AbstractExtensive characterization of xenobiotic metabolites remains a major challenge in mass spectrometry-based exposomics, as untargeted high-resolution mass spectrometry (HRMS) can detect extensive chemical diversity but often lacks sufficient structural annotation and prioritization strategies for unknown metabolites. Here, we developed a built-for-purpose HRMS-based workflow combining untargeted metabolomics, in silico metabolite prediction, structural annotation, and toxicity prioritization to characterize xenobiotic biotransformation products using Disperse Orange 61 (DO61) as a case study. Sprague–Dawley rats were exposed to DO61 via oral gavage, and liver, plasma, and urine samples were analyzed by ultra-high performance liquid chromatography (UHPLC) coupled to HRMS in positive and negative ionization modes. Statistically significant features were prioritized and screened against a BioTransformer-generated suspect library containing predicted Phase I, Phase II, reductive, and microbial transformations. HRMS-based annotation, supported by bromine isotope pattern analysis and MS/MS fragmentation, identified putative DO61 metabolites, while SIRIUS-derived molecular formula predictions provided additional confidence through fragmentation-based structural evaluation. The workflow experimentally confirmed reductive azo cleavage as an early DO61 transformation, producing two aromatic amine products, and revealed extensive downstream metabolism of the parent compound and both cleavage products. Computational toxicity prediction further prioritized confirmed and putatively annotated metabolites with potential mutagenic relevance for a future evaluation. This workflow demonstrates how integrating untargeted HRMS with computational approaches can facilitate metabolite discovery, improve structural annotation confidence, and prioritize metabolites for toxicological evaluation, providing a hypothesis discovery strategy for characterizing xenobiotic metabolism and supporting exposure and hazard assessment within the field of exposomics research.

Using an integrative spatial Bayesian framework that merges high-resolution environmental pesticide risk modelling with comprehensive cancer registry data, this analysis reveals spatial patterns of pe...

Scientists say tailored disease treatments require a comprehensive assessment of a person’s lifelong environmental exposures.

Scientists say tailored disease treatments require a comprehensive assessment of a person’s lifelong environmental exposures.