Clinical failure rates over the decades: yikes
science.org
science.org
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.
Those costs are strictly for the mandatory testing, not the precursor drug design, etc.
The cost and calendar time required to be sunk before 10% pay off is why you generally only have drug development done by (a) some of the largest corporations in the world or (b) fly-by-night companies hoping to sell their preliminary drugs/results to the former to complete testing.
Just to clarify, I think our numbers are in agreement here. From your numbers, phase I + II + III would be in the range $21-75M, all told. Let's double that to account for inflation (2012 is quite stale), to get $42-150M. In California, I'm going to estimate a "normal" engineer's compensation is $150-400k, and the company actually pays more than this because of benefits, HR, etc -- call it $250-600k. Let's say it's 350k fully loaded on average. Then the $42-150M number represents 120-430 engineer years. I'd totally believe that Boom has 120-400 engineers working full time, so if they make a major revision every year, then each major revision is like a full phase I-III progression!
I think there's some reverse-dunning-kruger going on in this thread, where because we're all engineers we think engineering is comparably easy. My claim is that it's actually pretty similar to medical testing, in terms of generic human effort (to the extent that can even be measured and quantified).
edit: According to wikipedia, Boom had 150 employees in 2021 [1]! I'm very proud of my estimate lol, it seems like it was right on the nose. Somebody give me a prize!
If it fails, you've produced near $0 value, aside from some more information (maybe) about how something doesn't work. Because those costs are strictly for the execution of the trial, not for the development work before/during/after it.
That's in contrast to your hardware prototyping example, where a failed prototype still usually represents >$0 development value.
The appropriate analogy would be if every time a hardware prototype failed testing, it was required by regulation to scrap it, go back to the design stage, and restart from there.
I must ask for some data to support this statement, and also your definition of "a large part".
Things start with academic literature, and models (qualitative or even numerically quantitative, human comprehensible, or computationally predicted effects, ...). Then academic level testing occurs, resulting in putative results, obtained on animal models, cell / tissue cultures, ... before proceeding to human trials. The failure rates are for this final step. Imagine being in control of some pharma fund, there is a huge stream of putative drugs emerging in the literature, and only limited budget AND limited test-patient slots. On average people in this position succeed in selecting one that performs as predicted only ~10% of the time. Thats not a simple exploration-exploitation trade-off. With so many candidates, assuming proper pre-human experiments, one would expect much better results, and you'd from a financial perspective redirect focus towards those drugs with high confidence from prior forms of non-human testing. Yet we see failure rates 80-90%! From a purely financial perspective, there is a huge incentive to place more selection emphasis on confidence, but either its not happening or institutions (public or private) are systematically dropping the ball.
To make the engineering analogy with design methodology: before testing a new implementation, we have the luxury to preselect implementations depending on their unit tests, considerably improving the success rate for the higher level implementation. Yet for the analogy in drug selection we fail miserably.
I don't believe the failure is proper to the selection process. Its that the true fitness function is unavailable, if we had it would simply be a matter of performing gradient descent.
But obviously earthly biology does not come with a reference manual of all niches, and analytic objective differentiable fitness functions.
To a large extent it is in fact still the exploration-exploitation curve, but a meta level.
We can't bypass natural selection, we can't turbocharge natural selection (like the Nazi's tried), not only because it is evil, but because the fitness statistic is for all purposes and intents, emergent in nature.
The average reader here will be very familiar with the concept of premature optimization: don't start micro optimizing your code in assembler before functional correctness, first go for correctness, then reason about the hot paths from profiling and investigate and optimize from there.
Historically, hospitals and medicine long predate modern science and biology.
It predates the discovery of natural selection, it predates the measurement of selection phenomena on shortlived organisms.
Healthcare is high inertia never-to-seldom-recognize-earlier-mistakes domain.
The justification of healthcare is never supported by some axiomatized formally verifiable system of ethics, it is justified on the basis of associations and vague nebulous historically grown rules.
Socialized healthcare is a world-widely supported doctrine. Don't wish unto others what you wouldn't wish onto yourself is another. There is some nebulous concept of a right to healthcare, but a right to what: some nebulous default "healthy" state? There is also a strong connection with egalitarianism, if someone gets sick from a flu we somehow believe it is desirable to help them overcome it, because we claim this flu could have afflicted any individual equally.
