Advancing Drug Repositioning for Alzheimer’s Disease using Real-world Data

利用真实世界数据推进阿尔茨海默病药物重新定位

• 批准号: 10330045
• 负责人: Jiang Bian
• 金额: $ 79.87万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10330045
• 关键词: Address; Adopted; Affect; Alzheimer' s Disease; Alzheimer' s disease care; Alzheimer' s disease related dementia; American; Animal Model; Behavioral; Biological Markers; Cause of Death; Chronic Disease; Clinical; Code; Cohort Studies; Collection; Community Healthcare; Complex; Consumption; Data; Data Set; Databases; Degenerative Disorder; Detection; Drug Compounding; Drug usage; Education; Electronic Health Record; Emotional; Environmental Risk Factor; Family; Family Caregiver; Financial Hardship; Future; Genetic; Genotype; Gold; Health; Health Sciences; Health system; Healthcare Systems; High Prevalence; Human; Link; Literature; Machine Learning; Mental Tests; Methods; Modeling; Molecular; Natural Language Processing; PF4 Gene; Pathogenesis; Patient Recruitments; Patients; Pennsylvania; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Physicians; Population; Prospective cohort study; Quality of life; Questionnaires; Recording of previous events; Research; Risk Factors; Sample Size; Signal Transduction; Smoking; Speed; Structure; Texas; Time; Treatment outcome; Universities; Update; Vital Statistics; Woman; analytical method; base; cancer therapy; care costs; cohort; computable phenotypes; cost estimate; dementia risk; design; drug development; drug repurposing; effective therapy; efficacy testing; efficacy validation; family burden; high dimensionality; improved outcome; indexing; knowledge base; men; mental state; mild cognitive impairment; novel therapeutics; open source; phenotyping algorithm; polygenic risk score; power analysis; prospective; social; success; translational impact; treatment effect

Project Summary: Alzheimer’s disease (AD) and AD-related dementias (ADRD) is the 6th leading cause of death affecting about 5.7 million Americans. Generally, one in five women and one in ten men are expected to develop AD/ADRD; and the number of people living with AD/ADRD is expected to grow to 14 million in the next two decades. The quality of life of AD/ADRD patients is gradually diminished and caring for AD/ADRD patients imposes tremendous emotional and financial burden on family caregivers, communities, and healthcare systems. However, up until now, there is no cure and not even effective treatment for AD/ADRD patients, probably due to the complex mechanisms involved in the pathogenesis of AD/ADRD. As drug development is becoming increasingly expensive and time-consuming (with estimated cost from $648 million8 to $2.5 billion9 and an average of 9-12 years for new drugs), drug repurposing, aiming to discover new uses of existing drugs, is one potential solution to speed up the drug development for AD/ADRD. However, previous attempts on drug repurposing for AD/ADRD based on omics data have not been successful so far, indicating that animal models may not translate to humans as readily as hoped. New methods that can speed up drug development for AD/ADRD are needed. In this study, we propose to detect drugs that can be potentially repurposed for AD/ADRD using 4 unique EHR data sets. This study will address the critical challenges of EHR-based drug repurposing including incomplete patient’s information and misclassification error associated bias. Aim 1 will focus on a drug repurposing knowledgebase for AD/ADRD, natural language processing methods to extract risk factors from clinical narratives, and phenotyping algorithms to accurately identify MCI and AD/ADRD patients to support the patient cohort construction. In Aim 2, we will develop drug repurposing methods that account for the high-dimensional of risk factors and misclassification error associated bias and apply them to detect drug repurposing signals using large collections of EHRs from (1) the OneFlorida network (2) the Cerner Health Facts database, (3) EHR from physician practice at University of Texas Health Science Center at Houston, and (4) EHR data from the University of Pennsylvania. In Aim 3, we propose to validate the top-ranked signals through a prospective cohort study. We will recruit patients and routinely collect detailed pragmatic information and genotypes to validate the efficacy of the identified drug signals. The success of our study will: (1) produce a knowledgebase with timely updated risk factors, biomarkers, genotypes, and drug signals for AD/ADRD, (2) develop an open- source drug repurposing package - RAIDER (Repurposing Alzheimer Impacting Drugs using Electronic health Records) for AD/ADRD, and (3) generate drug repurposing signals validated in a prospective cohort study, which will inform the design of future large-scale national trials for AD/ADRD.

项目概要： 阿尔茨海默病（AD）和AD相关痴呆（ADRD）是第六大死亡原因，影响约100万人。 5.7数百万美国人一般来说，五分之一的女性和十分之一的男性预计会发展AD/ADRD; 在未来20年，AD/ADRD患者的数量预计将增长到1400万。的 AD/ADRD患者的生活质量逐渐下降，对AD/ADRD患者的护理要求 对家庭照顾者、社区和医疗保健系统造成巨大的情感和经济负担。 然而，到目前为止，AD/ADRD患者没有治愈，甚至没有有效的治疗方法，可能是由于 参与AD/ADRD发病机制的复杂机制。随着药物开发变得越来越 越来越昂贵和耗时（估计费用从6.48亿美元8到25亿美元9， 新药平均9-12年），药物再利用，旨在发现现有药物的新用途，是一种 加快AD/ADRD药物开发的潜在解决方案。然而，以前的药物尝试 到目前为止，基于组学数据的AD/ADRD再利用还没有成功，这表明动物模型 可能不会像希望的那样容易地转化为人类。新方法可以加速药物开发， 需要AD/ADRD。 在这项研究中，我们建议使用4个独特的EHR检测可能用于AD/ADRD的药物 数据集。这项研究将解决基于EHR的药物再利用的关键挑战，包括不完整的 患者信息和错误分类错误相关偏倚。Aim 1将重点关注药物再利用 AD/ADRD知识库，自然语言处理方法从临床中提取风险因素 叙述和表型分析算法，以准确识别MCI和AD/ADRD患者， 队列建设在目标2中，我们将开发药物再利用方法， 的风险因素和错误分类错误相关的偏见，并将其应用于检测药物再利用信号 使用来自（1）OneFlorida网络（2）Cerner Health Facts数据库（3）的大量EHR集合， EHR来自休斯顿德克萨斯大学健康科学中心的医生实践，以及（4）EHR数据来自 宾夕法尼亚大学在目标3中，我们建议通过前瞻性的 队列研究我们将招募患者，并定期收集详细的实用信息和基因型， 验证所识别的药物信号的功效。本研究的成功将：（1）产生一个知识库 及时更新AD/ADRD的风险因素、生物标志物、基因型和药物信号，（2）开发一个开放的 源药物再利用包- RAIDER（使用电子健康再利用影响阿尔茨海默病的药物 记录），以及（3）生成在前瞻性队列研究中验证的药物再利用信号， 这将为未来AD/ADRD的大规模国家试验的设计提供信息。