AI will not suddenly lead to an Alzheimer’s cure

In biotech and pharma, often you hear people bemoan: everyone’s going after the same targets! What does that complaint mean? It means scientists have demonstrated that a particular gene or protein is causally related to why a disease happens. PD-L1, TNF-α, PCSK9. Companies can then develop drugs that interfere with the protein, or reduce/increase the expression of the gene, and tilt your cells away from a diseased state to a healthier state.

Amyloid beta clearly has something to do with Alzheimer’s, since plaques build up in the brains of Alzheimer’s patients. But there are now drugs that remove amyloid plaques, and they don’t stop Alzheimer’s progression much. Something more complicated must be going on that we don’t understand yet (certainly that I don't understand yet), making drug development tougher.

Given it is hard to take measurements of the active brain, building an understanding of Alzheimer's is slow-going and you have to be inventive. When making recommendations to Good Ventures of what Alzheimer's research to fund, my colleagues Chris Somerville and Heather Youngs have tried to fund the discovery and unusual exploration of newer targets, to avoid well-funded dominant theories. I'm interested in other unusual sources of "data" for that reason, and I bet the AIs will be, too. That linked study was only possible because members of an extended family in Colombia who are genetically predisposed to Alzheimer's volunteered to participate in science. In another study in Oxford, veterans who sustained traumatic brain injuries donated their brains after death to allow scientists to observe how neurodegeneration gets affected by, e.g., bullet fragments physically blocking certain changes in your brain from occurring.

Such studies often do not get funded, because competition for funding is fierce and there's much to do. We need to keep public science going!

Hepatitis B is already treatable with various medicines, so it's absolutely worth knowing if you have a chronic infection. However, there is no full cure yet, and hep B kills 500,000+ people a year globally by leading to cirrhosis and liver cancer. Existing animal models aren't great at recapitulating human-like infection and disease. At Open Philanthropy, we’re supporting work to try and solve that, to allow for more iteration and downselection of drug candidates before the clinic.

Shots of lenacapavir offer almost 100% protection against HIV for six months. The drug was approved for HIV prevention by the FDA this year.

In 2010, a scientist at Gilead spotted a poster at a conference describing Pfizer's molecule PF74, which targeted the capsid of HIV. The molecule had promise, but wasn't stable, so it couldn't be used as a drug – it had a 100% hepatic extraction rate, i.e. all of it gets eliminated in one pass through the liver. Pfizer no longer had commercial interest in the drug, so Gilead took it on. Starting with PF74, they began "adding an atom here, a molecular group there, until after six years they had a molecule that was 12,000 times more potent than PF74 and had a hepatic extraction of less than 1%." Designing stable, specific, potent molecules, and testing for those properties as you go in the lab, can be difficult – but can take you from a good idea that won't work in practice, to a miracle drug that prevents thousands of infections.

You can read more about the discovery story in this STAT profile, where that quote comes from. I'm actually understating things by saying "6 years of tweaking", because it took a lot of work at Pfizer to get to PF74, and the group at Gilead had been working on targeting the capsid before 2010 too, so some of what they'd learned probably informed later development. If you prefer podcasts, my friend Saloni Dattani and I went into depth on the discovery story of lenacapavir in the first episode of Hard Drugs.

Siddhartha Mukherjee wrote a great piece in The New Yorker this June:

“‘Cancer screening trials typically take 10–15 years to publish on mortality, and when positive it typically takes a further 10–15 years before a screening programme with high coverage is rolled out nationally.’ ... by the time the final results arrive the technology may already be obsolete. What if, after thirty years, a trial yields a modestly positive signal – just as a newer, better test emerges?”

Lecanemab and donanemab were approved in 2023 and 2024 for Alzheimer’s prevention after almost a decade of development. Brain bleeding and related deaths were observed in the phase 3 trials and post-approval, years after development began. (They are both still on the market, since some patients may be comfortable with their safety profile. We will learn more as more people take them.)

Only around 5% of Americans have taken part in a clinical trial. For hepatitis C, "it took six years to accumulate enough infections to reach a conclusion: the vaccine failed to provide adequate protection compared to the placebo."

Monoclonals have had 50 years now, are the second most approved modality after small molecules, and most people would consider them commodities. Yet the production methods still can’t make them below $10 per gram, so they’re still not cheap enough for some “obvious” applications like preventing kids getting malaria in a rainy season.

Research predominance in rich countries leads to skewed knowledge and product availability. If mycetoma, a disease where bacteria or fungi take over your foot, were common in the U.S., the NIH and pharma system would probably have developed broadly effective cures by now, and the CDC would have a plan for tracking cases to reduce spread. As it stands, we don’t even know how many people are affected, let alone have an adequate toolset to help them.

There are now many cures for hepatitis C, yet 10,000 people in the United States will die of cirrhosis or liver cancer caused by hep C this year, uncured. That’s despite the fact Egypt has shown screening people and treating those who test positive can drive down infections to near zero. Egypt is beating the US on this.

