Assessing Hepatotoxic Potential of Medication Needs Real-World Data

Real-world data on the incidence of severe acute liver injury (ALI) provides a more accurate tool for assessing the hepatotoxic potential of medications and highlights the need for epidemiologic investigations, investigators reported in JAMA Internal Medicine.

The researchers aimed to identify the most potentially hepatotoxic medications based on real-world incidence rates of severe ALI and to compare these rates with existing categorizations derived from published case reports. The analysis included data from 7,899,888 individuals without pre-existing liver or biliary disease who initiated one of 194 medications suspected of causing hepatotoxicity in an outpatient setting between 2000 and 2021. The study focused on real-world hospitalization rates for severe ALI, defined by specific clinical criteria, to assess the actual risk associated with these medications.

The results revealed that 17 medications had a rate of 5.0 or more severe ALI events per 10,000 person-years, identifying them as the most hepatotoxic. Notably, 11 of these 17 medications (64%) were not classified in the highest hepatotoxicity category according to cumulative case reports. Medications with the highest observed rates included stavudine, erlotinib, and isoniazid, with stavudine showing the highest rate at 86.4 events per 10,000 person-years. These findings suggest that case reports, which do not account for the size of the exposed population, may underestimate the hepatotoxic risk of certain medications.

The study concludes that real-world data on the incidence of severe ALI provides a more accurate tool for assessing the hepatotoxic potential of medications and highlights the need for epidemiologic investigations to validate safety signals from case reports. This approach could lead to better identification and management of drug-induced liver injuries.

Reference
Torgersen J, Mezochow AK, Newcomb CW, et al. Severe acute liver injury after hepatotoxic medication initiation in real-world data. JAMA Intern Med. 2024;184(8):943-952. doi:10.1001/jamainternmed.2024.1836