AI transcript
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0:00:21 Hi and welcome to the A16Z podcast.
0:00:22 I’m Hannah.
0:00:26 The advent of new gene and cell therapies are beginning to approach that holy grail of
0:00:31 medicine, that of a possible cure, but they are also more expensive than any medicines
0:00:32 ever sold before.
0:00:37 In this episode, MIT economist Andrew Lowe and A16Z general partner on the biofund Jorge
0:00:43 Conde discuss how exactly we place an economic value on a cure, the questions we still need
0:00:47 to figure out, like who should pay for what and how, and how we need to start thinking
0:00:51 about handling the coming influx of highly priced medicines like this into our healthcare
0:00:52 system.
0:00:57 Jorge and Andrew also talk about the economics and risk of drug discovery and development
0:01:02 overall and how our markets might just function more like biological systems than anything
0:01:03 else.
0:01:06 So what does an economist know about healthcare?
0:01:08 Yeah, that’s a good question.
0:01:12 I probably should start with a disclaimer, kind of like the disclaimer that you find
0:01:14 on drugs.
0:01:20 This podcast could cause confusion, irritability, and drowsiness, but it should all pass within
0:01:21 a few minutes.
0:01:22 That’s the black box warning.
0:01:23 Right.
0:01:24 Exactly.
0:01:25 My background’s really in financial economics.
0:01:29 I got interested in this because friends and family were dealing with cancers of various
0:01:33 different types, and so the more I learned about what they were going through, the more
0:01:37 I got curious about the underlying economics of cancer drug development.
0:01:42 A lot of the work that you have done as an economist has been around thinking about value.
0:01:46 We have one approved gene therapy that sparks therapeutics, gene therapy for a rare inherited
0:01:48 form of blindness.
0:01:51 There are many, many more in the clinic.
0:01:55 There are many engineered cells or cell therapies that are also in the clinic.
0:02:01 These therapies have one great promise that has been an all too short supply in our industry,
0:02:03 which is the promise of a potential cure.
0:02:08 How do we think about the value of a cure in a system, and specifically how do we think
0:02:14 about pain for cures in a system that really isn’t designed for that?
0:02:16 Yeah, that’s a really interesting challenge.
0:02:20 That challenge, by the way, is only going to grow because although we only have one gene
0:02:26 therapy right now on the market, there are over 300 gene therapy clinical trials registered
0:02:28 at clinicaltrials.gov.
0:02:32 My guess is that within the next three years, we’re probably going to see somewhere between
0:02:35 five and 10 gene therapy approvals.
0:02:40 We’re talking about a pretty significant impact on payers and patients.
0:02:45 Just to give people a sense of scale, so look, Sterna, the spark therapeutics, gene therapy
0:02:48 for blindness, what’s the sticker price on that?
0:02:55 The list price right now is $850,000 for both eyes, $425,000 per eye.
0:02:59 We’re talking about some really expensive therapies, and that’s not even the most expensive.
0:03:05 People are talking about therapies for, say, hemophilia A, that if it gets approved, a
0:03:09 price tag could be well north of a million and a half dollars.
0:03:13 When you think about these kind of numbers, it’s really shocking, but the way I think
0:03:20 about it as an economist is, is it justified given the kind of value that it creates for
0:03:21 consumers?
0:03:25 Typically, the way I think about value is very much the way that someone would think
0:03:28 about value from any kind of a commodity purchase.
0:03:35 If you buy a home, the price of the home should be related to some degree to the housing services
0:03:41 that the home will provide you over the course of your lifetime and beyond.
0:03:46 We typically think about a house is not simply what is going to provide you in terms of shelter
0:03:48 for the next month.
0:03:51 That would be an apartment that you would rent one month at a time.
0:03:56 When we buy a house, as opposed to renting an apartment, we’re buying a stream of future
0:03:58 housing services.
0:04:01 I think about that the same way that I think about gene therapies.
0:04:07 When you are cured of a disease, you’re basically getting a lifetime of health, as opposed to
0:04:13 renting health one pill at a time with a chronic manageable condition.
0:04:18 When thinking about that framework, it stands to reason that if you’re going to get value
0:04:23 from a particular cure over a period of your life, you ought to be able to amortize your
0:04:26 payments over that kind of a period.
0:04:32 If you think about the idea behind a mortgage for a home, we can also apply that to thinking
0:04:35 about a mortgage for drugs, drug mortgages.
0:04:37 To mortgage for a cure.
0:04:38 Yeah.
0:04:39 Who pays that mortgage?
0:04:41 Well, that’s a good question.
0:04:47 In my view, insurance companies should pay for it because that’s why we have health insurance.
0:04:51 There are challenges, though, with the current system in getting insurance companies to pay
0:04:56 for it simply because they’re going to have a hard time in terms of balancing the cost
0:05:00 of the insurance with the payouts that they have to make.
0:05:05 Typically the way insurance works, if you join an insurance plan, you pay premiums
0:05:09 every year and in the event that you get ill, they cover your cost.
0:05:13 And the idea is that the amount that you’re paying every year, on average, should roughly
0:05:18 equal the cost that they’re paying out, maybe a little bit less so that they can make a
0:05:21 profit from their business.
0:05:26 That economic calculus becomes very difficult when you’ve got patients that can leave a
0:05:28 plan after two or three years.
0:05:36 So imagine a health insurance company, A, paying for a cure for you and with the expectation
0:05:39 that now that you’re healthy, you’re going to be able to pay premiums for the rest of
0:05:42 your life and that will make up for the cost of the cure.
0:05:48 But suppose that after three years, you leave plan A and move to plan B. Now, plan B has
0:05:52 the benefit of your health and the premiums that you’re going to be paying for the rest
0:05:57 of your life, whereas plan A is stuck having paid for your therapy.
0:06:04 So the solution to this is to allow plan A to amortize, to space out the payment of your
0:06:07 therapy over the course of many years.
0:06:12 And if you leave after three years, the remaining payments, the remaining drug mortgages that
0:06:18 the health plan A was supposed to pay, that moves to plan B. And in a way, that creates
0:06:23 a level playing field for all insurance companies so nobody is disincentivized to provide those
0:06:24 cures to their patients.
0:06:30 So that’s a fascinating concept because one of the things when people think about insurance,
0:06:35 when they think about people moving around from plan to plan, and we have now legislation
0:06:41 around this, is, well, what’s the obligation of the payer to pay for a pre-existing condition?
0:06:45 We’ve always assumed a pre-existing condition as a disease, but what you’re describing
0:06:50 is in some cases in the future, the pre-existing condition might actually be a cure.
0:06:54 And so there’ll be a liability not associated with the treatment of a disease, but a liability
0:06:56 associated with the treatment for a cure.
0:06:57 Exactly.
0:07:02 And so we can actually fix the system with one stroke of a pen by changing in the Affordable
0:07:09 Care Act the sentence that prevents insurers from refusing to serve a planned participant
0:07:14 because of pre-existing conditions to change that to pre-existing financial conditions as
0:07:16 well.
0:07:20 And if we do that, then this whole issue about mobility is not a problem.
0:07:23 But good luck trying to get legislators to do that right now.
0:07:25 I think it’s going to be a challenge.
0:07:30 Do we know that debate has even taken place because it does feel like a logical solution?
0:07:35 As far as I know, the debate has not yet taken place, largely because there’s no need.
0:07:40 But if we institute these payment plans for one-time therapies and we’re able to start
0:07:46 developing cures for some of the larger prevalence diseases, for example, we’re on the verge of
0:07:50 developing a cure for sickle cell anemia, well, there are 100,000 patients in the United
0:07:55 States with sickle cell, and that’s going to hit health plans pretty hard.
0:08:00 A few weeks ago in the UK, a group announced the launch of a clinical trial for applying
0:08:04 gene therapy to deal with age-related macular degeneration.
0:08:09 There are 600,000 AMD patients in the UK, 2 million patients in the US.
0:08:13 If we have a gene therapy for AMD that succeeds, that’s going to be a real problem for our
0:08:15 health care system.
0:08:21 The concept of a drug mortgage or a cure, a mortgage for cures, it’s a fascinating one.
0:08:27 But when we use the analogy of a home, does it start to break down in the sense that I
0:08:34 know how much I’m going to pay for my mortgage because the value of the house is agreed upon
0:08:37 and determined when I buy that house.
