Life After Eroom’s Law(refoundable.com)
refoundable.com
Life After Eroom’s Law
https://refoundable.com/research/life-after-erooms-law-interview-with-jack-scannell.html
45 comments
Does anyone else see a blue background on this page?
No idea what's causing this for you. Can you say a bit more about your setup?
Just wanted to say that I enjoyed reading the article. I liked the overall quality and that important bits have citations. I hadn't heard of your website before but I'll be sure to read more of your work in the future.
Thank you. It takes a fair bit of time to put something like this together, so I appreciate your kind words.
[deleted]
"Eroom's law is the observation that drug discovery is becoming slower and more expensive over time"...
A more accurate definition pulled from the introduction:
> Eroom’s law is the observation that the cost of developing a new drug roughly doubled every nine years from 1950 through 2010. All in all, it shows a roughly 80-fold decline in the productivity of drug R&D.
> Eroom’s law is the observation that the cost of developing a new drug roughly doubled every nine years from 1950 through 2010. All in all, it shows a roughly 80-fold decline in the productivity of drug R&D.
[deleted]
Author here.
I interviewed Jack Scannell on the hypothesis that the man who wrote the now famous paper on Eroom's law would likely have many more insights to offer that didn't necessarily make it into print.
One of the oddest things about biotech and pharma is that cures trail improvement in technology by at least a couple of decades. That's terrible news if you're a patient/consumer though maybe great news if you're a lab technician.
I interviewed Jack Scannell on the hypothesis that the man who wrote the now famous paper on Eroom's law would likely have many more insights to offer that didn't necessarily make it into print.
One of the oddest things about biotech and pharma is that cures trail improvement in technology by at least a couple of decades. That's terrible news if you're a patient/consumer though maybe great news if you're a lab technician.
Hey refoundable - great interview! Love Jack's characterization of Alzheimers as chemical roulette.
Just wanted to let you know there's a typo in your first figure. "Thalyidamide" should be "thalidomide" [0]. It's also been sold as Thalomid.
[0] https://en.wikipedia.org/wiki/Thalidomide
Just wanted to let you know there's a typo in your first figure. "Thalyidamide" should be "thalidomide" [0]. It's also been sold as Thalomid.
[0] https://en.wikipedia.org/wiki/Thalidomide
Thanks for the great catch. Fixed.
The article mentions poor incentive structure around development of better disease models as an obstacle for some of the drug categories, is that an area where either the federal govt or the industry as a whole can step in and develop a collective solution?
That's certainly what's implied by Scannell's answer to Question 8, but such a solution comes with its own set of limitations related to design by committee.
Small startup teams are still the most effective way to solve hard problems in our society, and so I suspect that any definitive solution would likely be built and sold to the industry by such a team.
Small startup teams are still the most effective way to solve hard problems in our society, and so I suspect that any definitive solution would likely be built and sold to the industry by such a team.
The way your website handles font-size and zoom is really weird.
I feel like there should be a named law of hacker news for this. There's sometimes more discussion about how the page doesn't work without javascript, font colors are bad, page is slow, whatever, than about the content of the article. Although in this case most people are talking about the article.
Agreed though, there's some weird responsive CSS going on here that makes it impossible to resize fonts on a desktop.
Agreed though, there's some weird responsive CSS going on here that makes it impossible to resize fonts on a desktop.
I echo both observations and urge you to name that law.
Will look under the hood at the CSS.
Will look under the hood at the CSS.
Thanks for looking. I use the "zoom" feature of Chrome to make fonts more readable. The CSS on this page does not permit resizing of the type - a major faux pas for accessibility.
This is a fascinating article, and is even better with the conversation here added. Thanks for your efforts.
This is a fascinating article, and is even better with the conversation here added. Thanks for your efforts.
Elephant in the room: what was it you thought would stop it? If I have a great new idea for a high-performance computer chip, which would also cause them to be twice the cost, it won't get funded, because people will just settle for a higher performance chip in a few years at a lower cost.
