Prediction of Drug-Drug Interactions

药物不良反应是美国第四大死亡原因，占医院死亡人数的5%。 招生这些不良事件中很大一部分是由药物相互作用引起的， 经历这样的互动代表了患者访问他们的医生的最大恐惧之一。是 患者通常同时接受4种或更多种药物， 越来越多的老年病人在养老院。尽管这种多元化药房和社会化药房无处不在 药物间相互作用的影响尚未有结构化的尝试来预测和管理 复杂的多药物相互作用。我们建议采取以下措施，作为弥补这方面不足的第一步 研究三种药物混合物中发生的“三元”药物相互作用。我们将专注于代谢药物 肝脏和肠壁中CYP3A酶水平的相互作用，因为这些代表了 临床上重要的药物相互作用的一个最常见原因。我们先来量化一下 诱导肠和肝CYP3A酶的过程和浓度依赖性 原型诱导剂利福平我们将使用静脉咪达唑仑来反映肝脏CYP3A活性， 肠夹活检以反映肠CYP3A活性。这将使我们能够建立一个预测， 利福平诱导CYP3A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研究。这些研究将测试 假设同时给予两种CYP3A调节剂的作用可从个体预测 二元互动我们将使用静脉和口服咪达唑仑作为肠道和肝脏CYP3A的探针 活动这些三元相互作用将包括抑制剂的组合以及抑制剂和抑制剂的组合。 诱导剂利福平我们将为每种药物开发基于生理学的药代动力学模型 参与三元相互作用，以检查相互作用的可预测性。 我们还将检验三元体内相互作用可以从体外数据预测的假设。的 抑制剂、诱导剂和底物之间的相互作用将在亚细胞级分中定量，并培养 细胞体外参数估计值将纳入我们的生理药代动力学模型， 测试预测能力，并为化学品之间的复杂相互作用建立一个通用平台。

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.