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Research ArticleArticle

Integrated in Vitro Analysis for the in Vivo Prediction of Cytochrome P450-Mediated Drug-Drug Interactions

Dermot F. McGinnity, Nigel J. Waters, James Tucker and Robert J. Riley
Drug Metabolism and Disposition June 2008, 36 (6) 1126-1134; DOI: https://doi.org/10.1124/dmd.108.020446
Dermot F. McGinnity
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Nigel J. Waters
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James Tucker
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Robert J. Riley
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Abstract

Unbound IC50 (IC50,u) values of 15 drugs were determined in eight recombinantly expressed human cytochromes P450 (P450s) and human hepatocytes, and the data were used to simulate clinical area under the plasma concentration-time curve changes (δAUC) on coadministration with prototypic CYP2D6 substrates. Significant differences in IC50,u values between enzyme sources were observed for quinidine (0.02 μM in recombinant CYP2D6 versus 0.5 μM in hepatocytes) and propafenone (0.02 versus 4.1 μM). The relative contribution of individual P450s toward the oxidative metabolism of clinical probes desipramine, imipramine, tolterodine, propranolol, and metoprolol was estimated via determinations of intrinsic clearance using recombinant P450s (rP450s). Simulated δAUC were compared with those observed in vivo via the ratios of unbound inhibitor concentration at the entrance to the liver to inhibition constants determined against rP450s ([I]in,u/Ki) and incorporating parallel substrate elimination pathways. For this dataset, there were 20% false negatives (observed δAUC ≥ 2, predicted δAUC < 2), 77% correct predictions, and 3% false positives. Thus, the [I]in,u/Ki approach appears relatively successful at estimating the degree of clinical interactions and can be incorporated into drug discovery strategies. Using a Simcyp ADME (absorption, metabolism, distribution, elimination) simulator (Simcyp Ltd., Sheffield, UK), there were 3% false negatives, 94% correct simulations, and 3% false positives. False-negative predictions were rationalized as a result of mechanism-based inhibition, production of inhibitory metabolites, and/or hepatic uptake. Integrating inhibition and reaction phenotyping data from automated rP450 screens have shown applicability to predict the occurrence and degree of in vivo drug-drug interactions, and such data may identify the clinical consequences for candidate drugs as both “perpetrators” and “victims” of P450-mediated interactions.

Footnotes

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

  • doi:10.1124/dmd.108.020446.

  • ABBREVIATIONS: P450, cytochrome P450; DDI, drug-drug interaction; IVIVE, in vitro-in vivo extrapolation; AUC, area under the plasma concentration-time curve; [I]in,u, unbound maximum hepatic input concentration; rP450, recombinant cytochrome P450; δAUC, area under the plasma concentration-time curve change; RSP, robotic sample processor; HPLC, high-performance liquid chromatography; CLint, intrinsic clearance; MS, mass spectrometry; MS/MS, tandem mass spectrometry; fmx, fraction of total clearance metabolized by P450x; ka, absorption rate constant; IC50,u, unbound IC50; fuinc, unbound fraction in incubation; [I]in, maximum hepatic input concentration; fup, unbound fraction in plasma.

  • ↵1 Current affiliation: Metabolism and Pharmacokinetics Group, Novartis Institute for Biomedical Research, Horsham, West Sussex, UK.

    • Received January 14, 2008.
    • Accepted March 17, 2008.
  • The American Society for Pharmacology and Experimental Therapeutics
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Drug Metabolism and Disposition: 36 (6)
Drug Metabolism and Disposition
Vol. 36, Issue 6
1 Jun 2008
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Research ArticleArticle

Integrated in Vitro Analysis for the in Vivo Prediction of Cytochrome P450-Mediated Drug-Drug Interactions

Dermot F. McGinnity, Nigel J. Waters, James Tucker and Robert J. Riley
Drug Metabolism and Disposition June 1, 2008, 36 (6) 1126-1134; DOI: https://doi.org/10.1124/dmd.108.020446

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Research ArticleArticle

Integrated in Vitro Analysis for the in Vivo Prediction of Cytochrome P450-Mediated Drug-Drug Interactions

Dermot F. McGinnity, Nigel J. Waters, James Tucker and Robert J. Riley
Drug Metabolism and Disposition June 1, 2008, 36 (6) 1126-1134; DOI: https://doi.org/10.1124/dmd.108.020446
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