Literature DB >> 15205386

An evaluation of the in vitro metabolism data for predicting the clearance and drug-drug interaction potential of CYP2C9 substrates.

Tommy B Andersson1, Eva Bredberg, Hans Ericsson, Helena Sjöberg.   

Abstract

In the early drug discovery process, metabolic stability and cytochrome P450 inhibition are often used as an early selection tool to identify useful compounds for further development. The reliability of the data in this process is therefore crucial. In the present study, in vitro enzyme kinetic data were used to predict the in vivo clearance and drug-drug interaction potential of four well known CYP2C9 substrates (tolbutamide, fluvastatin, ibuprofen and diclofenac) that are frequently used as benchmark substances in screening programs. Quantitative predictions of hepatic clearance using the well stirred prediction model and CL(int) calculated from enzyme kinetic measurements were not useful. Including and excluding protein binding resulted in under- and overestimation, respectively, of in vivo clearance. The only predicted in vivo clearance that fell into the range of reported measured values was for fluvastatin when protein binding was not included. In an open, randomized, seven-armed, crossover study in healthy volunteers, tolbutamide, ibuprofen, and fluvastatin were investigated as inhibitors of the metabolism of diclofenac, and vice versa. None of the combinations was found to interact with each other in vivo. The in vitro drug-drug interaction potential was investigated by K(i) determinations of the same combinations. In contrast to clearance predictions, the interaction potential in vivo was best predicted when plasma protein binding was included in the various models used. This study points to the uncertainty in calculating in vivo kinetics from in vitro enzyme kinetic data. The in vitro metabolic screening can thus be questioned as a compound selection tool without a proven in vitro-in vivo correlation.

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Year:  2004        PMID: 15205386     DOI: 10.1124/dmd.32.7.715

Source DB:  PubMed          Journal:  Drug Metab Dispos        ISSN: 0090-9556            Impact factor:   3.922


  13 in total

1.  Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS).

Authors:  Manthena V Varma; Stefanus J Steyn; Charlotte Allerton; Ayman F El-Kattan
Journal:  Pharm Res       Date:  2015-07-09       Impact factor: 4.200

2.  Application of CYP3A4 in vitro data to predict clinical drug-drug interactions; predictions of compounds as objects of interaction.

Authors:  Kuresh A Youdim; Aref Zayed; Maurice Dickins; Alex Phipps; Michelle Griffiths; Amanda Darekar; Ruth Hyland; Odette Fahmi; Susan Hurst; David R Plowchalk; Jack Cook; Feng Guo; R Scott Obach
Journal:  Br J Clin Pharmacol       Date:  2008-02-14       Impact factor: 4.335

3.  Predictions of metabolic drug-drug interactions using physiologically based modelling: Two cytochrome P450 3A4 substrates coadministered with ketoconazole or verapamil.

Authors:  Nathalie Perdaems; Helene Blasco; Cedric Vinson; Marylore Chenel; Sarah Whalley; Fanny Cazade; François Bouzom
Journal:  Clin Pharmacokinet       Date:  2010-04       Impact factor: 6.447

4.  Efficacy of Tilorone Dihydrochloride against Ebola Virus Infection.

Authors:  Sean Ekins; Mary A Lingerfelt; Jason E Comer; Alexander N Freiberg; Jon C Mirsalis; Kathleen O'Loughlin; Anush Harutyunyan; Claire McFarlane; Carol E Green; Peter B Madrid
Journal:  Antimicrob Agents Chemother       Date:  2018-01-25       Impact factor: 5.191

5.  Repurposing Quinacrine against Ebola Virus Infection In Vivo.

Authors:  Thomas R Lane; Jason E Comer; Alexander N Freiberg; Peter B Madrid; Sean Ekins
Journal:  Antimicrob Agents Chemother       Date:  2019-08-23       Impact factor: 5.191

6.  Artemisinin and thiabendazole are potent inhibitors of cytochrome P450 1A2 (CYP1A2) activity in humans.

Authors:  Tashinga E Bapiro; Jane Sayi; Julia A Hasler; Mary Jande; Gerald Rimoy; Amos Masselle; Collen M Masimirembwa
Journal:  Eur J Clin Pharmacol       Date:  2005-10-29       Impact factor: 2.953

Review 7.  Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction.

Authors:  Haiyan Li; Jin Sun; Xiaowen Fan; Xiaofan Sui; Lan Zhang; Yongjun Wang; Zhonggui He
Journal:  J Comput Aided Mol Des       Date:  2008-06-24       Impact factor: 3.686

8.  A systems biology approach to dynamic modeling and inter-subject variability of statin pharmacokinetics in human hepatocytes.

Authors:  Joachim Bucher; Stephan Riedmaier; Anke Schnabel; Katrin Marcus; Gabriele Vacun; Thomas S Weiss; Wolfgang E Thasler; Andreas K Nüssler; Ulrich M Zanger; Matthias Reuss
Journal:  BMC Syst Biol       Date:  2011-05-06

9.  Mechanistic approaches to volume of distribution predictions: understanding the processes.

Authors:  Trudy Rodgers; Malcolm Rowland
Journal:  Pharm Res       Date:  2007-03-20       Impact factor: 4.580

10.  Repurposing the antimalarial pyronaridine tetraphosphate to protect against Ebola virus infection.

Authors:  Thomas R Lane; Christopher Massey; Jason E Comer; Manu Anantpadma; Joel S Freundlich; Robert A Davey; Peter B Madrid; Sean Ekins
Journal:  PLoS Negl Trop Dis       Date:  2019-11-21
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