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Physiologically-based pharmacokinetic modeling for the prediction of CYP2D6-mediated gene-drug-drug interactions

Publication date2019
Abstract

The aim of this work was to predict the extent of Cytochrome P450 2D6 (CYP2D6)-mediated drug-drug interactions (DDIs) in different CYP2D6 genotypes using physiologically-based pharmacokinetic (PBPK) modeling. Following the development of a new duloxetine model and optimization of a paroxetine model, the effect of genetic polymorphisms on CYP2D6-mediated intrinsic clearances of dextromethorphan, duloxetine, and paroxetine was estimated from rich pharmacokinetic profiles in activity score (AS)1 and AS2 subjects. We obtained good predictions for the dextromethorphan-duloxetine interaction (Ratio of predicted over observed area under the curve (AUC) ratio (Rpred/obs ) 1.38-1.43). Similarly, the effect of genotype was well predicted, with an increase of area under the curve ratio of 28% in AS2 subjects when compared with AS1 (observed, 33%). Despite an approximately twofold underprediction of the dextromethorphan-paroxetine interaction, an Rpred/obs of 0.71 was obtained for the effect of genotype on the area under the curve ratio. Therefore, PBPK modeling can be successfully used to predict gene-drug-drug interactions (GDDIs). Based on these promising results, a workflow is suggested for the generic evaluation of GDDIs and DDIs that can be applied in other situations.

Citation (ISO format)
STORELLI, Flavia, DESMEULES, Jules Alexandre, DAALI, Youssef. Physiologically-based pharmacokinetic modeling for the prediction of CYP2D6-mediated gene-drug-drug interactions. In: CPT: pharmacometrics & systems pharmacology, 2019, p. 1–10. doi: 10.1002/psp4.12411
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ISSN of the journal2163-8306
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Creation07/04/2019 4:19:00 PM
First validation07/04/2019 4:19:00 PM
Update time03/15/2023 5:48:12 PM
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