0:08:42 And in some ways, the equity I get in my home is the difference between what the value was
0:08:48 when I bought it versus the value when I sell it and I end up paying off the mortgage at
0:08:49 that point in time.
0:08:57 In the case of the cure, how do we get agreement on what the value of the cure is at the moment
0:08:59 of treatment?
0:09:05 Because I would imagine that for a patient, they’re going to put one value on it, society
0:09:08 may put a different value on it, the payer may put a different value on it.
0:09:14 So how do we get to agreement on value so we can from there determine the mortgage payments?
0:09:17 Well, different countries answer that question differently.
0:09:22 For example, in the United Kingdom, their process for determining what the price of
0:09:28 a drug should be or what an acceptable price is is a group of individuals that do pharmacoeconomic
0:09:31 analysis to try to calculate cost effectiveness.
0:09:34 This is an organization called NICE, N-I-C-E.
0:09:39 And this organization does economic studies to try to determine whether or not at a given
0:09:43 price it’s worthwhile for their society.
0:09:48 And not everybody agrees with what NICE comes up with, but the bottom line is that the National
0:09:52 Health Service in the United Kingdom will take the recommendations of NICE and follow
0:09:53 through.
0:09:59 Now, in our country, we would call that a death panel and it’s a politically loaded term,
0:10:02 but effectively that’s unfortunately what it is.
0:10:05 You have to make trade-offs between money and lives saved.
0:10:08 Now, in the United States, we have a very different system.
0:10:10 It’s a multi-payer system.
0:10:15 And at first I thought, oh, that just means it’s a free market, but in fact, the complexity
0:10:21 of the regulation surrounding the pharma industry is anything but a free market.
0:10:26 There are all sorts of incentives in certain cases that cause drug prices to be higher than
0:10:30 usual and in other cases to force them to be lower than usual.
0:10:35 I think that the best way to think about it is to start back and ask the question, what
0:10:37 do patients get out of it?
0:10:39 What is the value to a patient?
0:10:44 We can layer on top of that all sorts of other considerations, but to me, the patient should
0:10:46 be the first consideration.
0:10:48 And there are economists who study that all the time.
0:10:51 They come up with the notion of quality-adjusted life years.
0:10:54 And they ask the question, if you’re going to save a patient’s life, if you’re going
0:11:00 to literally cure the patient of an unfortunate early death, you can actually calculate the
0:11:04 number of quality-adjusted life years you’ve given back to that patient.
0:11:07 And that’s the beginning of where value comes from.
0:11:13 The other argument would be, if you cure a disease, you avoid the cost of treating that
0:11:15 disease over the lifetime.
0:11:21 So one other argument for pricing or giving value to a cure is to say, well, let’s take
0:11:25 the net present value of all of the avoided costs.
0:11:28 And so I’m going to ask you an overly simplified question.
0:11:36 Would a way to price these cures be, let’s take the value of the qualities, the quality-adjusted
0:11:43 life years, and add to that the net present value of the costs avoided?
0:11:48 Well, that’s certainly a consideration that people have used in making an argument one
0:11:50 way or the other for pricing.
0:11:53 But I think that you have to be careful because there are many other factors then that you
0:11:55 might want to also bring in.
0:12:01 For example, if a patient is now going to live a natural life, that patient will pay taxes
0:12:03 for the rest of his or her life.
0:12:06 Should we factor that into the calculus?
0:12:10 That patient, on the other hand, will be consuming other aspects of society.
0:12:15 And for example, if that particular patient ends up becoming unemployed, then they’ll
0:12:17 be drawing unemployment insurance.
0:12:21 So you can go down this rabbit hole of all sorts of costs and benefits that you have
0:12:23 to do the math for.
0:12:28 And unless we’re really set up to do that, it’s very easy to cherry-pick the particular
0:12:33 statistic that makes your case look stronger than the opposing side.
0:12:37 So I think that the approach that we seem to be gravitating towards is to try to come
0:12:42 up with an objective notion of value, and there’s an organization called ICER.
0:12:47 It’s a nonprofit organization that’s job is to focus on developing the cost-effectiveness
0:12:53 studies, maybe using that as a starting point, and then going from there to try to see whether
0:12:58 or not we can come up with a rational pricing model that basically all stakeholders can
0:12:59 live with.
0:13:03 As ICER looked into the spark therapeutic struggle, I believe they have.
0:13:08 I know that they did a study on gene therapies in general, and they were very positively
0:13:13 inclined, particularly given the cost of many of the diseases without the gene therapy.
0:13:16 Hemophilia is a good example, even at a price of, say, one and a half million.
0:13:19 We don’t actually know what the price will be.
0:13:23 One and a half million is actually a bargain relative to what it costs to deal with a current
0:13:30 hemophilia patient, which can be anywhere from 300,000 to 500,000 a year for the rest
0:13:32 of that patient’s natural life.
0:13:37 So at one and a half million, that seems to be a bargain from that kind of calculus.
0:13:40 So it’s a fascinating conundrum.
0:13:46 If we look at the history of the biotechnology industry, we’ve seen a small-scale version
0:13:50 of this challenge with rare genetic diseases.
0:13:55 So to pick one sort of well-known example, when Genzyme comes onto the scene, the founder
0:14:02 and CEO of that company, Henry Tirmier, pioneered the idea of, you know, we can develop and
0:14:08 commercialize therapies for these patients that have these rare diseases.
0:14:12 And there’s a lot of value associated with treating them and alleviating the conditions
0:14:14 to the extent that we can.
0:14:18 And as a result, we can charge, we can price it based on that value.
0:14:23 So those therapies were on the order of $100,000 and above.
0:14:29 Well, oh my gosh, how can we possibly pay $100,000 for a therapy?
0:14:35 And part of the rationale for doing it was, well, these are rare diseases, so collectively
0:14:42 the probability that you would have many patients in any given plan that are going to have this
0:14:49 therapy in general, on scale, these were going to be very much one-off events for most plans,
0:14:54 and therefore they could shoulder that kind of payment.
0:14:58 What’s fascinating about what we’re describing here, and you’ve given a lot of great examples,
0:15:06 like SMA, hemophilia, AMD, these are all diseases that even if they were individually rare, which
0:15:11 many of these are not, collectively this is going to be a common condition.
0:15:16 And so we’re going to see another reckoning, I think, across the industry in having to
0:15:23 think through this problem that Gensheim tackled from the micro scale, at the more macro scale.
0:15:26 And my take on this is, I’m an optimist.
0:15:31 My assumption is that the innovation is going to lead the regulation, the policy, the coverage
0:15:36 here, and that we will get to an answer on how to actually make sure that patients get
0:15:37 these cures.
0:15:42 Well, I’m absolutely optimistic that we’ll come up with an answer, but I don’t believe
0:15:46 we’re going to get to that answer until the system is forced to deal with this issue in
0:15:48 a direct way.
0:15:53 And maybe that ends up being when we develop a gene therapy for a really big indication
0:15:55 like Alzheimer’s.
0:15:58 There are currently 5 million patients that have Alzheimer’s.
0:16:03 If we develop a gene therapy to stop the progression of the disease or to be able to delay the
0:16:08 onset, that’s going to create tremendous pressure on the healthcare system.
0:16:11 And at that point, we’re really going to have to confront this as a nation.
0:16:14 And it’s complicated because you’re dealing with life and death issues.
0:16:19 When we think about pricing things like a car, it’s not a big deal because, well, if you
0:16:22 can’t afford a car, maybe you’ll get a cheaper car.
0:16:25 But if you need a drug, you can’t afford to get a cheaper drug if there’s only one drug
0:16:27 that cures your disease.
0:16:31 So I think that’s one of the reasons why economics, as much as I love the subject and feel that
0:16:34 it’s critical, these are not just economic considerations.
0:16:39 We have to bring in all of the relevant stakeholders to make these decisions, and that’s really
0:16:41 what our policymakers are trying to do.
0:16:46 It’s clear we’re on the cusp of a new age in medicine, and it’s going to be fascinating
0:16:48 to see how all of this plays out.
0:16:51 One of the things that I found most surprising about your work is what the work you’ve done
0:16:57 around trying to develop risk metrics for drug discovery.
0:17:00 Can we talk a little bit about some of the conclusions that you drew from that and how
0:17:01 we can apply them?
0:17:05 That’s a really interesting aspect of the healthcare industry that puzzled me for the
0:17:07 longest time.
