A Prescription for Healthcare Investing

[00:00:00] Dr. Andrew Lo: It all began about 15 years ago, where over a seven year period, six people close to me all died of cancer. I realized that finance actually plays a pretty big role in cancer drug development, I started thinking about applying all of the tools of financial analysis, to thinking about how to change the drug development process to reduce the risks, increase the expected reward, and therefore bring more money into the space to get more drugs developed.

[00:00:20] Sara Shores: Welcome to The Bid, where we break down what's happening in the markets and explore the forces changing the economy and finance. I'm your host, Sarah Shores, the Global Head of Strategic Product Management for the Portfolio Management Group at BlackRock. Today we're discussing the rapidly evolving space of biomedicine.

A convergence of breakthroughs in biology, medicine, and technology is driving the industry to an inflection point and opening up the door to a wealth of new discoveries. But funding new drug development is challenging. Trials are expensive, complex, and lengthy, and only a fraction of the therapeutics that go into clinical trials will ever come out the other side approved and effective for their intended use.

In fact, a 2021 joint paper by Bio Pharma Intelligence and QLS Advisors estimates the likelihood a drug is approved is less than 10%. Today I'm joined by Dr. Andrew, economist, professor and author who is working to accelerate the pace of change in biomedicine. Dr. Lo is currently a professor at the MIT Sloan School of Management and co-founder and chairman of QLS Advisors, a life sciences and technology advisory firm.

Andrew, welcome to The Bid.

[00:01:34] Dr. Andrew Lo: Thank you for having me..

[00:01:35] Sara Shores: Andrew, your title is Doctor, but you're not a medical doctor... in fact, you've spent most of your career studying financial markets. Tell us more about your journey from traditional finance to healthcare finance.

[00:01:45] Dr. Andrew Lo: Well, it was a long journey and it began about 15 years ago where over a seven year period, six people close to me all died of cancer. And during that period of time, I felt that somehow knowing me was carcinogenic. And then I realized, of course, that we all deal with that situation.

Everybody's been touched by cancer either directly or indirectly. And I realized at the time, A cancer patient needs a financial economist about as much as a fish needs a 401k plan, we have nothing to contribute to their wellbeing. And that really frustrated me. So I began to learn more about the disease and about how to treat it and then I realized that finance actually plays a pretty big role in cancer drug development, sometimes too big a role in some cases, driving the scientific agendas rather than vice versa. And so that's when I started thinking about applying all of the tools of financial analysis, things like portfolio theory, diversification, risk management, securitization, and so on, to thinking about how to change the drug development process to reduce the risks, increase the expected reward, and therefore bring more money into the space to get more drugs developed. Part of that process involved applying quantitative tools, things like machine learning. And other financial analysis. And, , over the course of the last few years, a number of my former students and I realized that these tools actually had quite a lot of commercial relevance.

So we created a company called QLS Advisors, Quantitative Life Sciences, because we wanted to take our tools for quantitative finance and apply them to biomedical context. And through that process began to help biotech and pharma C. Manage their pipeline assets more effectively, thereby lowering the cost of capital, increasing the throughput of all of these various different pipelines, and ultimately getting more therapies to patients faster and ultimately cheaper.

[00:03:40] Sara Shores: It sounds like your work is bringing the concepts of financial economics to solving both the diversification and risk management aspects of the challenge. Also in helping to identify where you might have the most successful potential trials, so you're really trying to solve both sides of the equation.

[00:03:59] Dr. Andrew Lo: That's right, and given that I'm from MIT, I've got to talk about that equation. So let me back up and talk about what I call the fundamental theorem of healthcare finance. And it's an equation! And the equation looks something like this. I wish I had a whiteboard here. On the left hand side of the equation is the expected value of a particular experimental drug candidate. On the right hand side are just three terms. One is the present value of all cash flows of drug sales. If that drug is approved, multiplied by the probability of. minus the cost of developing that drug. So pretty simple equation as far as these things go,

Financial economists can tell you a lot about the present value of future cash flows. Financial economists can tell you a lot about the costs, but the one thing that financial economists have nothing to say about is the probability of success. So really the key parameter in valuing healthcare assets is really that probability, what is the likelihood that a clinical trial will succeed or fail?

