Literature DB >> 21291341

In silico site of metabolism prediction of cytochrome P450-mediated biotransformations.

Ákos Tarcsay1, György M Keseru.   

Abstract

INTRODUCTION: Preclinical research involves the in vitro monitoring of metabolic stability to deliver compounds with improved ADME profiles. Prediction of the metabolically vulnerable points can substantially help in analyzing CYP-mediated metabolism data and support optimization efforts in drug discovery programs. Moreover, fast and reliable in silico predictions could accelerate the characterization of in vitro/in vivo metabolites. AREAS COVERED: This paper reviews in silico methods available for CYP-mediated site of metabolism (SOM) prediction. Comprehensive and practical knowledge in this field can guide the identification of best practice and may inspire ideas for the development of novel approaches. EXPERT OPINION: Comparison of the efficacy of SOM prediction methodologies revealed the general dependency on the studied isoform and substrate set. Increasing knowledge on P450 X-ray structures, on biotransformations and on the mechanistic details of the catalytic cycle revolutionized the prediction of SOM. Although no ultimate solution exits, combined methods covering both steric and electronic effects are preferred on most of the pharmaceutically relevant isoforms.

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Year:  2011        PMID: 21291341     DOI: 10.1517/17425255.2011.553599

Source DB:  PubMed          Journal:  Expert Opin Drug Metab Toxicol        ISSN: 1742-5255            Impact factor:   4.481


  7 in total

1.  Predicting drug metabolism by CYP1A1, CYP1A2, and CYP1B1: insights from MetaSite, molecular docking and quantum chemical calculations.

Authors:  Preeti Pragyan; Siddharth S Kesharwani; Prajwal P Nandekar; Vijay Rathod; Abhay T Sangamwar
Journal:  Mol Divers       Date:  2014-07-16       Impact factor: 2.943

Review 2.  Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Authors:  Johannes Kirchmair; Mark J Williamson; Jonathan D Tyzack; Lu Tan; Peter J Bond; Andreas Bender; Robert C Glen
Journal:  J Chem Inf Model       Date:  2012-02-17       Impact factor: 4.956

3.  Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software.

Authors:  Megan L Peach; Alexey V Zakharov; Ruifeng Liu; Angelo Pugliese; Gregory Tawa; Anders Wallqvist; Marc C Nicklaus
Journal:  Future Med Chem       Date:  2012-10       Impact factor: 3.808

4.  Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers.

Authors:  Jonathan D Tyzack; Hamse Y Mussa; Mark J Williamson; Johannes Kirchmair; Robert C Glen
Journal:  J Cheminform       Date:  2014-05-27       Impact factor: 5.514

5.  Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System.

Authors:  Shuai-Bing He; Man-Man Li; Bai-Xia Zhang; Xiao-Tong Ye; Ran-Feng Du; Yun Wang; Yan-Jiang Qiao
Journal:  Int J Mol Sci       Date:  2016-10-09       Impact factor: 5.923

6.  RD-Metabolizer: an integrated and reaction types extensive approach to predict metabolic sites and metabolites of drug-like molecules.

Authors:  Jiajia Meng; Shiliang Li; Xiaofeng Liu; Mingyue Zheng; Honglin Li
Journal:  Chem Cent J       Date:  2017-07-18       Impact factor: 4.215

7.  Modeling chemical interaction profiles: I. Spectral data-activity relationship and structure-activity relationship models for inhibitors and non-inhibitors of cytochrome P450 CYP3A4 and CYP2D6 isozymes.

Authors:  Brooks McPhail; Yunfeng Tie; Huixiao Hong; Bruce A Pearce; Laura K Schnackenberg; Weigong Ge; Luis G Valerio; James C Fuscoe; Weida Tong; Dan A Buzatu; Jon G Wilkes; Bruce A Fowler; Eugene Demchuk; Richard D Beger
Journal:  Molecules       Date:  2012-03-15       Impact factor: 4.411

  7 in total

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