A Translational Bioinformatics Approach in the Drug Interaction Research

药物相互作用研究中的转化生物信息学方法

• 批准号: 8761156
• 负责人: Lang Li
• 金额: $ 34.09万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8761156
• 关键词: Accident and Emergency department; Address; Algorithms; Antimalarials; Antipsychotic Agents; Applications Grants; Basic Science; Bioinformatics; Biological; Biological Assay; CYP3A4 gene; Cells; Chloroquine; Clinical; Clinical Research; Collaborations; Computerized Medical Record; Confounding Factors (Epidemiology); Data; Data Analyses; Data Set; Databases; Detection; Discipline; Drug Exposure; Drug Interactions; Drug Kinetics; Epidemiology; Genomics; Goals; Health; Healthcare Systems; Hospitalization; In Vitro; Incidence; Individual; Laboratory Study; Medical Records; Metabolism; Methodology; Methods; Mining; Modeling; Molecular; Muscle Weakness; Myopathy; Outcome; Pathway interactions; Patients; Performance; Pharmaceutical Preparations; Pharmacology; Polypharmacy; Proteins; Public Health; Reaction; Recombinants; Reporting; Research; Research Design; Risk; Scheme; Scientist; Scoring Method; Sum; System; Testing; Translational Research; Translations; United States; Visit; Work; base; case control; data mining; design; drug efficacy; drug metabolism; drug testing; epidemiology study; insight; novel; patient population; population based; public health relevance; quetiapine; research study; screening; simulation; statistics; surveillance study; theories; translational approach; unpublished works; uptake

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.

描述（由申请人提供）：药物相互作用 (DDI) 对公共健康构成越来越大的威胁，估计每年导致 195,000 人住院治疗和 74,000 人次急诊室就诊。当前的 DDI 研究通过计算和实验研究了药物相互作用的不同方面。尽管这些方法是互补的，但它们通常是独立进行的，没有协调。体外药理学实验使用完整细胞、微粒体蛋白片段或重组系统来研究药物相互作用机制。药物流行病学（大众）使用基于人群的方法和大型电子病历 (EMR) 数据库来研究 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 DDI，并将在体外进一步验证它们。以下是具体目标。在目标 1 中，提出了一种新颖的动态嵌套病例对照设计来检测单一药物或 DDI 对 ADR 的影响。开发了一种新的经验贝叶斯方法来测试药物和 DDI 引起的 ADR，并将估计错误发现率。在目标 2 中，提出了一种新颖的广义倾向评分方法来分析高维药物数据。与传统的倾向评分方法相比，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。目标 3，使用目标 1 和 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选测定来检测增加一种明确的 ADR（肌病）风险的新药和 DDI。在我们的初步工作中，使用我们提出的方法和 220 万条记录的 EMR 数据库，确定了六种肌病风险 DDI (p < 5¿10-6)，包括新发现的喹硫平和氯喹之间的相互作用。如果将它们放在一起，由于喹硫平通过 CYP3A4 途径抑制氯喹代谢并阻断 OATP1B1/1B3 跨膜转运蛋白，它们使肌病风险比单独增加的风险高 2.17 倍。因此，这项工作的成功执行将展示完整的转化范围，从基于 EMR 的 DDI 发现开始，到通过药理学实验阐明分子 DDI 机制结束。总之，这些初步数据表明，我们的转化方法对于临床 DDI 研究来说是一种高度可行且极其强大的方法，可能会对这一新兴的公共卫生危险产生有价值的见解。