Deciphering bisphenol A (BPA)-elicited osteoarthritis mechanisms through network toxicology and molecular docking, then de novo generation of novel therapeutic candidates.

OBJECTIVE: Bisphenol A (BPA), a pervasive environmental pollutant, is increasingly associated with osteoarthritis (OA) development, yet its molecular mechanisms remain unknown. Currently, there is no definitive cure for OA. METHODS: BPA targets were predicted using STITCH and Swiss Target Prediction, while OA-related targets were collected from GeneCards, OMIM, and the Therapeutic Target Database (TTD). Protein-protein interaction (PPI) networks were constructed using STRING and visualized in Cytoscape to identify hub targets. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed, and molecular docking with AutoDock evaluated BPA-core target interactions. We employed our Computational Analysis of Novel Drug Opportunities (CANDO) platform for de novo drug prediction. RESULTS: Systematic bioinformatics analysis identified 26 candidate targets, with ESR1, PTGS2, CCL2, FLNA, and TRPV1 as key hubs. Pathway analysis revealed involvement in calcium ion transport, muscle contraction, IL-17 signaling, and estrogen signaling. Molecular docking confirmed strong BPA-target binding affinities. CANDO predicted 14 potential OA treatments, including glucosamine, ibuprofen, celecoxib, indomethacin, palmitic acid, and linoleic acid. Notably, qRT-PCR validation revealed that ESR1, PTGS2, CCL2, and TRPV1 were highly expressed, whereas FLNA was expressed at lower levels in the osteoarthritis blood samples. CONCLUSIONS: This study elucidates BPA's molecular mechanisms in OA and identifies promising therapeutic candidates. The integration of network toxicology, molecular docking, and computational drug discovery provides a robust framework for understanding environmental toxicants and advancing OA therapies.