Historically, it's been estimated using just past successes and failures, or it's been estimated by asking experts in the field, so-called key opinion leaders. We call them KOLs for short, and asking their opinion about the likelihood that a trial will succeed or fail. But at the end of the. It's really a mathematical and statistical question, and that's where the tools of data science can come in.

Using hundreds of thousands of historical clinical trial outcomes, we can now estimate fairly precisely what the probability of success is, and using the tools of modern data science that gives us an edge that's very hard to come by, even with the very best K. Because it's just difficult to translate qualitative senses of whether the science is good or bad into quantitative estimates of outcomes.

[00:06:01] Sara Shores: Andrew, all that sounds fascinating and also quite complicated. What is the benefit to taking this more data driven? Thinking about medical innovation.

[00:06:11] Dr. Andrew Lo: Well, you're right, it is more complicated. In fact, I think it was Albert Einstein who said that a theory should be as simple as possible, but no simpler, and I'm afraid that in this case it requires this level of complexity. Because drug development is complex. And so the main benefit of using these tools is we get more accurate estimates of these probabilities of success, which means that we're going to make better financial decisions as investors in which trials to fund in which trials not to fund.

And as a result, more money will come into the space because investors now understand what the risks are. But one of the key benefits, is the fact that with a quantitative approach, as with other kinds of technologies, we can now scale much more quickly and broadly than before.

So rather than relying on a few key opinion leaders for these forecasts, we can now do this on an automated basis, on a nightly basis across hundreds of thousands of clinical trials across thousands of drug companies. And we can analyze this data at speeds that are impossible for humans to do. So, like much of technology, what it does is it gives us the ability to automate and scale in ways that we could not before.

[00:07:26] Sara Shores: I understand why looking at a lot of different trials would be helpful. Why is speed important? I would think that the pace of change was relatively slow. What's changing on a day to day basis?

[00:07:36] Dr. Andrew Lo: Well, that's a great point. It turns out that everything is changing on a daily basis in biomedicine. You know, for example, if an Alzheimer's drug gets developed and approved by the FDA, that's great news, and it may take years for that to happen, and then on one day you get the approval. Literally the very next day, what you'll see is implications for all other drugs that have been developed in the Alzheimer's space.

That doesn't happen over weeks or months. It happens almost instantaneously, and it happens across tens of companies in ways that humans can't possibly keep track of at the same speed as computers. So taking into account all of these various different features and being able to implement them on a regular basis across the various different companies and trials and events is something that machines were perfectly designed to do.

[00:08:27] Sara Shores: I recently watched a 2014 TED talk given by NYU professor Roger Stein, where he estimated there are roughly 20 year’s worth of clinical trials for potentially life-saving drugs that are sitting on the shelf untested. That's a heck of a bottleneck.

Is that still reflective of what you see today? And if so, why is it so hard to get things funded and approved?

[00:08:51] Dr. Andrew Lo: I think it's actually even worse today. Are so many ideas that scientists and clinicians have, but not nearly enough money to be able to take those ideas into practice. And part of the reason, in fact, the vast majority of the reasons that these drugs don't get developed is risk. And that's something that financial economists know something about.

[00:09:38] Sara Shores: So what role can finance and investors play in helping to solve that funding problem?

[00:09:44] Dr. Andrew Lo: Well, financial economists and financial practitioners have been managing risk for centuries using the financial system, and so in the same way that we manage risk in a portfolio of stocks and bonds, we can actually manage the risk of a portfolio of biomedical assets. Things like portfolio theory, securitization, diversification and other kinds of risk management tools actually can change the way drugs are developed by altering the risk reward trade off.

[00:10:22] Sara Shores: So all your experience in traditional finance, you're now bringing to bear to help solving that problem for, for medical breakthroughs. What aspects of the research and risk taking are unique to the medical field?

