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Research ArticleArticle

Physiologically Based Pharmacokinetic Modeling to Predict Drug-Drug Interactions Involving Inhibitory Metabolite: A Case Study of Amiodarone

Yuan Chen, Jialin Mao and Cornelis E. C. A. Hop
Drug Metabolism and Disposition February 2015, 43 (2) 182-189; DOI: https://doi.org/10.1124/dmd.114.059311
Yuan Chen
Drug Metabolism and Pharmacokinetics, Genentech Inc., South San Francisco, California
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Jialin Mao
Drug Metabolism and Pharmacokinetics, Genentech Inc., South San Francisco, California
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Cornelis E. C. A. Hop
Drug Metabolism and Pharmacokinetics, Genentech Inc., South San Francisco, California
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Abstract

Evaluation of drug-drug interaction (DDI) involving circulating inhibitory metabolites of perpetrator drugs has recently drawn more attention from regulatory agencies and pharmaceutical companies. Here, using amiodarone (AMIO) as an example, we demonstrate the use of physiologically based pharmacokinetic (PBPK) modeling to assess how a potential inhibitory metabolite can contribute to clinically significant DDIs. Amiodarone was reported to increase the exposure of simvastatin, dextromethorphan, and warfarin by 1.2- to 2-fold, which was not expected based on its weak inhibition observed in vitro. The major circulating metabolite, mono-desethyl-amiodarone (MDEA), was later identified to have a more potent inhibitory effect. Using a combined “bottom-up” and “top-down” approach, a PBPK model was built to successfully simulate the pharmacokinetic profile of AMIO and MDEA, particularly their accumulation in plasma and liver after a long-term treatment. The clinical AMIO DDIs were predicted using the verified PBPK model with incorporation of cytochrome P450 inhibition from both AMIO and MDEA. The closest prediction was obtained for CYP3A (simvastatin) DDI when the competitive inhibition from both AMIO and MDEA was considered, for CYP2D6 (dextromethorphan) DDI when the competitive inhibition from AMIO and the competitive plus time-dependent inhibition from MDEA were incorporated, and for CYP2C9 (warfarin) DDI when the competitive plus time-dependent inhibition from AMIO and the competitive inhibition from MDEA were considered. The PBPK model with the ability to simulate DDI by considering dynamic change and accumulation of inhibitor (parent and metabolite) concentration in plasma and liver provides advantages in understanding the possible mechanism of clinical DDIs involving inhibitory metabolites.

Footnotes

    • Received June 4, 2014.
    • Accepted October 15, 2014.
  • This study was funded by Genentech (a member of the Roche group). All authors were employees of Genentech when this work was carried out. They have no other conflicts of interest to declare.

  • dx.doi.org/10.1124/dmd.114.059311.

  • ↵Embedded ImageThis article has supplemental material available at dmd.aspetjournals.org.

  • Copyright © 2014 by The American Society for Pharmacology and Experimental Therapeutics
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Drug Metabolism and Disposition: 43 (2)
Drug Metabolism and Disposition
Vol. 43, Issue 2
1 Feb 2015
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Research ArticleArticle

PBPK Modeling of DDI Involving Inhibitory Metabolite

Yuan Chen, Jialin Mao and Cornelis E. C. A. Hop
Drug Metabolism and Disposition February 1, 2015, 43 (2) 182-189; DOI: https://doi.org/10.1124/dmd.114.059311

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Research ArticleArticle

PBPK Modeling of DDI Involving Inhibitory Metabolite

Yuan Chen, Jialin Mao and Cornelis E. C. A. Hop
Drug Metabolism and Disposition February 1, 2015, 43 (2) 182-189; DOI: https://doi.org/10.1124/dmd.114.059311
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