Rüdesheim Simeon, Selzer Dominik, Mürdter Thomas, Igel Svitlana, Kerb Reinhold, Schwab Matthias, Lehr Thorsten
Department of Clinical Pharmacy, Saarland University, 66123 Saarbrücken, Germany.
Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, 70376 Stuttgart, Germany.
Pharmaceutics. 2022 Aug 18;14(8):1734. doi: 10.3390/pharmaceutics14081734.
The cytochrome P450 2D6 () genotype is the single most important determinant of CYP2D6 activity as well as interindividual and interpopulation variability in CYP2D6 activity. Here, the CYP2D6 activity score provides an established tool to categorize the large number of CYP2D6 alleles by activity and facilitates the process of genotype-to-phenotype translation. Compared to the broad traditional phenotype categories, the CYP2D6 activity score additionally serves as a superior scale of CYP2D6 activity due to its finer graduation. Physiologically based pharmacokinetic (PBPK) models have been successfully used to describe and predict the activity score-dependent metabolism of CYP2D6 substrates. This study aimed to describe CYP2D6 drug-gene interactions (DGIs) of important CYP2D6 substrates paroxetine, atomoxetine and risperidone by developing a substrate-independent approach to model their activity score-dependent metabolism. The models were developed in PK-Sim, using a total of 57 plasma concentration-time profiles, and showed good performance, especially in DGI scenarios where 10/12, 5/5 and 7/7 of DGI AUC ratios and 9/12, 5/5 and 7/7 of DGI C ratios were within the prediction success limits. Finally, the models were used to predict their compound's exposure for different CYP2D6 activity scores during steady state. Here, predicted DGI AUC ratios were 3.4, 13.6 and 2.0 (poor metabolizers; activity score = 0) and 0.2, 0.5 and 0.95 (ultrarapid metabolizers; activity score = 3) for paroxetine, atomoxetine and risperidone active moiety (risperidone + 9-hydroxyrisperidone), respectively.
细胞色素P450 2D6（CYP2D6）基因型是CYP2D6活性以及CYP2D6活性个体间和群体间变异性的最重要单一决定因素。在此，CYP2D6活性评分提供了一种既定工具，可按活性对大量CYP2D6等位基因进行分类，并促进基因型到表型的转化过程。与宽泛的传统表型类别相比，CYP2D6活性评分因其更精细的分级，还可作为CYP2D6活性的更优衡量标准。基于生理的药代动力学（PBPK）模型已成功用于描述和预测CYP2D6底物的活性评分依赖性代谢。本研究旨在通过开发一种不依赖底物的方法来模拟其活性评分依赖性代谢，以描述重要CYP2D6底物帕罗西汀、托莫西汀和利培酮的CYP2D6药物-基因相互作用（DGI）。这些模型在PK-Sim中开发，使用了总共57个血浆浓度-时间曲线，表现良好，特别是在DGI情况下，DGI AUC比值的10/12、5/5和7/7以及DGI C比值的9/12、5/5和7/7在预测成功范围内。最后，这些模型用于预测稳态期间不同CYP2D6活性评分时其化合物的暴露情况。在此，帕罗西汀、托莫西汀和利培酮活性部分（利培酮 + 9-羟基利培酮）的预测DGI AUC比值分别为3.4、13.6和2.0（慢代谢者；活性评分 = 0）以及0.2、0.5和0.95（超快代谢者；活性评分 = 3）。
