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Hacker News
As someone who used to work in pharmaceutical R&D, the important thing to remember is the hurdle to get over hasn’t remained constant.
The FDA has gotten significantly more strict in its review than it was in the 1960s.
A good example is hERG inhibition as an off-target effect. It’s a receptor on heart muscles and will result in QT prolongation and potential arrhythmias.
It wasn’t discovered until the 1990s. Now every molecule is screened and many are dumped. The impact can vary but there are tons of drugs on the market now that are hERG inhibitors (many discovered after the fact).
It’s a good example of the increased rigor that the FDA applies to everything they review that past trials never had to face.
by refurb
I had to look what "clinical failure rate" means and I think I got the answer in this paper [1] and... I'm not going to say that it is worrisome. Unlike other industries, we can't accurately model in the pre-prototype phase how something will behave in living beings. And to use the author examples, cars and airliners are big and way simpler to model than the complex pathways that you find in biology.
We are chipping away at that problem but it's not like we have it mostly solved as other engineering areas of knowledge. I would argue that due that phase I successes isn't the benchmark, but phase II success should be the actual measure. I'm sure that if someone charts the accumulative success rate for each phase of clinical trials, you will see that phase I is the most brutal one.
by braiamp
As someone who works in research, this isn’t surprising at all. It’s hard to find something that treats (well most often reduces symptoms) of a particular disease. It’s even more difficult to find something that is also safe at the dose level it takes to treat said disease. Are there faster ways to do this? Probably not. AI is only going to help out so much, just like automated drug screening only helped so much since its introduction in the 90s.
by AbsurdCensor
Roughly equivalent to the percentage of startups that fail.
Lower numbers don’t mean we’re doing better, it means we’re trying less.
by jcims
Many comments so far seem to try to handwave away the 90 % failure as somehow "optimal" in the system, which seems absurd to me. It is clearly not advantageous for individual companies to keep a drug candidate alive long enough for it to fail in stage III or IV. One obvious question is why they don't and it is very, very tempting to speculate that it's because the problems are getting harder, we are targeting novel mechanisms etc. Again, I think this misses a simpler explanation:
As with many cases where companies make seemingly bad decisions, I think a lot of the explanation lies in system dynamics. Think about the incentive structure inside large pharma companies - it is generally not a career advancement move for a project manager to kill the drug candidate they oversee. It is career advancing to get it approved for the next stage. What could possibly go wrong in this world?
by mbnielsen
I'm actually surprised it isn't going up over time. That is naively what you would expect as the low-hanging fruit is plucked. So the fact that it's been stable is probably a sign that scientific advances are roughly keeping pace with the (presumably) increasing challenge of finding ever more targets for drugs.
by levocardia
Can’t agree with the premise. It seems that through changing regulatory regimes and technology a 1 in 10 chance is the economic optimum for this. Interesting stability, certainly, but I’d expect that as technology improves success rate, funding increases until the marginal project is unprofitable.
The better we get at doing things, the more ambitious we get. As an example, we can keep babies alive much earlier in gestation, so we try harder if they’re earlier than we would before. We should expect a homeostatic equilibrium between our skill and our ambition.
by arjie
> But let’s think about that 91% failure rate for a moment. When I bring this up in presentations, I invite the audience to consider what the auto industry would look like of 91% of new car designs proved unable to roll out of the factory, or if 91% of new airliner models were unable to leave the ground - and if you only found that out after spending all the R&D money to build them at full size and trying to fly them. No cutting-edge restaurant could survive if 91% of its innovative dishes proved inedible or outright poisonous. What other industries operate under these bizarre conditions?
This is bizarre, bordering on stupid. Frist of all, there likely is ~90% failure rate of prototypes; I feel that roughly matches my experience in engineering. Of course the design that makes it through the process, testing, refinement, and into mass production is not going to have a 90% failure rate, but that's a _finished product_, whereas clinical tests are just that -- tests. Finished cars are more analogous to individual pills coming out of the factory. And I'm not even sure the analogy would be very meaningful anyways, because we have different requirements for things of different impact and importance. A 10% manufacturing defect rate is fine in forks, but not for fire extinguishers.
