From The Scientist, 1 May 2019:
Australia’s government drug safety watchdog sounded the alarm about the oral antifungal agent terbinafine in 1996. The drug, sold under the brand name Lamisil by pharma giant Novartis, had come onto the market in 1993 for the treatment of fungal skin infections and thrush. But three years later, the agency had received a number of reports of suspected adverse reactions, including 11 reports of liver toxicity. By 2008, three deaths from liver failure and 70 liver reactions had been pinned on oral terbinafine.
Researchers in Canada identified the biochemical culprit behind terbinafine’s liver toxicity—a compound called TBF-A that appeared to be a metabolite of terbinafine—in 2001. Clinicians quickly learned to monitor and manage this potential toxicity during treatment, but no one could work out how the compound actually formed in the liver, or could experimentally reproduce its synthesis from terbinafine in the lab.
Then, in 2018, graduate student Na Le Dang at Washington University in St. Louis hit upon a way to use artificial intelligence (AI)—specifically, a machine learning algorithm—to work out the possible biochemical pathways terbinafine takes when it is metabolized by the liver. Trained on large numbers of known metabolic pathways, the algorithm had learned the most likely outcomes when different types of molecules were broken down in the organ. With that information, it was able to identify what no human could: that the metabolism of terbinafine to TBF-A was a two-step process. Read more.
Bianca Nogrady 