In the case of medicine, as society gets wealthier, it is willing/able to spend more on medicine. The only time you say "no thank you" to an improvement in medicine in exchange for higher costs, is when you're out of money.
There is no such thing as "good enough" medicine, since we all still die eventually. Therefore, we pay more for medicine, including pharmaceutical R&D, because we can, and we can never actually get as much as we want.
In the case of medicine, as society gets wealthier, it is willing/able to spend more on medicine. The only time you say "no thank you" to an improvement in medicine in exchange for higher costs, is when you're out of money.
There is no such thing as "good enough" medicine, since we all still die eventually. Therefore, we pay more for medicine, including pharmaceutical R&D, because we can, and we can never actually get as much as we want.
Most people tend to focus on the cost side of Eroom's law, but I find its most disturbing implication to be the other side: That between 1950 and 2010, we got ~100x worse at finding drugs that actually work.
That suggests that there's something deeply broken about the science, and not just about the regulation or the cost structure.
That suggests that there's something deeply broken about the science, and not just about the regulation or the cost structure.
Could you expand a bit on that?
I'm having a hard time making the connection between declining efficiency and there necessarily being something broken about the science.
To expand just a bit: Scannell seems to be primarily focused on the disease model as the root issue here. This is curious to me. My understanding is that drugs fail primarily in two ways:
1) It's ineffective - fails to treat the disease. Which is partially covered by model validity, but also impacted by pharmacokinetics and distribution. Essentially your molecule can work, it just can't get where it needs to go in a high enough concentration to make a difference.
2) It's unsafe - your molecule is toxic either acutely or long term.
The data[0] I'm aware of indicates that these issues occur with roughly equal frequency. (My assumptions being: failure in Phase 1 trials are an issue of safety, failure in phase 2 can be caused by safety or effectiveness). Which for me calls into question the focus solely on good disease models.
[0] https://www.nature.com/articles/nrd3078
I'm having a hard time making the connection between declining efficiency and there necessarily being something broken about the science.
To expand just a bit: Scannell seems to be primarily focused on the disease model as the root issue here. This is curious to me. My understanding is that drugs fail primarily in two ways:
1) It's ineffective - fails to treat the disease. Which is partially covered by model validity, but also impacted by pharmacokinetics and distribution. Essentially your molecule can work, it just can't get where it needs to go in a high enough concentration to make a difference.
2) It's unsafe - your molecule is toxic either acutely or long term.
The data[0] I'm aware of indicates that these issues occur with roughly equal frequency. (My assumptions being: failure in Phase 1 trials are an issue of safety, failure in phase 2 can be caused by safety or effectiveness). Which for me calls into question the focus solely on good disease models.
[0] https://www.nature.com/articles/nrd3078
Two things, one is that toxicity is overwhelmingly the failure mode and the other is that 1 (effectiveness) and 2 (toxicity) are actually closely related not independent.
Failures are overwhelmingly in Phase II, https://en.wikipedia.org/wiki/Phases_of_clinical_research And we know why they happen, we don't need to guess. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6609997/#:~:tex... Mostly it's that we discover previously unknown toxic effects. Between Phase I failures due to toxicity, Phase II failures which are half due to toxicity, and Phase III failures, and Phase III trails failing 17% of the time due to toxicity https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6092479/ it is overwhelmingly the biggest problem. Only half of Phase III trials fail due to a lack of efficacy.
At the same time effectiveness and safety are not independent variables. Safety often a matter of dose. The dose required to achieve a clinical effect may turn out to be unsafe.
The issues goes back largely to model validity. That our models don't allow us to accurately establish safety margins and dosages, in addition to often being misleading about the wider effects that drugs will have.
Failures are overwhelmingly in Phase II, https://en.wikipedia.org/wiki/Phases_of_clinical_research And we know why they happen, we don't need to guess. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6609997/#:~:tex... Mostly it's that we discover previously unknown toxic effects. Between Phase I failures due to toxicity, Phase II failures which are half due to toxicity, and Phase III failures, and Phase III trails failing 17% of the time due to toxicity https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6092479/ it is overwhelmingly the biggest problem. Only half of Phase III trials fail due to a lack of efficacy.
At the same time effectiveness and safety are not independent variables. Safety often a matter of dose. The dose required to achieve a clinical effect may turn out to be unsafe.
The issues goes back largely to model validity. That our models don't allow us to accurately establish safety margins and dosages, in addition to often being misleading about the wider effects that drugs will have.
I think we're saying the same thing. The disease model itself is necessarily limited. Sure you have a molecule that hits the target, great. But the struggle comes from being able to get that molecule to target in vivo without causing toxic effects.
Maybe we're just missing on how we're using words. I don't see how having a better disease model necessarily gets you to a better place on this. Sure, you can get better SAR and can decrease the dose. But as you point out, dosage is not just a function of SAR.
Having better tox models seems like the highest value, albeit very difficult, route here. Which to me is a separate, more general, problem than a specific disease model.
Maybe we're just missing on how we're using words. I don't see how having a better disease model necessarily gets you to a better place on this. Sure, you can get better SAR and can decrease the dose. But as you point out, dosage is not just a function of SAR.
Having better tox models seems like the highest value, albeit very difficult, route here. Which to me is a separate, more general, problem than a specific disease model.
> Having better tox models seems like the highest value, albeit very difficult, route here. Which to me is a separate, more general, problem than a specific disease model.
Agreed. Better toxicity testing would go a long way towards solving some aspects of the search problem.
Agreed. Better toxicity testing would go a long way towards solving some aspects of the search problem.
The FDA does not allow Pharma companies to create safe and slightly less effective drugs than the best-in-class, even if such drugs were 10-100x cheaper and would thus alleviate most people's complaints about outrageous drug prices.
Scannell's observations are focused on the problem of finding drugs that are more effective than the current best-in-class, the so-called Better than the Beatles problem.
This turns out to be a really hard search problem, given for instance that animal models make unreliable and poor substitutes for humans beings, or that you're generally not allowed to base drug approval on small but highly representative patient populations (with some exceptions). There are even more technical problems I won't get into here (see Question 9 in the interview).
In short, I see no reason why we couldn't be getting slightly less effective but way cheaper drugs today if the FDA was willing to slightly relax its use of the precautionary principle. But in order to keep pushing the frontier of drug efficacy, we'll need new technical breakthroughs to help us solve the search problem.
Scannell's observations are focused on the problem of finding drugs that are more effective than the current best-in-class, the so-called Better than the Beatles problem.
This turns out to be a really hard search problem, given for instance that animal models make unreliable and poor substitutes for humans beings, or that you're generally not allowed to base drug approval on small but highly representative patient populations (with some exceptions). There are even more technical problems I won't get into here (see Question 9 in the interview).
In short, I see no reason why we couldn't be getting slightly less effective but way cheaper drugs today if the FDA was willing to slightly relax its use of the precautionary principle. But in order to keep pushing the frontier of drug efficacy, we'll need new technical breakthroughs to help us solve the search problem.
The costs of drugs is usually profit seeking. Many drugs can already be produced and delivered cheaply if the economic system would allow it. Trying to patch the drug instead of the economics just gives people worse drugs.
I see no reason why we can't do both. We obviously need better drugs for the diseases we haven't yet cured, and we need cheaper drugs for the diseases we've already cured. The two are related yet distinct problems.
Incidentally, Scannell wrote one of the best teardowns of how drug pricing actually works: https://www.forbes.com/sites/matthewherper/2015/10/13/four-r...
Incidentally, Scannell wrote one of the best teardowns of how drug pricing actually works: https://www.forbes.com/sites/matthewherper/2015/10/13/four-r...
First, really appreciate the engagement here. This is a hugely important problem and this interview and your presentation of it is a great contribution.
