Knowledge-Driven Approaches to Create the MTox700+ Metabolite Panel for Predicting Toxicity.

Elena Sostare, Thomas N Lawson, Lucy R Saunders, John K Colbourne, Ralf J M Weber, Tomasz Sobanski, Mark R Viant

Toxicological sciences : an official journal of the Society of Toxicology 2022 Mar 28

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Endogenous metabolite levels describe the molecular phenotype that is most downstream from chemical exposure. Consequently, quantitative changes in metabolite levels have the potential to predict mode-of-action and adversity, with regulatory toxicology predicated on the latter. However, toxicity-related metabolic biomarker resources remain highly fragmented and incomplete. Although development of the S1500+ gene biomarker panel has accelerated the application of transcriptomics to toxicology, a similar initiative for metabolic biomarkers is lacking. Our aim was to define a publicly available metabolic biomarker panel, equivalent to S1500+, capable of predicting pathway perturbations and/or adverse outcomes. We conducted a systematic review of multiple toxicological resources, yielding 189 proposed metabolic biomarkers from existing assays (BASF, Bowes-44, and Tox21), 342 biomarkers from databases (Adverse Outcome Pathway Wiki, Comparative Toxicogenomics Database, QIAGEN Ingenuity Pathway Analysis, and Toxin and Toxin-Target Database), and 435 biomarkers from the literature. Evidence mapping across all 8 resources generated a panel of 722 metabolic biomarkers for toxicology (MTox700+), of which 462 (64%) are associated with molecular pathways and 575 (80%) with adverse outcomes. Comparing MTox700+ and S1500+ revealed that 418 (58%) metabolic biomarkers associate with pathways shared across both panels, with further metabolites mapping to unique pathways. Metabolite reference standards are commercially available for 646 (90%) of the panel metabolites, and assays exist for 578 (80%) of these biomarkers. This study has generated a publicly available metabolic biomarker panel for toxicology, which through its future laboratory deployment, is intended to help build foundational knowledge to support the generation of molecular mechanistic data for chemical hazard assessment. © The Author(s) 2022. Published by Oxford University Press on behalf of the Society of Toxicology.

Citation

Elena Sostare, Thomas N Lawson, Lucy R Saunders, John K Colbourne, Ralf J M Weber, Tomasz Sobanski, Mark R Viant. Knowledge-Driven Approaches to Create the MTox700+ Metabolite Panel for Predicting Toxicity. Toxicological sciences : an official journal of the Society of Toxicology. 2022 Mar 28;186(2):208-220