Real-World Adverse Event Signals of Immunotherapeutic Agents in Lung Cancer: A Pharmacovigilance Data Mining Approach

Background Real-world safety data for immune checkpoint inhibitors nivolumab, pembrolizumab, and durvalumab are essential to guide their clinical use for the treatment of lung cancer. This study analyzed their post-marketing safety profiles. Materials and Methods We employed drug safety signal detection methods, including the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), and other data mining algorithms, to analyze the intensity, occurrence patterns, and risk differentials of adverse drug event (ADE) signals for nivolumab, pembrolizumab, and durvalumab in the FAERS database from January 2004 to March 2026. Results A total of 19208 reports were included. ADEs were most frequent in males and patients aged 65 to 85 years or weighing 50 to 100 kg. Dominant reporting disproportionalities were observed for endocrine disorders and surgical complications. Subgroup analyses showed disproportionate reporting signals for urinary tract infections and rheumatoid arthritis in females, while elderly patients exhibited stronger reporting associations for cerebral infarction, pulmonary toxicity, and renal impairment. However, these subgroup findings are hypothesis-generating and may be influenced by multiplicity and sparse data bias. All three drugs showed disproportionate reporting of endocrine-related adverse events. Conclusion This analysis detected known safety signals through statistical disproportionalities and identified potential reporting associations worthy of further investigation as hypothesis-generating findings, particularly in specific demographic subgroups. These hypothesis-generating findings warrant prospective validation, continued pharmacovigilance, and investigation into combination therapy risks before guideline updates. Limitations Due to inherent biases and lack of exposure denominators in FAERS data, subgroup findings are exploratory and not causal, requiring prospective validation before clinical application.