Constructing Xenobiotic Maps of Metabolism to Predict the Role of Enzymes in DNA Adduct Formation

Abstract

Background : The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food additives...). Among environmental contaminants of concern, heterocyclic aromatic amines (HAA) are xenobiotics classified as possible or probable carcinogens (2A or 2B) by IARC for which low information exist in humans. While HAA is a family of more than thirty identified chemicals, the metabolism activation and DNA adduct formation have been fully characterized in human liver for few of them (MeIQx, PhIP, AalphaC). Results: We developed a modeling approach in order to predict all the possible metabolite derivatives of a xenobiotic. Our approach relies on the construction of an enriched and annotated map of derivative metabolites from an input metabolite. The pipeline assembles reaction prediction tools (SyGMa), sites of metabolism prediction tools (Way2Drug, SOMP and Fame 3), a tool to estimate the ability of a xenobotics to form DNA adducts (XenoSite Reactivity V1), and a filtering procedure based on Bayesian framework. This prediction pipeline was evaluated using caffeine and then applied to HAAs. The method was applied to determine enzyme profiles associated with the maximization of DNA adducts formation derived from each HAA. These profiles could be very different depending on the chemicals allowing to classify HAAs which have been grouped by their associated profiles. Conclusions: Overall, such a predictive toxicological model based on a in silico systems biology approach open perspectives to estimate genotoxicity of various chemical classes of environmental contaminants. Moreover, our approach based on enzymes profile determination open the perspective to predict various xenobiotics derived metabolites susceptible to bind DNA adducts in both normal and physiopathological situations.

A comprehensive review on the application of artificial intelligence in drug discovery.

“…Undirected graph recursive neural networks (UG-RNNs) and graph-based CNN are used to predict aqueous solubility [64]. RS-predictor (using hierarchical descriptor and quantum chemical, atom-based descriptor), SMARTCyp and Xenosite (combining ANN with topological, quantum chemical, and SMARTCyp descriptor), CypRules, MetaSite, Metapred, WhichCyp are available tools to predict sites of metabolism [65]. Many ML methods are used for toxicity study, i.e., SVM, relevance vector machine (RVM), regularized-RF, RVM boosting (RVMBoost), SVM boosting (SVMBoost), AdaBoost, and C5.0 trees.…”