0:17:11 When I first started looking at the way drugs were developed, I couldn’t understand why
0:17:17 it was the case that so many amazing breakthroughs have been made over the years while at the
0:17:21 same time, I kept hearing about the fact that there’s this valley of death and there’s not
0:17:25 enough funding at the early stages of drug discovery.
0:17:30 The more I looked into it, the more I realized that these incredible breakthroughs are actually
0:17:34 increasing the economic risks of drug discovery.
0:17:39 When I first looked at Eroom’s law, I never heard a professor at Eroom, it took me a while
0:17:44 to realize, “Oh, Eroom is more spelled backwards, the opposite of Moore’s law,” and the idea
0:17:49 that as we get smarter, as pioneers in the drug development field develop all of these
0:17:56 amazing therapies, that it actually gets harder from an investment’s perspective, that was
0:17:57 really counter-intuitive.
0:18:01 Usually, in my field, as we get smarter, typically things get easier.
0:18:06 The more you know about a company, for example, the less risky is an investment in that company.
0:18:07 That’s right.
0:18:08 But that’s not true with drug development.
0:18:12 Thinking about drug development requires thinking carefully about risk.
0:18:17 The more you know about the underlying biology of disease, the riskier it can get from a
0:18:19 financial perspective.
0:18:24 The reason for that is that as we learn more about these various different mechanisms of
0:18:30 disease and how to deal with them, it can actually increase the risk of a drug becoming
0:18:36 obsolete because some young upstart decides to try this new pathway that ends up working
0:18:37 really well.
0:18:40 A good example is combination therapies.
0:18:46 We now know that one or two drugs that don’t work particularly well can work magnificently
0:18:48 well when put together.
0:18:53 The best example is the HIV cocktail, the five antiretroviral therapies that by themselves
0:18:57 don’t do very much, but when you put them together, they can turn a deadly disease into
0:18:59 a chronic manageable condition.
0:19:02 That’s an interesting example, because if you look at HIV cocktail therapy, there was
0:19:08 a theory for why the combination of the compounds that go into the cocktail would work, to some
0:19:13 block the attachment of the virus to the cell, some don’t allow the cell to replicate.
0:19:18 That had a logical basis for the combination therapy, but that’s not always true.
0:19:24 Sometimes the synergy of therapies happened experimentally or they’re observed experimentally.
0:19:26 They’re not sort of a priori known.
0:19:32 But the fact that we now know that combination therapies can work, that means that from a
0:19:37 scientific as well as an ethical perspective, we’re obligated to search for combinations
0:19:38 to come up with new therapies.
0:19:41 In fact, some people say that we don’t need any more new drugs.
0:19:44 We’ve got all the drugs we need of the 2800 drugs that are approved.
0:19:48 All we need to do is to find the right combination to treat all disease.
0:19:51 So now that we are smarter and we know the combination therapies work, what does that
0:19:52 mean?
0:19:57 It means that the drug development landscape has become that much more complex.
0:20:01 And it also means that for existing pharma companies that have drugs that are producing
0:20:07 good revenues, their revenues can be wiped out by a new combination that just comes out
0:20:12 of the blue because some researchers hit upon that just by accident or by theory.
0:20:15 But the flip side of that is there is latency in the system, right?
0:20:21 In other words, it takes, assuming that one could hypothesize or test a combination, the
0:20:27 period of time from which that original hypothesis is tested until it’s actually impacting patient
0:20:29 care can be on the order of years.
0:20:34 It could be, but that just means that we now understand the risks can be drawn out over
0:20:36 a period of years.
0:20:41 And so that really surprised me in that idea that we actually, as we get smarter, things
0:20:43 could get riskier.
0:20:44 That’s fascinating.
0:20:50 So to set the table a bit in terms of the drug discovery and development paradigm, I think
0:20:56 in the U.S. alone, the biopharmaceutical industry spends about $75 billion per annum investing
0:20:57 in R&D.
0:21:00 And a big chunk of that is the D side of the equation, right?
0:21:05 The statistic that gets thrown around a lot is that it takes on average about 10 to 15
0:21:12 years for a new drug to reach approval, to get FDA approval and reach patients.
0:21:17 And when you account for all of the failures along the way, the total amount of investment
0:21:23 to get to that one drug 10 to 15 years later is something on the order of $2.5 billion.
0:21:28 If you just look at sort of the survival statistics, it’s something on the order of 1 in 10,000
0:21:32 compounds actually makes it all the way through this gauntlet.
0:21:35 Are those statistics real?
0:21:37 Have they been born out in the work that you’ve done?
0:21:45 Well, the short answer is no, mainly because what we’re trying to do is to understand how
0:21:48 these statistics change over time.
0:21:53 So the numbers that you cited are accurate when you look at the entire expanse of history.
0:21:57 But the problem with that is that medicine has changed a lot.
0:22:02 In particular, if we think about the fact that it was only since 2003 that the human
0:22:06 genome was sequenced, we’re not even 20 years out.
0:22:11 And nowadays, we understand that sequencing the human genome is fundamental for lots of
0:22:14 diseases and therapies like cancer, immunotherapies.
0:22:19 So we’re in a very different period today than we were even 10 years ago.
0:22:24 So when you take a look at statistics like it takes $2.6 billion to develop a drug or
0:22:28 the probability of success for developing a drug in oncology is 5%, those numbers are
0:22:34 aggregated over a period of time and over a particular sample of firms.
0:22:37 What we’ve done over the course of the last couple of years is to try to come up with
0:22:43 better numbers, numbers that use a larger data set over a longer period of time, but
0:22:47 looking at it through time so that we can actually see what the trends are.
0:22:49 So here’s a case in point.
0:22:55 If you take data from 2000 to 2015, so that’s 15 years of data, the probability of success
0:23:00 in oncology in cancer drugs is actually less than 5%.
0:23:06 But if you look at the last five years, the probability of success is triple the historical
0:23:07 rate.
0:23:12 So just within the last five years, the probability of success in oncology is about 10% to 15%.
0:23:16 Especially if you look at lead indications and you add biomarkers, so when you stratify
0:23:19 the data in that way, you get a very different picture.
0:23:24 Even if the probability of success is 10% to 15%, that obviously means that 85% to 90%
0:23:25 can still fail.
0:23:30 The main reason that drugs tend to fail is either because the target that you’re trying
0:23:36 to hit with your drug, with your molecule, doesn’t necessarily modulate the disease.
0:23:39 That doesn’t have the intended effect.
0:23:44 Or it does modulate the disease, but it’s also important elsewhere, so you get sort
0:23:48 of on target toxicity for that molecule.
0:23:54 Or you made the wrong molecule and the molecule either hits the target, maybe not hard enough
0:23:58 or hits that target and a lot of other targets, and so then you get off target toxicity.
0:24:03 We’re learning a lot more about biology and what confounded you and as you entered in
0:24:05 the space that actually has increased the risk.
0:24:10 How do you sort of connect the dots between that insight with the last data point you just
0:24:13 gave us that the success rates are actually going up and not down?
0:24:16 How do I connect those two thoughts in my mind?
0:24:20 Because I would imagine if the risk was going up, that means success is going down, but it
0:24:22 sounds like you’re saying both are happening.
0:24:25 That’s right, and that’s really a fascinating conundrum.
0:24:31 First of all, when my coauthors and I calculate success rates, what we’re looking at is path
0:24:37 by path success rates, meaning if you start off with a compound at phase one, what are
0:24:41 the chances that that will eventually become a drug?
0:24:44 That is, you can file successfully for a new drug application.
0:24:51 We’re actually following a drug coupled with an indication through the entire process.
0:24:56 It is true that when you look at it path by path, the success rates are going up.
0:25:01 At the same time, what I was talking about was the financial risks of investing in these
0:25:05 things, and that’s also going up.
0:25:06 Here’s the key.
0:25:12 The key is that how many investors do you know that put money in a phase one trial and leave
0:25:16 it there and follow through until NDA?
0:25:17 Very, very few.
0:25:23 Yeah, so they basically may trade out of that investment at a value inflection point, whether
0:25:27 that’s an IPO or an acquisition, and that’s uncoupled with the approval of the drug.
0:25:28 That’s right.
0:25:34 In fact, those are the good events, but more often than not, they get wiped out because
0:25:40 a compound has these off-target effects, toxicity, or it’s not doing what it’s supposed to do.
0:25:45 As a result, they basically get the plug pulled on the project, and they lose all of their
0:25:46 initial investment.