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- Hacker News
- As someone who used to work in pharmaceutical R&D, the important thing to remember is the hurdle to get over hasn’t remained constant.
The FDA has gotten significantly more strict in its review than it was in the 1960s.
A good example is hERG inhibition as an off-target effect. It’s a receptor on heart muscles and will result in QT prolongation and potential arrhythmias.
It wasn’t discovered until the 1990s. Now every molecule is screened and many are dumped. The impact can vary but there are tons of drugs on the market now that are hERG inhibitors (many discovered after the fact).
It’s a good example of the increased rigor that the FDA applies to everything they review that past trials never had to face.
by refurb - I had to look what "clinical failure rate" means and I think I got the answer in this paper [1] and... I'm not going to say that it is worrisome. Unlike other industries, we can't accurately model in the pre-prototype phase how something will behave in living beings. And to use the author examples, cars and airliners are big and way simpler to model than the complex pathways that you find in biology.
We are chipping away at that problem but it's not like we have it mostly solved as other engineering areas of knowledge. I would argue that due that phase I successes isn't the benchmark, but phase II success should be the actual measure. I'm sure that if someone charts the accumulative success rate for each phase of clinical trials, you will see that phase I is the most brutal one.
by braiamp - As someone who works in research, this isn’t surprising at all. It’s hard to find something that treats (well most often reduces symptoms) of a particular disease. It’s even more difficult to find something that is also safe at the dose level it takes to treat said disease. Are there faster ways to do this? Probably not. AI is only going to help out so much, just like automated drug screening only helped so much since its introduction in the 90s.by AbsurdCensor
- Roughly equivalent to the percentage of startups that fail.
Lower numbers don’t mean we’re doing better, it means we’re trying less.
by jcims - Many comments so far seem to try to handwave away the 90 % failure as somehow "optimal" in the system, which seems absurd to me. It is clearly not advantageous for individual companies to keep a drug candidate alive long enough for it to fail in stage III or IV. One obvious question is why they don't and it is very, very tempting to speculate that it's because the problems are getting harder, we are targeting novel mechanisms etc. Again, I think this misses a simpler explanation:
As with many cases where companies make seemingly bad decisions, I think a lot of the explanation lies in system dynamics. Think about the incentive structure inside large pharma companies - it is generally not a career advancement move for a project manager to kill the drug candidate they oversee. It is career advancing to get it approved for the next stage. What could possibly go wrong in this world?
by mbnielsen - I'm actually surprised it isn't going up over time. That is naively what you would expect as the low-hanging fruit is plucked. So the fact that it's been stable is probably a sign that scientific advances are roughly keeping pace with the (presumably) increasing challenge of finding ever more targets for drugs.by levocardia
- Can’t agree with the premise. It seems that through changing regulatory regimes and technology a 1 in 10 chance is the economic optimum for this. Interesting stability, certainly, but I’d expect that as technology improves success rate, funding increases until the marginal project is unprofitable.
The better we get at doing things, the more ambitious we get. As an example, we can keep babies alive much earlier in gestation, so we try harder if they’re earlier than we would before. We should expect a homeostatic equilibrium between our skill and our ambition.
by arjie - > But let’s think about that 91% failure rate for a moment. When I bring this up in presentations, I invite the audience to consider what the auto industry would look like of 91% of new car designs proved unable to roll out of the factory, or if 91% of new airliner models were unable to leave the ground - and if you only found that out after spending all the R&D money to build them at full size and trying to fly them. No cutting-edge restaurant could survive if 91% of its innovative dishes proved inedible or outright poisonous. What other industries operate under these bizarre conditions?
This is bizarre, bordering on stupid. Frist of all, there likely is ~90% failure rate of prototypes; I feel that roughly matches my experience in engineering. Of course the design that makes it through the process, testing, refinement, and into mass production is not going to have a 90% failure rate, but that's a _finished product_, whereas clinical tests are just that -- tests. Finished cars are more analogous to individual pills coming out of the factory. And I'm not even sure the analogy would be very meaningful anyways, because we have different requirements for things of different impact and importance. A 10% manufacturing defect rate is fine in forks, but not for fire extinguishers.