Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Comput Mol Sci
Impact Factor: 14.016

Recent advances in the prediction of non‐ CYP450 ‐mediated drug metabolism

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Computational models of drug metabolism prediction have focused mainly on cytochrome P450 enzymes, because drug–drug interactions, reactive metabolite formation, hepatotoxicity, idiosyncratic adverse drug interactions, and/or loss of efficacy of many drugs were the results of interactions with CYP450s. Metabolic regioselectivity and isoform specificity prediction models for CYP450‐catalyzed reactions have reached approximately 95% accuracy. Thus, a new drug candidate is less likely to show unexpected metabolic profile due to metabolism via CYP450 pathways. For such candidates, secondary metabolic Phase I and II enzymes are likely to play an expected (or unexpected) role in drug metabolism. The importance of flavin monooxygenases (FMOs), aldehyde and alcohol dehydrogenase, monoamine oxidase from the Phase I and UDP‐glucuronosyltransferase (UGT), sulfotransferase, glutathione S‐transferase, and methyltransferase from Phase II has increased and United States Food and Drug Administration guidelines on NDA have specific recommendations for in vitro and in vivo testing against these enzymes. Thus, there is an urgent requirement of reliable predictive models for drug metabolism catalyzed by these enzymes. In this review, we have classified drug metabolism prediction models (site of metabolism, isoform specificity, and kinetic parameter) for these enzymes into Phase I and II. When such models are unavailable, we discuss the Quantitative Structure Activity Relationship (QSAR), pharmacophore, docking, dynamics, and reactivity studies performed for the prediction of substrates and inhibitors. Recently published models for FMO and UGT are discussed. The need for comprehensive, widely applicable, sequential primary and secondary metabolite prediction is highlighted. Potential difficulties and future prospectives in the development of such models are discussed. WIREs Comput Mol Sci 2017, 7:e1323. doi: 10.1002/wcms.1323

Crystal structure (1GSE) of ethacrynic acid conjugated to GSH bond in the GST binding site. Glutathione is covalently bonded to ethacrynic acid.
[ Normal View | Magnified View ]
Schematic representation of the protocol employed by Sharma et al. for generating CoMFA models for prediction of SULT substrates.
[ Normal View | Magnified View ]
SN2 type mechanism of sulfotransferase reaction with hydroxyl group containing drugs and xenobiotics.
[ Normal View | Magnified View ]
Flow‐chart employed by Sorich et al. for the generation of multiple pharmacophores for the prediction of substrates and nonsubstrates of UGTs.
[ Normal View | Magnified View ]
Proposed polar mechanism for the oxidation of amine with hydrogens on α‐carbon atoms.
[ Normal View | Magnified View ]
Indole‐2,3‐dione derivatives with selectivity toward the isoforms of aldehyde dehydrogenase (ALDH). The bold values indicate that the activity of the structures above are high for the corresponding isoforms.
[ Normal View | Magnified View ]
Possible mechanisms of nephrotoxicity of acyclovir.
[ Normal View | Magnified View ]
Abacavir metabolism by alcohol dehydrogenase (ADH) isoforms to reactive metabolites.
[ Normal View | Magnified View ]
Accessibility and reactivity are important factors determining FMO3‐mediated drug metabolism.
[ Normal View | Magnified View ]
Crystal structures of yeast FMO. Panel (a) shows the FAD cofactor bound in the big domain while NADP is seen interacting with the small domain. Panel (b) shows the FAD cofactor in the big domain, while NADP has been displaced by the substrate (methimazole). Electron density analysis has shown the presence of dioxygen in both these crystal structures.
[ Normal View | Magnified View ]
A comprehensive and chemically intuitive classification of site(s) of metabolism (SOMs) based on the phase and type of metabolic reactions.
[ Normal View | Magnified View ]
Relative contribution of different Phase II metabolic enzymes toward drug metabolism. UDP‐glucuronosyltransferase (UGT), sulfotransferase (SULT), N‐acetyltransferase‐1 (NAT1), N‐acetyltransferase ‐2 (NAT2), glutathione S‐transferase (GST), GST‐α (GSTA), GST‐π (GSTP), GST‐μ (GSTM), GST‐θ (GSTT), catechol O‐methyltransferase (COMT), histamine N‐methyltransferase (HMT), thiopurine S‐methyltransferase (TPMT).
[ Normal View | Magnified View ]
Relative contribution of different Phase I metabolic enzymes toward drug metabolism. Flavin monooxygenases, quinone oxidoreductases (NQ0), and dihydropyrimidine dehydrogenase (DPD) are included in others.
[ Normal View | Magnified View ]
Summary of work flow used for SOM prediction model development for seven metabolic reaction types.
[ Normal View | Magnified View ]
Regioselectivity of methylation and demethylation of nitrocatechol derivatives by COMT and P450.
[ Normal View | Magnified View ]
COMT catalyzed methylation of dopamine, quercetin, and luteolin. SAM, S‐adenosyl‐l‐methionine; SAH, S‐adenosylhomocysteine. cLog P values for the substrates and metabolites show that methylation decreases the polarity of substrates marginally.
[ Normal View | Magnified View ]
Curcumin analogues with inhibitory potential against purified GST enzymes.
[ Normal View | Magnified View ]

Related Articles

In silico toxicology: computational methods for the prediction of chemical toxicity
Computational toxicology: a tool for all industries
Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening

Browse by Topic

Software > Molecular Modeling
Structure and Mechanism > Reaction Mechanisms and Catalysis
Computer and Information Science > Chemoinformatics

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts