Unravelling drug-induced hepatic steatosis: Clinical sub-phenotypes, outcome prediction, and identification of high-concern drugs and hazardous chemical attributes

Abstract

Background: Drug-induced liver injury (DILI) is a rare but serious adverse drug reaction with a wide range of clinical presentations. While three major DILI phenotypes—hepatocellular, cholestatic, and mixed—are well established, other injury patterns such as steatosis-associated DILI (DIS) are harder to identify and remain underexplored. Existing limited evidence suggests that steatosis is frequent among DILI patients, yet this subtype and its causative drugs have not been systematically characterized in large patient cohorts. Goals: This study aims to fill this knowledge gap by collecting information from DIS case reports through AI-supported literature review and data extraction. It also seeks to uncover clinical variables linked to poor outcomes and recovery, and to underscore higher-risk drugs and their distinctive chemical features. Methods: A comprehensive literature search on DIS was conducted using keywords and Boolean operators in seven databases. Relevant studies were screened using Sysrev, an AI-supported application for article review and data extraction. Clinical, biochemical, and histopathological data were collected, and drug properties were obtained from databases or by QSAR modeling. The resulting database was subjected to statistical and multivariate analyses, including logistic regression. Results: Data from 251 DIS cases associated with 34 drugs were collected. DIS patients clustered into three subphenotypes with different predominant outcomes: recovery, chronicity, or fatality. Chronic cases showed fibrosis and steatohepatitis with macrosteatosis, while fatal cases often involved microsteatosis and lactic acidosis. Drugs like Didanosine, Zidovudine, Valproate, and Tetracycline were linked to fatal outcome. A predictive model using clinical and chemical features allowed to identify high-concern drugs and hazardous chemical attributes.