At the time of writing, there’s an active measles outbreak in Canada. Canada had achieved measles elimination status in 1998. Measles is technologically “solved”, but not everyone trusts the solutions or their messengers.

No, that’s one possible example. Here are two more (also science fiction):

• Everyone who wants one gets a blood test that predicts their chance of developing Alzheimer’s later in life. They can then make changes to their diet, exercise differently, and start preventive drugs like the ones described above before they have any symptoms. That’s not a cure, because disease never manifested – but if it worked, it could be better than a cure, since e.g. your hippocampus wouldn’t have shrunk yet compared to the example above. The bar for safety on preventive measures is higher, though; if Memorvio and Cathespian have a safety profile that risks brain haemorrhage in 1 in every 500 people, you might take them if dementia is closing in on you, but not risk it if your chance of dementia is low enough or far enough in the future.
• You take a daily medicine that reduces cellular aging, aiming to live longer, and it drops your dementia risk too.

I'm not a doctor, or an Alzheimer's expert; I'm not confident in these bullets, and you should seek out better advice if you need it. But, from what I've read:

• Exercise (presumably because it reduces inflammation and increases blood flow, but maybe other stuff’s going on too?)
• Reducing high blood pressure
• Cognitive engagement like reading, puzzles, probably good conversations, probably writing?
• The shingles vaccine reduced new dementia diagnoses by 20% over 7 years in this study (not randomised, but a neat natural experiment)
• Maybe GLP-1s like Ozempic work too – awaiting randomized results in September!

The future gets shaped by what we work on today. I don't know what the future will look like, I'm trying to make educated guesses based on what I see now, plan appropriately, and stay adaptable!

Fair, me too, but most of the same bottlenecks will apply in that case. You’ll want to know if it’s safe, to track patients for longer than just that day, it will cost a lot to make and monitor, etc.

I chose to focus on one disease for this post, to make it more concrete. By definition it's harder to cure all diseases than one disease (curing all diseases involves at least curing Alzheimer's!), so the arguments here should also apply to that harder task.

"Aging" is harder to define medically than Alzheimer's, so would have been harder to write about. But the same bottlenecks would apply – and note that prevention generally has a higher safety bar to cross than treatment, because healthy people might not want to take as much risk. That's why vaccine safety is such a hot topic.

Then, as a side note, I'm not totally sure you can replace brain cells (neurons, say) without changing who you are. So, focusing on cellular aging in organs where cells are used to getting replaced more often (the liver, blood, and skin) may be "easier" than the brain.

This kind of thinking is a little too zoomed out for my tastes. A less extreme version, that intelligence is helpful for solving problems, is true, but does not have as singular implications. The more extreme version is too simple, and applying simple theories to our messy world tends to go wrong. This one in particular is, shall we say, not a terribly green flag when I see it pop up. So I suggest – descriptively and normatively – more pluralism.

Then hey, if you're going for one-thing-explains-all-other-things – energy is a better pick than intelligence anyway. It's better defined, and thermodynamics runs the universe. Go work on fusion!

In my experience, scientists and bioengineers making new tools often do not think about how those tools could be used by bad actors, because they're focused on solving problems. For example, when designing therapeutics, you want to make sure the patient's immune system doesn't reject the drugs, thinking they're invaders. Several scientists I've spoken to have been interested in making tools to evade the immune system for this reason, without noticing that evading the immune system is something you might want to be careful making general tools for. This makes me wonder, of course, what parallel blindspots-of-emphasis I have in things I work on.

The conversations about where these boundaries lie should be nuanced, in my opinion, not blunt. Biological research, and indeed computational biology, has been more helpful than harmful to the human species so far, so we can't have a general aversion to it. The same goes for machine learning – it's been great so far, and some AI systems have much higher risk of going off the rails than others. We can't have a general rule against machine learning, though we should watch out for dangerous ways AI systems can be designed.

I have included AI investments in the table where I think the benefit is clear, and the risk is not clear enough to stay away. Those are judgment calls that scientists, biosafety experts, and the public need to make together. I agree it is important not to be credulous about all AI systems, and to take seriously the risks. Part of why I've written this piece is to inject more nuance into the conversation about under what circumstances some of the hoped-for benefits from AI would, in fact, arise.

Firstly, I personally bet you'll need more data and controlled experiments – but I don't know.

Secondly, the next steps from such a blueprint are not simply bureaucratic. Don't imagine a blueprint in the sense of a chemical formula; curing all cases of Alzheimer's is unlikely to be possible with one small molecule. The blueprint may be for how to make a factory that makes a complicated type of drug the pharmaceutical industry hasn't made before, and that they'd mess up for a while until getting it right. In that world, humans still would not yet collectively have the 1) knowledge or 2) capability to cure Alzheimer’s. So it does not count as having an Alzheimer’s cure. If you get Alzheimer’s, you will not be cured with that blueprint.

