How AI is stopping the next great flu before it starts
Conventional methods for drug and vaccine development are wildly inefficient. Researchers can spend nearly a decade laboriously vetting candidate molecule after candidate molecule via intensive trial and error techniques. According to a 2019 study by the Tufts Center for the Study of Drug Development, developing a single drug treatment costs $2.6 billion on average — more than double what it cost in 2003 — with only around 12 percent entering clinical development ever gaining FDA approval.
“You always have the FDA,” Dr. Eva-Maria Strauch, Assistant Professor of Pharmaceutical & Biomedical Sciences at University of Georgia, told Engadget. “The FDA really takes five to 10 years to approve a drug.”
However, with the help of machine learning systems, biomedical researchers can essentially flip the trial-and-error methodology on its head. Instead of systematically trying each potential treatment manually, researchers can use an AI to sort through massive databases of candidate compounds and recommend the ones most likely to be effective.
“A lot of the questions that are really facing drug development teams are no longer the sorts of questions that people think that they can handle from just sorting through data in their heads,” S. Joshua Swamidass, a computational biologist at Washington University, told The Scientist in 2019. “There’s got to be some sort of systematic way of looking at large amounts of data . . . to answer questions and to get insight into how to do things.”
For example, terbinafine is an oral antifungal medication that was marketed in 1996 as Lamifil, a treatment for thrush. However, within three years multiple people had reported adverse effects of taking the medication and by 2008, three people had died of liver toxicity and another 70 had been sickened. Doctors discovered that a metabolite of terbinafine (TBF-A) was the cause of the liver damage but at the time couldn’t figure out how it was being produced in the body.
This metabolic pathway remained a mystery to the medical community for a decade until 2018 when Washington University graduate student Na Le Dang trained an AI on metabolic pathways and had the machine figure out the potential ways in which the liver could break down terbinafine into TBF-A. Turns out that creating the toxic metabolite is a two-step process, one that is far more difficult to identify experimentally but simple enough for an AI‘s powerful pattern recognition capabilities to spot.
In fact, more than 450 medicines have been pulled from the market in the past 50 years, many for causing liver toxicity like Lamifil did. Enough that the FDA launched the Tox21.gov website, an online database of molecules and their relative toxicity against various important human proteins. By training an AI on this dataset, researchers hope to more quickly determine whether a potential treatment will cause serious side effects or not.
“We’ve had a challenge in the past of essentially, ‘Can you predict the toxicity of these compounds in advance?'” Sam Michael CIO for the National Center for Advancing Translational Sciences which helped create the database, told Engadget. “This is the exact opposite of what we do for small molecule screening for pharmaceuticals. We don’t want to find a hit, we want to say ‘Hey, there’s a likelihood for this [compound to be toxic].
