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

Xenobiotic
metabolism is a key consideration in evaluating the
hazards and risks posed by environmental chemicals. A number of software
tools exist that are capable of simulating metabolites, but each reports
its predictions in a different format and with varying levels of detail.
This makes comparing the performance and coverage of the tools a practical
challenge. To address this shortcoming, we developed a metabolic simulation
framework called MetSim, which comprises three main components. A
graph-based schema was developed to allow metabolism information to
be harmonized. The schema was implemented in MongoDB to store and
retrieve metabolic graphs for subsequent analysis. MetSim currently
includes an application programming interface for four metabolic simulators:
BioTransformer, the OECD Toolbox, EPA’s chemical transformation
simulator (CTS), and tissue metabolism simulator (TIMES). Lastly,
MetSim provides functions to help evaluate simulator performance for
specific data sets. In this study, a set of 112 drugs with 432 reported
metabolites were compiled, and predictions were made using the 4 simulators.
Fifty-nine of the 112 drugs were taken from the Small Molecule Pathway
Database, with the remainder sourced from the literature. The human
models within BioTransformer and CTS (Phase I only) and the rat models
within TIMES and the OECD Toolbox (Phase I only) were used to make
predictions for the chemicals in the data set. The recall and precision
(recall, precision) ranked in order of highest recall for each individual
tool were CTS (0.54, 0.017), BioTransformer (0.50, 0.008), Toolbox in vitro (0.40, 0.144), TIMES in vivo (0.40,
0.133), Toolbox in vivo (0.40, 0.118), and TIMES in vitro (0.39, 0.128). Combining all of the model predictions
together increased the overall recall (0.73, 0.008). MetSim enabled
insights into the performance and coverage of in silico metabolic simulators to be more efficiently derived, which in turn
should aid future efforts to evaluate other data sets.