An integrated computational framework for hazard identification of emerging contaminants: a case study of 6PPD

With the rapid expansion in the diversity and production of chemical, the associated environmental pollution and health risks have become increasingly prominent, underscoring the urgent need for rapid identification of potential high-risk contaminants. In this study, we propose an integrated computational framework for the rapid hazard assessment of emerging contaminants. N-(1,3-Dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD), a widely used rubber antioxidant, was selected as a case study. A total of 24 transformation products were obtained, including both previously reported compounds (e.g., 6PPD-quinone) and newly predicted ones. Following structural optimization via Gaussian-based quantum chemical calculations, frontier molecular orbital analysis was conducted to elucidate plausible biotransformation mechanisms of previously unreported metabolites. Quantitative structure-activity relationship models for ten aquatic species were developed to construct species sensitivity distributions, yielding a predicted no-effect concentration (PNEC) of 1550 ng/L. Several metabolites exhibited higher toxicity than the parent compound. Reverse molecular docking was performed against 23,391 human proteins and integrated with physiologically based toxicokinetic modeling. The results revealed a predominance of hepatic bioactivity and high-affinity binding to key metabolic enzymes (CYP4F3, CYP2J2, HSD17B6, and HSD17B2), suggesting potential molecular initiating events. The preliminary zebrafish experiments provided supportive evidence for these potential mechanisms, including oxidative stress, inflammatory responses, and hepatic dysfunction, while no significant effects were observed at PNEC-level concentrations. Overall, this study demonstrates a practical and generalizable framework for early hazard screening of emerging contaminants, providing a valuable tool for environmental risk assessment and management.