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Research ArticleArticle

A Combined Model for Predicting CYP3A4 Clinical Net Drug-Drug Interaction Based on CYP3A4 Inhibition, Inactivation, and Induction Determined in Vitro

Odette A. Fahmi, Tristan S. Maurer, Mary Kish, Edwin Cardenas, Sherri Boldt and David Nettleton
Drug Metabolism and Disposition August 2008, 36 (8) 1698-1708; DOI: https://doi.org/10.1124/dmd.107.018663
Odette A. Fahmi
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Tristan S. Maurer
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Mary Kish
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Edwin Cardenas
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Sherri Boldt
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David Nettleton
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This article has a correction. Please see:

  • CORRECTION TO “A COMBINED MODEL FOR PREDICTING CYP3A4 CLINICAL NET DRUG-DRUG INTERACTION BASED ON CYP3A4 INHIBITION, INACTIVATION, AND INDUCTION DETERMINED IN VITRO” - September 01, 2008

Abstract

Although approaches to the prediction of drug-drug interactions (DDIs) arising via time-dependent inactivation have recently been developed, such approaches do not account for simple competitive inhibition or induction. Accordingly, these approaches do not provide accurate predictions of DDIs arising from simple competitive inhibition (e.g., ketoconazole) or induction of cytochromes P450 (e.g., phenytoin). In addition, methods that focus upon a single interaction mechanism are likely to yield misleading predictions in the face of mixed mechanisms (e.g., ritonavir). As such, we have developed a more comprehensive mathematical model that accounts for the simultaneous influences of competitive inhibition, time-dependent inactivation, and induction of CYP3A in both the liver and intestine to provide a net drug-drug interaction prediction in terms of area under the concentration-time curve ratio. This model provides a framework by which readily obtained in vitro values for competitive inhibition, time-dependent inactivation and induction for the precipitant compound as well as literature values for fm and FG for the object drug can be used to provide quantitative predictions of DDIs. Using this model, DDIs arising via inactivation (e.g., erythromycin) continue to be well predicted, whereas those arising via competitive inhibition (e.g., ketoconazole), induction (e.g., phenytoin), and mixed mechanisms (e.g., ritonavir) are also predicted within the ranges reported in the clinic. This comprehensive model quantitatively predicts clinical observations with reasonable accuracy and can be a valuable tool to evaluate candidate drugs and rationalize clinical DDIs.

Footnotes

  • This work was presented in part at the 2005 and 2006 Institute for Scientific Exchange Conferences on Drug-Drug Interactions and at the 2006 International Symposium on Microsomes and Drug Oxidations meeting.

  • Article, publication date, and citation information can be found at http://dmd.aspetjournals.org.

  • doi:10.1124/dmd.107.018663.

  • ABBREVIATIONS: P450, cytochrome P450; DDI, drug-drug interaction; AUC, area under the concentration-time curve; GMFE, geometric mean fold error; TDI, time-dependent inactivation; q.d., every day; GFE, geometric fold error.

    • Received August 30, 2007.
    • Accepted May 16, 2008.
  • The American Society for Pharmacology and Experimental Therapeutics
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Drug Metabolism and Disposition: 36 (8)
Drug Metabolism and Disposition
Vol. 36, Issue 8
1 Aug 2008
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Research ArticleArticle

A Combined Model for Predicting CYP3A4 Clinical Net Drug-Drug Interaction Based on CYP3A4 Inhibition, Inactivation, and Induction Determined in Vitro

Odette A. Fahmi, Tristan S. Maurer, Mary Kish, Edwin Cardenas, Sherri Boldt and David Nettleton
Drug Metabolism and Disposition August 1, 2008, 36 (8) 1698-1708; DOI: https://doi.org/10.1124/dmd.107.018663

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Research ArticleArticle

A Combined Model for Predicting CYP3A4 Clinical Net Drug-Drug Interaction Based on CYP3A4 Inhibition, Inactivation, and Induction Determined in Vitro

Odette A. Fahmi, Tristan S. Maurer, Mary Kish, Edwin Cardenas, Sherri Boldt and David Nettleton
Drug Metabolism and Disposition August 1, 2008, 36 (8) 1698-1708; DOI: https://doi.org/10.1124/dmd.107.018663
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