DOI: https://doi.org/10.2478/acph-20260022
Article ID: 260022 (16 pages)
Article
Adverse event reporting of golimumab: A disproportionality analysis of the FDA Adverse Event Reporting System (FAERS) Database
WENJIE MA, NUAN JIA, HUI LIAO, ZHILEI LI, XIAODAN LI
Abstract
This study conducted a retrospective disproportionality analysis of 50,031 adverse drug event (ADE) reports from the FDA Adverse Event Reporting System (FAERS) database spanning Q2 2009 to Q3 2025, in which golimumab was identified as the primary suspect drug. Four disproportionality algorithms were simultaneously applied: the reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS). A total of 397 positive signals were identified across 25 system organ classes (SOCs). The SOCs with concordant positive signals across all four algorithms were identified for infections and infestations and surgical and medical procedures. High-frequency signals included pneumonia, lower respiratory tract infection, influenza, nasopharyngitis, sinusitis and cellulitis. Comparison with the current prescribing information revealed several previously undocumented ADEs, including urinary tract infection, ear infection, flavivirus infection, Zika virus infection and Dengue fever. Twenty-nine positive malignancy signals absent from the prescribing information were identified, including basal cell carcinoma, colorectal cancer, cervical cancer and breast cancer. Time-to-onset analysis revealed that the risk of ADEs was the highest within the first few months of treatment initiation. Clinicians should rigorously monitor for infectious complications and malignancies when prescribing golimumab to ensure patient safety.
Keywords
golimumab, FAERS, ADE, signal mining
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