Literature DB >> 14683480

Reaction phenotyping in drug discovery: moving forward with confidence?

J Andrew Williams1, Susan I Hurst, Jonathan Bauman, Barry C Jones, Ruth Hyland, John P Gibbs, R Scott Obach, Simon E Ball.   

Abstract

For the pharmaceutical industry, one of the challenges in evaluating the risk of future compound attrition at the discovery stage is the successful prediction of the major routes of clearance in humans. For compounds cleared by metabolism, such information will help to avoid the development of compounds that will exhibit large interpatient differences in pharmacokinetics via 1). routes of metabolism catalyzed by functionally polymorphic enzymes and/or 2). clinically significant metabolic drug-drug interactions, in the later stages of development. The degree of intersubject variability that is acceptable for a drug candidate is uncertain in the discovery stage where knowledge of other important factors is limited or unavailable (i.e. therapeutic index, pharmacodynamic variability, etc). Reaction phenotyping is the semi-quantitative in vitro estimation of the relative contributions of specific drug-metabolizing enzymes to the metabolism of a test compound. However, reaction phenotyping in the discovery stage of drug development is complicated by the absence of radiolabelled parent compound or metabolite bioanalytical standards relative to later stages of development. In this commentary, some of the approaches, based on published data, which can be taken to overcome these challenges are discussed. In addition, knowledge of the molecular structure (i.e. specific chemical substituents), physicochemical properties, and routes of clearance in animals can all help in making a successful prediction for the routes of clearance in humans. In combination, the objective of these studies should be to reduce to a minimum the risk of finding significant inter-patient differences in pharmacokinetics at a later stage in development due to significant metabolism by polymorphic enzymes or drug-drug interactions. Consequently, this data should be used to avoid costly late stage attrition.

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Year:  2003        PMID: 14683480     DOI: 10.2174/1389200033489235

Source DB:  PubMed          Journal:  Curr Drug Metab        ISSN: 1389-2002            Impact factor:   3.731


  6 in total

1.  From the Cover: Three-Dimensional (3D) HepaRG Spheroid Model With Physiologically Relevant Xenobiotic Metabolism Competence and Hepatocyte Functionality for Liver Toxicity Screening.

Authors:  Sreenivasa C Ramaiahgari; Suramya Waidyanatha; Darlene Dixon; Michael J DeVito; Richard S Paules; Stephen S Ferguson
Journal:  Toxicol Sci       Date:  2017-09-01       Impact factor: 4.849

2.  Prediction of in vivo drug-drug interactions from in vitro data: impact of incorporating parallel pathways of drug elimination and inhibitor absorption rate constant.

Authors:  Hayley S Brown; Kiyomi Ito; Aleksandra Galetin; J Brian Houston
Journal:  Br J Clin Pharmacol       Date:  2005-11       Impact factor: 4.335

Review 3.  The use of pharmacokinetic and pharmacodynamic data in the assessment of drug safety in early drug development.

Authors:  D K Walker
Journal:  Br J Clin Pharmacol       Date:  2004-12       Impact factor: 4.335

Review 4.  Chemical inhibitors of cytochrome P450 isoforms in human liver microsomes: a re-evaluation of P450 isoform selectivity.

Authors:  Siamak Cyrus Khojasteh; Saileta Prabhu; Jane R Kenny; Jason S Halladay; Anthony Y H Lu
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2011-02-19       Impact factor: 2.441

5.  General framework for the quantitative prediction of CYP3A4-mediated oral drug interactions based on the AUC increase by coadministration of standard drugs.

Authors:  Yoshiyuki Ohno; Akihiro Hisaka; Hiroshi Suzuki
Journal:  Clin Pharmacokinet       Date:  2007       Impact factor: 6.447

6.  Induction of CYP2B6 and CYP3A4 expression by 1-aminobenzotriazole (ABT) in human hepatocytes.

Authors:  Kyunghee Yang; Kwi Hye Koh; Hyunyoung Jeong
Journal:  Drug Metab Lett       Date:  2010-08
  6 in total

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