[00:10:34] Dr. Andrew Lo:. There are three aspects of drug development that make it unique. Virtually no other industry faces all three issues. And then a big fourth one that I'll talk about in a minute. . The three issues have to do with the fact that drug development requires large amounts of capital, typically one or two orders of magnitude more capital than in other industries just to get proof of concept. We're looking at something like 10 to a hundred million dollars of investment, and that's not even all the way to clinical approval. That's really just to see whether or not the drug has some kind of efficacy for a given disease. Second, it typically takes five to 15. Of clinical testing.

So before you ever get the chance to approve a drug, you have to go through the process of phase one, phase two, phase three, clinical trials, and third, we're talking about very low success rates in the area of cancer, which I know best. The probability of success of developing a cancer drug historically is a little less than 5%.

That's a greater than 95% failure. And so all three of these characteristics makes it really difficult to invest in drug development from beginning to end. And the final whammy that makes us really tough is the fact that we're dealing with life and death issues. So there's an ethical dimension to drug development that doesn't exist for many other industries.

And so taken together, that creates huge challenges that really need to be addressed in ways that are different from what we've been doing over the course of the last few decades.

[00:12:10] Sara Shores: So it seems a miracle that anyone ever invests in drug development. Based upon some of those challenges, how can we try to mitigate those risks?

[00:12:19] Dr. Andrew Lo: Well, this is where finance comes in. If you take a look at the kinds of risks that drug developers are facing, you can really translate almost all of those risks with the possible exception of the ethical dimension. You can translate almost all of those risks into financial risk taking, and that's where financial engineering comes in.

Thinking about developing. As a portfolio, multiple shots on goal, to use a hockey or a soccer term, is really effective way of reducing that risk. The standard ideas of diversification play a really critical role. In how we think about risk. And then of course all of the tools of the modern financial trade, things like hedging strategies, using derivative securities, thinking about portfolio construction tools.

All of those tools can actually be applied to drug development. But with the understanding that we're talking about relatively binary risks. So we can't apply the tools as is, we have to alter them. We have to extend them to the case where you're really looking at zero one outcomes, either a drug succeeds or it fails. That kind of risk is something that we don't see in very many industries, and that's one of the reasons why we need to develop new tools to apply these kind of financial engineering concepts to drug development.

[00:13:32] Sara Shores: We live in a world where data is more abundant than ever, and we see companies embracing technology and big data across just about every industry and every discipline in ways that have really started to change the limits of the possible. Andrew, I know you have a lot of experience in Quantitative Finance, and you've been working in quantitative investing for, for decades. What opportunities do you see to apply these techniques around unstructured data and technology to the field of healthcare investing?

[00:13:59] Dr. Andrew Lo: First of all, you're absolutely right that data has completely transformed most industries, including healthcare. We all know about the importance of electronic medical records and how

they're changing patient lives by understanding what are the key drivers of health and disease. But that's also happening on the drug and device development side.

We now have large amounts of data, of clinical trial outcomes, and what that means is that we can apply the traditional tools of modern artificial intelligence, things like machine learning, uh, and other kinds of pattern recognition techniques to try to identify which clinical trials are likely to fail. So using these kind of machine learning forecasts, we can come up with better odds for drug developers and increase the likelihood that a portfolio of investment projects in drug development can actually give better returns to investors. In very much the same way that we apply these tools to quantitative portfolio management, we can actually apply them to quantitative healthcare portfolio management.

And that's the exciting part. There are not very many people working on it just. But I suspect that over the course of the next 10 or 20 years, you're going to see many, many more breakthroughs in this area. And what that will mean ultimately is more drugs to patients faster, uh, and better.

[00:15:13] Sara Shores: That's fascinating. You give us a few examples of some of the features that you might look like. Look, can you give us an example of some of the features that you might look at to try to identify what trials are more likely to be successful?

[00:15:25] Dr. Andrew Lo: Sure there are a number of them, some of which are pretty intuitive, but some of which are subtle. So for example, an intuitive feature that we use is track records. If a drug developer has succeeded in getting a FDA approval for a prior drug, It turns out that that actually dramatically increases the chances of success for that particular trial.