I think one of the things that gets lost when we talk about Eroom's law is that the original data points were established before congress passed the Kefauver-Harris amendments in 1962 which set standards for clinical trials, iNDA process, and basically required that drugs show efficacy before they could be marketed.
An important part of those amendments is they made the drug companies go back and review the 4,000 drugs already on the market and provide evidence on their efficacy. It took FDA a long time to work through that backlog, but when they did:
"In January 1968, the Drug Efficacy Study panels finally reported their conclusions to the FDA. They had reviewed over 16,500 therapeutic claims for 4,000 pre-1962 drugs. Only 434, about 12 percent of those examined, delivered on all their promised claims. Seven hundred and sixty-nine were marked as 'ineffective'" [0].
I bring that up to say two things:
1) Our baseline in examining Eroom's law is a bit skewed because standards have been going up since the graph begins.
2) We should be careful in how we change those standards. Many of them were bought with patients lives.
I need to go now, but I do want to address your comment on pricing later.
Thank you again. Really great work.
[0] Pharma - Gerald Posner - Pg 224. https://www.amazon.com/Pharma-Greed-Lies-Poisoning-America-e...
I think one of the things that gets lost when we talk about Eroom's law is that the original data points were established before congress passed the Kefauver-Harris amendments in 1962 which set standards for clinical trials, iNDA process, and basically required that drugs show efficacy before they could be marketed.
An important part of those amendments is they made the drug companies go back and review the 4,000 drugs already on the market and provide evidence on their efficacy. It took FDA a long time to work through that backlog, but when they did:
"In January 1968, the Drug Efficacy Study panels finally reported their conclusions to the FDA. They had reviewed over 16,500 therapeutic claims for 4,000 pre-1962 drugs. Only 434, about 12 percent of those examined, delivered on all their promised claims. Seven hundred and sixty-nine were marked as 'ineffective'" [0].
I bring that up to say two things:
1) Our baseline in examining Eroom's law is a bit skewed because standards have been going up since the graph begins.
2) We should be careful in how we change those standards. Many of them were bought with patients lives.
I need to go now, but I do want to address your comment on pricing later.
Thank you again. Really great work.
[0] Pharma - Gerald Posner - Pg 224. https://www.amazon.com/Pharma-Greed-Lies-Poisoning-America-e...
No disagreement on the skewed stats. And I won't dispute the need for a minimum standard of efficacy.
However, if anything, the problem that we've had over the last 60 years is setting the standard of efficacy too high. This caused the Better than the Beatles problem, and placed Pharma companies in an impossible situation where they have to run faster and faster just to stay in the same place.
Moving forward, if we're not going to lower the efficacy standard, then the questions that Scannell raises about model validity and the search problem writ large become even more pressing and important.
On the other hand, if we're not going to solve the search problem, then the pragmatic solution would be to lower the efficacy bar somewhat so Pharma companies can at least create slightly less effective but way cheaper drugs.
In theory, we should be able to do both. In practice, we're likely to get one way before the other. The current path we're on of fewer drugs and higher prices is simply unsustainable.
However, if anything, the problem that we've had over the last 60 years is setting the standard of efficacy too high. This caused the Better than the Beatles problem, and placed Pharma companies in an impossible situation where they have to run faster and faster just to stay in the same place.
Moving forward, if we're not going to lower the efficacy standard, then the questions that Scannell raises about model validity and the search problem writ large become even more pressing and important.
On the other hand, if we're not going to solve the search problem, then the pragmatic solution would be to lower the efficacy bar somewhat so Pharma companies can at least create slightly less effective but way cheaper drugs.
In theory, we should be able to do both. In practice, we're likely to get one way before the other. The current path we're on of fewer drugs and higher prices is simply unsustainable.
This is helpful. Let me ask a question.
Why do you think that cheaper development costs will ultimately lead to lower drug prices?
I think we would both agree that drugs are not priced based on cost. Even if R&D were 10X cheaper - all else equal - prices are not coming down.
