Literature DB >> 23307233

Application of in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) modelling to investigate the impact of the CYP2C8 polymorphism on rosiglitazone exposure.

Karen Rowland Yeo1, Jane R Kenny, Amin Rostami-Hodjegan.   

Abstract

PURPOSE: To predict the impact of the CYP2C8 3 genotype on rosiglitazone exposure in the absence and presence of trimethoprim.
METHODS: Prior in vitro and in vivo information for rosiglitazone and trimethoprim were collated from the literature. Specifically, data on the frequency of the different allelic forms of CYP2C8 and their metabolic activity for rosiglitazone were incorporated into a physiologically-based pharmacokinetic (PBPK) model within the Simcyp Simulator (V11.1) to predict differences in the relative exposure of rosiglitazone according to CYP2C8 3 genotype in a virtual population.
RESULTS: Following multiple doses of 8 mg rosiglitazone, the predicted mean AUC(0-24) was 37 % lower in CYP2C8 3 homozygotes compared with wildtype homozygotes (p <  0.001), which was consistent with the 36 % lower value observed in vivo (p <  0.001) Kirchheiner et al. (Clin Pharmacol Ther 80:657-667, 2006). Predicted median AUC ratios of rosiglitazone in the presence and absence of trimethoprim ranged from 1.35 to 1.66 for ten virtual trials of subjects with the CYP2C8 1/1 genotype, which included the observed value of 1.42. In subjects with the CYP2C8 1/3 genotype, the predicted AUC ratios for all trials were higher than the observed value of 1.18 Kirchheiner et al. (Clin Pharmacol Ther 80:657-667, 2006).
CONCLUSIONS: Investigating the drug interactions in individuals with rare allelic forms of drug metabolising enzymes is fraught with many practical problems. Current study demonstrates the utility of prior in vitro metabolism data from such allelic forms to predict the relative exposure of a drug as a function of genotype. However, in vitro inhibition data obtained in one allelic variant (e.g. CYP2C8 1) may not be adequate to predict the in vivo interactions in another allele (e.g. CYP2C8 3), since the inhibitory characteristics of perpetrator might be different in each allelic variant in the same way as that of metabolism of the victim drug by such variants of the enzyme.

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Year:  2013        PMID: 23307233     DOI: 10.1007/s00228-012-1467-3

Source DB:  PubMed          Journal:  Eur J Clin Pharmacol        ISSN: 0031-6970            Impact factor:   2.953


  43 in total

1.  Linkage between the CYP2C8 and CYP2C9 genetic polymorphisms.

Authors:  Umit Yasar; Stefan Lundgren; Erik Eliasson; Anna Bennet; Björn Wiman; Ulf de Faire; Anders Rane
Journal:  Biochem Biophys Res Commun       Date:  2002-11-22       Impact factor: 3.575

2.  The influence of nonspecific microsomal binding on apparent intrinsic clearance, and its prediction from physicochemical properties.

Authors:  Rupert P Austin; Patrick Barton; Scott L Cockroft; Mark C Wenlock; Robert J Riley
Journal:  Drug Metab Dispos       Date:  2002-12       Impact factor: 3.922

3.  Predicting drug clearance from recombinantly expressed CYPs: intersystem extrapolation factors.

Authors:  N J Proctor; G T Tucker; A Rostami-Hodjegan
Journal:  Xenobiotica       Date:  2004-02       Impact factor: 1.908

4.  Prediction of in vivo drug clearance from in vitro data. I: impact of inter-individual variability.

Authors:  E M Howgate; K Rowland Yeo; N J Proctor; G T Tucker; A Rostami-Hodjegan
Journal:  Xenobiotica       Date:  2006-06       Impact factor: 1.908

5.  Polymorphisms in human CYP2C8 decrease metabolism of the anticancer drug paclitaxel and arachidonic acid.

Authors:  D Dai; D C Zeldin; J A Blaisdell; B Chanas; S J Coulter; B I Ghanayem; J A Goldstein
Journal:  Pharmacogenetics       Date:  2001-10

6.  Prediction of the effects of genetic polymorphism on the pharmacokinetics of CYP2C9 substrates from in vitro data.

Authors:  Makiko Kusama; Kazuya Maeda; Koji Chiba; Akinori Aoyama; Yuichi Sugiyama
Journal:  Pharm Res       Date:  2008-12-12       Impact factor: 4.200

7.  Drug metabolism by CYP2C8.3 is determined by substrate dependent interactions with cytochrome P450 reductase and cytochrome b5.

Authors:  Rüdiger Kaspera; Suresh B Naraharisetti; Eric A Evangelista; Kristin D Marciante; Bruce M Psaty; Rheem A Totah
Journal:  Biochem Pharmacol       Date:  2011-06-24       Impact factor: 5.858

8.  Characterization of the cytochrome P450 enzymes involved in the in vitro metabolism of rosiglitazone.

Authors:  S J Baldwin; S E Clarke; R J Chenery
Journal:  Br J Clin Pharmacol       Date:  1999-09       Impact factor: 4.335

9.  Steady state pharmacokinetics of trimethoprim 300 mg once daily in healthy volunteers assessed by two independent methods.

Authors:  B Odlind; P Hartvig; K E Fjellström; B Lindström; S Bengtsson
Journal:  Eur J Clin Pharmacol       Date:  1984       Impact factor: 2.953

10.  Pharmacokinetics of rosiglitazone in patients with varying degrees of renal insufficiency.

Authors:  Martha C Chapelsky; Kathleen Thompson-Culkin; Ann K Miller; Marshall Sack; Robert Blum; Martin I Freed
Journal:  J Clin Pharmacol       Date:  2003-03       Impact factor: 3.126

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  5 in total

Review 1.  Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification.

Authors:  Jennifer E Sager; Jingjing Yu; Isabelle Ragueneau-Majlessi; Nina Isoherranen
Journal:  Drug Metab Dispos       Date:  2015-08-21       Impact factor: 3.922

2.  A Physiologically-Based Pharmacokinetic Model of Trimethoprim for MATE1, OCT1, OCT2, and CYP2C8 Drug-Drug-Gene Interaction Predictions.

Authors:  Denise Türk; Nina Hanke; Thorsten Lehr
Journal:  Pharmaceutics       Date:  2020-11-10       Impact factor: 6.321

3.  Investigation of the Impact of CYP3A5 Polymorphism on Drug-Drug Interaction between Tacrolimus and Schisantherin A/Schisandrin A Based on Physiologically-Based Pharmacokinetic Modeling.

Authors:  Qingfeng He; Fengjiao Bu; Hongyan Zhang; Qizhen Wang; Zhijia Tang; Jing Yuan; Hai-Shu Lin; Xiaoqiang Xiang
Journal:  Pharmaceuticals (Basel)       Date:  2021-02-27

4.  Physiologically based pharmacokinetic (PBPK) modeling for prediction of celecoxib pharmacokinetics according to CYP2C9 genetic polymorphism.

Authors:  Young-Hoon Kim; Pureum Kang; Chang-Keun Cho; Eui Hyun Jung; Hye-Jeong Park; Yun Jeong Lee; Jung-Woo Bae; Choon-Gon Jang; Seok-Yong Lee
Journal:  Arch Pharm Res       Date:  2021-07-25       Impact factor: 4.946

5.  Streamlining physiologically-based pharmacokinetic model design for intravenous delivery of nanoparticle drugs.

Authors:  Anh-Dung Le; Helen J Wearing; Dingsheng Li
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2022-02-07
  5 in total

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