Then even with a drug candidate in hand, it will take years to work through “the system” not simply because the system is bad or outdated. The system is not the FDA; if the FDA disappeared it would still take time. The safety data takes time to collect and could throw up surprises in the real world as people and their doctors use a physical instantiation of the drug outlined in the blueprint; manufacturing something at all and manufacturing it reliably are two different things; monitoring the correct/safe way to inject the product at the right personalised dose may take specialist doctors in short supply; etc.

Rate-limiting over what time period? Decades matter! And how sure are you discovery is the rate-limiting step? We have the technology to surgically remove blood clots from the brain when someone is having a stroke, and we are rate-limited on specialist surgeons, so most people with strokes don't get the surgery.

I cannot rule out that superhuman AI systems would be able to predict safety almost-perfectly, without more data and controlled experiments (though I'd be surprised).

That said, I don’t trust anything at the 99 out of 100 level that could kill me, and drugs that are powerful enough to alter your brain to end Alzheimer’s could clearly alter your brain negatively too (e.g., the current available Alzheimer’s drugs give 10-20% of patients microbleeds). Then aside from the chemical prediction, black market drugs also introduce practical risks of incorrect manufacturing, and I’m glad someone (currently, in the US, the FDA) does manufacturing site visits to assure quality.

I'm not assuming that. I'm hopeful the clinical trial system will adapt! Some drugs, like Gleevec, turn out to be so good in phase 2 trials that drug regulators just approve the drug and ask for a phase 3 to happen later, so patients can benefit sooner. I'm hopeful for more cases like that. Safety is harder to speed through, though.

I agree that may happen. I put question marks in that row of the table because I have no idea where it will all net out.

I’m open to that being true too – and expect that would help a lot with the first four bottlenecks in the list (understanding, replacing wet lab models with better computational models, designing drugs, and reducing the length of efficacy trials since better drugs are statistically easier to prove). I think the next six are likely still to apply, though.

I sympathise with this over the long run, and it's familiar in the history of science. E.g. Edward Jenner could not have predicted subunit vaccines, because he made a smallpox vaccine before the germ theory of disease was understood and accepted, let alone the understanding of which subunits of a pathogen could be used on their own to prompt a safer, still-effective immune response. (He had no way to know what an antigen was. Or indeed what any protein was. Or indeed an antibody.)

But 5–10 years is not that long, even with vastly more powerful (software-based) AI. It takes time to gather data. It takes time to change human beings’ opinions.

Maybe! That’s getting pretty into magic territory, though. Our biological concepts are not perfect, but I bet “DNA”, “proteins”, and “neurons” will survive in some form in the future. I don’t think future science is going to be entirely inexplicable to humans. (And, if it were, there would be limits to its democratic desirability.)

And even so, you need to get through the 10th bottleneck: trust. Superhuman AI would have to bring some of us along, and that will probably require some explanation using concepts we understand.

OK. You try them first! I’ll wait for the safety data.

More philosophically, “nanobots” is a concept that fills in at the level of abstraction where you might just be recreating the functions of cells, or a human body, inside your body. Something will have to give those nanobots energy, something will have to give them ways of receiving and sending signals, of helping protect you from invaders (immune system bots)… your body is about to get full up, and in order not to double your weight, those bots may have to start replacing some of your cells… maybe we could make them out of carbon and hydrogen and…

Regardless, nanobots would not be cheap to make, nor cheap to administer, in the next 5–10 years, per bottleneck #7. Nanobots do not escape bottlenecks #8 or #9, really, either.

Here I plead the 5th. Or the 10th. By which I mean I refer you to my 5 to 10 year timeframe.

Biomanufacturing sites are difficult to build for reasons unrelated to "intelligence"; you need reliability, no contamination, etc. And delivery and monitoring still involves human doctors, flesh and bone, who patients will talk to in ways they don't talk to robots.

(Here's a video of the train station construction.)

Small molecule drugs are already commoditised to the extent that it costs pennies to make enough active ingredient to cure someone of malaria. 600,000 people die of malaria each year.

1) There are a lot of stacked assumptions there within an internally coherent logic that I think sounds simpler than it would turn out in reality. I think it's more complicated. See the answers above, in particular to the "intelligence is upstream" question.

2) I still don’t think this vision gets around bottlenecks #5 through #10, without complementary non-AI/non-robotic investments. Perhaps the vision involves AI systems deciding to make more of those investments because we humans were too slow to... but a lot of those investments will be the non-AI ones I write about in this post!

3) In that industrially explosive world, for a while at least, Alzheimer’s would not make the cut on the list of concerns.

I am writing about the next 5 to 10 years only. I can revisit this one later if we see more evidence for it (interest rates spiking to fuel investment, rapid improvement in robotics, cost of production of one new drug modality dropping much quicker than usual, etc).