Identification of Potential Drug-drug Interactions in Alzheimer's Disease Patients

项目摘要 当一种药物影响另一种药物的水平或活性时，就会发生药物相互作用（DDI）。 阿尔茨海默病（AD）患者存在DDI的巨大风险。然而，关于潜在DDI的信息 在临床数据库中仍然缺乏AD患者中发生的情况。在本提案中，我们将开发和评估有效的 可从FDA不良事件报告记录中识别潜在DDI的计算模型 系统（FAERS），世界上最大的药品上市后监测数据库。约900万 将分析FAERS（2004年至2019年）的ADE记录。几种数据挖掘算法，多项 伽马泊松收缩（MGPS），贝叶斯置信传播神经网络（BCPNN），以及 将应用关联规则来检测来自这些报告的DDI信号。一些混杂因素的影响 还将使用多变量逻辑分析研究影响DDI的因素（例如年龄、种族、性别和剂量） 回归分析通过计算模型识别的DDI将通过以下回顾性分析进行验证： 南方大学伯德阿尔茨海默病研究所的AD患者的电子健康记录（EHR） 佛罗里达州（USF）。此外，还将开发一个网络服务器AD_DDI，以向公众提供预测信息 模型和结果。该项目的成功完成将为医生或 药剂师更恰当地为AD患者开药。

Project Summary A drug-drug interaction (DDI) occurs when one drug influences the level or activity of another drug. There is a big risk of DDI in Alzheimer’s disease (AD) patients. However, information about potential DDIs occur in AD patients is still lacking in clinical databases. In this proposal, we will develop and evaluate efficient computational models that can identify potential DDIs from the records in FDA Adverse Event Reporting System (FAERS), the largest drug post-marketing surveillance database in the world. Approximately 9 million ADE records from FAERS (from 2004 to 2019) will be analyzed. Several data mining algorithms, multi-item Gamma Poisson shrinkage (MGPS), Bayesian Confidence Propagation Neural Network (BCPNN), and association rule will be applied to detect DDI signals from these reports. The effects of some confounding factors (such as age, race, gender, and dosage) on DDIs will be also investigated using multivariate logistic regression. The DDIs identified by the computational model will be validated through a retrospective analysis of electronic health records (EHRs) of AD patients at the Byrd Alzheimer's Institute at the University of South Florida (USF). In addition, a web server, AD_DDI, will be developed to provide public access to the prediction model and results. The successful completion of this project will provide useful information for doctors or pharmacists to prescribe drugs for AD patients more appropriately.