0:25:52 A good example is a drug Velcade, which is a very successful drug, but it probably wiped
0:25:58 out two or three sets of investors before it actually got to approval.
0:25:59 That’s the problem.
0:26:02 Drug development is actually not a marathon.
0:26:07 Actually, I think it’s more of a relay race of marathons, and the problem is that very
0:26:11 often we drop the baton, and that’s the challenge.
0:26:16 That’s the increase in risk, while at the same time, we are also increasing approval
0:26:17 rates.
0:26:20 It’s ironic, but they’re both true.
0:26:25 The work that you’ve done on understanding risk and benefit and drug discovery, does
0:26:27 it hold true across different disease areas?
0:26:32 Because we’ve spent a lot of time talking about cancer and combination therapies.
0:26:37 You mentioned HIV as well, but how universal a truth is this across disease areas?
0:26:41 Well, there are a couple of things that are common across disease areas, and then there’s
0:26:44 certain aspects that are very different.
0:26:50 The commonality is that drug development takes a very long time.
0:26:55 Because of the length, if the funding gets interrupted midway, it really destroys a tremendous
0:26:57 amount of value.
0:27:01 Where there are differences, though, in the risks across the therapeutic areas is that
0:27:03 certain areas are just harder than others.
0:27:08 For example, we now know that Alzheimer’s is probably the most difficult challenge facing
0:27:11 our nation and frankly the world today.
0:27:17 It’s since 2003, the probability of success for Alzheimer’s drug development is zero.
0:27:22 We have had literally no drugs since 2003 to treat Alzheimer’s.
0:27:26 So we need to think about a different approach for that particular area.
0:27:28 Now let me turn to vaccines.
0:27:33 The probability of success for developing a vaccine is north of 30%.
0:27:36 So it seems like vaccines is a slam dunk.
0:27:40 And yet, why aren’t we developing vaccines for lots of the diseases that we need to
0:27:41 deal with?
0:27:44 Because the economics of vaccines- Financial incentives are not in place.
0:27:45 Exactly, they don’t work.
0:27:50 So that’s another area where despite the differences in the probability of success, the economics
0:27:54 are really preventing us from dealing with that terrible set of afflictions.
0:28:00 Going back to the first point you made earlier in terms of the risk going up as we learn
0:28:01 more about disease.
0:28:08 I think that’s very true in the world where each drug discovery program was its own sort
0:28:10 of bespoke thing.
0:28:16 So you made a target, you made a molecule for a specific target and if that was successful,
0:28:17 fantastic.
0:28:27 But when you had a second program as an innovator, there’s really not a whole lot of knowledge
0:28:33 or value you could take from that first program into the next one.
0:28:36 Value doesn’t really accrue from one program to the next.
0:28:39 And of course, by extension, if there’s a failure, there’s not a whole lot of experience
0:28:44 that you can take from that first program into the second.
0:28:48 But that starts to change when you start to talk about things like gene therapy and cell
0:28:54 therapy in that if we figure out how to deliver genes to specific cell types, in other words
0:29:00 if the vehicles get very sophisticated, that could be used across a number of diseases.
0:29:02 And that was one of the big risk factors for gene therapy.
0:29:07 So if we went back over the last couple of decades as this approach was being developed,
0:29:11 the same as I’m sure going to be very true for things like CAR-T, what follows in terms
0:29:14 of engineered cells, there are going to be built off of some of the innovations, all
0:29:17 of the components that are used in these kinds of therapies.
0:29:19 So value will start to accrue over time.
0:29:25 We’ll have therapeutic platforms that could be very broadly applied and won’t be as bespoke.
0:29:29 Have you given thought as to how that impacts sort of the risk model?
0:29:34 Because in this case, it might be that as we learn more risk actually goes down because
0:29:38 you know if it worked in delivering a gene to one cell, it is more likely to work in
0:29:41 delivering a different gene to a different cell.
0:29:42 So you’re absolutely right.
0:29:46 You give a great example where as we learn more about delivering genes, we’re going to
0:29:49 actually reduce the risk across all gene therapies.
0:29:51 We only have one approved gene therapy, of course, but there are a number of clinical
0:29:56 trials and many of them are using this AAV vector.
0:29:59 What happens if we discover that there are some off-target effects?
0:30:03 That’s going to affect all of these gene therapies at the same time.
0:30:09 So in a way, using these commonly used techniques, that’s a good thing because we’ve learned
0:30:14 that they seem to work, but at the same time they also build certain kinds of common risk
0:30:17 factors across all of these therapies.
0:30:22 So I think we need to be aware of that and we need to assess that kind of risk when we’re
0:30:24 dealing with these types of therapies.
0:30:25 It cuts both ways.
0:30:26 Obviously, we learn more.
0:30:27 I see.
0:30:28 Yes, it’s systemic risk.
0:30:29 But it’s systemic.
0:30:30 Exactly right.
0:30:31 I see.
0:30:32 Yeah.
0:30:34 So you’ve mentioned that one of your early surprises or at least one of your early insights
0:30:39 when you came into the healthcare space is that in many ways it’s not really a rational
0:30:40 market.
0:30:46 There are structurally a lot of impediments to have a sort of truly well-functioning market
0:30:48 in healthcare.
0:30:55 There’s regulation, there’s cross-incentives, there’s general issues around economic feasibility
0:31:00 of being able to run healthcare systems in the first place.
0:31:05 And I read a quote from you where you said, “Markets behave more like biological systems
0:31:07 than physical devices.”
0:31:09 Can you explain what you mean by that?
0:31:14 Well, for one thing, we ought to think about using evolutionary biology as a paradigm for
0:31:18 understanding how markets change over time.
0:31:24 Markets are not immutable objects that are set in a particular path that never changes.
0:31:28 Because we’ve got a variety of different species that are interacting with each other, we actually
0:31:33 understand that now markets can be very different from even one day to the next depending on
0:31:35 who shows up.
0:31:39 And once you understand the kind of paradigm that biologists use to think about various
0:31:43 interactions in their domain, you can see that it applies almost directly.
0:31:44 It’s not even metaphorical.
0:31:50 It is actually literal that markets are biological adaptations to dealing with certain challenges
0:31:52 that we face every day.
0:31:57 And through that lens, all sorts of behaviors that we’re puzzling from a pure traditional
0:32:00 economic perspective become a lot more understandable.
0:32:06 Take for example, how stock markets have these incredible booms and busts.
0:32:10 We think that that’s irrational because, well, either you’re overvaluing stocks during the
0:32:15 boom or you’re dramatically undervaluing them during the bust.
0:32:19 But that doesn’t take into account the fact that at the early stages of innovation, nobody
0:32:22 knows whether or not that’s going to have any value.
0:32:23 Gene therapy is a good example.
0:32:27 The first gene therapy was a big leap of faith.
0:32:31 And in fact, even before that first gene therapy, there was an example in Philadelphia of a
0:32:35 gene therapy trial that went awry and a patient died.
0:32:36 The Jesse Gelsinger.
0:32:37 Exactly.
0:32:43 We need to develop an understanding about these kinds of therapeutics and technologies.
0:32:48 And in order for us to do that, we have to take these kinds of leaps of faith.
0:32:53 And as these leaps of faith turn out to work out, we become eventually over enthusiastic
0:32:56 and the market overshoots.
0:33:01 And once we realize that they’ve overshot, we then start to unwind and correct and the
0:33:03 market sometimes crashes.
0:33:08 So that kind of feast and famine, the boom and bust cycle, that may not be rational
0:33:14 from a strictly physical perspective, but from a biological perspective, it’s actually
0:33:15 expected.
0:33:17 Truly evolutionary landscape.
0:33:18 Red and tooth and claw.
0:33:19 Biology, economics.
0:33:20 Absolutely.
0:33:21 Thank you, professor.
0:33:21 Thank you.
0:33:31 [BLANK_AUDIO]
0:00:10 tax or investment advice or be used to evaluate any investment or security and is not directed
0:00:14 at any investors or potential investors in any A16Z fund.
0:00:19 For more details, please see a16z.com/disclosures.
0:00:21 Hi and welcome to the A16Z podcast.
0:00:22 I’m Hannah.
0:00:26 The advent of new gene and cell therapies are beginning to approach that holy grail of
0:00:31 medicine, that of a possible cure, but they are also more expensive than any medicines
0:00:32 ever sold before.