So what is the mechanism you think will lead to lower prices given lower costs of R&D?
Another question: Maybe as a patient (or a doctor), I like the better than the beetles problem. I don't need a thousand drugs on the market to treat every indication. Especially ones that have not passed a high bar for efficacy. I need a few drugs, preferably outside of patent protection, with enough diversity in structure to avoid specific toxicity effects.
If lowering the efficacy standards doesn't get me treatment for new indications, why would I want it? It's not clear to me that solving better than the beetles gets me to new indications. Presumably if I'm in a regime where BTTB applies, I've got a drug for that.
Why do you think that cheaper development costs will ultimately lead to lower drug prices?
I think we would both agree that drugs are not priced based on cost. Even if R&D were 10X cheaper - all else equal - prices are not coming down.
So what is the mechanism you think will lead to lower prices given lower costs of R&D?
Another question: Maybe as a patient (or a doctor), I like the better than the beetles problem. I don't need a thousand drugs on the market to treat every indication. Especially ones that have not passed a high bar for efficacy. I need a few drugs, preferably outside of patent protection, with enough diversity in structure to avoid specific toxicity effects.
If lowering the efficacy standards doesn't get me treatment for new indications, why would I want it? It's not clear to me that solving better than the beetles gets me to new indications. Presumably if I'm in a regime where BTTB applies, I've got a drug for that.
> Why do you think that cheaper development costs will ultimately lead to lower drug prices?
If the cost of drug development were to drop by a factor of 100-1000, say from the $3B it costs today to the $3-30M range, that would likely unleash a massive wave of decentralization in the industry that would make it very hard to justify outrageously high drug prices for long.
However, short of a large drop of that magnitude, I agree that we'll probably continue to see Pharma companies charging as much as they can get away with and the political climate allows.
> I don't need a thousand drugs on the market to treat every indication. [...] If lowering the efficacy standards doesn't get me treatment for new indications, why would I want it?
It's true that the Better than the Beatles problem rewards consumers with cheaper drugs, but on the flipside, it robs them of the benefit of newer drugs.
As a result, all the technical capabilities that the Pharma industry gained into developing drugs that are less toxic or better targeted do not get translated into consumer benefit when the BTTB problem is in the way.
Moreover, once an area of drug development stops seeing new commercial success in decades, hysteresis sets in and can drive Pharma companies out of such areas, which has largely been the case with anti-infectives since the 1980s. (See Question 11 in the interview)
If the cost of drug development were to drop by a factor of 100-1000, say from the $3B it costs today to the $3-30M range, that would likely unleash a massive wave of decentralization in the industry that would make it very hard to justify outrageously high drug prices for long.
However, short of a large drop of that magnitude, I agree that we'll probably continue to see Pharma companies charging as much as they can get away with and the political climate allows.
> I don't need a thousand drugs on the market to treat every indication. [...] If lowering the efficacy standards doesn't get me treatment for new indications, why would I want it?
It's true that the Better than the Beatles problem rewards consumers with cheaper drugs, but on the flipside, it robs them of the benefit of newer drugs.
As a result, all the technical capabilities that the Pharma industry gained into developing drugs that are less toxic or better targeted do not get translated into consumer benefit when the BTTB problem is in the way.
Moreover, once an area of drug development stops seeing new commercial success in decades, hysteresis sets in and can drive Pharma companies out of such areas, which has largely been the case with anti-infectives since the 1980s. (See Question 11 in the interview)
[deleted]
Again want to say I really appreciate how thoughtful you are about this.
I just want to offer a few of my thoughts because you've been so generous with yours. So you know, I work in the industry. More on the biz side than the scientific. And I am intensely interested in this issue.
100-1000 times cheaper I don't think is achievable in the medium term, if ever. As long as we're running clinical trials I think costs will stay constrained to about a 5x decrease. A 100-1000x reduction, I think, implies a world where we can largely simulate the impact of drugs without resort to in vivo testing. A future I believe in, but one that has a lot big unknown technical obstacles to overcome - like entirely new fields of science and engineering. I'm thinking here on a 50-100 year horizon.
