| アブストラクト | BACKGROUND AND PURPOSE: Immune checkpoint inhibitors (ICIs) improve cancer outcomes but are also associated with immune-related adverse events (irAEs), which pose significant challenges for clinical management. EXPERIMENTAL APPROACH: An observational pharmacovigilance analysis on FDA Adverse Event Reporting System was performed to identify ICI-related adverse event (AE) signals. Fatality kinetics simulation and multivariate logistic regression were used to investigate patterns of fatal AEs and multisignal involvement. A machine learning framework, SAFE-ICI, was developed to predict short-term risk and outcomes of fatal irAEs occurring within the first 90 days of ICI therapy. KEY RESULTS: The analysis identified 358 significant AE signals associated with ICI therapies across 18 organ systems. PD-1/PD-L1 therapies were associated with 54 fatal irAEs, including 23 in non-small cell lung cancer (NSCLC), 5 in melanoma, 6 in renal cell carcinoma (RCC) and 20 in other cancers. Combination therapies were associated with 20 fatal irAEs, including 3 in NSCLC, 6 in melanoma, 7 in RCC and 4 in other cancers, with stable involvement of multiple AE signals. The SAFE-ICI model demonstrated robust performance in predicting fatal irAE risk, successfully stratifying patients into low- and high-risk phenotypes with significantly different survival benefits, in both the discovery and holdout validation cohorts. CONCLUSION AND IMPLICATIONS: Our findings highlight the potential of machine learning to improve pharmacovigilance systems and aid clinicians in enhancing patient outcomes during ICI therapy. |
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| ジャーナル名 | British journal of pharmacology |
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| Pubmed追加日 | 2025/9/15 |
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| 投稿者 | Yan, Dongxue; Lyu, Beibei; Yu, Jie; Bao, Siqi; Zhang, Zicheng; Zhou, Meng; Sun, Jie |
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| 組織名 | School of Biomedical Engineering, Eye Hospital, Wenzhou Medical University,;Wenzhou, China. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/40948045/ |
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