Congener-resolved hazard identification of ginkgolic acids as emerging natural contaminants.

Ginkgolic acids (GAs) are alkylphenolic constituents widely present in Ginkgo biloba-derived products and plant residues that may enter environmental systems through poorly regulated consumer goods, industrial processing, and waste streams. Owing to inconsistent regulatory control and limited environmental monitoring, GAs are increasingly regarded as emerging natural contaminants. However, their congener-specific hazard profiles and mechanistic relevance to key detoxification organs remain insufficiently resolved, hindering informed hazard prioritization for environmental and public health assessment. A congener-resolved, hazard-oriented toxicological framework is presented here to identify high-priority GAs congeners by integrating network toxicology, deep learning-based hazard prediction, molecular docking, transcriptomics, and targeted experimental validation. Network analysis revealed 52 hepatotoxicity- and 21 nephrotoxicity-associated targets linked to GAs exposure. Among five major congeners, GA C17:2 was prioritized as a high-concern candidate based on integrated in silico hazard signals and stable interactions with ten core toxicity-related proteins. GA C17:2 elicited potent cytotoxicity in hepatic and renal cell models, accompanied by lipid metabolic disruption and activation of ferroptosis-, apoptosis-, and inflammation-associated responses. In a short-term in vivo exposure model, GA C17:2 induced dose-dependent liver and kidney injury, characterized by PPARA suppression, ACSL4 upregulation, lipid peroxidation, and dysregulation of PPAR signaling, ferroptosis, and fatty acid degradation pathways. Positioned as a mechanistic hazard identification study rather than a quantitative environmental risk assessment, this work provides structure-informed biological evidence linking GAs congeners to adverse molecular responses in detoxification organs. Our findings identify GA C17:2 as a high-priority congener for environmental hazard screening and establish a mechanism-informed framework to support future monitoring strategies, contaminant classification, and risk-oriented investigations of emerging natural contaminants.