Drug-induced autoimmune-like hepatitis: A disproportionality analysis based on the FAERS database

Area of Science:

• Pharmacovigilance and Drug Safety
• Hepatology
• Autoimmune Diseases

Background:

• Drug-induced autoimmune-like hepatitis (DI-ALH) is a severe liver condition with potential for acute liver failure.
• Existing knowledge on drug associations with DI-ALH is limited, necessitating comprehensive real-world data analysis.
• The U.S. Food and Drug Administration's Adverse Event Reporting System (FAERS) database offers a vast resource for pharmacovigilance.

Purpose of the Study:

• To systematically identify drugs associated with DI-ALH using a large-scale pharmacovigilance database.
• To analyze adverse event reports within the FAERS database to detect drug-DI-ALH signals.
• To provide a comprehensive assessment of drug-related risks for DI-ALH.

Main Methods:

• Analysis of DI-ALH reports from the FAERS database (Q1 2004 - Q1 2024).
• Utilized four signal detection methods: Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM).
• Established predefined thresholds for each method to confirm significant drug-adverse event associations.

Main Results:

• Extracted 5,723 DI-ALH reports, identifying 50 drugs with strong associations.
• Biologics, statins, antibiotics, and antiviral drugs were the most common categories.
• Nitrofurantoin, minocycline, and nivolumab showed the strongest signals; mesalazine, aldesleukin, onasemnogene abeparvovec-xioi, and nefazodone were newly identified.

Conclusions:

• This study offers a comprehensive assessment of drugs linked to DI-ALH, validating findings across multiple detection methods.
• Identified both known and previously unreported drug associations, enhancing understanding of drug-induced liver injury.
• Findings support improved pharmacovigilance and clinical risk assessment, though FAERS limitations and need for clinical validation exist.