Discovering new drugs is an expensive proposition. From 2012 to 2022, adjusting for inflation, spending on pharmaceutical research and development increased by almost half to roughly $250bn, according to Bernstein Research. Yet the number of novel drug approvals remained broadly flat. Artificial intelligence could help.
Birthing a new treatment is fraught with challenges. Looking into hospital-acquired bacterial pneumonia in a 1,000 patient Phase 3 trial cost just shy of $90,000 per patient, according to research by Tufts and Duke universities. Insufficient guinea pigs are another problem; in one study, more than two-thirds of UK trials failed to enrol sufficient candidates.
Jack Scannell and fellow researchers, writing over a decade ago, dubbed this Eroom’s Law. This is the backwards version of Moore’s Law, which predicts that the number of transistors that can be squeezed on to a microchip doubles every two years. The number of new drugs per $1bn spent on R&D has halved about every nine years since 1950. Trials from Phase 1 to launch still take a decade on average, calculates McKinsey, and even then only one in 10 succeeds.