Robustness Analyses Substantially Narrow in silico Network-Toxicology Signals Linking Selected Pharmaceuticals and Caffeine to Vitiligo.

Background: Network-toxicology and molecular-docking pipelines are widely used to link environmental chemicals to disease targets, but their outputs are rarely subjected to external validation, background-aware significance testing, or promiscuity controls. Methods: This fully computational (in silico) study applied a conventional network-toxicology and molecular-docking pipeline to vitiligo and eleven compounds-caffeine, four antibiotics, two angiotensin-receptor blockers, two statins, a proton-pump inhibitor and an antidepressant-adopted verbatim from a published study, holding chemical input constant. The output was stress-tested by core-tier threshold sensitivity, external cross-platform validation in two independent cohorts with the receiver-operating-characteristic (ROC) direction fixed in advance, background-restricted overlap-significance testing, and caffeine-exclusion sensitivity analysis. Results: The 102-gene target set did not intersect the high-confidence vitiligo core tier (overlap = 0, stable above 0.170) and gave a 22-gene extended-tier intersection, in which HIF1A was the highest-degree node and five key differentially expressed genes discriminated lesional from non-lesional skin at areas under the ROC curve (AUC) of 0.73-0.87. Every check degraded this. The overlap was enriched against a protein-coding background (fold 2.49, p = 5.1×10 Conclusion: Validation and robustness checks converted an apparently supported mechanistic network into a result with no surviving gene-level candidate. The most consequential was a library default: within-cohort ROC direction selection turned a contradicted gene into the apparently strongest validation. Such checks should be standard, not optional. No causal or exposure-related association was demonstrated.