THe healthcare zooko's triangle looks like this, you can't have all 3 of the following, so once you have unconditionally selected one desideratum, you will be left torn by the decision between only one of the remaining desiderata:
1. egalitarian access to healthcare 2. working healthcare solutions towards "individual health", i.e. improving procreation rates statistically 3. maintained fitness of the collective human genome distribution
Assuming without compromise 1: egalitarian access to healthcare: ===========
To the extent a healthcare instrument (drugs, or tools like glasses) works (2), it undoes decreased genetic procreation rates, inducing a higher incidence rate in future generations, reducing the fitness of the human genome (3): we have not cured or treated this individual, we have traded innate health, fitness and quality of life of future generations for the convenience and comfort of individuals in the current generations.
To the extend we insist to maintain genetic fitness, it would require the healthcare instrument to be ineffective and thus not influence procreation statistics of fertile individuals. To the extent a healthcare instrument doesn't affect the procreation statistics of a fertile individual, the healthcare instrument didn't improve quality of life for this individual (and is effectively a quack measure). For example without the flu shot, an fertile individual might have met a potential mate, or been in the mood to mate with an already associated partner, but an individual without the flu shot might have felt too sick, or been to repulsive for a mate or partner during sickness. Natural selection first and foremost is about procreation probabilities and rates, and much rarer the individually stronger but collectively weaker signal of death.
For example: there is wide consensus that pre-modern tribal hunter/gatherer humans only had sub 5% incidence rates of poor vision requiring corrective glasses according to modern standards. The selective pressure on eyesight was so strong that even 1 or 2 generations of modern medicine, results in large majority of population requiring prescription glasses, the loss of a strong selective pressure quickly results in drift away from fitness.
Assuming without compromise 2. "individually effective" healthcare instruments: =============
If we select to keep (1) egalitarian access then it will be at the expense of (3) maintained fitness in the humanities collective genome: the egalitarian access to the individually effective healthcare measure, will result in the loss of genetic utility, since the utility is supported and provided externally instead of innately.
If we select to maintain the collective genetic fitness of humanity (3), it can be attained while maintaining "individually effective healthcare" measures (drugs, crutches, pacemakers,...), but only if we relax (1) egalitarian access to healthcare: a modern nation state can admit migration or otherwise support the transfer of genetic material from regions with no or little healthcare, or from regions where such healthcare was only recently introduced. Steady state reliance on such a stream of wild-type humans, is basically bio-colonialism: it requires a region where humans face natural selection without help from healthcare, and their genes are used to improve or maintain fitness of modern nation states elsewhere.
Assuming without compromise (3) maintaining fitness of humanities collective genome:
If we insist on maintaining (1) egalitarian access; then it will be at the cost of (2) healthcare that objectively improves the quality of life and thus procreation statistics in the case of fertile individuals. So we could all enjoy egalitarian access to non-functional medicine.
If we insist on maintaining (2) individually effective healthcare measures, which improve quality of life and hence procreation statistics of fertile individuals, we have to sacrifice (1) egalitarian access: if a sufficient collection of humans is basically deprived of healthcare access, then yes we could maintain fitness by selecting their genetics for procreation.
It's a veritable zooko's triangle, and an absolute nightmare once comprehension sinks in.
If for every QAPR (quality adjusted procreation rate) were taken into account for healthcare interventions, they'd all go negative! whatever current "meritocratic assessment" of healthcare instruments is basically fraud from the perspective of genetics and natural selection, like those video's you see from parents climbing school walls in India to help their children cheat on some national level exams, and somehow this being normalized by society...
It is not a question of allegiance with or against modern healthcare, it is a question of internal consistency of what is known about natural selection, selection pressure, healthcare, etc. We only know how to trade with loss (but you don't need a degree in medicine to attain that skill).
Modern medicine stems from a premature optimization objective (and the original goals did not involve (1) egalitarian access -for nobility and their armies- nor did the original goals involve (3) maintaining genetic fitness for future generations -the statistics of natural selection and concepts like selection pressure were only poorly understood).
Socialized healthcare institutions continued their "mission goals" without establishing an existence result first, they basically tried to fulfill and democratize the nebulous informal desiderata the earlier forms of healthcare aspired to... It's historically grown holy-grail level provable unobtainium. Its a pointless crusade worse than fighting windmills.
