandson, a boy named Andrew. Great name, by the way. Andrew was diagnosed in utero with Gaucher syndrome. What are the chances of that? I asked Harvey about this. It was a very emotional conversation. I asked him, "Harvey, in 1983, when you started down this path to develop a drug for Gauchers, did you have any idea that you carried the gene that your grandson, who was as yet unborn, was going to have the disease? Did you have any idea?" And he said, "No, I had no idea. I thought I was doing some cool science that could actually help some patients, so that was, you know, All good. It turns out that Andrew in 2012 developed the symptoms of full-blown Gaucher's, but he's doing just fine, thank you very much, leading a totally normal life thanks to the drug that grandpa helped to develop. This is why I want to be Harvey Lodish. I am not an MD or PhD in biology. I don't have the ability to develop a drug that will help save my as-yet unborn grandchildren. But I realized something. I realized that I could be Harvey Lodish. We can all be Harvey Lodish if we invest in the therapeutics that will help save our as-yet unborn grandchildren. Finance doesn't have to be a zero-sum game always. At the right scale, with the right kind of financing, we can actually do well By doing good. Sorry, I teach MBA students, so I have to make this a little bit more dynamic. And so with all of your interest and help in bridging the gap between finance and medicine, I believe that we're gonna be seeing some amazing therapeutics over the last few years. We can all be Harvey Lodish. And with that, I wanna thank you, and happy to answer questions or discussion.

Thanks a lot, Andy. So we have a huge number of questions, so I have to be selective. I apologize for the audience, Extremely interesting and inspiring talk. I wanted to— I got the impression there is a private solution, essentially it's more teaching, educating, developing the methods rather than NGOs or the PPP partnership with the public sector. Is this the correct impression I got, or is there any role for the government to intervene and help out or coordinate or Can you elaborate on this space a little bit?

Several questions are approached. Great, that's a great question. And there's absolutely a role for government. In fact, the reason that we have had such success in the private sector today is precisely because of government policy over the course of the last several decades. I'll give you an example. Right now, cancer has a tremendous number of new drugs coming onto market. I think last year there may have been like 27 cancer drugs approved in one year, one year. So right now it looks like there's all this low-hanging fruit, particularly applying gene therapy, cellular therapy, immunotherapy, all of these new tools. How did that happen? It happened because in 1971, President Nixon declared war on cancer and created all sorts of cancer centers around the country to fund basic scientific research in cancer biology, which was not something that the private sector was going to do because there's no way to earn a rate of return because many of these ideas is just moving forward knowledge. It's not necessarily patentable. And so a lot of these ideas ultimately fed into the low-hanging fruit that we are benefiting from today. Uh, there was estimates that The NIH has, over the course of the last 40 years, put about $100 billion of money out there to develop cancer biology. So we don't get these wonderful pieces of low-hanging fruit unless we plant the seeds decades before. That's where government policy plays an important role. Another example is certain areas of not necessarily market failures, but market challenges. And I'll give you an example, Alzheimer's. Alzheimer's is clearly a really important problem. Over 5 million Americans suffer from Alzheimer's. And last year we spent just dealing with the medical needs of Alzheimer's patients. Medicare and Medicaid spent $200 billion on Alzheimer's patients alone. Okay, so this is a serious problem. What's the probability of success for developing an Alzheimer's drug? Well, you can estimate it. And if you use the last 10 years of data, the answer is the probability of success is zero. The last time we had an Alzheimer's drug developed successfully approved by the FDA was 2003. So it's been 18 years, almost two decades before we have a single new Alzheimer's drug, despite the fact that we've got 5 million patients. Why? It's because we don't understand the biology of Alzheimer's. And that's an example where government policy can play a huge role, and it has. So we declared a kind of a war on Alzheimer's with the BRAIN Initiative that was launched by Barack Obama a few years ago. And now the funding for Alzheimer's at NIH has gone up by several fold. So I expect that in another 5 to 10 years, we're gonna see intellectual property being generated that will then turn into valuable companies. And there may be an Alzheimer's drug approved sooner rather than later because of that effort. So that's an example of government policy. Government policy can actually lower the risk and raise the Sharpe ratio of these investments by being able to provide funding for things that will create the kind of understanding, the knowledge, the intellectual property that the private sector can then take the rest of the way.

So you focused very much on the US, how it's structured in the US. If you take a more international perspective, do other countries have a different way to— so you said the early phase should be done by the government and then the later phase should be done by the private sector ideally. Some people might argue, you know, they take the benefits from all the early investments done by the government. If you look across the globe, if you look, you know, the Europe India, China, do you see totally different ways to do it, or do you think the US is so far ahead, so it's really, you know, there's not so much alternative models we can learn from, from other continents?