0:00:37 In this episode, MIT economist Andrew Lowe and A16Z general partner on the biofund Jorge
0:00:43 Conde discuss how exactly we place an economic value on a cure, the questions we still need
0:00:47 to figure out, like who should pay for what and how, and how we need to start thinking
0:00:51 about handling the coming influx of highly priced medicines like this into our healthcare
0:00:52 system.
0:00:57 Jorge and Andrew also talk about the economics and risk of drug discovery and development
0:01:02 overall and how our markets might just function more like biological systems than anything
0:01:03 else.
0:01:06 So what does an economist know about healthcare?
0:01:08 Yeah, that’s a good question.
0:01:12 I probably should start with a disclaimer, kind of like the disclaimer that you find
0:01:14 on drugs.
0:01:20 This podcast could cause confusion, irritability, and drowsiness, but it should all pass within
0:01:21 a few minutes.
0:01:22 That’s the black box warning.
0:01:23 Right.
0:01:24 Exactly.
0:01:25 My background’s really in financial economics.
0:01:29 I got interested in this because friends and family were dealing with cancers of various
0:01:33 different types, and so the more I learned about what they were going through, the more
0:01:37 I got curious about the underlying economics of cancer drug development.
0:01:42 A lot of the work that you have done as an economist has been around thinking about value.
0:01:46 We have one approved gene therapy that sparks therapeutics, gene therapy for a rare inherited
0:01:48 form of blindness.
0:01:51 There are many, many more in the clinic.
0:01:55 There are many engineered cells or cell therapies that are also in the clinic.
0:02:01 These therapies have one great promise that has been an all too short supply in our industry,
0:02:03 which is the promise of a potential cure.
0:02:08 How do we think about the value of a cure in a system, and specifically how do we think
0:02:14 about pain for cures in a system that really isn’t designed for that?
0:02:16 Yeah, that’s a really interesting challenge.
0:02:20 That challenge, by the way, is only going to grow because although we only have one gene
0:02:26 therapy right now on the market, there are over 300 gene therapy clinical trials registered
0:02:28 at clinicaltrials.gov.
0:02:32 My guess is that within the next three years, we’re probably going to see somewhere between
0:02:35 five and 10 gene therapy approvals.
0:02:40 We’re talking about a pretty significant impact on payers and patients.
0:02:45 Just to give people a sense of scale, so look, Sterna, the spark therapeutics, gene therapy
0:02:48 for blindness, what’s the sticker price on that?
0:02:55 The list price right now is $850,000 for both eyes, $425,000 per eye.
0:02:59 We’re talking about some really expensive therapies, and that’s not even the most expensive.
0:03:05 People are talking about therapies for, say, hemophilia A, that if it gets approved, a
0:03:09 price tag could be well north of a million and a half dollars.
0:03:13 When you think about these kind of numbers, it’s really shocking, but the way I think
0:03:20 about it as an economist is, is it justified given the kind of value that it creates for
0:03:21 consumers?
0:03:25 Typically, the way I think about value is very much the way that someone would think
0:03:28 about value from any kind of a commodity purchase.
0:03:35 If you buy a home, the price of the home should be related to some degree to the housing services
0:03:41 that the home will provide you over the course of your lifetime and beyond.
0:03:46 We typically think about a house is not simply what is going to provide you in terms of shelter
0:03:48 for the next month.
0:03:51 That would be an apartment that you would rent one month at a time.
0:03:56 When we buy a house, as opposed to renting an apartment, we’re buying a stream of future
0:03:58 housing services.
0:04:01 I think about that the same way that I think about gene therapies.
0:04:07 When you are cured of a disease, you’re basically getting a lifetime of health, as opposed to
0:04:13 renting health one pill at a time with a chronic manageable condition.
0:04:18 When thinking about that framework, it stands to reason that if you’re going to get value
0:04:23 from a particular cure over a period of your life, you ought to be able to amortize your
0:04:26 payments over that kind of a period.
0:04:32 If you think about the idea behind a mortgage for a home, we can also apply that to thinking
0:04:35 about a mortgage for drugs, drug mortgages.
0:04:37 To mortgage for a cure.
0:04:38 Yeah.
0:04:39 Who pays that mortgage?
0:04:41 Well, that’s a good question.
0:04:47 In my view, insurance companies should pay for it because that’s why we have health insurance.
0:04:51 There are challenges, though, with the current system in getting insurance companies to pay
0:04:56 for it simply because they’re going to have a hard time in terms of balancing the cost
0:05:00 of the insurance with the payouts that they have to make.
0:05:05 Typically the way insurance works, if you join an insurance plan, you pay premiums
0:05:09 every year and in the event that you get ill, they cover your cost.
0:05:13 And the idea is that the amount that you’re paying every year, on average, should roughly
0:05:18 equal the cost that they’re paying out, maybe a little bit less so that they can make a
0:05:21 profit from their business.
0:05:26 That economic calculus becomes very difficult when you’ve got patients that can leave a
0:05:28 plan after two or three years.
0:05:36 So imagine a health insurance company, A, paying for a cure for you and with the expectation
0:05:39 that now that you’re healthy, you’re going to be able to pay premiums for the rest of
0:05:42 your life and that will make up for the cost of the cure.
0:05:48 But suppose that after three years, you leave plan A and move to plan B. Now, plan B has
0:05:52 the benefit of your health and the premiums that you’re going to be paying for the rest
0:05:57 of your life, whereas plan A is stuck having paid for your therapy.
0:06:04 So the solution to this is to allow plan A to amortize, to space out the payment of your
0:06:07 therapy over the course of many years.
0:06:12 And if you leave after three years, the remaining payments, the remaining drug mortgages that
0:06:18 the health plan A was supposed to pay, that moves to plan B. And in a way, that creates
0:06:23 a level playing field for all insurance companies so nobody is disincentivized to provide those
0:06:24 cures to their patients.
0:06:30 So that’s a fascinating concept because one of the things when people think about insurance,
0:06:35 when they think about people moving around from plan to plan, and we have now legislation
0:06:41 around this, is, well, what’s the obligation of the payer to pay for a pre-existing condition?
0:06:45 We’ve always assumed a pre-existing condition as a disease, but what you’re describing
0:06:50 is in some cases in the future, the pre-existing condition might actually be a cure.
0:06:54 And so there’ll be a liability not associated with the treatment of a disease, but a liability
0:06:56 associated with the treatment for a cure.
0:06:57 Exactly.
0:07:02 And so we can actually fix the system with one stroke of a pen by changing in the Affordable
0:07:09 Care Act the sentence that prevents insurers from refusing to serve a planned participant
0:07:14 because of pre-existing conditions to change that to pre-existing financial conditions as
0:07:16 well.
0:07:20 And if we do that, then this whole issue about mobility is not a problem.
0:07:23 But good luck trying to get legislators to do that right now.
0:07:25 I think it’s going to be a challenge.
0:07:30 Do we know that debate has even taken place because it does feel like a logical solution?
0:07:35 As far as I know, the debate has not yet taken place, largely because there’s no need.
0:07:40 But if we institute these payment plans for one-time therapies and we’re able to start
0:07:46 developing cures for some of the larger prevalence diseases, for example, we’re on the verge of
0:07:50 developing a cure for sickle cell anemia, well, there are 100,000 patients in the United
0:07:55 States with sickle cell, and that’s going to hit health plans pretty hard.
0:08:00 A few weeks ago in the UK, a group announced the launch of a clinical trial for applying
0:08:04 gene therapy to deal with age-related macular degeneration.
0:08:09 There are 600,000 AMD patients in the UK, 2 million patients in the US.
0:08:13 If we have a gene therapy for AMD that succeeds, that’s going to be a real problem for our
0:08:15 health care system.
0:08:21 The concept of a drug mortgage or a cure, a mortgage for cures, it’s a fascinating one.
0:08:27 But when we use the analogy of a home, does it start to break down in the sense that I
0:08:34 know how much I’m going to pay for my mortgage because the value of the house is agreed upon
0:08:37 and determined when I buy that house.
0:08:42 And in some ways, the equity I get in my home is the difference between what the value was
0:08:48 when I bought it versus the value when I sell it and I end up paying off the mortgage at
0:08:49 that point in time.
0:08:57 In the case of the cure, how do we get agreement on what the value of the cure is at the moment
0:08:59 of treatment?
0:09:05 Because I would imagine that for a patient, they’re going to put one value on it, society
0:09:08 may put a different value on it, the payer may put a different value on it.
