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There is no good reason for mosquitoes to exist in wealthy countries today. New technologies can get rid of the mosquito species that cause diseases like dengue, yellow fever, and malaria; and others can prevent them from carrying those diseases. worksinprogress.co/issue/mos… So why do mosquitoes still exist? In a new article for Works in Progress, Maya Rosen explains how state incapacity and risk aversion led to over a decade of delays and billions of unnecessary bites in the United States. Oxitec, for example, engineered an Aedes aegypti mosquito in such a way that its female offspring would die before adulthood. Through sustained releases, they showed they could reduce local mosquito populations by 80% or more, in field trials. They sought approval from the USDA in 2010, which then passed it to the FDA, which passed it to the EPA. In 2020, the EPA allowed them to conduct limited field trials in the Florida Keys. Two years ago, their experimental permit expired, and they still need another review before it can be used routinely. Their proposed field trial in California was withdrawn after public opposition. Another company, MosquitoMate, developed a technique involving Wolbachia, which also suppresses local mosquitoes. They then spent over fifteen years working through the system before finally receiving commercial registration nationwide in 2024. It seems that, sometimes, the barrier to getting rid of diseases isn’t technology at all. It’s policy and political will. New products shouldn’t get stuck in regulatory review for so long, especially if the delays are about basic questions like which agency has oversight over the product, rather than testing for safety. Though some of those uncertainties were being addressed, the broader changes haven’t materialized. After Biden’s 2022 executive order, the agencies planned to update how their coordinated framework was implemented. But Trump rescinded the order last year. Meanwhile, the EPA unit that reviews new mosquito control products is still a small office backlogged in reviewing a wide range of products. Mosquitoes are a policy choice. We can get rid of them. Will we choose to? worksinprogress.co/issue/mos…

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1. I love the idea of curing most (or all?) diseases. I just wrote an article about it worksinprogress.news/p/futur… and gave a TED talk about it too! ted.com/talks/saloni_dattani… We should be a lot more ambitious with medical innovation, and the history of science makes me more, not less, optimistic about what’s possible. Many diseases we once thought were untreatable turned out not to be. 2. But what’s possible isn’t the same as what’s going to happen, and it’s very different from claiming it’s possible within a timeline of 10 years. Even if we grant exponential improvement in our technological capabilities, we still wouldn’t be able to cure most diseases within ten years, because technology is just one part of the equation. One clear illustration of this is Eroom’s law, where the cost of drug development has risen despite massive technological advancement. 3. In our episode ‘Will AI solve medicine?’ (harddrugs.worksinprogress.co…) Jacob Trefethen and I talk about how funding, institutions, incentives, better measurement, and a lack of data mean that we’ll still have massive gaps in solving diseases despite progress in AI. These problems fall most heavily on neglected diseases, like rare and tropical diseases, where funding is often the barrier to progress. But they also affect diseases whose clinical trials are long and onerous, disease progression itself is slow, or poorly measured. 4. All this means that, if we want to speed up medical innovation and scale it up worldwide, we should also be unblocking the other barriers to medical progress with clinical trial reform, better data collection, better measurement instruments, better funding models, and more. (See our ‘Clinical Trials Abundance blog’ for some ideas clinicaltrialsabundance.blog…) It’s going to take a while before we solve these to the point where we can cure most diseases! 5. I thought the policy proposals in ‘Policy on the AI Exponential’ relating to drug development were quite vague and too brief to be useful. But even if they were all implemented ideally, we would still be lacking cures for diseases for which financing or clinical trial infrastructure are the barrier, or where we’d need entirely new drug modalities to reach targets safely. FWIW, I’m fairly skeptical of the use of ‘synthetic control arms’ if we actually want reliably effective medicines, not just faster clinical trials. But platform trials, which include shared control groups that many drugs are tested against, probably get at the intention. 6. I’m very skeptical of the idea that “many of the steps in the clinical process that previously required expensive and slow experiments may soon be done via AI simulation or analysis” ... but maybe this depends on one’s definition of ‘many’. Human biology is extremely complicated and interactions between drugs and molecules in our body are very hard to predict, in part because of the number of combinations and biological states, and the level of measurement that’s possible in the lab; the way we get around this is through experimental data collection, i.e. clinical trials, but for some diseases, progression is very slow, so we’re not going to be able to collect that amount of data in time, even with perfect measurement. Unfortunately public health datasets typically don’t include much drug-human body data, with the exception of, e.g., prescription datasets, which would be somewhat useful for analyzing historical drugs. Getting pharma companies to pool sufficient individual participant data from clinical trials to improve these predictions, though, that would be exciting. 7. I sometimes worry about the deluge of AI ‘slop’ science that’s going to enter the academic literature. We already needed to reform peer review, catch scientific fraud, improve data quality and rigour before AI (see e.g. my articles ‘Real peer review has never been tried’ worksinprogress.co/issue/rea… and ‘The speed of science’ worksinprogress.co/issue/the…), but now it seems like science reform is even more important. I could imagine a Pangram-like model for AI-written science papers, but that seems like it merely catches the tip of the iceberg of how deep the slop might go, if it involves unpublished data, for example. If AI use increases exponentially, we might end up seeing an AI science slop tsunami, which could affect predictions based on the literature. What’s the solution to this?

It's finally here! I gave a TED talk in April in Vancouver, and it was released online today. You can watch it here: ted.com/talks/saloni_dattani… I've also adapted it into an article, which was published last week: worksinprogress.co/issue/fut…

New episode of HARD DRUGS! Are miracle drugs hiding in plain sight? GLP-1 drugs have transformed diabetes and obesity, can help treat heart disease, kidney disease, sleep apnea, and may also work against dementia. But they’re far from the only medicine with more multiple uses. Aspirin, colchicine, minoxidil, Botox, and, famously, Viagra were all found to treat more conditions than expected. (Did you know that BCG vaccines are used to treat bladder cancer?) In fact, about a third of FDA-approved small molecule drugs are later approved for a second use. But sometimes, it takes several decades - and public funding - to learn whether existing drugs could work for more conditions. Moxidectin, for example, went untested for human diseases for decades; we now know that it can cure ~70% of whipworm infections in kids. Aspirin was used for over a century before we found it had cardiovascular benefits. Why? The commercial incentive to test further is time-limited: once a drug goes off-patent and generics can enter, drug developers can get undercut, so it’s no longer worth it to run expensive clinical trials to test other uses. The problem is even larger for tropical diseases, which are often not profitable to treat in the first place. The result is that we’re missing out on knowing about a lot of additional uses of already-approved drugs! There may be more miracle drugs hiding in plain sight. And economists estimate the ‘missing innovation’ would be worth 100 to 400 billion dollars in social value annually(!) How do we solve this? In this episode, @JacobTref and I chat about all the above and more!