The news came as a shock to many people, and AbbVie's share prices tumbled. However, Weida Tong, a researcher at the FDA's National Center for Toxicological Research (NCTR) in Jefferson, Arkansas, could have predicted this outcome. He and his colleagues had recently developed an algorithm to assess a drug's potential for causing liver injury. Tong's team had not assessed these particular drugs before they were approved, but after the agency issued its warning, the researchers entered the data for Viekira Pak into their algorithm and found that it predicted the drug cocktail might have toxic effects on the liver.
When a drug receives FDA approval, the presumption is that it is safe. However, liver injury can be hard to predict, and animal studies do not always identify compounds that might harm human livers. Even human safety studies can miss the signs, in part because the potential for injury can depend on an individual's genetic makeup. “In the area of liver safety, I don't believe there's been any progress whatsoever in the last 30 years,” says Paul Watkins, a toxicologist and director of the Institute for Drug Safety Sciences, a joint venture between The Hamner Institutes for Health Sciences and the University of North Carolina at Chapel Hill. Tong and his four-member team hope to change that by developing models that can predict which medicines might cause trouble, before drugmakers embark on costly clinical trials and dangerous drugs reach the public.
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