Prediction of Drug-Drug Interactions

药物不良反应是美国第四大致死原因，占医院总数的5% 招生。这些不良事件中有很大一部分是由于药物与药物的相互作用和机会 体验这样的互动是患者去看医生时最害怕的事情之一。它是 患者同时接受4种或更多药物治疗的情况很常见，而10种或更多药物在 养老院中老年患者的数量不断增加。尽管这种无处不在的多药房和社交 药物-药物相互作用的影响一直没有系统地尝试预测和管理 复杂的多种药物相互作用。我们提议迈出第一步，通过以下方式弥补这一缺口 研究3种药物混合物中发生的“三元”药物相互作用。我们将专注于代谢性药物 肝脏和肠壁中细胞色素P3A酶水平的相互作用，因为这些酶代表 临床上重要的药物相互作用的单一最常见原因。我们将首先量化时间 黄曲霉毒素诱导肠道和肝脏细胞色素P3A酶的过程和浓度依赖关系 典型诱导剂利福平。我们将使用静脉注射咪达唑仑来反映肝脏细胞色素P3A活性和 肠挤压活检以反映肠道细胞色素P3A的活性。这将使我们能够建立一个可预测的、 利福平诱导细胞色素P3A的生理学药代动力学模型。 在随后的研究中，我们进行了三元药物相互作用的研究。这些研究将测试 两种细胞色素P3A调节剂同时给药的效应可从个体预测的假设 二元相互作用。我们将使用静脉和口服咪达唑仑作为肠道和肝脏细胞色素P3A的探针 活动。这些三元相互作用将包括缓蚀剂的组合和缓蚀剂和 诱导剂，利福平。我们将建立每种药物的生理学药代动力学模型。 参与三元交互作用，以检查交互作用的可预测性。 我们还将测试这一假设，即可以从体外数据预测体内的三元相互作用。这个 抑制剂、诱导剂和底物之间的相互作用将以亚细胞组分进行量化并进行培养 细胞。体外参数估计将被纳入我们的生理药代动力学模型中 测试预测能力，为化学品之间的复杂相互作用搭建通用平台。

Adverse drug reactions are the fourth leading cause of death in the US and account for 5% of hospital admissions. A large percentage of these adverse events result from drug-drug interactions and the chance of experiencing such an interaction represents one of the greatest fears of patients visiting their doctor. It is common for patients to receive 4 or more drugs simultaneously and 10 or more is common among the growing population of elderly patients in nursing homes. Despite this ubiquitous polypharmacy and social impact of drug-drug interactions there has been no structured attempt to predict and therefore manage complex multi-drug interactions. We are proposing to take the first step in remedying this shortfall by studying "ternary" drug interactions occurring within mixtures of 3 drugs. We will focus on metabolic drug interactions at the level of the CYP3A enzymes in the liver and intestinal wall because these represent the single most common cause of clinically important drug-drug interactions. We will first quantify the time course and concentration dependence of the induction of intestinal and hepatic CYP3A enzymes by the prototypical inducer, rifampin. We will use intravenous midazolam to reflect hepatic CYP3A activity and intestinal pinch biopsies to reflect intestinal CYP3A activity. This will allow us to build a predictive, physiologically based pharmacokinetic model of CYP3A induction by rifampin. In the subsequent studies we conduct ternary drug interaction studies. These studies will test the hypotheses that the effect of two CYP3A modulators given simultaneously is predictable from the individual binary interactions. We will use intravenous and oral midazolam as probes of intestinal and hepatic CYP3A activity. These ternary interactions will include combinations of inhibitors and combinations of inhibitor and the inducer, rifampin. We will develop physiologically based pharmacokinetic models of each of the drugs involved in the ternary interactions to examine the predictability of the interactions. We will also test the hypothesis that the ternary in vivo interactions can be predicted from in vitro data. The interactions between inhibitors, inducer and substrate will be quantified in subcellular fractions and cultured cells. The in vitro parameter estimates will be incorporated into our physiological pharmacokinetic models to test the predictive power and build a universal platform for complex interactions between chemicals.