MetSim: Integrated Programmatic Access and Pathway Management for Xenobiotic Metabolism Simulators

Area of Science:

• Environmental Chemistry
• Computational Toxicology
• Bioinformatics

Background:

• Evaluating environmental chemical risks requires understanding xenobiotic metabolism.
• Existing metabolite simulation tools have varied formats and limited comparability.
• Comparing the performance and coverage of these tools is a significant challenge.

Purpose of the Study:

• To develop a standardized framework, MetSim, for harmonizing and comparing in silico metabolic simulators.
• To create a system for evaluating the performance and coverage of different metabolic simulation tools.

Main Methods:

• Developed a graph-based schema for harmonizing metabolism data.
• Implemented the schema in MongoDB for storing and retrieving metabolic graphs.
• Integrated four simulators: BioTransformer, OECD Toolbox, EPA's CTS, and TIMES.
• Compiled a dataset of 112 drugs and 432 metabolites for prediction.
• Evaluated simulator performance using recall and precision metrics.

Main Results:

• Individual simulator recall ranged from 0.39 to 0.54.
• Combining predictions from all simulators increased overall recall to 0.73.
• MetSim facilitated efficient derivation of insights into simulator performance.

Conclusions:

• MetSim provides a valuable platform for assessing in silico metabolic simulators.
• Standardized comparison aids in selecting appropriate tools for chemical risk evaluation.
• Future efforts can leverage MetSim to evaluate other datasets and improve predictive accuracy.