Literature DB >> 11922955

Computer systems for the prediction of xenobiotic metabolism.

Jan Langowski1, Anthony Long.   

Abstract

The aim of pharmaceutical research and development is to ensure a continuing pipeline of new chemical entities (NCEs) displaying high therapeutic efficacy with few or no side effects. Failure of promising lead candidates late in the drug discovery processes is regarded as commercially unacceptable in today's increasingly competitive business environment. An inappropriate ADME/Toxicity profile in humans is the major cause of failure of lead candidates in late clinical stages of drug development. Combinatorial chemistry techniques coupled with high throughput screening protocols means that pharmaceutical companies are now dealing with an unprecedented number of NCEs on an annual basis. As a consequence, screening for undesirable ADME/Toxicity properties in the early stages of drug development, preferably pre-synthesis, is now considered the essential paradigm. In silico assessment of NCEs is rapidly emerging as the next wave of technology for early ADME/Toxicity prediction. In this review, we discuss the major commercially available products for the assessing the potential metabolic activity of xenobiotic substances in mammalian systems.

Entities:  

Mesh:

Substances:

Year:  2002        PMID: 11922955     DOI: 10.1016/s0169-409x(02)00011-x

Source DB:  PubMed          Journal:  Adv Drug Deliv Rev        ISSN: 0169-409X            Impact factor:   15.470


  17 in total

1.  QSAR classification of metabolic activation of chemicals into covalently reactive species.

Authors:  Chin Yee Liew; Chuen Pan; Andre Tan; Ke Xin Magneline Ang; Chun Wei Yap
Journal:  Mol Divers       Date:  2012-02-28       Impact factor: 2.943

2.  In silico enzymatic synthesis of a 400,000 compound biochemical database for nontargeted metabolomics.

Authors:  Lochana C Menikarachchi; Dennis W Hill; Mai A Hamdalla; Ion I Mandoiu; David F Grant
Journal:  J Chem Inf Model       Date:  2013-09-12       Impact factor: 4.956

Review 3.  In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling.

Authors:  S Ekins; J Mestres; B Testa
Journal:  Br J Pharmacol       Date:  2007-06-04       Impact factor: 8.739

4.  Evaluation of descriptors and classification schemes to predict cytochrome substrates in terms of chemical information.

Authors:  John H Block; Douglas R Henry
Journal:  J Comput Aided Mol Des       Date:  2008-01-23       Impact factor: 3.686

Review 5.  Role of biotransformation studies in minimizing metabolism-related liabilities in drug discovery.

Authors:  Yue-Zhong Shu; Benjamin M Johnson; Tian J Yang
Journal:  AAPS J       Date:  2008-03-13       Impact factor: 4.009

6.  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

7.  A probabilistic method to report predictions from a human liver microsomes stability QSAR model: a practical tool for drug discovery.

Authors:  Ignacio Aliagas; Alberto Gobbi; Timothy Heffron; Man-Ling Lee; Daniel F Ortwine; Mark Zak; S Cyrus Khojasteh
Journal:  J Comput Aided Mol Des       Date:  2015-02-24       Impact factor: 3.686

8.  Encoding microbial metabolic logic: predicting biodegradation.

Authors:  Bo Kyeng Hou; Lynda B M Ellis; Lawrence P Wackett
Journal:  J Ind Microbiol Biotechnol       Date:  2004-07-10       Impact factor: 3.346

9.  QSAR analysis of the inhibition of recombinant CYP 3A4 activity by structurally diverse compounds using a genetic algorithm-combined partial least squares method.

Authors:  Suchada Wanchana; Fumiyoshi Yamashita; Mitsuru Hashida
Journal:  Pharm Res       Date:  2003-09       Impact factor: 4.200

10.  E-zyme: predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs.

Authors:  Yoshihiro Yamanishi; Masahiro Hattori; Masaaki Kotera; Susumu Goto; Minoru Kanehisa
Journal:  Bioinformatics       Date:  2009-06-15       Impact factor: 6.937

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.