Pharmacogenetics and Drug Interactions

描述(申请人提供)：越来越多的数据表明，人类药物代谢酶的遗传变异不仅可以深刻地影响药物新陈代谢的速度，而且还可以在伴随药物剂量的情况下观察到药物-药物相互作用的程度。这项研究的长期目标是了解P450酶基因多态性对药物相互作用易感性的影响，并开发预测相互作用潜力变化的模型；量化这些影响将提高临床剂量调整的准确性。前一次授权期的一个主要发现是，在体外模型和在氟比洛芬(CYP2C9探针底物)和氟康唑(CYP2C9抑制剂)相互作用的初步人体临床研究中，功能降低的CYP2C9变体(即CYP2C9*3)显示出不同程度的抑制。这一发现隐含着一种假设，即由CYP2C9代谢的药物部分和基因之间的相互作用决定了观察到的药物相互作用的程度。这项初步研究具有重要的临床意义，因为它表明，不同基因型别的个体在联合用药时可能需要不同的剂量调整，特别是对于治疗指数较小的药物，如苯妥英钠。目前的竞争性更新建立在上述发现的基础上：a)验证基于酶的药代动力学模型，该模型使用基因特异性抑制常数(Ki)来确定药物相互作用的程度和对药物代谢部分的影响，以及b)建立纯合子不良代谢和杂合子部分零代谢的体外模型，用于预测与基因相关的药物-药物相互作用。通过加强对基因对相互作用的影响的了解，并开发利用体外数据和感兴趣化合物的已知代谢特征的预测模型，可以更有效地管理药物相互作用，从而改善患者的结果。公共卫生相关性：一个人的基因构成可以影响药物的处置和作用，以及对药物相互作用的敏感性。更好地了解这些基因变化及其对药物处置的影响将导致改进药物治疗和更好地管理药物之间的相互作用。

DESCRIPTION (provided by applicant): Data are accumulating to suggest that genetic variation in human drug metabolizing enzymes can profoundly affect not only the rate of drug metabolism but also the degree of drug-drug interactions observed upon concomitant drug dosing. The long term goals of this research are to understand the effects of genetic polymorphisms in P450 enzymes on susceptibility to drug- drug interactions and to develop models to predict these changes in interaction potential; quantifying these effects will improve the accuracy of clinical dosing adjustments. A major finding from the previous granting period was that CYP2C9 variants with reduced function (i.e., CYP2C9*3) exhibit altered degrees of inhibition in both an in vitro model and in an initial in vivo human clinical study of the interaction of flurbiprofen (CYP2C9 probe substrate) and fluconazole (CYP2C9 inhibitor). Implicit in this finding is the hypothesis that an interplay between fraction of drug metabolized by CYP2C9 and genotype determines the extent of drug interaction observed. This preliminary study has significant clinical importance as it suggests that individuals of differing genotypes may require different adjustments of doses upon drug co-administration and in particularly for narrow therapeutic index drugs, such as phenytoin. The current competing renewal builds upon the above findings to: a) validate an enzyme-based pharmacokinetic model that uses a genotype-specific inhibition constant (Ki) to determine the extent of the drug interaction and the impact on the fraction of drug metabolized, and b) develop an in vitro model of homozygous poor metabolism and heterozygous partial null metabolism for predicting genotype-dependent drug-drug interactions. Through enhanced understanding of the effect of genotype on interactions and development of a predictive model that utilizes both in vitro data and known metabolism characteristics of the compound of interest, drug interactions can be managed more effectively resulting in improved patient outcomes. PUBLIC HEALTH RELEVANCE: A person's genetic makeup can affect drug disposition and action, as well as the susceptibility to drug interactions. Improved understanding of these genetic changes and their effects on drug disposition will lead to improved drug therapy and better management of drug-drug interactions.