Artificial Intelligence Shakes Up Drug Discovery

The pharmaceutical industry is looking to machine learning to overcome complex challenges in drug development.

Written byBianca Nogrady
| 8 min read
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Modified from © istock.com, the scientist staff

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.

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Meet the Author

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    Bianca Nogrady is a freelance science journalist and author who is yet to meet a piece of research she doesn't find fascinating. In addition to The Scientist, her words have appeared in outlets including Nature, The Atlantic, Wired UK, The Guardian, Undark, MIT Technology Review, and the BMJ. She is also author of Climate Change: How We Can Get To Carbon Zero, The End: The Human Experience Of Death, editor of the 2019 and 2015 Best Australian Science Writing anthologies, and coauthor of The Sixth Wave: How To Succeed In A Resource-Limited World. She is based in Sydney, Australia.

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