Second, if the drug has developed, second, if the drug has gotten an approval for a different disease, that also makes it much more likely that it will get approved for this new one. But an example of a feature that's a bit more subtle is if the particular clinical trial happens to recruit patients faster than, It turns out that that's actually a pretty good sign for approval.

Now, you might ask, why is that the case? Why should it be that if a clinical trial recruits patients much more quickly, that it's more likely to succeed? Well, so we talked to a number of clinicians about this, and they said that they've actually observed this anecdotally in the past. And the way it works is that if an experimental treatment is really effective, well the patients usually see an effect right away, and that means that they're doctors.

Realize that this is working right away. And what do the doctors do? They call all their friends. And so the other doctors that have patients with similar diseases, they will try to get their patients into that trial. So basically, purely from word of mouth, you'll see very effective clinical trials recruit patients exponentially faster than other trials, and that's something that we can detect with these machine learning tools.

[00:17:03] Sara Shores: Fascinating in the way that you're bringing in both the unique aspects of drug development alongside the more traditional mean variants along. Traditional investing concepts and really marrying the two. You must see a lot of different projects Concepts in your work are there any recent medical breakthroughs that you're particularly excited about?

[00:17:22] Dr. Andrew Lo: Wow. You know, I think there are many different breakthroughs that have been going on, and that's one of the reasons why I've been spending more and more of my research time in healthcare finance because we see so many breakthroughs happening right now, not enough funding to be able to take advantage of them.

And this is really an inflection point in the whole biomedical ecosystem. So just to name three examples. Number one, Gene therapy, the idea of being able to deal with certain genetic diseases by taking the correct form of a gene, inserting it into a virus, injecting it into a patient, and having that virus replace the defective gene with the correct one. That's a reality. We actually have approved products right now that

have. cured certain diseases, a one time treatment that basically reduces the patient to a totally normal life expectancy that actually is happening.

Second, gene editing the idea of not only being able to change the genetic structure one at a time, but to literally create new genes that will deal with disease at the very core of how they're being generated. Dr. Jennifer Doudna from U.C. Berkeley won Nobel Prize along with her co-authors for this amazing technology. And it is being implemented now in a number of biotech startups.

And third, all of the medical devices that are now allowing us to detect disease in some cases before even any obvious signs are occurring in humans, that's giving us a head start that we never had before.

And there are many other examples, but those are just a few that give me confidence that over the course of the next 10 years, we're going to see some tremendous progress in dealing with all kinds of human diseases.

[00:19:02] Sara Shores: So inspiring to hear how much potential there is and even a greater need then for us , to solve the funding problem, to be able to help some of those developments. You've described a really different model for funding therapeutic development that could open up a meaningful new source of capital for innovation.

What areas of research might now get attention that didn't perhaps get that attention before?

[00:19:23] Dr. Andrew Lo: Well, the real benefit of having additional funding right now, is that the riskiest and most significant breakthroughs that have not yet been developed can now be undertaken. Typically, what investors are willing to fund are opportunities where they understand the risk and reward, but sometimes in order to deal with disease, at its very core, you have to swing for the fences, which means you're going to strike out more often than not.

And it's really those really risky but extraordinarily transformative. The. For example, not just dealing with cancer at the tumor level, but figuring out how to prevent cancer by developing vaccines against certain kinds of cancers. Those are examples of things that we may want to take on if we had the funding, but where if we don't have enough funding, they're going to be focusing on the lower hanging fruit.

And sometimes a lower hanging fruit is not going to provide the same kind of therapeutic benefits as when you go for the really tough diseases and the really experimental but breakthrough the.

[00:20:26] Sara Shores: Andrew. Wow. This has been such an inspiring discussion. I'm thrilled that we had the chance to share your story and the role that investors can play in helping to bring life changing new therapeutics to patients all over the world. Thank you again for joining us for the bid today.

[00:20:41] Dr. Andrew Lo: Thanks so much for having me.

[00:20:42] Sara Shores: Thank you for joining us for this episode of the Bid. Make sure you subscribe to the bid wherever you get your podcasts.