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MetSim: Integrated Programmatic Access and Pathway Management for Xenobiotic Metabolism Tools
Citation:
Groff, L., I. Shah, AND G. Patlewicz. MetSim: Integrated Programmatic Access and Pathway Management for Xenobiotic Metabolism Tools. SOT, Nashville, TN, March 19 - 23, 2023. https://doi.org/10.23645/epacomptox.22637485
Impact/Purpose:
Poster presented to the Society of Toxicology 62nd Annual Meeting and ToxExpo March 2023
Description:
Metabolic similarity is a key consideration in read-across but approaches to characterise and quantify the contribution metabolism plays are still evolving. A major challenge lies in the lack of a standardized database of human xenobiotic metabolism pathways for environmental chemicals. To address this issue, we developed a metabolic simulation framework called MetSim, comprised of three main components. First, we propose a harmonised graph-based representation for managing xenobiotic metabolism pathway information between different in silico tools and empirical evidence from the literature. This schema is implemented in a Mongo database to store, retrieve and analyze large-scale metabolic graphs. Second, MetSim includes a standardised application programming interface (API) for available metabolic simulators, including BioTransformer, the OECD Toolbox, and Tissue Metabolism Simulator (TIMES). Third, MetSim includes functions to systematically evaluate the performance of metabolism simulators using recall, precision and overall accuracy on benchmark data sets. Here we report on the overall architecture of MetSim, and performance results for two data sets: (a) 59 drugs (mostly NSAIDs) and their 179 published metabolites, and (b) 718 diverse substances in the EPA Distributed Structure-Searchable Toxicity (DSSTox) database and their 1632 metabolites. The 59 drugs were processed with MetSim using BioTransformer (CypReact model with 3 cycles of human Phase I metabolism), TIMES (in vivo rat simulator model) and OECD Toolbox (in vitro rat Liver S9), producing 11202, 590, and 539 metabolites, respectively. The recall for Biotransformer, TIMES and OECD Toolbox was 0.62, 0.41 and 0.52, respectively. For the larger DSSTox dataset, two cycles of human phase I (CypReact) and one cycle of phase II metabolism were modeled using BioTransformer, and both TIMES and OECD Toolbox using the same two rat liver models, producing 60097, 6654, and 5204 metabolites, respectively. The recall for Biotransformer, TIMES and OECD Toolbox was 0.16, 0.41 and 0.38, respectively. We summarized the performance of these tools by data set, chemical class (using ClassyFire) and metabolic simulator. All tools performed well for phenanthrenes, piperadines, lactams, and azoles but poorly for pyrrolines, organonitrogen compounds, and nucleotide analogues. BioTransformer performed well for benzoxazines, benzothaizepines, and quinolines, but poorly for steroids, benzothiazines, and diazines. Conversely, TIMES and the OECD Toolbox performed well for steroids, benzothiazines, and diazines, but poorly for benzoxazines, diazinines and organooxygen compounds. MetSim provides useful data and insights on the performance and limitations of in silico metabolism tools, which will inform our subsequent efforts in characterising metabolic similarity. This abstract does not reflect EPA policy.
URLs/Downloads:
DOI: MetSim: Integrated Programmatic Access and Pathway Management for Xenobiotic Metabolism Tools
LGROFF_SOT2023POSTER_V2.PDF (PDF, NA pp, 774.586 KB, about PDF)