Chaudhry Shafqat Rasul, Muhammad Sajjad, Eidens Moritz, Klemm Marco, Khan Dilaware, Efferth Thomas, Weisshaar Maria-Paz
Department of Neurosurgery, University of Bonn, Sigmund-Freudstrasse 25, D-53105 Bonn, Germany.
Curr Drug Metab. 2014;15(7):711-8. doi: 10.2174/1389200215666141125121952.
Interindividual variability in drug response depends on a number of genetic and environmental factors. The metabolic enzymes are well known for their contribution to this variability due to drug-drug interactions and genetic polymorphisms. The phase I drug metabolism is highly dependent upon the cytochrome P450 mono-oxygenases (CYP) and their genetic polymorphism leads to the variable internal drug exposures. The highly polymorphic CYP2C9, CYP2C19 and CYP2D6 isozymes are responsible for metabolizing a large portion of routinely prescribed drugs and contribute significantly to adverse drug reactions and therapeutic failures. In this review, two attractive and easily implementable approaches are highlighted to recommend drug doses ensuring similar internal exposures in the face of these polymorphisms. The first approach relies on subpopulation-based dose recommendations that consider the original population dose as an average of the doses recommended in genetically polymorphic subpopulations. By using bioequivalence principles and assuming linear gene-dose effect, dose recommendations can be made for different metabolic phenotypes. The second approach relates area under the curve to two characteristic parameters; the contribution ratio (CR), computes for the contribution of the metabolic enzyme and the fractional activity (FA), considers the impact of the genetic polymorphism. This approach provides valid and error free internal drug exposure predictions and can take into consideration genetic polymorphisms and drug interactions and/ or both simultaneously. Despite certain advantages and limitations, both approaches provide a good initial frame-work for devising models to predict internal exposure and individualize drug therapy, one of the promises from human genome project.
药物反应的个体间差异取决于多种遗传和环境因素。代谢酶因其通过药物相互作用和基因多态性对这种差异的影响而广为人知。由于药物 - 药物相互作用和基因多态性，I 相药物代谢高度依赖于细胞色素 P450 单加氧酶（CYP），其基因多态性导致药物体内暴露量的变化。高度多态的 CYP2C9、CYP2C19 和 CYP2D6 同工酶负责代谢大部分常规处方药，并对药物不良反应和治疗失败有显著影响。在本综述中，重点介绍了两种有吸引力且易于实施的方法，以在面对这些多态性时推荐能确保相似体内暴露量的药物剂量。第一种方法依赖于基于亚群体的剂量推荐，即将原始群体剂量视为基因多态性子群体中推荐剂量的平均值。通过使用生物等效性原则并假设线性基因 - 剂量效应，可以针对不同的代谢表型做出剂量推荐。第二种方法将曲线下面积与两个特征参数相关联；贡献比（CR），用于计算代谢酶的贡献，以及分数活性（FA），考虑基因多态性的影响。这种方法能提供有效且无误差的药物体内暴露预测，并且可以同时考虑基因多态性和药物相互作用及/或两者。尽管有一定的优点和局限性，但这两种方法都为设计预测体内暴露和使药物治疗个体化的模型提供了一个良好的初始框架，这是人类基因组计划的承诺之一。
