Literature DB >> 25941268

Quantitative Rationalization of Gemfibrozil Drug Interactions: Consideration of Transporters-Enzyme Interplay and the Role of Circulating Metabolite Gemfibrozil 1-O-β-Glucuronide.

Manthena V S Varma1, Jian Lin2, Yi-an Bi2, Emi Kimoto2, A David Rodrigues2.   

Abstract

Gemfibrozil has been suggested as a sensitive cytochrome P450 2C8 (CYP2C8) inhibitor for clinical investigation by the U.S. Food and Drug Administration and the European Medicines Agency. However, gemfibrozil drug-drug interactions (DDIs) are complex; its major circulating metabolite, gemfibrozil 1-O-β-glucuronide (Gem-Glu), exhibits time-dependent inhibition of CYP2C8, and both parent and metabolite also behave as moderate inhibitors of organic anion transporting polypeptide 1B1 (OATP1B1) in vitro. Additionally, parent and metabolite also inhibit renal transport mediated by OAT3. Here, in vitro inhibition data for gemfibrozil and Gem-Glu were used to assess their impact on the pharmacokinetics of several victim drugs (including rosiglitazone, pioglitazone, cerivastatin, and repaglinide) by employing both static mechanistic and dynamic physiologically based pharmacokinetic (PBPK) models. Of the 48 cases evaluated using the static models, about 75% and 98% of the DDIs were predicted within 1.5- and 2-fold of the observed values, respectively, when incorporating the interaction potential of both gemfibrozil and its 1-O-β-glucuronide. Moreover, the PBPK model was able to recover the plasma profiles of rosiglitazone, pioglitazone, cerivastatin, and repaglinide under control and gemfibrozil treatment conditions. Analyses suggest that Gem-Glu is the major contributor to the DDIs, and its exposure needed to bring about complete inactivation of CYP2C8 is only a fraction of that achieved in the clinic after a therapeutic gemfibrozil dose. Overall, the complex interactions of gemfibrozil can be quantitatively rationalized, and the learnings from this analysis can be applied in support of future predictions of gemfibrozil DDIs.
Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics.

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Year:  2015        PMID: 25941268     DOI: 10.1124/dmd.115.064303

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


  16 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.  Projecting ADME Behavior and Drug-Drug Interactions in Early Discovery and Development: Application of the Extended Clearance Classification System.

Authors:  Ayman F El-Kattan; Manthena V Varma; Stefan J Steyn; Dennis O Scott; Tristan S Maurer; Arthur Bergman
Journal:  Pharm Res       Date:  2016-09-12       Impact factor: 4.200

3.  A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Model of Dabigatran Etexilate, Dabigatran and Dabigatran Glucuronide in Healthy Adults and Renally Impaired Patients.

Authors:  Daniel Moj; Hugo Maas; André Schaeftlein; Nina Hanke; José David Gómez-Mantilla; Thorsten Lehr
Journal:  Clin Pharmacokinet       Date:  2019-12       Impact factor: 6.447

4.  Predictive Performance of Physiologically-Based Pharmacokinetic Models in Predicting Drug-Drug Interactions Involving Enzyme Modulation.

Authors:  Chia-Hsiang Hsueh; Vicky Hsu; Yuzhuo Pan; Ping Zhao
Journal:  Clin Pharmacokinet       Date:  2018-10       Impact factor: 6.447

5.  Implications of intercorrelation between hepatic CYP3A4-CYP2C8 enzymes for the evaluation of drug-drug interactions: a case study with repaglinide.

Authors:  Kosuke Doki; Adam S Darwich; Brahim Achour; Aleksi Tornio; Janne T Backman; Amin Rostami-Hodjegan
Journal:  Br J Clin Pharmacol       Date:  2018-03-06       Impact factor: 4.335

6.  Peroxisome Proliferator-Activated Receptor α Activation Suppresses Cytochrome P450 Induction Potential in Mice Treated with Gemfibrozil.

Authors:  Cunzhong Shi; Luo Min; Julin Yang; Manyun Dai; Danjun Song; Huiying Hua; Gangming Xu; Frank J Gonzalez; Aiming Liu
Journal:  Basic Clin Pharmacol Toxicol       Date:  2017-05-10       Impact factor: 4.080

7.  Prediction of Cyclosporin-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Model Characterizing Interplay of Drug Transporters and Enzymes.

Authors:  Yiting Yang; Ping Li; Zexin Zhang; Zhongjian Wang; Li Liu; Xiaodong Liu
Journal:  Int J Mol Sci       Date:  2020-09-24       Impact factor: 5.923

Review 8.  Drug interactions of meglitinide antidiabetics involving CYP enzymes and OATP1B1 transporter.

Authors:  Naina Mohamed Pakkir Maideen; Gobinath Manavalan; Kumar Balasubramanian
Journal:  Ther Adv Endocrinol Metab       Date:  2018-04-06       Impact factor: 3.565

9.  Quantitative Prediction of Drug-Drug Interactions Involving Inhibitory Metabolites in Drug Development: How Can Physiologically Based Pharmacokinetic Modeling Help?

Authors:  I E Templeton; Y Chen; J Mao; J Lin; H Yu; S Peters; M Shebley; M V Varma
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2016-09-19

10.  Use of Physiologically Based Pharmacokinetic Modeling to Evaluate the Effect of Chronic Kidney Disease on the Disposition of Hepatic CYP2C8 and OATP1B Drug Substrates.

Authors:  Ming-Liang Tan; Ping Zhao; Lei Zhang; Yunn-Fang Ho; Manthena V S Varma; Sibylle Neuhoff; Thomas D Nolin; Aleksandra Galetin; Shiew-Mei Huang
Journal:  Clin Pharmacol Ther       Date:  2018-10-26       Impact factor: 6.875

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