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. 2019 Aug;8(8):567-576.
doi: 10.1002/psp4.12411. Epub 2019 Jul 3.

Physiologically-Based Pharmacokinetic Modeling for the Prediction of CYP2D6-Mediated Gene-Drug-Drug Interactions

Flavia Storelli  1   2 , Jules Desmeules  1   2   3   4 , Youssef Daali  1   2   3   4
Affiliations

Physiologically-Based Pharmacokinetic Modeling for the Prediction of CYP2D6-Mediated Gene-Drug-Drug Interactions

Flavia Storelli et al. CPT Pharmacometrics Syst Pharmacol. 2019 Aug.

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.

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Conflict of interest statement

The authors declared no competing interests for this work.

Figures

Figure 1
Figure 1
Simulated vs. observed duloxetine pharmacokinetic profiles. Green lines represent mean simulated pharmacokinetic profile, and gray lines represent the 5% and 95% percentiles of model‐predicted pharmacokinetic profiles. The x‐axis represents time after drug intake (hour), and the y‐axis represents plasma concentration (ng/mL). Observed data were taken from published trials: (a–d)42 (e, f)43 (g, h)44 (i)45 and (j)46
Figure 2
Figure 2
Sensitivity analysis of CYP2D6‐mediated clearance of both victim and inhibitors drugs on the extent of drug–drug interactions. AUC, area under the curve; CLint, intrinsic clearance; CYP, cytochrome P450; Vmax, maximal velocity.
Figure 3
Figure 3
Study workflow. In a first step, physiologically‐based pharmacokinetic (PBPK) models for substrates and inhibitors were built, optimized, or used unchanged from verified library compounds in Simcyp version 17. In a second step, the Cytochrome P450 2D6 (CYP2D6) genotype‐dependent CYP2D6‐mediated clearance of substrate and inhibitors were estimated from existing in vivo data in humans. Then the third step consisted in the simulations of genotype‐dependent drug–drug interactions (DDIs) to compare with existing DDI trials.
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