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Published on: September 28, 2013
Immunotherapy-related adverse events (irAEs): extraction from FDA drug labels and comparative analysis
1Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.
Objectives:
Immune checkpoint inhibitors (ICIs) have dramatically improved outcomes in cancer patients. However, ICIs are associated with significant immune-related adverse events (irAEs) and the underlying biological mechanisms are not well-understood. To ensure safe cancer treatment, research efforts are needed to comprehensively detect and understand irAEs.
Materials And Methods:
We manually extracted and standardized irAEs from The U.S Food and Drug Administration (FDA) drug labels for six FDA-approved ICIs. We compared irAE profile similarities among ICIs and 1507 FDA-approved non-ICI drugs. We investigated how irAEs have differential effects on human organs by classifying irAEs based on their targeted organ systems. Finally, we identified broad-spectrum (nontarget-specific) and narrow-spectrum (target-specific) irAEs.
Results:
A total of 893 irAEs were extracted. 31.4% irAEs were shared among ICIs as compared to the 8.0% between ICIs and non-ICI drugs. irAEs were resulted from both on- and off-target effects: irAE profiles were more similar for ICIs with same target than different targets, demonstrating the on-target effects; irAE profile similarity for ICIs with the same target is less than 50%, demonstrating unknown off-target effects. ICIs significantly target many organ systems, including endocrine system (3.4-fold enrichment), metabolism (3.7-fold enrichment), immune system (3.6-fold enrichment), and autoimmune system (4.8-fold enrichment). We identified 21 broad-spectrum irAEs shared among all ICIs, 20 irAEs specific for PD-L1/PD-1 inhibition, and 28 irAEs specific for CTLA-4 inhibition.
Discussion And Conclusion:
Our study presents the first effort toward building a standardized database of irAEs. The extracted irAEs can serve as the gold standard for automatic irAE extractions from other data resources and set a foundation to understand biological mechanisms of irAEs.
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