Decoding fatal toxic effects in checkpoint inhibitor therapy using real-world pharmacovigilance data and machine learning.

Abstract

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.