This would also entail a huge change for the regulatory environment, but I think we're so far in the future in this scenario that it's hard to predict what that will look like.
I think a 2x reduction is possible in the near-medium term. If we can change the probability of technical success for trials through better tox models - not testing - that creates an enormous amount of value. The stuff people are doing to design better molecules pales in comparison to doing this. I think the math and know-how exists to do it now - the challenges are more institutional - Who pays? Who provides data? Lot's of coordination problems between actors who are in economic knife fights with each other, though people are trying.
Data problems too. Pharma companies are terrible at databases. It's a mess.
Also, it's better to solve tox because it's not disease specific. The effort invested in building the model (or models) will be distributed over many development life-cycles and would remain valuable after a good drug for an indication is invented.
This is a huge win for the world because it makes diseases that are important but not economic easier to justify. The phrase 'important but not economic' churns my stomach, but there it is.
None of this will fix pricing. Not as long as the patent structure stays the same. If you can legally do it, people will do it. Doesn't matter what it costs. Coke is still charging you two bucks for something that costs them cents to produce, and they don't have a patent.
If you want a near term future with lower drug prices (as I do), I fear your route lies through congress. I think it can happen, but I'm an optimist.
Thanks again for your thoughts and your work. It's good to see other people pulling in the same direction.
I just want to offer a few of my thoughts because you've been so generous with yours. So you know, I work in the industry. More on the biz side than the scientific. And I am intensely interested in this issue.
100-1000 times cheaper I don't think is achievable in the medium term, if ever. As long as we're running clinical trials I think costs will stay constrained to about a 5x decrease. A 100-1000x reduction, I think, implies a world where we can largely simulate the impact of drugs without resort to in vivo testing. A future I believe in, but one that has a lot big unknown technical obstacles to overcome - like entirely new fields of science and engineering. I'm thinking here on a 50-100 year horizon.
This would also entail a huge change for the regulatory environment, but I think we're so far in the future in this scenario that it's hard to predict what that will look like.
I think a 2x reduction is possible in the near-medium term. If we can change the probability of technical success for trials through better tox models - not testing - that creates an enormous amount of value. The stuff people are doing to design better molecules pales in comparison to doing this. I think the math and know-how exists to do it now - the challenges are more institutional - Who pays? Who provides data? Lot's of coordination problems between actors who are in economic knife fights with each other, though people are trying.
Data problems too. Pharma companies are terrible at databases. It's a mess.
Also, it's better to solve tox because it's not disease specific. The effort invested in building the model (or models) will be distributed over many development life-cycles and would remain valuable after a good drug for an indication is invented.
This is a huge win for the world because it makes diseases that are important but not economic easier to justify. The phrase 'important but not economic' churns my stomach, but there it is.
None of this will fix pricing. Not as long as the patent structure stays the same. If you can legally do it, people will do it. Doesn't matter what it costs. Coke is still charging you two bucks for something that costs them cents to produce, and they don't have a patent.
If you want a near term future with lower drug prices (as I do), I fear your route lies through congress. I think it can happen, but I'm an optimist.
Thanks again for your thoughts and your work. It's good to see other people pulling in the same direction.
As I understand most of the cost of a new drug is the testing and certification. It's unlikely that a new drug with the same testing/certification requirements is significantly cheaper than an old one.
Well, are you aware that the common model of cancer (genetic) is almost certainly wrong? And that we've wasted 50+ years, and something like an infinite amount of money on it?
https://www.amazon.com/Cancer-Metabolic-Disease-Management-P...
https://www.amazon.com/Tripping-over-Truth-Overturning-Entre...
https://www.amazon.com/Cancer-Metabolic-Disease-Management-P...
https://www.amazon.com/Tripping-over-Truth-Overturning-Entre...
