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

Published in CPT: pharmacometrics & systems pharmacology. 2019, p. 1-10
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.
PMID: 31268632
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Article (Published version) (568 Kb) - public document Free access
Research groups Groupe Desmeules Jules (pharmacologie/toxicologie) (567)
Pharmaco-omiques et médecine de précision (1003)
Swiss National Science Foundation: 320030_182361
(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 https://archive-ouverte.unige.ch/unige:121056

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Deposited on : 2019-07-22

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