There are ways used right now that are working towards reducing those false findings by looking at actual humans, their biomarkers and whenever or not there's an associated molecule to the condition that we would like to pass onto others. It will not kill prototypes, instead it will discourage us from going through a prototype at all by going for better candidates instead.
Prototypes, when they fail, are also often tweaked into something that works. A lot of phase 3 failures appear to have been complete dead-ends.
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.
Those failures can include drugs that would be considerer miraculous 10 or 20 years ago. You might have a drug that functionally cures HIV, but failed to outperform the standard of care in a RCT.
You have drugs like Lorbrena for non-small cell lung cancer where the median life extension is unknown because >50% of patients are alive 7 years out. Or Keytruda where 50% of advanced melenoma patients appear functionally cured 10 years post treatment.
Why would anyone make a drug if the current prevalent treatment was as good as a functional cure?
What do lorbrena and keytruda have to do with the rate of failures being constant?
The most parsimonious explanation is either our models or methods (or both) are garbage. Something’s missing. The failure rate is insane, and writing it off as ambition or “biology is hard” rather than digging in does us no favors.
Both models and methods are challenging. Biology is hard.
Who isn't digging in exactly?
Pharmaceuticals are more than a two trillion dollar market with Millions of scientists and engineers and doctors working diligently to refine the process.
There's tens of billions of dollars to gain for any company that comes up with even minor improvements to success rate.
Keep in mind that we’re talking about the success rate of the last step in the process: human trials. For every human trial there were millions of drug candidates that were considered and rejected. Tens of thousands of those candidates were actually synthesized and tested in animal models or in cell cultures. In the 80s these numbers were all lower. Back then only thousands of candidates would be considered, and only hundreds would be synthesized for actual testing.
Worse, for every candidate that gets to the human trial stage there were multiple projects that rejected all of their candidates and were discontinued. The numbers here are pretty vague, but past surveys have found that the number of failures at this stage is really high too, maybe as high as 90%.
Pharma companies are hardly unaware of this problem. They regularly spend years (even decades) and hundreds of millions of dollars on drug projects that never reach human trials. They spend even more on the ones that reach human trials and then fail. As a result they have spent billions on new methods, new systems, new techniques, etc, all intended to reduce that risk. It appears that all they have managed to do is keep pace with the increasing difficulty of drug development.
I don’t necessarily agree that this is inevitable, but it is understandable.
> The most parsimonious explanation is either our models or methods (or both) are garbage.
I disagree with that. In spite of the difficulties we have managed to get around 50 new drugs approved every year for the last several decades. Sometimes more, sometimes less, of course, but as far as I know there’s been no obvious trend upwards or downwards. That doesn’t seem like garbage to me.
> If the goal were flight, would jumping off cliffs with wings like birds be “ambitious”?
Not any more, no, but it was quite ambitious back in the 1890s. Back then there was a single scholarly study on the lift generated by wings, and it was completely wrong. The Wright brothers built their own wind tunnel and ran their own tests to get reliable data to base the Flyer’s wings on.
If you’re talking about copycat mechanisms, that is fine but we’re still left discussing successes when OP is about failures
You spend the 1 billion for a ticket to win 30 billion/yr for the next several years.
Is it rational to spend 1$ to make $100 10% of the time? A 99% failure rate would be break even.
You can spend that $1B to win a new category or that same money to maybe be non-inferior to Keytruda, but maybe fail. I think copycat design does happen, but I mostly notice it when drugs are being developed simultaneously at different firms. Perhaps it happens more beyond this, I'm unsure.
Otherwise, it makes more sense to try to find an indication where you are approved and have no competition.
I'm not talking about biosimilars or strict copycats.
Completely novel indications are a tiny portion of Pharma development.
Lucrative and technologically viable untreated indications are few and far between.
Meanwhile, you have huge proven markets for treatments that impact millions of people with corresponding Revenue.
If you are skeptical, you can simply go look at the development Pipelines for the top 10 pharmaceutical Giants and see how many of their new programs are for indications with existing treatments versus orphan.
In recent years, approximately one-third of all novel new drugs approved annually by the FDA have carried a Breakthrough Therapy Designation. This corresponds to drugs that claim a substantial improvement over the standard of care for their target disease.