Well, I think there's certainly things that we can learn from every country, because there are all sorts of different experiments that are effectively being conducted. That's a really important question, because there are some key differences. So the first observation that I would make is that the United States has traditionally been the largest funder of fundamental scientific research. So NIH has a budget of about $30 billion every year. That's the annual budget. And it's been going on for decades. So I think the US is very much far ahead, but the other countries are catching up. And particularly in Asia, China, Singapore, uh, they've made very big investments in the life sciences over the course of the last 10 years. So I think that the balance of talent and power is shifting, but even more important is the healthcare systems because the healthcare systems, what we are willing to pay in terms of drug prices, in terms of reimbursement policies, in terms of coverage, that very much determines how much innovation goes on. And up until now, the United States has been the most valuable market for any drug company to participate in. So even countries that are far away from us, even those countries, when they launch a drug, they will typically launch it first here in the US because we are paying the highest prices. Now, there's been a lot of debate about that, right? Many people say that that's not a feature, that's a bug. We should not be paying higher prices than people in Canada or in the UK or in Asia. And this goes to a much deeper question that I am definitely not qualified to discuss, but many healthcare economists have written a lot of very interesting things about, which is what is the best healthcare system? Is it single-payer? Is it multi-payer? Is it some kind of a hybrid? And I think that that's where we have a lot to learn. Obviously, the UK is a single-payer system. Canada, single-payer. Germany, multi-payer system. The United States, it's a free-for-all. So we have very, very different kinds of incentives that we are providing healthcare companies. And that's something that I think can really use a lot of innovation and more research.

So can I come back to the individual risk of a little startup, or not a little one, but essentially a biomedical startup? There's also a risk that— two more risks essentially— that you cannot sell your company early enough. How would you elaborate on that risk? And there's also the risk that, you know, suddenly a competitor pops up and steals the market from you. So how do you take this into account? So if you fund more projects, there might be a competing product coming, and like your soccer players, the third-year, third-grade kids running together, and they discover suddenly the same thing or similar things.

So Yeah, so that, that's a, that's a really important question. And there are several different questions underlying it. So let me take a couple of aspects. The first has to do with what happens if you, you get interrupted in the middle of a project and, uh, or you can't raise money. That is absolutely a huge problem that you can eliminate, as you pointed out, by having a longer-term financing. So for example, if you financed your portfolio, not by equity, but by debt, by long-term debt, by 10-year debt, by 20-year debt, then you actually will not run into that problem. But who would lend you money if you're not generating any cash flows? So the key about debt financing has to be how to deal with that. So that suggests that maybe a hybrid portfolio where you have late-stage assets that are generating cash flows and early-stage assets that will not generate cash flows for a decade, a hybrid portfolio where you diversify the various different stages that you're in could be financed using these bio bonds. That's a possibility. We don't know yet because it hasn't been done, but I think that's a real issue. But the overarching objective is we have a beta for a biotech company of 2. If we had financing that could take that biotech company from beginning all the way to end— by end, I mean FDA approval. If we could have financing for that entire process of clinical experimentation, then the beta goes from 2 down to 0, because then all that's left is just scientific risk, right? So this suggests that having a large enough portfolio, having a longer-term financing, maybe getting sovereign wealth funds that have much longer horizons than hedge funds or short-term financing, maybe that will allow us to reach the full potential. Now, the other thing that you mentioned was about biotech companies basically getting made obsolete by other technologies. That's absolutely a risk. That's part of innovation. And rather than worry about that risk, what we ought to be doing is to encourage more innovation by pooling the risk over a much larger portfolio. So again, portfolio theory and diversification can deal with that kind of risk. And by the way, that risk is real. To give you an illustration, in the year 2000, if you took the list of the top 30 best-selling drugs, the ones that generated the most revenue, and you ask the question, where did those 30 drugs come from, pharma or biotech or academic medicine? The answer is that 26 out of the top 30 drugs were developed by Big Pharma. Only 4 came out of biotech or academic medicine. That was in the year 2000. Fast forward to the year 2018, which is the most recent year that we have data for this. In the year 2018, the top 30 best-selling drugs in that year, 26 of them came out of biotech or academic medicine. 26. Out of the top 10, 9 of them out of 10 came out of biotech and academic medicine. So the balance of power has shifted from Big Pharma to biotech. But it's not that Big Pharma is lazy or, you know, failing. It is that Big Pharma's incentives, their skills, their objectives are focused on late-stage drug development, regulatory approval, marketing, distributing, and licensing.

Very good. So thanks a lot, Andy. It was very insightful. I learned a lot, and I think the audience also appreciate it. But we went way over time. I just want to conclude. No, it's, uh, that's good. It's, um, um, we always conclude with a positive note. So if you project into the future, how do you see the future evolving, let's say 5 or 10 years, how will the pharma industry, how will biotech industry, and how would the financing— how do you envision it? I mean, you gave a clear vision, but do you think it will happen? And will we bring the 10 years down to 5 years? And this, you know, opens up another possibilities. Perhaps you can just end with one positive note.

We always end with a positive note at the end. I absolutely think that we're going to see tremendous progress both on the scientific front and the financing front. And the more financing that comes into the industry, the faster is the scientific progress that we're going to make. And believe it or not, thanks to COVID-19, thanks to the pandemic, I believe that government policy will now be much more focused on providing innovation and support for infectious diseases, which was an area that was not getting enough attention prior to the pandemic. So now I think we have a chance of being able to deal much more successfully with the next pandemic and with all sorts of healthcare issues.

Thanks a lot, Andy. Appreciate it. Hope to see you soon in the real world, and let's stay in touch.

Thank you so much. It's been a pleasure and an honor, and I wish you all good luck, and please stay safe and healthy.

You too. Bye-bye.

Bye-bye.