0:09:14 So how do we get to agreement on value so we can from there determine the mortgage payments?
0:09:17 Well, different countries answer that question differently.
0:09:22 For example, in the United Kingdom, their process for determining what the price of
0:09:28 a drug should be or what an acceptable price is is a group of individuals that do pharmacoeconomic
0:09:31 analysis to try to calculate cost effectiveness.
0:09:34 This is an organization called NICE, N-I-C-E.
0:09:39 And this organization does economic studies to try to determine whether or not at a given
0:09:43 price it’s worthwhile for their society.
0:09:48 And not everybody agrees with what NICE comes up with, but the bottom line is that the National
0:09:52 Health Service in the United Kingdom will take the recommendations of NICE and follow
0:09:53 through.
0:09:59 Now, in our country, we would call that a death panel and it’s a politically loaded term,
0:10:02 but effectively that’s unfortunately what it is.
0:10:05 You have to make trade-offs between money and lives saved.
0:10:08 Now, in the United States, we have a very different system.
0:10:10 It’s a multi-payer system.
0:10:15 And at first I thought, oh, that just means it’s a free market, but in fact, the complexity
0:10:21 of the regulation surrounding the pharma industry is anything but a free market.
0:10:26 There are all sorts of incentives in certain cases that cause drug prices to be higher than
0:10:30 usual and in other cases to force them to be lower than usual.
0:10:35 I think that the best way to think about it is to start back and ask the question, what
0:10:37 do patients get out of it?
0:10:39 What is the value to a patient?
0:10:44 We can layer on top of that all sorts of other considerations, but to me, the patient should
0:10:46 be the first consideration.
0:10:48 And there are economists who study that all the time.
0:10:51 They come up with the notion of quality-adjusted life years.
0:10:54 And they ask the question, if you’re going to save a patient’s life, if you’re going
0:11:00 to literally cure the patient of an unfortunate early death, you can actually calculate the
0:11:04 number of quality-adjusted life years you’ve given back to that patient.
0:11:07 And that’s the beginning of where value comes from.
0:11:13 The other argument would be, if you cure a disease, you avoid the cost of treating that
0:11:15 disease over the lifetime.
0:11:21 So one other argument for pricing or giving value to a cure is to say, well, let’s take
0:11:25 the net present value of all of the avoided costs.
0:11:28 And so I’m going to ask you an overly simplified question.
0:11:36 Would a way to price these cures be, let’s take the value of the qualities, the quality-adjusted
0:11:43 life years, and add to that the net present value of the costs avoided?
0:11:48 Well, that’s certainly a consideration that people have used in making an argument one
0:11:50 way or the other for pricing.
0:11:53 But I think that you have to be careful because there are many other factors then that you
0:11:55 might want to also bring in.
0:12:01 For example, if a patient is now going to live a natural life, that patient will pay taxes
0:12:03 for the rest of his or her life.
0:12:06 Should we factor that into the calculus?
0:12:10 That patient, on the other hand, will be consuming other aspects of society.
0:12:15 And for example, if that particular patient ends up becoming unemployed, then they’ll
0:12:17 be drawing unemployment insurance.
0:12:21 So you can go down this rabbit hole of all sorts of costs and benefits that you have
0:12:23 to do the math for.
0:12:28 And unless we’re really set up to do that, it’s very easy to cherry-pick the particular
0:12:33 statistic that makes your case look stronger than the opposing side.
0:12:37 So I think that the approach that we seem to be gravitating towards is to try to come
0:12:42 up with an objective notion of value, and there’s an organization called ICER.
0:12:47 It’s a nonprofit organization that’s job is to focus on developing the cost-effectiveness
0:12:53 studies, maybe using that as a starting point, and then going from there to try to see whether
0:12:58 or not we can come up with a rational pricing model that basically all stakeholders can
0:12:59 live with.
0:13:03 As ICER looked into the spark therapeutic struggle, I believe they have.
0:13:08 I know that they did a study on gene therapies in general, and they were very positively
0:13:13 inclined, particularly given the cost of many of the diseases without the gene therapy.
0:13:16 Hemophilia is a good example, even at a price of, say, one and a half million.
0:13:19 We don’t actually know what the price will be.
0:13:23 One and a half million is actually a bargain relative to what it costs to deal with a current
0:13:30 hemophilia patient, which can be anywhere from 300,000 to 500,000 a year for the rest
0:13:32 of that patient’s natural life.
0:13:37 So at one and a half million, that seems to be a bargain from that kind of calculus.
0:13:40 So it’s a fascinating conundrum.
0:13:46 If we look at the history of the biotechnology industry, we’ve seen a small-scale version
0:13:50 of this challenge with rare genetic diseases.
0:13:55 So to pick one sort of well-known example, when Genzyme comes onto the scene, the founder
0:14:02 and CEO of that company, Henry Tirmier, pioneered the idea of, you know, we can develop and
0:14:08 commercialize therapies for these patients that have these rare diseases.
0:14:12 And there’s a lot of value associated with treating them and alleviating the conditions
0:14:14 to the extent that we can.
0:14:18 And as a result, we can charge, we can price it based on that value.
0:14:23 So those therapies were on the order of $100,000 and above.
0:14:29 Well, oh my gosh, how can we possibly pay $100,000 for a therapy?
0:14:35 And part of the rationale for doing it was, well, these are rare diseases, so collectively
0:14:42 the probability that you would have many patients in any given plan that are going to have this
0:14:49 therapy in general, on scale, these were going to be very much one-off events for most plans,
0:14:54 and therefore they could shoulder that kind of payment.
0:14:58 What’s fascinating about what we’re describing here, and you’ve given a lot of great examples,
0:15:06 like SMA, hemophilia, AMD, these are all diseases that even if they were individually rare, which
0:15:11 many of these are not, collectively this is going to be a common condition.
0:15:16 And so we’re going to see another reckoning, I think, across the industry in having to
0:15:23 think through this problem that Gensheim tackled from the micro scale, at the more macro scale.
0:15:26 And my take on this is, I’m an optimist.
0:15:31 My assumption is that the innovation is going to lead the regulation, the policy, the coverage
0:15:36 here, and that we will get to an answer on how to actually make sure that patients get
0:15:37 these cures.
0:15:42 Well, I’m absolutely optimistic that we’ll come up with an answer, but I don’t believe
0:15:46 we’re going to get to that answer until the system is forced to deal with this issue in
0:15:48 a direct way.
0:15:53 And maybe that ends up being when we develop a gene therapy for a really big indication
0:15:55 like Alzheimer’s.
0:15:58 There are currently 5 million patients that have Alzheimer’s.
0:16:03 If we develop a gene therapy to stop the progression of the disease or to be able to delay the
0:16:08 onset, that’s going to create tremendous pressure on the healthcare system.
0:16:11 And at that point, we’re really going to have to confront this as a nation.
0:16:14 And it’s complicated because you’re dealing with life and death issues.
0:16:19 When we think about pricing things like a car, it’s not a big deal because, well, if you
0:16:22 can’t afford a car, maybe you’ll get a cheaper car.
0:16:25 But if you need a drug, you can’t afford to get a cheaper drug if there’s only one drug
0:16:27 that cures your disease.
0:16:31 So I think that’s one of the reasons why economics, as much as I love the subject and feel that
0:16:34 it’s critical, these are not just economic considerations.
0:16:39 We have to bring in all of the relevant stakeholders to make these decisions, and that’s really
0:16:41 what our policymakers are trying to do.
0:16:46 It’s clear we’re on the cusp of a new age in medicine, and it’s going to be fascinating
0:16:48 to see how all of this plays out.
0:16:51 One of the things that I found most surprising about your work is what the work you’ve done
0:16:57 around trying to develop risk metrics for drug discovery.
0:17:00 Can we talk a little bit about some of the conclusions that you drew from that and how
0:17:01 we can apply them?
0:17:05 That’s a really interesting aspect of the healthcare industry that puzzled me for the
0:17:07 longest time.
0:17:11 When I first started looking at the way drugs were developed, I couldn’t understand why
0:17:17 it was the case that so many amazing breakthroughs have been made over the years while at the
0:17:21 same time, I kept hearing about the fact that there’s this valley of death and there’s not
0:17:25 enough funding at the early stages of drug discovery.