The new drug could be cheaper to manufacture, fewer side effects, a full cure in stead of a functional cure.
How will your clinical trial of the ostensible full cure work if standard of care is curing people?
I am not sure whether you ask for economic why, or an overall why.
The overall why seems easier to explain:
a. The new drug may be cheaper.
b. The new drug may be safer. (e.g. no risk of anaphylaxis etc.)
c. The new drug may not need cold storage (huge problem outside the First World).
d. The new drug may have other properties that the original does not have (e.g. being taken once a day instead of four times a day, not requiring people to stay off specific food etc.)
e. The side effects (such as vomiting) may be lower.
In general, it is always better to have alternatives in medicine.
And frankly your comment about "facile thinking" is totally unwelcome. Have you seen someone vomiting their guts out after chemo? That is torture.
Oncology is one field where every less punishing treatment would be useful. Some patients have to be taken off otherwise life-saving treatments because they cannot tolerate them. That is true even with well-treatable cancers such as Hodgkins, which 90+ per cent of patients survive.
The search for different formulations of an active agent seems a little unrelated (sibling comment to yours) but I concede that I didn't exclude that in my earlier argument.
--
My point is that several posters here are giving arguments from first principles ('yes of course we would want to keep trying to make more medications because more options is better than fewer options') but not engaging with the enormous cost such a rationale would entail.
And the notorious inhaled insulin that Pfizer launch (and horribly failed).
There are many ways to differentiate drugs, not just efficacy.
Anti-vegf treatments for blindness are a good example if you want to research. They all have basicly the same effect in terms of letters preserved on an eye chart if dosed as perscribled.
It's a 15 billion dollar per year Market that resolves on the basis of the longest dose interval and side effects.
You can look at the bevicizumab, to ranibizumab, to aflibercept, to brolucizumab, to faricimab drug programs. There are also dozens of failed programs in this area and perpetually new drugs and Gene Therapies looking to break into the market.
Perhaps an even more salient example would be pill based GLPs looking exploit the listed factors
I explained in sibling that I understood the Lowe article to be about new research (as a researcher, I don't often think about how hard it is to make a GLP survive the stomach but I do think about how incretin biology works). I guess these VEGF agents you list feel like a very straightforward engineering question (can we make an antibody that targets VEGF, humanize its Fc etc) while Lowe brings up conceptual areas like PCSK9, HMG-CoA reductase.
I still maintain that the 1% better thing is not going to lead to incredible success because rational actors aren't going to change their prescribing patterns each time a new agent gets approved.
They want the best, and are willing to sepnd 10x the price or switch treatment to secure that 1% survival, extra week of dosing interval, or the ability to inject at home via mail instead of some sweaty infusion clinic.
I used to think like you before transitioning from medical devices to pharmaceutical development.
The USA is the only real Market that matters for drug development. Unlike the with marginal benefit pricing and National negotiation, the US market is consumer Centric, with patients and their providers wanting the best, even if it is marginal. That 1% means that you can either take the market share or 10x the price.
> I guess these VEGF agents you list feel like a very straightforward engineering question
Each of those straightforward engineering questions takes about 100,000 labor-years and a billion dollars to get approval.
Some providers did opt to self-compound avastin for off label use mostly in Europe but also in the US.
The idea that Pharma companies would pass on developing a product 1% better than keytruda, or that hcps would pass on buying is laughable
Yes, the science advances, previously high-hanging fruits become low-hanging become high-hanging again [1], but the tooling also advances: we now have databases like OpenTargets which let us more easily evaluate potential drug targets. Failure is so much more than that, though: a program can fail after you've shown efficacy in animals, sometimes it just doesn't happen in the human subjects. Or you fail to find the right measurement (endpoint). A million ways to die.
[1] Gene editing is an example: impossible, then very possible, but now the blocker is public perception which in turn blocks investment.
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?
Since this isn't happening, it seems this might not be the explanation.
It is not software development where you can start a project every month see how it goes and drop it or pivot.
I guess they use a lot of computer aided models before they even start serious parts but I believe this discussion is not about failure rates on that stage because then it would be 99%
You're grossly oversimplifying the process. The decision to "kill" a drug is huge, especially if it's already in the clinic (per the article). That decision will be taken by a large group of people, not an individual - and certainly not a "project manager".
