Computational toxicology prioritization of CYP3A4 and DPP7 as candidate triclosan-relevant molecular targets in...

Triclosan (TCS), a synthetic broad-spectrum antimicrobial and novel persistent organic pollutant, is detected in over 75% of human urine samples, yet its molecular targets in ulcerative colitis (UC) remain unclear. We integrated network toxicology, multi-cohort machine learning, molecular docking, 100-ns all-atom molecular dynamics (MD) simulation, and single-cell transcriptomics to address this gap.Intersecting UC differentially expressed genes with database-derived TCS targets yielded 479 shared candidate targets; because these databases include indirect and probabilistic associations,this set was treated as hypothesis-generating. KEGG enrichment identified the AGE-RAGE signaling pathway, PPAR signaling, and TNF signaling among the top enriched pathways. LASSO regression and random forest algorithms distilled a 10-gene diagnostic signature that achieved cross-cohort AUC of 0.910-1.000 across six independent GEO datasets, consistent with strong discriminative performance.Molecular docking predicted Vina scores of -7.1 kcal/mol (CYP3A4) and -7.0 kcal/mol (DPP7);redocking and decoy analyses supported protocol specificity but not biochemical binding or inhibition. Over 100 ns of MD simulation, the docked poses remained stable for both targets. Single-cell RNA sequencing GSE214695 independently corroborated cell-type-specific CYP3A4 downregulation in epithelial cells and broad DPP7 upregulation across immune subsets in UC.CellOracle-based in silico perturbation predicted that CYP3A4 suppression would impair cytoplasmic translation, while DPP7 suppression would polarize innate inflammation toward an IL-1β-driven phenotype. Together, these analyses computationally prioritize CYP3A4 and DPP7 as candidate TCS-relevant targets in UC and generate testable hypotheses for future experimental validation; they do not by themselves establish direct target engagement, inhibition, or causal contribution to UC.