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

阿尔茨海默氏病患者潜在药物相互作用的鉴定

• 批准号: 10267731
• 负责人: Feng Cheng
• 金额: $ 7.48万
• 依托单位: UNIVERSITY OF SOUTH FLORIDA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10267731
• 关键词: Adverse Drug Experience Report; Adverse drug event; Adverse event; Age; Algorithms; Alzheimer' s Disease; Alzheimer' s disease patient; Big Data; Bradycardia; Cholinesterase Inhibitors; Clinical; Clinical Pharmacists; Combination Medication; Complex; Computer Models; Consciousness Disorders; Data; Databases; Digestive System Disorders; Disease; Drug Combinations; Drug Interactions; Drug Prescriptions; Elderly; Electronic Health Record; Event; Florida; Galantamine; Gender; Health; Healthcare; Impairment; Individual; Institutes; Life; Logistic Regressions; Manuals; Marketing; Medical; Metabolism; Methods; Modeling; Names; Outcome; Patients; Pharmaceutical Preparations; Pharmacists; Polypharmacy; Race; Recording of previous events; Records; Reporting; Research; Resources; Risk; Scientist; Signal Transduction; Statistical Algorithm; System; Tacrine; Testing; Unconscious State; United States Food and Drug Administration; Universities; Validation; Work; clinical center; clinical database; data mining; donepezil; dosage; drug resource; drug testing; neural network; older patient; patient health information; pediatric patients; predictive modeling; rivastigmine; tool; virtual; web server

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.

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)患者有很大的DDI风险。但是，有关潜在DDI的信息 阿尔茨海默病患者的发病情况尚缺乏临床数据库。在这份提案中，我们将开发和评估高效 可从FDA不良事件报告记录中识别潜在DDIS的计算模型 世界上最大的药品上市后监测数据库FAERS。大约900万 将分析FAERS的ADE记录(从2004年到2019年)。几种数据挖掘算法，多项 伽马泊松收缩(MGPS)、贝叶斯置信度传播神经网络(BCPNN)和 将应用关联规则来检测这些报告中的DDI信号。一些令人困惑的东西的影响 影响DDIS的因素(如年龄、种族、性别和剂量)也将使用多变量Logistic进行调查 回归。由计算模型确定的DDIS将通过回顾分析 南方大学伯德阿尔茨海默病研究所AD患者的电子健康记录(EHR) 佛罗里达州(美国联邦)。此外，还将开发一个名为AD_DDI的网络服务器，以供公众查阅预测 模型和结果。该项目的成功完成将为医生或 药剂师给AD患者开药更合适。