It's worth noting that the most effectives breakthroughs against cancer in recent decades (Immunotherapy, angiogenesis-suppressing drugs) have largely been based on ideas that were first dismissed and widely ridiculed by senior scientists working on cancer research.
I suspect that in another 50 years, when the war against cancer is (finally) over, we will end up with a completely different perspective on the history of cancer research than the often self-serving and propagandized perspective we have today.
I suspect that in another 50 years, when the war against cancer is (finally) over, we will end up with a completely different perspective on the history of cancer research than the often self-serving and propagandized perspective we have today.
Just how radical were these ideas you speak of from the accepted dogma?
On angiogenesis, see Judah Folkman: https://www.tobinproject.org/about/judah-folkman
On cancer immunotherapy, see Fran Visco's story as partially told by Daniel Sarewitz (section titled "War on Cancer"): https://www.thenewatlantis.com/publications/saving-science
On cancer immunotherapy, see Fran Visco's story as partially told by Daniel Sarewitz (section titled "War on Cancer"): https://www.thenewatlantis.com/publications/saving-science
Thanks! They certainly like very illuminating insights. From my viewpoint epigenetics seems like the forerunner to many future revelations too.
>Generally, he’d get the same kind of answer: the industry had run out of “low-hanging fruit.”
>In the course of his own later investigations, Scannell began to focus on a serious technical problem called “model validity,”
(Sentences adjacent in original.)
The problem, here, I think, is backwards. I have a little window into the world of drug discovery by a decade-long interest in nootropics (tl;dr: I don't take them anymore).
These statements aren't wrong, but they obscure the history. The way we discovered antidepressants was mostly like this:
- tuberculosis patients treated with hydrazines become "inappropriately" happy
- modified hydrazines first used to treat depression
- hydrazines shown to increase serotonin levels by monoamine oxidase inhibition, now called MAOIs
- serotonin hypothesis of depression developed
- serotonin-targeting drugs developed for depression
There are a few more steps here, but the point is: the low-hanging fruit was the hydrazine, then we climbed the tree and got SSRIs.
The problem is that we started climbing all the trees with low fruit but never developed a way to find new trees. We also stopped looking for low-hanging fruit, in that we don't test random drugs on people the way we used to in the '40s. Everything has to be justified by an existing theory even though the existing theories are nowhere near comprehensive or complete.
I don't think we should start testing random drugs on people, but I do think we need to avoid this framing of the problem. We don't understand human biology that well. Our primary focus needs to be not on developing drugs per se but on understanding the extremely intricate hydrogen-bonded nanomachines they're supposed to modify.
But relative to that, our efforts to find drugs should be tempered by a certain humility about the validity of even our most "well-understood" theories. Most of them were developed by accident. The whole risk-averse investing and management apparatus wants reliable theories, so researchers need to be louder about the fact that in many biology problems we don't have reliable theories. We have moderately supported working models.
>In the course of his own later investigations, Scannell began to focus on a serious technical problem called “model validity,”
(Sentences adjacent in original.)
The problem, here, I think, is backwards. I have a little window into the world of drug discovery by a decade-long interest in nootropics (tl;dr: I don't take them anymore).
These statements aren't wrong, but they obscure the history. The way we discovered antidepressants was mostly like this:
- tuberculosis patients treated with hydrazines become "inappropriately" happy
- modified hydrazines first used to treat depression
- hydrazines shown to increase serotonin levels by monoamine oxidase inhibition, now called MAOIs
- serotonin hypothesis of depression developed
- serotonin-targeting drugs developed for depression
There are a few more steps here, but the point is: the low-hanging fruit was the hydrazine, then we climbed the tree and got SSRIs.
The problem is that we started climbing all the trees with low fruit but never developed a way to find new trees. We also stopped looking for low-hanging fruit, in that we don't test random drugs on people the way we used to in the '40s. Everything has to be justified by an existing theory even though the existing theories are nowhere near comprehensive or complete.