0:17:30 The more I looked into it, the more I realized that these incredible breakthroughs are actually
0:17:34 increasing the economic risks of drug discovery.
0:17:39 When I first looked at Eroom’s law, I never heard a professor at Eroom, it took me a while
0:17:44 to realize, “Oh, Eroom is more spelled backwards, the opposite of Moore’s law,” and the idea
0:17:49 that as we get smarter, as pioneers in the drug development field develop all of these
0:17:56 amazing therapies, that it actually gets harder from an investment’s perspective, that was
0:17:57 really counter-intuitive.
0:18:01 Usually, in my field, as we get smarter, typically things get easier.
0:18:06 The more you know about a company, for example, the less risky is an investment in that company.
0:18:07 That’s right.
0:18:08 But that’s not true with drug development.
0:18:12 Thinking about drug development requires thinking carefully about risk.
0:18:17 The more you know about the underlying biology of disease, the riskier it can get from a
0:18:19 financial perspective.
0:18:24 The reason for that is that as we learn more about these various different mechanisms of
0:18:30 disease and how to deal with them, it can actually increase the risk of a drug becoming
0:18:36 obsolete because some young upstart decides to try this new pathway that ends up working
0:18:37 really well.
0:18:40 A good example is combination therapies.
0:18:46 We now know that one or two drugs that don’t work particularly well can work magnificently
0:18:48 well when put together.
0:18:53 The best example is the HIV cocktail, the five antiretroviral therapies that by themselves
0:18:57 don’t do very much, but when you put them together, they can turn a deadly disease into
0:18:59 a chronic manageable condition.
0:19:02 That’s an interesting example, because if you look at HIV cocktail therapy, there was
0:19:08 a theory for why the combination of the compounds that go into the cocktail would work, to some
0:19:13 block the attachment of the virus to the cell, some don’t allow the cell to replicate.
0:19:18 That had a logical basis for the combination therapy, but that’s not always true.
0:19:24 Sometimes the synergy of therapies happened experimentally or they’re observed experimentally.
0:19:26 They’re not sort of a priori known.
0:19:32 But the fact that we now know that combination therapies can work, that means that from a
0:19:37 scientific as well as an ethical perspective, we’re obligated to search for combinations
0:19:38 to come up with new therapies.
0:19:41 In fact, some people say that we don’t need any more new drugs.
0:19:44 We’ve got all the drugs we need of the 2800 drugs that are approved.
0:19:48 All we need to do is to find the right combination to treat all disease.
0:19:51 So now that we are smarter and we know the combination therapies work, what does that
0:19:52 mean?
0:19:57 It means that the drug development landscape has become that much more complex.
0:20:01 And it also means that for existing pharma companies that have drugs that are producing
0:20:07 good revenues, their revenues can be wiped out by a new combination that just comes out
0:20:12 of the blue because some researchers hit upon that just by accident or by theory.
0:20:15 But the flip side of that is there is latency in the system, right?
0:20:21 In other words, it takes, assuming that one could hypothesize or test a combination, the
0:20:27 period of time from which that original hypothesis is tested until it’s actually impacting patient
0:20:29 care can be on the order of years.
0:20:34 It could be, but that just means that we now understand the risks can be drawn out over
0:20:36 a period of years.
0:20:41 And so that really surprised me in that idea that we actually, as we get smarter, things
0:20:43 could get riskier.
0:20:44 That’s fascinating.
0:20:50 So to set the table a bit in terms of the drug discovery and development paradigm, I think
0:20:56 in the U.S. alone, the biopharmaceutical industry spends about $75 billion per annum investing
0:20:57 in R&D.
0:21:00 And a big chunk of that is the D side of the equation, right?
0:21:05 The statistic that gets thrown around a lot is that it takes on average about 10 to 15
0:21:12 years for a new drug to reach approval, to get FDA approval and reach patients.
0:21:17 And when you account for all of the failures along the way, the total amount of investment
0:21:23 to get to that one drug 10 to 15 years later is something on the order of $2.5 billion.
0:21:28 If you just look at sort of the survival statistics, it’s something on the order of 1 in 10,000
0:21:32 compounds actually makes it all the way through this gauntlet.
0:21:35 Are those statistics real?
0:21:37 Have they been born out in the work that you’ve done?
0:21:45 Well, the short answer is no, mainly because what we’re trying to do is to understand how
0:21:48 these statistics change over time.
0:21:53 So the numbers that you cited are accurate when you look at the entire expanse of history.
0:21:57 But the problem with that is that medicine has changed a lot.
0:22:02 In particular, if we think about the fact that it was only since 2003 that the human
0:22:06 genome was sequenced, we’re not even 20 years out.
0:22:11 And nowadays, we understand that sequencing the human genome is fundamental for lots of
0:22:14 diseases and therapies like cancer, immunotherapies.
0:22:19 So we’re in a very different period today than we were even 10 years ago.
0:22:24 So when you take a look at statistics like it takes $2.6 billion to develop a drug or
0:22:28 the probability of success for developing a drug in oncology is 5%, those numbers are
0:22:34 aggregated over a period of time and over a particular sample of firms.
0:22:37 What we’ve done over the course of the last couple of years is to try to come up with
0:22:43 better numbers, numbers that use a larger data set over a longer period of time, but
0:22:47 looking at it through time so that we can actually see what the trends are.
0:22:49 So here’s a case in point.
0:22:55 If you take data from 2000 to 2015, so that’s 15 years of data, the probability of success
0:23:00 in oncology in cancer drugs is actually less than 5%.
0:23:06 But if you look at the last five years, the probability of success is triple the historical
0:23:07 rate.
0:23:12 So just within the last five years, the probability of success in oncology is about 10% to 15%.
0:23:16 Especially if you look at lead indications and you add biomarkers, so when you stratify
0:23:19 the data in that way, you get a very different picture.
0:23:24 Even if the probability of success is 10% to 15%, that obviously means that 85% to 90%
0:23:25 can still fail.
0:23:30 The main reason that drugs tend to fail is either because the target that you’re trying
0:23:36 to hit with your drug, with your molecule, doesn’t necessarily modulate the disease.
0:23:39 That doesn’t have the intended effect.
0:23:44 Or it does modulate the disease, but it’s also important elsewhere, so you get sort
0:23:48 of on target toxicity for that molecule.
0:23:54 Or you made the wrong molecule and the molecule either hits the target, maybe not hard enough
0:23:58 or hits that target and a lot of other targets, and so then you get off target toxicity.
0:24:03 We’re learning a lot more about biology and what confounded you and as you entered in
0:24:05 the space that actually has increased the risk.
0:24:10 How do you sort of connect the dots between that insight with the last data point you just
0:24:13 gave us that the success rates are actually going up and not down?
0:24:16 How do I connect those two thoughts in my mind?
0:24:20 Because I would imagine if the risk was going up, that means success is going down, but it
0:24:22 sounds like you’re saying both are happening.
0:24:25 That’s right, and that’s really a fascinating conundrum.
0:24:31 First of all, when my coauthors and I calculate success rates, what we’re looking at is path
0:24:37 by path success rates, meaning if you start off with a compound at phase one, what are
0:24:41 the chances that that will eventually become a drug?
0:24:44 That is, you can file successfully for a new drug application.
0:24:51 We’re actually following a drug coupled with an indication through the entire process.
0:24:56 It is true that when you look at it path by path, the success rates are going up.
0:25:01 At the same time, what I was talking about was the financial risks of investing in these
0:25:05 things, and that’s also going up.
0:25:06 Here’s the key.
0:25:12 The key is that how many investors do you know that put money in a phase one trial and leave
0:25:16 it there and follow through until NDA?
0:25:17 Very, very few.
0:25:23 Yeah, so they basically may trade out of that investment at a value inflection point, whether
0:25:27 that’s an IPO or an acquisition, and that’s uncoupled with the approval of the drug.
0:25:28 That’s right.
0:25:34 In fact, those are the good events, but more often than not, they get wiped out because
0:25:40 a compound has these off-target effects, toxicity, or it’s not doing what it’s supposed to do.
0:25:45 As a result, they basically get the plug pulled on the project, and they lose all of their
0:25:46 initial investment.
0:25:52 A good example is a drug Velcade, which is a very successful drug, but it probably wiped
0:25:58 out two or three sets of investors before it actually got to approval.
0:25:59 That’s the problem.
0:26:02 Drug development is actually not a marathon.
