Abstract
BackgroundA number of computational tools for metabolism prediction have been developed over the last 20 years to predict the structures of small molecules undergoing biological transformation or environmental degradation. These tools were largely developed to facilitate absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, although there is now a growing interest in using such tools to facilitate metabolomics and exposomics studies. However, their use and widespread adoption is still hampered by several factors, including their limited scope, breath of coverage, availability, and performance.ResultsTo address these limitations, we have developed BioTransformer, a freely available software package for accurate, rapid, and comprehensive in silico metabolism prediction and compound identification. BioTransformer combines a machine learning approach with a knowledge-based approach to predict small molecule metabolism in human tissues (e.g. liver tissue), the human gut as well as the environment (soil and water microbiota), via its metabolism prediction tool. A comprehensive evaluation of BioTransformer showed that it was able to outperform two state-of-the-art commercially available tools (Meteor Nexus and ADMET Predictor), with precision and recall values up to 7 times better than those obtained for Meteor Nexus or ADMET Predictor on the same sets of pharmaceuticals, pesticides, phytochemicals or endobiotics under similar or identical constraints. Furthermore BioTransformer was able to reproduce 100% of the transformations and metabolites predicted by the EAWAG pathway prediction system. Using mass spectrometry data obtained from a rat experimental study with epicatechin supplementation, BioTransformer was also able to correctly identify 39 previously reported epicatechin metabolites via its metabolism identification tool, and suggest 28 potential metabolites, 17 of which matched nine monoisotopic masses for which no evidence of a previous report could be found.ConclusionBioTransformer can be used as an open access command-line tool, or a software library. It is freely available at https://bitbucket.org/djoumbou/biotransformerjar/. Moreover, it is also freely available as an open access RESTful application at www.biotransformer.ca, which allows users to manually or programmatically submit queries, and retrieve metabolism predictions or compound identification data.
Highlights
Metabolism is key to the production of energy, the generation of cellular building blocks as well as the activation, detoxification, and elimination of metabolic by-products or xenobiotics
Comparative evaluation of BioTransformer and Meteor Nexus in the prediction of human single‐step metabolism of small molecules The first test involved a comparative assessment of the performance of BioTransformer and Meteor Nexus (v.3.0.1) [26] in predicting single-step human metabolism of 40 pharmaceuticals and pesticides, randomly selected from DrugBank [38] and T3DB [42]
This test set was limited to these compound classes because Meteor Nexus’ biotransformation dictionary and associated rule bases are limited to pharmaceuticals and pesticides
Summary
Metabolism is key to the production of energy (catabolism), the generation of cellular building blocks (anabolism) as well as the activation, detoxification, and elimination of metabolic by-products or xenobiotics. Non-essential metabolites include metabolites generated through the activation, detoxification and elimination of metabolic by-products or xenobiotics. Xenobiotics are compounds such as pharmaceuticals and personal care products (PPCPs), pesticides, plant or food compounds, food additives, surfactants, solvents, and other man-made or biologically foreign substances. A number of computational tools for metabolism prediction have been developed over the last 20 years to predict the structures of small molecules undergoing biological transformation or environmental deg‐ radation These tools were largely developed to facilitate absorption, distribution, metabolism, excretion, and toxic‐ ity (ADMET) studies, there is a growing interest in using such tools to facilitate metabolomics and exposomics studies. Their use and widespread adoption is still hampered by several factors, including their limited scope, breath of coverage, availability, and performance
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