A Translational Bioinformatics Approach in the Drug Interaction Research

描述(由申请人提供)：药物-药物相互作用(DDIS)对公众健康构成越来越大的威胁，估计每年导致195,000人住院和74,000人到急诊室就诊。目前的DDI研究从计算和实验两个方面调查药物相互作用的不同方面。虽然这些方法是相辅相成的，但它们通常是独立进行的，不需要协调。体外药理学实验使用完整的细胞、微粒体蛋白质组分或重组系统来研究药物相互作用的机制。药物流行病学(Popo)使用以人群为基础的方法和大型电子医疗记录(EMR)数据库来调查DDI对药物疗效和药物不良反应(ADR)的贡献。在这项拨款提案中，将开发新的生物信息学数据挖掘方法来从EMR中挖掘DDiS，并将在体外进一步验证它们。以下是具体目标。在目标1中，提出了一种新的动态嵌套病例对照设计，以检测单一药物或DDI对ADR的影响。发展了一种新的经验贝叶斯方法来测试药物和DDI诱导的ADR，它将估计错误发现率。在目标2中，提出了一种新的广义倾向评分方法来分析高维用药数据。与传统的倾向评分法相比，该方法在识别高度相关药物的ADR效应方面具有更强的能力。目的3，使用AIMS 1和AIMS 2中开发的单变量和多变量数据挖掘方法，我们将使用EMR数据库和高通量酶筛查方法，检测增加一种明确定义的ADR-肌病风险的新药和DDIS。在我们的初步工作中，使用我们提出的方法和一个220,000个记录的电子病历数据库，识别了6个肌病风险DDis(p&lt；5�10-6)，包括新发现的奎硫平和氯喹之间的相互作用。如果加在一起，它们会使肌病风险增加2.17倍，这是由于奎硫平通过CYP3A4途径抑制氯喹代谢和阻断OATP1B1/1B3跨膜转运蛋白而导致的单独风险。因此，这项工作的成功执行将展示一个完整的翻译范围，从基于EMR的DDI发现开始，以通过药理学实验阐明分子DDI机制结束。综上所述，这些初步数据表明，我们的翻译方法是一种高度可行和极其强大的临床DDI研究方法，可能会为这一新出现的公共健康危险提供有价值的见解。

DESCRIPTION (provided by applicant): Drug-drug interactions (DDIs) represent an increasing threat to public health, causing an estimated 195,000 annual hospitalizations and 74,000 emergency room visits. Current DDI research investigates different aspects of drug interactions, both computationally and experimentally. Although these approaches are complementary, they are usually conducted independently and without coordination. In vitro pharmacology experiments use intact cells, microsomal protein fractions, or recombinant systems to investigate drug interaction mechanisms. Pharmaco-epidemiology (in populo) uses a population-based approach and large electronic medical record (EMR) databases to investigate the contribution of a DDI to drug efficacy and adverse drug reactions (ADRs). In this grant proposal, novel bioinformatics data mining approaches will be developed to mine DDIs from EMR, and they will be further validated in vitro. The following are specific aims. In Aim 1, a nove dynamic nested case-control design is proposed to detect of either single drug or DDI effects on the ADR. A new empirical Bayes method is developed to test the drug and DDI-induced ADRs, and it will estimate false discovery rates. In Aim 2, a novel generalized propensity score method is proposed to analyze high dimensional medication data. This method possesses more power in identifying ADR effects from highly correlated drugs, than the conventional propensity score method. Aim 3, using the univariate and multivariate data mining methods developed in aims 1 and 2, we will detect novel drugs and DDIs that increase the risk of one well-defined ADR, myopathy, using the EMR database and high-throughput enzymatic screening assays. In our preliminary work, using our proposed methodology and a 2.2 million record EMR database, six myopathy risk DDIs were identified (p < 5�10-6), including a newly discovered interaction between quetiapine and chloroquine. If taken together, they increase myopathy risk 2.17-fold higher than their added individual risks due to quetiapine inhibition of chloroquine metabolism by the CYP3A4 pathway and blockage of the OATP1B1/1B3 transmembrane transporter. Thus, the successful execution of this work will demonstrate a complete translational scope, starting with EMR-based DDI discovery, and ending with the elucidation of molecular DDI mechanisms through pharmacological experiments. Together, these preliminary data demonstrate that our translational approach is a highly feasible and extremely powerful method for clinical DDI research, likely to yield valuable insight into this emerging public health peril.