Lower numbers don’t mean we’re doing better, it means we’re trying less.
ugh, I don't know
Their goal often seems not to be toward a successful, sustainable product. They just market their vision in the hopes of being acquired.
A few fewer failures might not be entirely a bad thing.
The missing ingredient of course is the people. We cannot let this country pretend it's functional when it invests so poorly. Any sane version of the us will expect years if nit decades of economic pain before these investments pay off
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.
That should have been the end if the comment; the follow-up by demonstrating such expertise just has me laughing.
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.
Plenty of successful drugs from the 50’s had hERG activity, yet the impact on safety was marginal. Today plenty of programs are killed over it.
Increase safety scrutiny does increase safety, but at a cost.
Is there some evidence of these two claims? Obviously many experts think otherwise.
All of these are rather potent hERG inhibitors, yet were approved and widely used before that activity was discovered. Pulling them off the market would have been quite disruptive considering some of them are the only effective treatment for rather serious diseases.
Of course it's a matter of opinion, but I would argue some of these workhorse medications would have had their program killed by the manufacturer (fear the FDA won't approve) or the FDA itself (risk of fatal arrhythmias) if they were developed today.
Yet the risk is managed by flagging the contraindication when combined with other drugs (since the inhibition is often additive).
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
Is an utterly irrelevant comparison. For physical thing we have engineering, and the practical application of the trades and craft, trial and error.
For modern medicine, we're only just starting to come out of the wild west era. Or perhaps slightly further along than that.
Drug design seems a lot more binary. You can find a new pathway, but drugs themselves are fairly simply molecules, and you can't iteratively 'fix bugs' the way you can in an engine or a piece of software.
The engine is a given, it's almost astronomically complicated, you know a lot less about how it works than you'd like to, and you're trying to change how it works while it's running without breaking anything, using tiny rigid parts that have to snap into place correctly and can't be bent to fit.
High failure rates aren't surprising.
You might have something like Anti–vascular endothelial growth factor therapy, where you have a binding target structure, and iterate the uses and molecular structure around it, or combine it with other structures and binding sites.
You might go from mab to fab, or to bispecific mab using CrossMab IgG architecture, or bispecific fab using dutafab fragment architechture. This analogizes to mixing and matching different jet engine technologies into different platofrms.
VEGF targeting biologics have netted >150 billion dollars to date, and this will only grow faster in the future.
iterative GLP-1 technologies will be much the same, where people are literally iteratively fixing bugs.
The development pipeline for both is littred with failed iterations.
Why do you say that? What's the evidence? We continue to have virtually no clue how to make drugs, per TFA.
Drug development is a hard problem because the solution space is poorly constrained: biochemistry is complex and messy, expecting one chemical substance to have narrow positive effects is probably hopeless.
So the claim is that we're out of the wild west because there's a set of things we've made advancements on, despite drug development getting harder and harder?
The only reason people are even dying from cancer at such high rates is because we solved other easier illnesses, and people live way longer as a result. People 100 years ago weren't dying to cancer like they are now, because they were already dead from other more common, simpler, diseases.
Something like cancer is extraordinarily complex. It's nothing like a bacteria or a virus. I mean, think about it, we have to devise methods of targeting cancer cells without injuring human cells around them - when the bodies own immune system cannot differentiate them.
And we have devised those methods! Chemotherapy, immunotherapy, radiation therapy. It's truly incredible.
I don't think its irrelevant, but a better comparison would be to what vacuum tube development looked like before we understood electrons. There were some very whacky designs and most of them didn't work for crap.
And, yeah, I would argue that PCR shifted us from the alchemy phase of biology to the science phase and now mRNA has shifted us from the science phase of biology to t he engineering phase of biology. We're just getting started.
Anyone smarter than a 10 year old would not we actually know almost everything about car design. How it works is not at all hidden. We have iterated on pretty much the same thing for 100 years. The current state is about efficiency, materials and manufacturing.
When you have deep knowledge and experience in a field you will get near 100%.
> For modern medicine, we're only just starting to come out of the wild west era. Or perhaps slightly further along than that.
You could say the same about deep learning. Yet we see improvements every day.
But also it strikes me, perhaps we're also approaching the limits of "drugs" as in compounds delivered by topical, oral ingestion, injectable etc? Perhaps the human creature needs upgrades that go beyond "just add chemicals systemically"?
