This is a largely agreed upon problem. What isn't clear is why it seems to get sidestepped in discussion so frequently. No one knows how a molecule will interact with human biology. It can't be simulated computationally to generate data (too slow), there isn't a lot of data in the grand scheme of things, so you can't train a model to actually predict the interaction of a molecule with a human.
And nothing I've said here is an original thought or piece of insight.
One might argue that we have calibrated drug safety to such a high degree that the aggregate negative social impacts are far higher than the costs of side effects. Of course we don’t want another thalidomide catastrophe but I think there are large potential test groups to mine who are much less sensitive to the risks. For example, the elderly and those with terminal diagnoses. Right now we only permit drug testing in very limited cases for the latter group, and only when the treatment is related to their terminal diagnosis. I’m seeing older people taking all kinds of experimental anti-aging drugs right now, either off label, or novel. We’re not collecting any of this data empirically because what they’re doing is not legal.
Further, [we are getting much better at simulating drug interactions.](https://pubmed.ncbi.nlm.nih.gov/41155903/) I strongly suspect AI will offer new probabilistic models to funnel novel drugs into more targeted trials which are much more likely to succeed. If we had 90% certainty of success and high certainty of lower adverse reactions I think we could allow much less stringent human trials. That means much lower cost and much faster turn-around.
My wife is a doctorate in pharmacology, I made exactly this argument to her but she dismantled it pretty quickly.
a) We do test on terminally ill people, but after testing in other models.
b) Thalidomide was all roses compared to the horrors you can inflict.
c) Profit-driven industry makes extremely perverse incentives.
Still I do come down more on your side of the argument, and we have historically made massive leaps doing some horrific things.
I wonder if we can get better models based off of human cell-lines / tissues? Like CRISPR did?
> Profit-driven industry makes extremely perverse incentives
People who are specialists in something other than economics often make this argument but it's often a very weak argument, IMO. Liability means that you are incentivized to be quite cautious in pushing a potentially dangerous product. Particularly with pharmaceuticals a class action can be easily and successfully argued in court. Moreover, a relatively small number of dangerous flops will tank your reputation with consumers. I would also add that you can't make any more money selling drugs to people you have killed.
The worst money incentive for drug manufacturers is that market forces push them towards recurring treatments for diseases that effect a large number of people. However that seems less than perverse to me.
> I would also add that you can't make any more money selling drugs to people you have killed.
That assumes that people are playing a long game. If you're looking for your payday and exit, and there are many people out there who are only looking for the next payday, this argument falls apart.
> Moreover, a relatively small number of dangerous flops will tank your reputation with consumers.
One success the anti-vax people have achieved is to muddy the waters around this. Your public reputation is no longer correlated to actual performance.
> Liability means that you are incentivized to be quite cautious in pushing a potentially dangerous product
There's a long list of people throughout history who do not have this caution at all.
You seem to be taking a theoretical economics approach. Reality is different. People are not perfectly rational actors, and consequences are not consistent.
The antivax people are still fringe and by and large people trust that the drugs they take are safe and effective.
Payday and exit is pretty hard in biotech as you need to conjure up billions of dollars in capital, which doesn't often flow to people who payday-and-exit.
What happens in reality is that people try really hard to make safe and effective drugs, they mostly fail, and the ones that are approved are generally safe and effective.
> b) Thalidomide was all roses compared to the horrors you can inflict.
Just to expand on this for the audience. Even a perfectly reasonable synthesis of MPPP, a synthetic opioid, can produce MPTP[1], which will destroy all of your dopaminergic neurons and give you permanent brain damage. This was discovered entirely by accident. Or, Karen Wetterhahn, who got a single drop of organic mercury on her gloved hand and spent the next year suffering from her brain rotting from the inside out.
This only barely scratches the surface from what I can remember, but there's absolutely, unambiguously a lot of good reasons why these barriers are there. These are rules that have been put down through blood and tears.
d) elderly and infirm people are in a weakened state and so are less likely to recover from accidental complications your drug might introduce
e) sickness is itself a confounder for the toxicology effect of a drug. Suppose you take a drug and your liver shuts down - did the drug cause it or was it your underlying illness? This can lead to programs being prematurely shut down and actually prevent the development of treatments that might have gone on to succeed.
Testing a drug in a terminally ill person for something other than their illness is not going to produce meaningful data.
You misunderstand how hard it is to pull signal from a drug being put into a human body, which is why we need large numbers of people who meet extremely narrow criteria (and don't have things like a terminal cancer).
Your latter idea is a good one, and hopefully something AI will help with. As you imply: we will know empirically when it's ready because trial failure rates will trend toward zero. Today we're closer to 90% failures and not even clearly trending in the correct direction.
You're aware that, as a healthy volunteer, you could go sign up for a Phase 1 study today, right? You can even earn a pretty penny doing so.
It's quite a leap to go from the study you linked to thinking AI will create models that will be useful for new trials. The big leaps in AI have required enormous amounts of data. There is no such analogous data sets in biology and no foreseeable way to get it.
It really is hard to see AI improving the trial process meaningfully. We are more likely to see progress with better trial design and regulatory regimes.
They use more sophisticated domides based off Thalidomide these days (2nd gen Lenalidomide, 3rd gen Pomalidomide, and next gen Mezidomide is in trials).
But yes, it was very effective as a treatment. When it was approved for cancer use, we actually had no idea how it worked, only that it worked really well. Thalidomide has saved a few orders of magnitude more lives than it took/damaged.
Anthropic and others already have "AI-powered wet labs". Not sure to what extent they are able to automate lab operations. I recall seeing a post about specifically running automated in-vitro experiments controlled by agents, though. I don't think it's being sidestepped, I think it is being handled. Or at least they are trying; maybe the labs operate at a "Theranos-spraying-blood-everywhere" level.
its pretty obvious why that gets sidestepped isnt it? There are multiple companies trying to go public with trillion dollar valuations on the premise that AI will change everything, it would be mad if they weren't spending hundreds of millions to billions in shaping public opinion that everything can be solved with AI.
And nothing I've said here is an original thought or piece of insight.