Pharmacogenetics and Drug Interactions

描述（由申请方提供）：数据的积累表明，人类药物代谢酶的遗传变异不仅会深刻影响药物代谢速率，还会影响伴随药物给药后观察到的药物相互作用程度。本研究的长期目标是了解P450酶遗传多态性对药物相互作用敏感性的影响，并开发模型来预测相互作用潜力的这些变化;量化这些影响将提高临床剂量调整的准确性。上一个授权期的一个主要发现是功能降低的CYP 2C 9变体（即，CYP 2C 9 *3）在体外模型和氟比洛芬（CYP 2C 9探针底物）与氟康唑（CYP 2C 9抑制剂）相互作用的初始体内人体临床研究中均显示出抑制程度的改变。这一发现隐含的假设是，CYP 2C 9代谢的药物分数和基因型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的个体在药物联合给药时可能需要不同的剂量调整，特别是对于窄治疗指数药物，如苯妥英。目前的竞争性更新基于上述发现，以：a）验证基于酶的药代动力学模型，该模型使用基因型特异性抑制常数（Ki）确定药物相互作用的程度和对药物代谢分数的影响，以及B）开发纯合代谢不良和杂合部分无效代谢的体外模型，用于预测基因型依赖性药物-药物相互作用。通过增强对基因型对相互作用的影响的理解和开发利用体外数据和目标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，从而改善患者结局。公共卫生相关性：一个人的基因组成可以影响药物的处置和行动，以及对药物相互作用的敏感性。对这些遗传变化及其对药物处置的影响的更好理解将导致改善药物治疗和更好地管理药物相互作用。

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.