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.

项目总结:

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