Literature DB >> 20565339

The challenges of in silico contributions to drug metabolism in lead optimization.

Roy J Vaz1, Ismael Zamora, Yi Li, Stephan Reiling, Jian Shen, Gabriele Cruciani.   

Abstract

IMPORTANCE OF THE FIELD: The site of metabolism (SOM) predictions by CYP 3A4 are extremely important during the drug discovery process especially during the lead discovery or library design phases. With the ability to rapidly characterize metabolites from these enzymes, the challenges facing in silico contribution change during the drug optimization phase. Some of the challenges are addressed in this article. Some aspects of the SOM prediction software and methodology are discussed in this opinion article and examples of software utility in overcoming metabolic instability in drug optimization are shown. AREAS COVERED IN THIS REVIEW: SOM prediction by various approaches is discussed. Two ways of overcoming metabolic instability, blocking the metabolic softspots and rational modification of the instable molecule to avoid interaction with the CYP pocket, are discussed. The contribution plot in MetaSite and its use are discussed. WHAT THE READER WILL GAIN: The reader will gain an understanding of possible approaches to either blocking the metabolic softspot or rationally modifying the molecule using MetaSite software or docking approaches. Blocking metabolism using fluorination has risks especially introducing multifluorinated benzene rings in the molecule. TAKE HOME MESSAGE: During the lead optimization phase of drug discovery, when metabolic instability is an issue in a series, in silico approaches can be used to modify the molecule in order to decrease clearance due to metabolism, even that due to CYP3A4.

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Year:  2010        PMID: 20565339     DOI: 10.1517/17425255.2010.499123

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


  7 in total

1.  Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds.

Authors:  Fangping Mu; Clifford J Unkefer; Pat J Unkefer; William S Hlavacek
Journal:  Bioinformatics       Date:  2011-04-08       Impact factor: 6.937

2.  In vivo-in vitro-in silico pharmacokinetic modelling in drug development: current status and future directions.

Authors:  Olavi Pelkonen; Miia Turpeinen; Hannu Raunio
Journal:  Clin Pharmacokinet       Date:  2011-08       Impact factor: 6.447

3.  Shaping the future of safer innovative drugs in Europe.

Authors:  Jordi Mestres; Sharon D Bryant; Ismael Zamora; Johann Gasteiger
Journal:  Nat Biotechnol       Date:  2011-09-08       Impact factor: 54.908

4.  In Silico Augmentation of the Drug Development Pipeline: Examples from the study of Acute Inflammation.

Authors:  Gary An; John Bartels; Yoram Vodovotz
Journal:  Drug Dev Res       Date:  2011-03-01       Impact factor: 4.360

Review 5.  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

6.  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

7.  In silico mechanistic profiling to probe small molecule binding to sulfotransferases.

Authors:  Virginie Y Martiny; Pablo Carbonell; David Lagorce; Bruno O Villoutreix; Gautier Moroy; Maria A Miteva
Journal:  PLoS One       Date:  2013-09-06       Impact factor: 3.240

  7 in total

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