I don't think we should start testing random drugs on people, but I do think we need to avoid this framing of the problem. We don't understand human biology that well. Our primary focus needs to be not on developing drugs per se but on understanding the extremely intricate hydrogen-bonded nanomachines they're supposed to modify.
But relative to that, our efforts to find drugs should be tempered by a certain humility about the validity of even our most "well-understood" theories. Most of them were developed by accident. The whole risk-averse investing and management apparatus wants reliable theories, so researchers need to be louder about the fact that in many biology problems we don't have reliable theories. We have moderately supported working models.
My understanding is that MAOIs are still more effective for mood disorders than SSRIs, but have the unfortunate issue of being toxic when taken with just about anything (and not just drugs, e.g. most fermented foods have fairly negative interaction effects with MAOIs).
I agree that discovery is far from a straight-forward process, and that many of the cavalier ways in which it happened in decades past would be criminally prosecuted and beyond the pale today.
But I don't think that there was ever much low-hanging fruit to begin with. Discoveries always look "obvious" ex post and almost never ex ante.
I find it best to think of "low-hanging fruit" as a euphemism that polite people have agreed to use in order to excuse the massive failure that has taken place and stop curious people like Scannell from asking too many questions.
But I don't think that there was ever much low-hanging fruit to begin with. Discoveries always look "obvious" ex post and almost never ex ante.
I find it best to think of "low-hanging fruit" as a euphemism that polite people have agreed to use in order to excuse the massive failure that has taken place and stop curious people like Scannell from asking too many questions.
The current Covid-19 vaccine seems a crucial example of how important model validity can be. The mod-mRNA vaccines can apparently be created in a matter of hours and validated on animal subjects within days/weeks, yet it has taken us almost a year with extensive testing to be sure it will work and be safe. And even that is considered a record breaking time.
It cannot be overstated how big an advancement in medical science it would be if we could show that all such vaccines are safe and (probably) effective.
It cannot be overstated how big an advancement in medical science it would be if we could show that all such vaccines are safe and (probably) effective.
You're right on the money.
Any breakthrough that could either dramatically shorten the feedback cycle (from years to weeks), or reduce the cost of drug development by 100-1000x would unlock so much value as to completely reshape the structure of the Pharma industry.
The consolidation of the industry in recent decades could be justified by the high fixed costs of drug development that made it necessary to seek greater economies of scale, but in a world where the cost of drug development drops precipitously, the pendulum could swing in the other direction, unleashing a wave of decentralization in the industry.
Absent this kind of breakthrough, I expect only more intense regulatory and pricing pressures for the Pharma industry in coming years.
Any breakthrough that could either dramatically shorten the feedback cycle (from years to weeks), or reduce the cost of drug development by 100-1000x would unlock so much value as to completely reshape the structure of the Pharma industry.
The consolidation of the industry in recent decades could be justified by the high fixed costs of drug development that made it necessary to seek greater economies of scale, but in a world where the cost of drug development drops precipitously, the pendulum could swing in the other direction, unleashing a wave of decentralization in the industry.
Absent this kind of breakthrough, I expect only more intense regulatory and pricing pressures for the Pharma industry in coming years.
It's a tough lift to demonstrate that all such modified-mRNA vaccines are safe and likely effective. This trial is a good validation of off-target safety, and that's a huge achievement. However, you still have on-target safety concerns (e.g. cross reactivity). Then, for each pathogen, the selected antigen expressed needs to 1. generate an antibody response and 2. provide protection from infection.
Antibody titers can de-risk 1 pretty effectively. But, without human challenge trials, you're stuck waiting for nature to test 2. Even using non-human primates as the model to test infection has many pitfalls as the species barrier is often strong with infectious diseases. Human immunology is often idiosyncratic when compared with closely related primates.
Antibody titers can de-risk 1 pretty effectively. But, without human challenge trials, you're stuck waiting for nature to test 2. Even using non-human primates as the model to test infection has many pitfalls as the species barrier is often strong with infectious diseases. Human immunology is often idiosyncratic when compared with closely related primates.