抗悪性腫瘍薬に関連する間質性肺疾患のシグナル層別化：FAERSデータベースを用いた多手法シグナル検出分析

This study evaluated the signal of association of interstitial lung disease (ILD) associated with antineoplastic drugs using a multi-method signal detection approach and the FDA Adverse Event Reporting System (FAERS) database. ILD reports linked to antineoplastic drugs from the first quarter of 2004 through the second quarter of 2024 were retrieved from FAERS and analyzed using 5 pharmacovigilance methods: reporting odds ratio (ROR), proportional reporting ratio, MHRA, Multi-item Gamma Poisson Shrinker, and Bayesian Confidence Propagation Neural Network. Drugs were classified according to their primary mechanism of action. A total of 38,950 reports of ILD were analyzed. Of these, 47.66% were in male patients, and 37.57% were aged 65 to 80 years. Geographically, Japan accounted for 43.74% of the reports. Serious outcomes were documented in 99.29% of cases, predominantly hospitalization (51.64%) and death (26.36%). Signal detection analysis identified 1210 potential drug-ILD associations. Among these, 605 demonstrated robust evidence, satisfying the criteria for 3 or more methods. Notably, non-small cell lung cancer was associated with a high burden of ILD, particularly among elderly patients. The strongest signal was observed for trastuzumab deruxtecan, with a ROR of 41.3 (95% CI: 35.8-47.7), followed by gefitinib and lenvatinib. Analysis of treatment duration revealed that immunotherapy agents exhibited the shortest median durations, ranging from 32 to 43 days. Targeted therapies showed a broader range of 24 to 379 days, while chemotherapy drugs displayed the greatest variability, with median durations spanning 4 to 76 days. ILD signal strength varies across antineoplastic drug classes, with targeted therapies and immune checkpoint inhibitors showing heightened signals. Multi-method analysis suggests the potential need for adequate monitoring.