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Currently, genotyping of patients for polymorphic enzymes responsible for metabolic elimination is considered a possibility to adjust drug dose levels. For a patient to profit from this procedure, the interindividual differences in drug metabolism within one genotype should be smaller than those between different genotypes. We studied a large cohort of healthy young adults (283 subjects), correlating their CYP2C9 genotype to a simple phenotyping metric, using flurbiprofen as probe drug. Genotyping was conducted for CYP2C9*1, *2, *3. The urinary metabolic ratio MR (concentration of CYP2C9-dependent metabolite divided by concentration of flurbiprofen) determined two hours after flurbiprofen (8.75 mg) administration served as phenotyping metric. Linear statistical models correlating genotype and phenotype provided highly significant allele-specific MR estimates of 0.596 for the wild type allele CYP2C9*1, 0.405 for CYP2C9*2 (68 % of wild type), and 0.113 for CYP2C9*3 (19 % of wild type). If these estimates were used for flurbiprofen dose adjustment, taking 100 % for genotype *1/*1, an average reduction to 84 %, 60 %, 68 %, 43 %, and 19% would result for genotype *1/*2, *1/*3, *2/*2, *2/*3, and *3/*3, respectively. Due to the large individual variation within genotypes with coefficients of variation >= 20% and supposing the normal distribution, one in three individuals would be out of the average optimum dose by more than 20 %, one in 20 would be 40% off. Whether this problem also applies to other CYPs and other drugs has to be investigated case by case. Our data for the given example, however, puts the benefit of individual drug dosing to question, if it is exclusively based on genotype.
This study should contribute to the important field of pharmacogenetics by: firstly, establishing an easy and safe phenotyping method that combines the activity determination of all three previously mentioned CYPs (CYP2D6, CYP2C9, and CYP2C19) into one phenotyping cocktail and secondly, improving the knowledge about the predictive power of the genotype for the measured phenotype. It was indeed possible to develop a save, easy-to-use, fast and simultaneous phenotyping procedure for the important genetic polymorphic enzymes CYP2D6 and CYP2C9. To accomplish that, interaction studies with the chosen probe drugs dextromethorphan (DEX, CYP2D6), flurbiprofen (FLB, CYP2C9) and omeprazole (OME, CYP2C19) were conducted. It could be proven that DEX and FLB can be administered in combination, whereas OME alters the phenotyping results of CYP2C9. This is a new finding as in 2004 a phenotyping cocktail was published that used FLB and OME in combination. However, to our knowledge, no interaction tests were carried in that study. The new phenotyping procedure is not only verified by prior probe drug interaction studies, it also has other advantages over phenotyping cocktails found in literature. Firstly, save probe drugs are used in very small doses. This is possible due to the new sensitive LC-MS/MS methods that were evaluated. Secondly, the new phenotyping procedure is very fast and on-invasive. Urine has to be collected only for 2 h and the results also suggest that the time consuming glucuronide cleavage of the CYP2D6 dependent metabolite dextrorphan, usually carried out before CYP2D6 phenotyping, may be unnecessary. Most importantly, however, new insights into the phenotype prediction from genotype for CYP2C9 and CYP2D6 could be gained within this study. Nearly 300 phenotyped Caucasian subjects were also genotyped for the most important known variant alleles for CYP2D6, CYP2C9 and CYP2C19 using several established and newly developed genoptyping methods. Therefore, a direct correlation between phenotype and genotype could be conducted for CYP2D6 and CYP2C9. Employing linear modeling, it was possible to assign activity coefficients to each of the detected CYP2D6 and CYP2C9 alleles, thereby estimating their contribution to the resulting enzyme activity. This might facilitate the prediction of the CYP2D6 and CYP2C9 metabolic status of a subject knowing only its respective genotypes. Especially the new CYP2D6 genotype phenotype correlation model might allow for more precise phenotype prediction for the included variant alleles than was possible until now. Taken together, this study substantially contributes to the important research field of pharmacogenetics by (i) developing a save and easy-to-use phenotyping combination for CYP2D6 and CYP2C9, and (ii) by establishing activity coefficients for each of the detected CYP2D6 and CYP2C9 alleles, thereby allowing for a more precise prediction of the phenotype from genotype.