0:26:07 Actually, I think it’s more of a relay race of marathons, and the problem is that very
0:26:11 often we drop the baton, and that’s the challenge.
0:26:16 That’s the increase in risk, while at the same time, we are also increasing approval
0:26:17 rates.
0:26:20 It’s ironic, but they’re both true.
0:26:25 The work that you’ve done on understanding risk and benefit and drug discovery, does
0:26:27 it hold true across different disease areas?
0:26:32 Because we’ve spent a lot of time talking about cancer and combination therapies.
0:26:37 You mentioned HIV as well, but how universal a truth is this across disease areas?
0:26:41 Well, there are a couple of things that are common across disease areas, and then there’s
0:26:44 certain aspects that are very different.
0:26:50 The commonality is that drug development takes a very long time.
0:26:55 Because of the length, if the funding gets interrupted midway, it really destroys a tremendous
0:26:57 amount of value.
0:27:01 Where there are differences, though, in the risks across the therapeutic areas is that
0:27:03 certain areas are just harder than others.
0:27:08 For example, we now know that Alzheimer’s is probably the most difficult challenge facing
0:27:11 our nation and frankly the world today.
0:27:17 It’s since 2003, the probability of success for Alzheimer’s drug development is zero.
0:27:22 We have had literally no drugs since 2003 to treat Alzheimer’s.
0:27:26 So we need to think about a different approach for that particular area.
0:27:28 Now let me turn to vaccines.
0:27:33 The probability of success for developing a vaccine is north of 30%.
0:27:36 So it seems like vaccines is a slam dunk.
0:27:40 And yet, why aren’t we developing vaccines for lots of the diseases that we need to
0:27:41 deal with?
0:27:44 Because the economics of vaccines- Financial incentives are not in place.
0:27:45 Exactly, they don’t work.
0:27:50 So that’s another area where despite the differences in the probability of success, the economics
0:27:54 are really preventing us from dealing with that terrible set of afflictions.
0:28:00 Going back to the first point you made earlier in terms of the risk going up as we learn
0:28:01 more about disease.
0:28:08 I think that’s very true in the world where each drug discovery program was its own sort
0:28:10 of bespoke thing.
0:28:16 So you made a target, you made a molecule for a specific target and if that was successful,
0:28:17 fantastic.
0:28:27 But when you had a second program as an innovator, there’s really not a whole lot of knowledge
0:28:33 or value you could take from that first program into the next one.
0:28:36 Value doesn’t really accrue from one program to the next.
0:28:39 And of course, by extension, if there’s a failure, there’s not a whole lot of experience
0:28:44 that you can take from that first program into the second.
0:28:48 But that starts to change when you start to talk about things like gene therapy and cell
0:28:54 therapy in that if we figure out how to deliver genes to specific cell types, in other words
0:29:00 if the vehicles get very sophisticated, that could be used across a number of diseases.
0:29:02 And that was one of the big risk factors for gene therapy.
0:29:07 So if we went back over the last couple of decades as this approach was being developed,
0:29:11 the same as I’m sure going to be very true for things like CAR-T, what follows in terms
0:29:14 of engineered cells, there are going to be built off of some of the innovations, all
0:29:17 of the components that are used in these kinds of therapies.
0:29:19 So value will start to accrue over time.
0:29:25 We’ll have therapeutic platforms that could be very broadly applied and won’t be as bespoke.
0:29:29 Have you given thought as to how that impacts sort of the risk model?
0:29:34 Because in this case, it might be that as we learn more risk actually goes down because
0:29:38 you know if it worked in delivering a gene to one cell, it is more likely to work in
0:29:41 delivering a different gene to a different cell.
0:29:42 So you’re absolutely right.
0:29:46 You give a great example where as we learn more about delivering genes, we’re going to
0:29:49 actually reduce the risk across all gene therapies.
0:29:51 We only have one approved gene therapy, of course, but there are a number of clinical
0:29:56 trials and many of them are using this AAV vector.
0:29:59 What happens if we discover that there are some off-target effects?
0:30:03 That’s going to affect all of these gene therapies at the same time.
0:30:09 So in a way, using these commonly used techniques, that’s a good thing because we’ve learned
0:30:14 that they seem to work, but at the same time they also build certain kinds of common risk
0:30:17 factors across all of these therapies.
0:30:22 So I think we need to be aware of that and we need to assess that kind of risk when we’re
0:30:24 dealing with these types of therapies.
0:30:25 It cuts both ways.
0:30:26 Obviously, we learn more.
0:30:27 I see.
0:30:28 Yes, it’s systemic risk.
0:30:29 But it’s systemic.
0:30:30 Exactly right.
0:30:31 I see.
0:30:32 Yeah.
0:30:34 So you’ve mentioned that one of your early surprises or at least one of your early insights
0:30:39 when you came into the healthcare space is that in many ways it’s not really a rational
0:30:40 market.
0:30:46 There are structurally a lot of impediments to have a sort of truly well-functioning market
0:30:48 in healthcare.
0:30:55 There’s regulation, there’s cross-incentives, there’s general issues around economic feasibility
0:31:00 of being able to run healthcare systems in the first place.
0:31:05 And I read a quote from you where you said, “Markets behave more like biological systems
0:31:07 than physical devices.”
0:31:09 Can you explain what you mean by that?
0:31:14 Well, for one thing, we ought to think about using evolutionary biology as a paradigm for
0:31:18 understanding how markets change over time.
0:31:24 Markets are not immutable objects that are set in a particular path that never changes.
0:31:28 Because we’ve got a variety of different species that are interacting with each other, we actually
0:31:33 understand that now markets can be very different from even one day to the next depending on
0:31:35 who shows up.
0:31:39 And once you understand the kind of paradigm that biologists use to think about various
0:31:43 interactions in their domain, you can see that it applies almost directly.
0:31:44 It’s not even metaphorical.
0:31:50 It is actually literal that markets are biological adaptations to dealing with certain challenges
0:31:52 that we face every day.
0:31:57 And through that lens, all sorts of behaviors that we’re puzzling from a pure traditional
0:32:00 economic perspective become a lot more understandable.
0:32:06 Take for example, how stock markets have these incredible booms and busts.
0:32:10 We think that that’s irrational because, well, either you’re overvaluing stocks during the
0:32:15 boom or you’re dramatically undervaluing them during the bust.
0:32:19 But that doesn’t take into account the fact that at the early stages of innovation, nobody
0:32:22 knows whether or not that’s going to have any value.
0:32:23 Gene therapy is a good example.
0:32:27 The first gene therapy was a big leap of faith.
0:32:31 And in fact, even before that first gene therapy, there was an example in Philadelphia of a
0:32:35 gene therapy trial that went awry and a patient died.
0:32:36 The Jesse Gelsinger.
0:32:37 Exactly.
0:32:43 We need to develop an understanding about these kinds of therapeutics and technologies.
0:32:48 And in order for us to do that, we have to take these kinds of leaps of faith.
0:32:53 And as these leaps of faith turn out to work out, we become eventually over enthusiastic
0:32:56 and the market overshoots.
0:33:01 And once we realize that they’ve overshot, we then start to unwind and correct and the
0:33:03 market sometimes crashes.
0:33:08 So that kind of feast and famine, the boom and bust cycle, that may not be rational
0:33:14 from a strictly physical perspective, but from a biological perspective, it’s actually
0:33:15 expected.
0:33:17 Truly evolutionary landscape.
0:33:18 Red and tooth and claw.
0:33:19 Biology, economics.
0:33:20 Absolutely.
0:33:21 Thank you, professor.
0:33:21 Thank you.
0:33:31 [BLANK_AUDIO]
with Andrew Lo (@AndrewWLo) and Jorge Conde (@JorgeCondeBio)
The advent of new gene and cell therapies are beginning to approach that holy grail of medicine—that of a possible cure. But they are also more expensive than any medicines ever sold before. In this episode, MIT economist Andrew Lo and a16z General Partner on the Bio Fund Jorge Conde discuss how exactly we place an economic value on a cure; the questions we still need to figure out, like who should pay for what and how; and how we need to start thinking about handling the coming influx of highly priced medicines like these into our healthcare system.
If we think about these payments as a kind of ‘mortgage for a cure,’ what happens when your gene therapy mortgage defaults? How would payment plans like these move between insurance plans? Lo and Conde also discuss the broader context in our healthcare system, the economics and risk of drug discovery and development overall – and finally, how our markets might just function more like biological systems than anything else.