Impact of Genetic Polymorphism on Drug-Drug Interactions Mediated by Cytochromes: A General Approach

• Published in: The AAPS journal Vol. 15; no. 4; pp. 1242 - 1252
• Main Authors: Tod, Michel, Nkoud-Mongo, Christina, Gueyffier, François
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.10.2013
• Subjects: Biochemistry; Biomedical and Life Sciences; Biomedicine; Biotechnology; Cytochrome P-450 CYP3A - genetics; Cytochrome P-450 CYP3A - physiology; Drug Interactions - genetics; Forecasting; Humans; Pharmacology/Toxicology; Pharmacy; Polymorphism, Genetic - genetics; Research Article; drug interactions; genetic polymorphism; quantitative prediction; cytochromes
• ISSN: 1550-7416; 1550-7416
• DOI: 10.1208/s12248-013-9530-2

Currently, quantitative prediction of the impact of genetic polymorphism and drug-drug interactions mediated by cytochromes, based on in vivo data, is made by two separate methods and restricted to a single cytochrome. We propose a unified approach for describing the combined impact of drug-drug interactions and genetic polymorphism on drug exposure. It relies on in vivo data and uses the following three characteristic parameters: one for the victim drug, one for the interacting drug, and another for the genotype. These parameters are known for a wide range of drugs and genotypes. The metrics of interest are the ratio of victim drug area under the curve (AUC) in patients with genetic variants taking both drugs, to the AUC in patients with either variant or wild-type genotype taking the victim drug alone. The approach was evaluated by external validation, comparing predicted and observed AUC ratios found in the literature. Data were found for 22 substrates, 30 interacting drugs, and 38 substrate-interacting drug couples. The mean prediction error of AUC ratios was 0.02, and the mean prediction absolute error was 0.38 and 1.34, respectively. The model may be used to predict the variations in exposure resulting from a number of drug-drug–genotype combinations. The proposed approach will help (1) to identify comedications and population at risk, (2) to adapt dosing regimens, and (3) to prioritize the clinical pharmacokinetic studies to be done.