Pharmacovigilance Signal Detection and Mutually Exclusive Driver Mutations of the PI3K/AKT Pathway in Breast Cancer Treated With Capivasertib.

This study is aimed at comprehensively evaluating the real-world safety profile and underlying molecular mechanisms of the AKT inhibitor capivasertib by integrating pharmacovigilance data, computational biology, and multiomics analyses. Adverse event (AE) reports from the FAERS database were analyzed using disproportionality algorithms (ROR, PRR, BCPNN, EBGM) to detect significant safety signals. To elucidate potential toxicological mechanisms, we employed network toxicology and molecular docking, further validated by 100 ns molecular dynamics (MD) simulations. Additionally, bulk genomic cohorts (e.g., TCGA, METABRIC) and single-cell RNA sequencing (scRNA-seq) datasets were utilized to assess mutation patterns and delineate key targets within the tumor microenvironment (TME). Analysis of 22,143 AE reports yielded 133 significant safety signals, predominantly involving metabolism (e.g., hyperglycemia), the gastrointestinal system (e.g., nausea, stomatitis), and dermatological conditions (e.g., rash). Pathway enrichment highlighted the PI3K-AKT, HIF-1, and EGFR signaling networks. Integrative analyses identified critical toxicity-related modulators, notably AKT1, IGF1, PTEN, TP53, and GSK3B. Crucially, MD simulations robustly confirmed the thermodynamic stability of the capivasertib-GSK3B complex. Genomic profiling revealed pronounced mutual exclusivity among PIK3CA, AKT1, and PTEN alterations. Furthermore, scRNA-seq analysis demonstrated that GSK3B overexpression defines a highly aggressive, proliferative malignant subpopulation that profoundly reshapes intercellular communication with stromal fibroblasts. By seamlessly bridging real-world pharmacovigilance with advanced structural biology and single-cell transcriptomics, this study delineates the comprehensive safety landscape of capivasertib. Our findings provide crucial clinical alerts for AE monitoring and offer deep mechanistic insights to optimize personalized therapeutic management in breast cancer.