Quantitative prediction of the impact of drug interactions and genetic polymorphisms on cytochrome P450 2C9 substrate exposure.

Autor: Castellan AC; Université de Lyon, F-69000, Lyon, Université Lyon 1, CNRS UMR5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Modélisation et Evaluation des Thérapeutiques, 7 rue Guillaume Paradin, 69007 Lyon, France. castellan_charlotte@yahoo.fr, Tod M, Gueyffier F, Audars M, Cambriels F, Kassaï B, Nony P
Jazyk: angličtina
Zdroj: Clinical pharmacokinetics [Clin Pharmacokinet] 2013 Mar; Vol. 52 (3), pp. 199-209.
DOI: 10.1007/s40262-013-0031-3
Abstrakt: Background and Objective: Cytochrome P450 (CYP) 2C9 is the most common CYP2C enzyme and makes up approximately onethird of total CYP protein content in the liver. It metabolises more than 100 drugs. The exposure of drugs mainly eliminated by CYP2C9 may be dramatically modified by drug-drug interactions (DDIs) and genetic variations. The objective of this study was to develop a modelling approach to predict the impact of genetic polymorphisms and DDIs on drug exposure in drugs metabolised by CYP2C9. We then developed dosing recommendations based on genotypes and compared them to current Epar/Vidal dosing guidelines.
Methods: We created two models. The genetic model was designed to predict the impact of CYP2C9 polymorphisms on drug exposure. It links the area under the concentration-time curve (AUC) ratio (mutant to wild-type patients) to two parameters: the fractional contribution of CYP2C9 to oral clearance in vivo (i.e. CR or contribution ratio), and the fractional activity of the allele combination with respect to the homozygous wild type (i.e. FA or fraction of activity). Data were available for 77 couples (substrate, genotype). We used a three-step approach: (1) initial estimates of CRs and FAs were calculated using a first bibliographic dataset; (2) external validation of these estimates was then performed through the comparison between the AUC ratios predicted by the model and the observed values, using a second published dataset; and (3) refined estimates of CRs and FAs were obtained using Bayesian orthogonal regression involving the whole dataset and initial estimates of CRs and FAs. Posterior distributions of AUC ratios, CRs and FAs were estimated using Monte-Carlo Markov chain simulation. The drug interaction model was designed to predict the impact of DDIs on drug exposure. It links the AUC ratio (ratio of drug given in combination to drug given alone) to several parameters: the CR, the inhibition ratio (IR) of an inhibitor, and the increase in clearance (IC) due to an inducer. Data were available for 80 DDIs. IRs and ICs were calculated using the interaction model and an external validation was performed. Doses adjustments were calculated in order to obtain equal values for drug exposure in extensive and poor metabolisers and then compared to Epar/Vidal dosing guidelines.
Results: CRs were assessed for 26 substrates, FAs for five genotype classes including CYP2C9*2 and *3 allelic variants, IRs for 27 inhibitors and ICs for two inducers. For the genetic model, the mean prediction error of AUC ratios was -0.01, while the mean prediction absolute error was 0.36. For the drug interaction model, the mean prediction error of AUC ratios was 0.01, while the mean prediction absolute error was 0.22. Of the 26 substrates and CYP2C9*2 and *3 variants investigated, 30 couples (substrate, genotype) lead to a dose adjustment, as opposed to only ten couples identified in the Epar/Vidal recommendations.
Conclusion: These models were already used for CYP2D6. They are accurate at predicting the impact of drug interactions and genetic polymorphisms on CYP2C9 substrate exposure. This approach will contribute to the development of personalized medicine, i.e. individualized drug therapy with specific dosing recommendations based on CYP genotype or drug associations.
Databáze: MEDLINE
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