Genotype-based quantitative prediction of drug exposure for drugs metabolized by CYP2D6.

We propose a framework to enable quantitative prediction of the impact of CYP2D6 polymorphisms on drug exposure. It relies mostly on in vivo data and uses two characteristic parameters: one for the drug and the other for the genotype. The metric of interest is the ratio of drug area under the curve (AUC) in patients with mutant genotype to the AUC in patients with wild-type genotype. Any combination of alleles, as well as duplications, may be accommodated in the framework. Estimates of the characteristic parameters were obtained by orthogonal regression for 40 drugs and five classes of genotypes, respectively, including poor, intermediate, and ultrarapid metabolizers (PMs, IMs, and UMs). The mean prediction error of AUC ratios was -0.05, and the mean prediction absolute error was 0.20. An external validation was also carried out. The model may be used to predict the variations in exposure induced by all drug-genotype combinations. An application of this model to a rare combination of alleles (*4*10) is described.