Frontiers in pharmacology · 2025
An integrated approach based on FDA adverse event reporting system, network pharmacology, molecular docking, and molecular dynamics simulation analysis to study the cardiac adverse reactions and mechanism of action of osimertinib.
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Wang Wenjuan, Liu Hong, Wen Yongqing, Zhang Yanhua, Ma Xu
Аннотация
OBJECTIVES: This study investigates osimertinib-induced cardiac adverse reactions (CAR) using real-world FDA Adverse Event Reporting System (FAERS) data and explores molecular mechanisms via network pharmacology, molecular docking, and dynamics simulations.
METHODS: We analyzed osimertinib-related adverse events from Q4 2015 to Q4 2024 using FAERS data, applying reporting odds ratio (ROR) and Bayesian confidence propagation neural network (BCPNN) methods. Potential CAR targets were identified via PharmMapper, Swiss Target Prediction, and GeneCards. Protein-protein interaction (PPI) networks, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, molecular docking, and dynamics simulations were performed.
RESULTS: Among 15,382 reports, 274 were CAR-related, including pericardial effusion, cardiomyopathy, and cardiac dysfunction (25.00% mortality). Key targets (AKT1, ESR1, EGFR, SRC, ALB, CASP3) and pathways (PI3K-Akt, Ras, MAPK, calcium, JAK-STAT, TNF) were identified. Molecular docking confirmed strong binding affinity with binding energies below -7.5 kJ/mol for key targets (AKT1: -9.9 kJ/mol; ALB: -8.4 kJ/mol). Molecular dynamics simulations (100 ns) demonstrated stable binding of osimertinib-AKT1/ALB complexes, with average RMSD values of 0.52 nm and 0.50 nm, respectively, and binding free energies of -44.63 kJ/mol (AKT1) and -42.92 kJ/mol (ALB).
CONCLUSION: This study clarifies osimertinib-induced CAR mechanisms involving multi-target interactions and pathway dysregulation, aiding clinical safety and future research.
METHODS: We analyzed osimertinib-related adverse events from Q4 2015 to Q4 2024 using FAERS data, applying reporting odds ratio (ROR) and Bayesian confidence propagation neural network (BCPNN) methods. Potential CAR targets were identified via PharmMapper, Swiss Target Prediction, and GeneCards. Protein-protein interaction (PPI) networks, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, molecular docking, and dynamics simulations were performed.
RESULTS: Among 15,382 reports, 274 were CAR-related, including pericardial effusion, cardiomyopathy, and cardiac dysfunction (25.00% mortality). Key targets (AKT1, ESR1, EGFR, SRC, ALB, CASP3) and pathways (PI3K-Akt, Ras, MAPK, calcium, JAK-STAT, TNF) were identified. Molecular docking confirmed strong binding affinity with binding energies below -7.5 kJ/mol for key targets (AKT1: -9.9 kJ/mol; ALB: -8.4 kJ/mol). Molecular dynamics simulations (100 ns) demonstrated stable binding of osimertinib-AKT1/ALB complexes, with average RMSD values of 0.52 nm and 0.50 nm, respectively, and binding free energies of -44.63 kJ/mol (AKT1) and -42.92 kJ/mol (ALB).
CONCLUSION: This study clarifies osimertinib-induced CAR mechanisms involving multi-target interactions and pathway dysregulation, aiding clinical safety and future research.