Computational Prediction of Metabolism: Sites, Products, SAR, P450 Enzyme Dynamics, and Mechanisms

Abstract

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.

Structural Diversity of Eukaryotic Membrane Cytochrome P450s

“…Structures of drug-metabolizing P450s can be used to improve predictions of sites of metabolism and provide information for drug redesign to overcome metabolic barriers to improve efficacy or to reduce the likelihood of drug-drug interactions (62)(63)(64)(65). Hepatic clearance of drugs by P450-mediated metabolism is the most prevalent pathway for elimination of the 200 most prescribed drugs and is attributed in order of frequency to microsomal P450s 3A4, 2C9, 2D6, 2C19, 1A2, 2C8, and 2B6 (66).…”

Prediction of the site of CYP3A4 metabolism of tolterodine by molecular dynamics simulation from multiple initial structures of the CYP3A4-tolterodine complex

“…For prediction of the former, docking simulation using the substrate and a CYP3A4 structure is widely used. There are many methods for SOM prediction [6], including SMARTCyp [7] and MetaSite [8]. The reported methods use a set of oxidative reactivities of fragment molecules (precalculated by quantum mechanical techniques such as density functional theory [DFT]) in combination with accessibility estimation methods such as the use of topological accessibility descriptors and superimposition of substrates with binding site grids.…”

In Silico Prediction of Human Sulfotransferase 1E1 Activity Guided by Pharmacophores from Molecular Dynamics Simulations

“…Acting in phase I metabolism, the enzyme family of cytochrome P450 is estimated to transform about 75% of marketed drugs (1) and numerous in silico approaches for the prediction of cytochrome P450-mediated metabolism have emerged to date (2,3). Although the majority of metabolism prediction studies focuses on phase I, the significance of phase II metabolism is generally underestimated (4) and to this day, computer-based models for the prediction of phase II metabolism remain scarce (5).…”