Combining network toxicology, machine learning and molecular docking to explore the mechanism of bisphenol A-induced Asthma

Bisphenol A (BPA), a globally prevalent industrial chemical found in numerous products, may contribute to asthma pathogenesis via multi-system physiological disturbances. This study systematically investigates the molecular mechanisms of BPA-induced asthmatic toxicity. Using databases like ChEMBL, STITCH, SwissTargetPrediction, and GEO, potential targets related to BPA and asthma were identified. Core targets were determined using machine learning algorithms: LASSO, RF, and SVM. Enrichment analyses and immune infiltration analysis were conducted to understand the biological impact. Finally, molecular docking was performed on the core targets. Through network toxicology, we identified 11 targets, followed by machine learning algorithms to narrow down to 4 core targets, including AR, NCOA3, ADORA3, and HSP90AA1. The results of enrichment analyses and immune infiltration analysis showed that BPA induces asthma through multiple mechanisms especially inflammation regulation, hormone signaling and immune balance. Molecular docking confirmed the strong binding between BPA and the core targets, especially ADORA3. These findings provide actionable targets for environmental asthma prevention strategies and highlight the urgency of BPA exposure regulation.