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Clinical Pharmacokinetics

, Volume 45, Issue 10, pp 1035–1050 | Cite as

Prediction of In Vivo Drug-Drug Interactions from In Vitro Data

Factors Affecting Prototypic Drug-Drug Interactions Involving CYP2C9, CYP2D6 and CYP3A4
  • Hayley S. Brown
  • Aleksandra Galetin
  • David Hallifax
  • J. Brian Houston
Original Research Article

Abstract

Background

Quantitative predictions of in vivo drug-drug interactions (DDIs) resulting from metabolic inhibition are commonly made based upon the inhibitor concentration at the enzyme active site [I] and the in vitro inhibition constant (Ki). Previous studies have involved the use of various plasma inhibitor concentrations as surrogates for [I] along with Ki values obtained from published literature. Although this approach has resulted in a high proportion of successful predictions, a number of falsely predicted interactions are also observed.

Objectives

To focus on three issues that may influence the predictive value of the [I]/Ki ratio approach: (i) the use of unbound Ki (Ki,u) values generated from standardised in vitro experiments compared with literature values; (ii) the selection of an appropriate [I]; and (iii) incorporation of the impact of intestinal metabolic inhibition for cytochrome P450 (CYP) 3A4 predictions. To this end we have selected eight inhibitors of CYP2C9, CYP2D6 and CYP3A4 and 18 victim drugs from a previous database analysis to allow prediction of 45 clinical DDI studies.

Methods

In vitro kinetic and inhibition studies were performed in human liver microsomes using prototypic probe substrates of CYP2C9 and CYP2D6, with various inhibitors (miconazole, sulfaphenazole, fluconazole, ketoconazole, quinidine, fluoxetine, fluvoxamine). The Ki estimates obtained were corrected for non-specific microsomal binding, and the Ki,u was incorporated into in vivo predictions using various [I] values. Predictions for CYP3A4 were based upon in vitro data obtained from a previous publication within our laboratory, and an assessment of the impact of the interaction in the gut wall is included. Predictions were validated against 45 in vivo studies and those within 2-fold of the in vivo ratio of area under the plasma concentration-time curve of the substrate, in the presence and absence of the inhibitor (AUCi/AUC) were considered successful.

Results

Predictions based upon the average systemic total plasma drug concentration ([I]av) [incorporating the effects of parallel drug elimination pathways] and the Ki,u value resulted in 91% of studies predicted to within 2-fold of the in vivo AUCi/AUC. This represents a 35% improvement in prediction accuracy compared with predictions based upon total Ki values obtained from various published literature sources. A corresponding reduction in bias and an increase in precision were also observed compared with the use of other [I] surrogates (e.g. the total and new unbound maximum hepatic input plasma concentrations). No significant improvement in prediction accuracy was observed by incorporating consideration of gut wall inhibition for CYP3A4.

Conclusion

DDI predictions based upon the use of Ki,u data obtained under a set of optimal standardised conditions were significantly improved compared with predictions using in vitro data collated from various sources. The use of [I]av as the [I] surrogate generated the most successful predictions as judged by several criteria. Incorporation of either plasma protein binding of inhibitor or gut wall CYP3A4 inhibition did not result in a general improvement of DDI predictions.

Keywords

Fluconazole Quinidine Felodipine Fluvoxamine Tolbutamide 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was funded by a consortium of pharmaceutical companies (AstraZeneca, GlaxoSmithKline, Bristol Myers Squibb, F. Hoffmann La Roche, Novartis, Pfizer and Servier) within the Centre for Applied Pharmacokinetic Research at the University of Manchester. H.S. Brown was financially supported by a Bristol Myers Squibb studentship. The authors wish to thank Kiyomi Ito and Susan Murby for valuable contributions to this work.

The authors have no conflict of interests directly relevant to the content of this study.

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Copyright information

© Adis Data Information BV 2006

Authors and Affiliations

  • Hayley S. Brown
    • 1
  • Aleksandra Galetin
    • 1
  • David Hallifax
    • 1
  • J. Brian Houston
    • 1
  1. 1.School of Pharmacy and Pharmaceutical SciencesUniversity of ManchesterManchesterUK

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