Literature DB >> 23393219

Quantitative prediction of repaglinide-rifampicin complex drug interactions using dynamic and static mechanistic models: delineating differential CYP3A4 induction and OATP1B1 inhibition potential of rifampicin.

Manthena V S Varma1, Jian Lin, Yi-An Bi, Charles J Rotter, Odette A Fahmi, Justine L Lam, Ayman F El-Kattan, Theunis C Goosen, Yurong Lai.   

Abstract

Repaglinide is mainly metabolized by cytochrome P450 enzymes CYP2C8 and CYP3A4, and it is also a substrate to a hepatic uptake transporter, organic anion transporting polypeptide (OATP)1B1. The purpose of this study is to predict the dosing time-dependent pharmacokinetic interactions of repaglinide with rifampicin, using mechanistic models. In vitro hepatic transport of repaglinide, characterized using sandwich-cultured human hepatocytes, and intrinsic metabolic parameters were used to build a dynamic whole-body physiologically-based pharmacokinetic (PBPK) model. The PBPK model adequately described repaglinide plasma concentration-time profiles and successfully predicted area under the plasma concentration-time curve ratios of repaglinide (within ± 25% error), dosed (staggered 0-24 hours) after rifampicin treatment when primarily considering induction of CYP3A4 and reversible inhibition of OATP1B1 by rifampicin. Further, a static mechanistic "extended net-effect" model incorporating transport and metabolic disposition parameters of repaglinide and interaction potency of rifampicin was devised. Predictions based on the static model are similar to those observed in the clinic (average error ∼19%) and to those based on the PBPK model. Both the models suggested that the combined effect of increased gut extraction and decreased hepatic uptake caused minimal repaglinide systemic exposure change when repaglinide is dosed simultaneously or 1 hour after the rifampicin dose. On the other hand, isolated induction effect as a result of temporal separation of the two drugs translated to an approximate 5-fold reduction in repaglinide systemic exposure. In conclusion, both dynamic and static mechanistic models are instrumental in delineating the quantitative contribution of transport and metabolism in the dosing time-dependent repaglinide-rifampicin interactions.

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Year:  2013        PMID: 23393219     DOI: 10.1124/dmd.112.050583

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


  14 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.  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

3.  Effect of gemfibrozil and rifampicin on the pharmacokinetics of selexipag and its active metabolite in healthy subjects.

Authors:  Shirin Bruderer; Marc Petersen-Sylla; Margaux Boehler; Tatiana Remeňová; Atef Halabi; Jasper Dingemanse
Journal:  Br J Clin Pharmacol       Date:  2017-08-16       Impact factor: 4.335

4.  Effect of a single-dose rifampin on the pharmacokinetics of pitavastatin in healthy volunteers.

Authors:  Yao Chen; Wei Zhang; Wei-hua Huang; Zhi-rong Tan; Yi-cheng Wang; Xi Huang; Hong-Hao Zhou
Journal:  Eur J Clin Pharmacol       Date:  2013-07-07       Impact factor: 2.953

5.  A Generic Model for Quantitative Prediction of Interactions Mediated by Efflux Transporters and Cytochromes: Application to P-Glycoprotein and Cytochrome 3A4.

Authors:  Michel Tod; S Goutelle; N Bleyzac; L Bourguignon
Journal:  Clin Pharmacokinet       Date:  2019-04       Impact factor: 6.447

6.  Physiologically Based Pharmacokinetic (PBPK) Modeling of Pitavastatin and Atorvastatin to Predict Drug-Drug Interactions (DDIs).

Authors:  Peng Duan; Ping Zhao; Lei Zhang
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2017-08       Impact factor: 2.441

Review 7.  Sandwich-Cultured Hepatocytes as a Tool to Study Drug Disposition and Drug-Induced Liver Injury.

Authors:  Kyunghee Yang; Cen Guo; Jeffrey L Woodhead; Robert L St Claire; Paul B Watkins; Scott Q Siler; Brett A Howell; Kim L R Brouwer
Journal:  J Pharm Sci       Date:  2016-02       Impact factor: 3.534

Review 8.  Prediction of pharmacokinetics and drug-drug interactions when hepatic transporters are involved.

Authors:  Rui Li; Hugh A Barton; Manthena V Varma
Journal:  Clin Pharmacokinet       Date:  2014-08       Impact factor: 6.447

9.  Reduced physiologically-based pharmacokinetic model of repaglinide: impact of OATP1B1 and CYP2C8 genotype and source of in vitro data on the prediction of drug-drug interaction risk.

Authors:  Michael Gertz; Nikolaos Tsamandouras; Carolina Säll; J Brian Houston; Aleksandra Galetin
Journal:  Pharm Res       Date:  2014-03-13       Impact factor: 4.200

10.  Mechanism-based pharmacokinetic modeling to evaluate transporter-enzyme interplay in drug interactions and pharmacogenetics of glyburide.

Authors:  Manthena V S Varma; Renato J Scialis; Jian Lin; Yi-An Bi; Charles J Rotter; Theunis C Goosen; Xin Yang
Journal:  AAPS J       Date:  2014-05-17       Impact factor: 4.009

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