Every Cure’s drug repurposing strategy isn't what you think, and it could change rare disease treatment

There are over 10,000 rare diseases — 95% of which don't have a cure. It can cost billions of dollars and take more than a decade to develop a new treatment. Because patient populations in the rare disease space can sometimes only consist of a few thousand — or even a few dozen people — drug companies have been hesitant to invest that kind of time and capital developing a drug that's unlikely to turn a profit. Drug repurposing flips that model on its head. Repurposing looks for new ways to use existing drugs to treat diseases that they weren't originally designed for. And now there are groups that are using AI to make that process more efficient. When Dr. David Fajgenbaum and his colleagues were looking to launch Every Cure, a non-profit focused on drug repurposing, they had a big decision to make. "If you want to repurpose drugs, there are two ways you can go. One is you can say, we are going to open up our shop and let patients and disease groups come to us and say, 'Hey, will you find a drug for my disease?'" Fajgenbaum said. "Or you take another approach ... using AI to find basically the lowest-hanging fruit across all drugs and all diseases." Fajgenbaum and his co-founders chose the second option. Every Cure doesn't search for a specific treatment for a specific disease. Instead, it looks to see if there are any existing drugs that can help any existing disease. The group looks for drug-disease matches, and then connects with the patients that can benefit. This marks a big departure from the way that rare disease treatments have traditionally been developed. "The way that research has always been done, if you want someone to do your research, you go to them, you give them money," Fajgenbaum said. "So people come to us, and they're like, we want you to work on our disease, here's money, and we're like, no, no, no, we can't … we don't do it that way." He added, "Getting that messaging out has been a little difficult." That's why, despite Fajgenbaum's long track ...