Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis - Boyce, Matthew; Favela, Kristin A.; Bonzo, Jessica A.; Chao, Alex; Lizarraga, Lucina E.; Moody, Laura R.; Owens, Elizabeth O.; Patlewicz, Grace; Shah, Imran; Sobus, Jon R.; Thomas, Russell S.; Williams, Antony J.; et al. (Frontiers in Toxicology, 2023)
Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analyses (NTA), which often aim to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) LCMS to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from EPA’s ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some – but not all – cases, SSA methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals.