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MWAS+ – A Novel Drug Repurposing Strategy for ADRD Prevention

MWAS — 预防 ADRD 的新型药物再利用策略

基本信息

批准号：
10677666
负责人：
ALI AHMED
金额：
$ 70.76万
依托单位：
GEORGE WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-15 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10677666
关键词：
Acceleration Accidents Aducanumab African American African American population Alzheimer&apos s Disease Alzheimer&apos s disease related dementia American Artificial Intelligence Autopsy Bioinformatics Blinded Brain Clinical Clinical Data Clinical Trials Cohort Studies Combined Modality Therapy Data Databases Dementia Development Disease Dose Drug Prescriptions Elderly Electronic Health Record Emotional FDA approved Family Financial Hardship Future Learning Life Link Machine Learning Medicare Medicare claim Methods Modeling Natural Language Processing Neurologist Outcome Pathology Pathway interactions Patients Pharmaceutical Preparations Pharmacy facility Phase Phenotype Positioning Attribute Prevention Procedures Process Public Health Randomized, Controlled Trials Recommendation Recording of previous events Records Sample Size Signal Transduction Societies Statistical Methods Structural Models Structure Study models Surrogate Endpoint Symptoms Testing Time Training United States Centers for Medicare and Medicaid Services United States Department of Veterans Affairs Validation Veterans Woman candidate identification candidate selection case control clinical database cohort cost cost effective deep learning deep learning model design dosage drug candidate drug repurposing experience fitness follow-up frailty genome wide association study innovation insight interest learning strategy novel novel therapeutics patient subsets prevent response screening therapy development tool

项目摘要

Nearly 6 million Americans ≥65 years suffer from Alzheimer’s disease (AD) or AD-related dementias (ADRD). AD/ADRD poses significant emotional, physical, and financial burdens on patients, families, and societies. There is no cure for AD/ADRD, and apart from the June 2021 controversial “accelerated approval” of aducanumab, no new symptom-modifying drug has been approved since 2003, highlighting the need for AD/ADRD prevention. Currently, no drug is available to delay the onset of AD/ADRD. The prohibitive cost of developing new drugs or repositioning partially developed drugs for AD/ADRD treatment would be even more prohibitive for AD/ADRD prevention as the latter would require larger sample size and longer follow-up. An alternative cost-effective and efficient approach is to repurpose from >20,000 FDA-approved drugs for AD/ADRD prevention. However, repurposing of drugs is often accidental. A timely and purposeful discovery of new clinical benefits of old drugs requires a systematic examination of large comprehensive clinical databases with longitudinal records and long follow-up, using innovative, sophisticated mixed machine learning and statistical tools. This application has been prepared in response to the NIA PAR-20-156 entitled “Translational Bioinformatics Approaches to Advance Drug Repositioning and Combination Therapy Development for Alzheimer’s Disease”. We propose a 3-Step Medication-Wide Association Study Plus (MWAS+) approach. Our MWAS+ will employ innovative explainable deep (machine) learning, a powerful artificial intelligence tool for noisy, nonlinear data. We will use Veterans Affairs (VA) electronic health record (EHR) data of >3 million Veterans ≥65 years (54,411 women; 202,000 African American), ~600 prescription drugs (each used by ≥10,000 Veterans), ≥10 years of history and ~200,000 AD/ADRD cases. In Step 1 (Aim 1), we will conduct a hypothesis-free exploratory case-control MWAS (akin to GWAS) to identify drugs associated with AD/ADRD in the VA EHR data. Drugs identified in Aim 1 will be reviewed by a panel of experts for plausible mechanistic pathways and 10 drugs will be recommended for hypothesis testing in Step 2 using VA EHR data (Aim 2) and external validation in Step 3 using Medicare data (Aim 3). In Aims 2 and 3, we will conduct outcome-blinded cohort studies using new user design. Marginal structural models and other causal inference methods, including doubly-robust inference procedures, will be used to estimate time- fixed (“intent-to-treat”) and time-varying (“as-treated”) effects of those drugs on incident AD/ADRD. The proposed project is highly significant because it will rigorously accelerate the identification of already approved drugs that have a high potential to be repurposed to delay and prevent AD/ADRD, a rapidly growing public health crisis. The project is innovative as it combines state-of-the-art deep learning and statistical methods to conduct an MWAS+ study that has never been used before for AD/ADRD prevention. In addition, the VA EHR contains high quality clinical data including pharmacy fill records and rich phenotypic information including fitness and frailty. Findings from this project will inform future clinical trials to repurpose approved drugs for AD/ADRD prevention.

近600万65岁的美国人患有阿尔茨海默病(AD)或与AD相关的痴呆(ADRD)。 AD/ADRD给患者、家庭和社会带来了巨大的情感、身体和经济负担。那里对AD/ADRD没有治愈作用，除了2021年6月有争议的阿杜卡努单抗被加速批准外，没有自2003年以来，批准了一种新的改善症状的药物，突出了预防AD/ADRD的必要性。目前，尚无延缓AD/ADRD发病的药物。研发新药的高昂成本或将部分开发的药物重新定位于AD/ADRD治疗将使AD/ADRD更加望而却步预防，因为后者需要更大的样本量和更长的后续行动。一种经济实惠的替代方案有效的方法是改变FDA批准的20,000种药物的用途，用于预防AD/ADRD。然而，毒品用途的改变往往是偶然的。及时、有目的地发现旧药的临床新益处需要对大型综合临床数据库进行系统检查，这些数据库具有纵向记录和详细的后续，使用创新的、复杂的混合机器学习和统计工具。此应用程序已被为响应NIA PAR-20-156《先进药物的翻译生物信息学方法》而编写阿尔茨海默病的重新定位和联合疗法的开发“。我们建议分三步走全药物联合研究加(MWAS+)方法。我们的MWAS+将采用创新的可解释深度(机器)学习，这是一种针对噪声、非线性数据的强大人工智能工具。我们将使用退伍军人 300万退伍军人≥65年的事务(VA)电子健康记录数据(54,411名女性；202,000名非裔美国人)，约600种处方药(每种由≥10,000名退伍军人使用)，≥10年历史和约200,000 AD/ADRD案例。在步骤1(目标1)中，我们将进行一项无假设的探索性病例对照研究(类似于在退伍军人事务部EHR数据中识别与AD/ADRD相关的药物。将对目标1中确定的药物进行审查由一个专家小组为合理的机械途径和10种药物推荐的假说在步骤2中使用VA EHR数据进行测试(目标2)，在步骤3中使用医疗保险数据进行外部验证(目标3)。在……里面目标2和3，我们将使用新的用户设计进行结果盲队列研究。边际构造模型其他因果推理方法，包括双稳健推理程序，将被用来估计时间- 这些药物对AD/ADRD事件的固定(“意向治疗”)和时变(“治疗”)效果。建议数该项目意义重大，因为它将严格加快已经批准的药物的识别速度，有很高的潜力被重新利用，以延迟和预防AD/ADRD，这是一种迅速增长的公共卫生危机。该项目具有创新性，因为它结合了最先进的深度学习和统计方法来进行以前从未用于AD/ADRD预防的Mis+研究。此外，VA EHR还含有高高质量的临床数据，包括药房填写记录和丰富的表型信息，包括健康和虚弱。该项目的发现将为未来的临床试验提供信息，以改变已批准用于预防AD/ADRD的药物的用途。