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: 10374177
• 负责人: Jiang Bian
• 金额: $ 76.13万
• 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 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10374177
• 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; Mental Tests; Methods; Modeling; Molecular; Natural Language Processing; PF4 Gene; Pathogenesis; Patient Recruitments; Patients; Pennsylvania; Persons; 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; knowledgebase; machine learning model; 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.

项目摘要： 阿尔茨海默氏病（AD）和与广告相关的痴呆症（ADRD）是第六个主要的死亡原因，影响 570万美国人。通常，预计五分之一的女性和十分之一的男性将发展广告/adrd。 预计在接下来的二十年中，拥有广告/ADRD的人数预计将增长到1400万。这 AD/ADRD患者的生活质量逐渐降低，并且不可能照顾AD/ADRD患者 对家庭护理人员，社区和医疗保健系统的巨大情感和经济燃烧。 但是，到目前为止，对于AD/ADRD患者，还没有治愈，甚至还没有有效的治疗 与AD/ADRD发病机理有关的复杂机制。随着药物开发的发展 越来越昂贵且耗时的时间（估计成本从6.48亿美元至25亿美元，一个 新药平均9 - 12年），重新利用药物，旨在发现现有药物的新用途，是一种 为了加快AD/ADRD的药物开发的潜在解决方案。但是，以前尝试药物 基于OMIC数据的AD/ADRD重新利用到目前为止尚未成功，表明动物模型 可能不会像希望的那样转化为人类。可以加快药物开发的新方法 需要广告/adrd。 在这项研究中，我们建议检测可以使用4个独特的EHR进行AD/ADRD的药物 数据集。这项研究将解决基于EHR的药物重新定位的关键挑战，包括不完整 患者的信息和错误分类错误与偏见有关。 AIM 1将重点放在药物上 AD/ADRD的知识库，自然语言处理方法，从临床中提取危险因素 叙述和表型算法准确识别MCI和AD/ADRD患者以支持患者 队列建筑。在AIM 2中，我们将开发用于高维的药物重新利用方法 风险因素和错误分类错误与偏差相关的错误，并将其应用于检测药物重新利用信号 使用（1）Oneflorida网络（2）Cerner Health Facts数据库的大量EHR集合，（3） 来自休斯敦的德克萨斯大学健康科学中心物理实践的EHR，（4）EHR数据来自 宾夕法尼亚大学。在AIM 3中，我们建议通过预期验证排名最高的信号 队列研究。我们将招募患者，并经常收集详细的务实信息和基因型 验证已确定的药物信号的有效性。我们研究的成功将：（1）产生一个知识库 有了及时更新的AD/ADRD的风险因素，生物标志物，基因型和药物信号，（2） 来源药物重新利用软件包-RAIDER（使用电子健康重新利用阿尔茨海默氏症对药物的影响 记录）ad/adrd和（3）在一项前瞻性队列研究中验证的药物重新利用信号， 这将为AD/ADRD的未来大规模全国试验的设计提供信息。

项目成果

Meta-analysis of Association between Newer Glucose-Lowering Drugs and Risk of Parkinson's Disease.

新型降血糖药物与帕金森病风险之间关联的荟萃分析。

• DOI: 10.1002/mdc3.13893
• 发表时间: 2023
• 期刊: Movement disorders clinical practice
• 影响因子: 4
• 作者: Tang,Huilin;Lu,Ying;Okun,MichaelS;Donahoo,WilliamT;Ramirez-Zamora,Adolfo;Wang,Fei;Huang,Yu;Chen,Wei-Han;Virnig,BethA;Bian,Jiang;Guo,Jingchuan
• 通讯作者: Guo,Jingchuan