I'm thinking of things like targetted delivery by nanobots, gene editing, replacing body parts with engineered/mechanical versions...
For example maybe we can obviate statins for those who have genetically high cholesterol by editing their genes, or maybe just replace the failing biological pump (heart) with a mechanical one?
(Cue all the folks with the "One does not simply" memes :) )
There's always AI simulation on the way. Deepmind's Isomorphic labs is working on it https://www.isomorphiclabs.com/articles/the-isomorphic-labs-...
And an AI designed drug for idiopathic pulmonary fibrosis from Insilico Medicine is going to clinical trials https://www.artificialintelligence-news.com/news/insilico-me...
He starts off on the wrong note, there is nothing at all wrong with a high rate of clinical failures. If anything, the reasonable argument standard might be that this rate is too low. It implies researchers are trying things that they expect to have a 10% chance of working out. That means we're missing out on all the cures and techniques that have a 1% chance of working out but but nonetheless do work.
Failed attempts cost society nearly nothing and successes will have compounding benifits for, y'know, lets optimistically say the human race survives for centuries. 1% or 0.1% success rates sound completely reasonable with that sort of lopsided risk profile. There isn't much of a reason not to try anything and everything that has the faintest chance of helping and see what happens.
If this try or die mentality does work, why don't we try every possible amino acid chain in every configuration possible? It's an infinitesimally small chance but try or die "outweighs". This is evidently absurd.
> If this try or die mentality does work, why don't we try every possible amino acid chain in every configuration possible?
You tell me. If someone wants to start working through the amino acids one by one I'm not going to say they should be stopped; sincerest good luck to them. I'm just not going to be the one funding it.
> You could solve all forms of cancer tomorrow, which would be a great thing, but you aren't "saving humanity".
This is a straw man. "Saving humanity", whatever that means, is outside the scope of medicine and I never argued for it. Nobody did. It isn't relevant.
TLDR: I just have an issue with their "we should try every combination with no regards to constraining factors" approach
https://www.complexsystemspodcast.com/episodes/ruxandra-tesl...
The world does not have unlimited resources. Money spent on one project is money that can’t be spent on another.
The failure rate does not take into account the millions of molecules that are tested by various methods of protein folding or binding properties. The 90% remains stable with time because on one side an end product is more difficult to produce, on the other the technology of molecule discovery has improved in the same amount.
Would make a small correction. It does not have to be people. It can be also be "doctors" or "governments". It is easier to convince or coerce/fool a lesser number of humans (doctors) or a single government regulatory body than to fool/convince every one who use the product, because the people can directly evaluate the product.
And when a single doctor is coerced, then the product is forced on hundreds of their patients. When a government is coerced, then the product is forced on tens of millions of people..
[1] https://en.wikipedia.org/wiki/Food_and_Drug_Administration_M...
110M+ animals suffering and dying for nothing each year. And people ask why I choose cruelty-free.
If they could do that today, they would. Trial criteria are already incredibly narrow specifically to try to encode as much of this knowledge as the company has prior to starting the trial. But it empirically turns out they don't have nearly enough to matter.
It's really not a problem that pre-filtering to that responders group produces too small a market. There are plenty of drug programs going after way-too-small markets because of all these wacky perverse incentives created by insurers and regulators to incentivize the creation of billion-dollar++ drugs for diseases virtually no one has.
It's pretty low effort to jeer from the outside and say "do better" for an industry that's been trying so many approaches to do so, without any ideas yourself.
The techniques have gotten a lot more sophisticated, but unfortunately a lot of the low hanging, "easy" fruit have been used up. Biology is hard.
There are breakthroughs that I could have never imagined such as mRNA vaccines.
Life science on the other hand is actually on a bleeding edge. Something actually revolutionary might emerge tomorrow after a grad student has an acid trip tonight.
In those industries, a person who deeply understands the theory behind the technology is assured of some job security. That's my job.
One area that has experienced more rapid innovation is software. And maybe it's not quite at the failure rate of drug development, but I think it's noticeably higher than for most "hardware" projects.
Even today, I am on a newsletter that has said AI drug will pass clinical trial in 2022 ... 23 .. 24 .. 25. Even the candidates that they talk about are mostly just repurposing. Don't get me wrong, ML has a solid place in clinical research but not like this.