Physiologically based pharmacokinetic modelling and in vivo [I]/Ki accurately predict P-glycoprotein-mediated drug-drug interactions with dabigatran etexilate

Authors

  • Yuansheng Zhao,

    1. The Hamner Institutes for Health Sciences, Research Triangle Park, NC, USA
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  • Zhe-Yi Hu

    Corresponding author
    1. Department of Clinical Pharmacy, College of Pharmacy, University of Tennessee Health Science Center, Memphis, TN, USA
    • Correspondence

      Dr Zhe-Yi Hu, University of Tennessee Health Science Center, College of Pharmacy, Department of Clinical Pharmacy, 881 Madison Ave., Room 328, Memphis, TN 38163, USA. E-mail: zhu13@uthsc.edu

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Abstract

Background and purpose

In vitro inhibitory potency (Ki)-based predictions of P-glycoprotein (P-gp)-mediated drug-drug interactions (DDIs) are hampered by the substantial variability in inhibitory potency. In this study, in vivo-based [I]/Ki values were used to predict the DDI risks of a P-gp substrate dabigatran etexilate (DABE) using physiologically based pharmacokinetic (PBPK) modelling.

Experimental approach

A baseline PBPK model was established with digoxin, a known P-gp substrate. The Km (P-gp transport) of digoxin in the baseline PBPK model was adjusted to Kmi to fit the change of digoxin pharmacokinetics in the presence of a P-gp inhibitor. Then ‘in vivo’ [I]/Ki of this P-gp inhibitor was calculated using Kmi/Km. Baseline PBPK model was developed for DABE, and the ‘in vivo’ [I]/Ki was incorporated into this model to simulate the static effect of P-gp inhibitor on DABE pharmacokinetics. This approach was verified by comparing the observed and the simulated DABE pharmacokinetics in the presence of five different P-gp inhibitors.

Key results

This approach accurately predicted the effects of five P-gp inhibitors on DABE pharmacokinetics (98–133% and 89–104% for the ratios of AUC and Cmax respectively). The effects of 16 other P-gp inhibitors on the pharmacokinetics of DABE were also confidently simulated.

Conclusions and implications

In vivo’ [I]/Ki and PBPK modelling, used in combination, can accurately predict P-gp-mediated DDIs. The described framework provides a mechanistic basis for the proper design of clinical DDI studies, as well as avoiding unnecessary clinical DDI studies.

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