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Decoding fatal toxic effects in checkpoint inhibitor therapy using real-world pharmacovigilance data and machine
Dongxue Yan1, Beibei Lyu1, Jie Yu1
1School of Biomedical Engineering, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
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.
Insights
Immune checkpoint inhibitors (ICIs) can cause fatal immune-related adverse events (irAEs). A new machine learning model, SAFE-ICI, predicts short-term risk of fatal irAEs within 90 days of ICI therapy.
Area of Science:
- Pharmacovigilance
- Oncology
- Machine Learning
Background:
- Immune checkpoint inhibitors (ICIs) improve cancer treatment outcomes.
- Immune-related adverse events (irAEs) are a significant clinical challenge associated with ICIs.
Purpose of the Study:
- To identify adverse event (AE) signals related to ICI therapies.
- To develop a machine learning model (SAFE-ICI) for predicting the risk and outcomes of fatal irAEs within the first 90 days of ICI therapy.
Main Methods:
- Observational pharmacovigilance analysis using the FDA Adverse Event Reporting System.
- Fatality kinetics simulation and multivariate logistic regression to analyze fatal AE patterns.
- Development and validation of the SAFE-ICI machine learning framework.
Main Results:
- Identified 358 significant AE signals across 18 organ systems linked to ICI therapies.
- PD-1/PD-L1 therapies were associated with 54 fatal irAEs; combination therapies with 20.
- The SAFE-ICI model accurately stratified patients into low- and high-risk groups for fatal irAEs, showing different survival benefits.
Conclusions:
- Machine learning holds potential for enhancing pharmacovigilance systems.
- The SAFE-ICI model can aid clinicians in improving patient outcomes during ICI therapy.
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