Literature DB >> 27422672

Evaluation of CYP2B6 Induction and Prediction of Clinical Drug-Drug Interactions: Considerations from the IQ Consortium Induction Working Group-An Industry Perspective.

Odette A Fahmi1, Mohamad Shebley2, Jairam Palamanda2, Michael W Sinz2, Diane Ramsden2, Heidi J Einolf2, Liangfu Chen2, Hongbing Wang2.   

Abstract

Drug-drug interactions (DDIs) due to CYP2B6 induction have recently gained prominence and clinical induction risk assessment is recommended by regulatory agencies. This work aimed to evaluate the potency of CYP2B6 versus CYP3A4 induction in vitro and from clinical studies and to assess the predictability of efavirenz versus bupropion as clinical probe substrates of CYP2B6 induction. The analysis indicates that the magnitude of CYP3A4 induction was higher than CYP2B6 both in vitro and in vivo. The magnitude of DDIs caused by induction could not be predicted for bupropion with static or dynamic models. On the other hand, the relative induction score, net effect, and physiologically based pharmacokinetics SimCYP models using efavirenz resulted in improved DDI predictions. Although bupropion and efavirenz have been used and are recommended by regulatory agencies as clinical CYP2B6 probe substrates for DDI studies, CYP3A4 contributes to the metabolism of both probes and is induced by all reference CYP2B6 inducers. Therefore, caution must be taken when interpreting clinical induction results because of the lack of selectivity of these probes. Although in vitro-in vivo extrapolation for efavirenz performed better than bupropion, interpretation of the clinical change in exposure is confounded by the coinduction of CYP2B6 and CYP3A4, as well as the increased contribution of CYP3A4 to efavirenz metabolism under induced conditions. Current methods and probe substrates preclude accurate prediction of CYP2B6 induction. Identification of a sensitive and selective clinical substrate for CYP2B6 (fraction metabolized > 0.9) is needed to improve in vitro-in vivo extrapolation for characterizing the potential for CYP2B6-mediated DDIs. Alternative strategies and a framework for evaluating the CYP2B6 induction risk are proposed.
Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics.

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Year:  2016        PMID: 27422672     DOI: 10.1124/dmd.116.071076

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


  17 in total

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2.  Ser100-Phosphorylated RORα Orchestrates CAR and HNF4α to Form Active Chromatin Complex in Response to Phenobarbital to Regulate Induction of CYP2B6.

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3.  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

4.  Quantitative Prediction of Drug Interactions Caused by Cytochrome P450 2B6 Inhibition or Induction.

Authors:  Veronica Di Paolo; Francesco Maria Ferrari; Italo Poggesi; Luigi Quintieri
Journal:  Clin Pharmacokinet       Date:  2022-07-20       Impact factor: 5.577

5.  Common Polymorphisms of CYP2B6 Influence Stereoselective Bupropion Disposition.

Authors:  Evan D Kharasch; Amanda Crafford
Journal:  Clin Pharmacol Ther       Date:  2018-08-09       Impact factor: 6.875

6.  Stereoselective Bupropion Hydroxylation by Cytochrome P450 CYP2B6 and Cytochrome P450 Oxidoreductase Genetic Variants.

Authors:  Pan-Fen Wang; Alicia Neiner; Evan D Kharasch
Journal:  Drug Metab Dispos       Date:  2020-04-01       Impact factor: 3.922

7.  Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models.

Authors:  Diane Ramsden; Cody L Fullenwider
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2022-03-28       Impact factor: 2.569

8.  Evaluation of a Novel Renewable Hepatic Cell Model for Prediction of Clinical CYP3A4 Induction Using a Correlation-Based Relative Induction Score Approach.

Authors:  Rongjun Zuo; Feng Li; Sweta Parikh; Li Cao; Kirsten L Cooper; Yulong Hong; Jin Liu; Ronald A Faris; Daochuan Li; Hongbing Wang
Journal:  Drug Metab Dispos       Date:  2017-01-06       Impact factor: 3.922

9.  Comprehensive PBPK model to predict drug interaction potential of Zanubrutinib as a victim or perpetrator.

Authors:  Kun Wang; Xueting Yao; Miao Zhang; Dongyang Liu; Yuying Gao; Srikumar Sahasranaman; Ying C Ou
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2021-05-02

10.  PBPK modeling to predict drug-drug interactions of ivosidenib as a perpetrator in cancer patients and qualification of the Simcyp platform for CYP3A4 induction.

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Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2021-05-01
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