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：
10446705
负责人：
ALI AHMED
金额：
$ 76.41万
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/10446705
关键词：
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 Life Link Machine Learning Medicare Medicare claim Methods Modeling Natural Language Processing Neurologist Outcome Pathology Pathway interactions Patients Pharmaceutical Preparations Pharmacy facility Phase Phenotype Prevention Procedures Process Public Health Randomized Controlled Trials 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 base 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 statistical and machine learning 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.

接近600万美国人≥65岁，患有阿尔茨海默氏病（AD）或与广告相关的痴呆症（ADRD）。 AD/ADRD对患者，家庭和社会构成了重大的情感，身体和金融燃烧。那里无法治愈AD/ADRD，除了2021年6月有争议的Aducanumab的“加速批准”之外，没有自2003年以来，已批准了新的症状改良药物，强调了对AD/ADRD预防的需求。目前，尚无药物延迟AD/ADRD的发作。开发新药或重新定位部分用于AD/ADRD治疗的药物将更加禁止用于AD/ADRD 预防措施将需要更大的样本量和更长的随访。替代性成本效益和有效的方法是从> 20,000名FDA批准的药物进行改革以预防广告/ADRD。然而，重新利用毒品通常是偶然的。及时，有目的地发现旧药物的新临床益处需要系统检查具有纵向记录和长期记录的大型综合临床数据库随访，使用创新，复杂的混合机器学习和统计工具。这个应用程序已经根据NIA PAR-20-156的响应，题为“转化生物信息学方法以推动药物对阿尔茨海默氏病的重新定位和组合疗法开发”。我们提出了三步全面的协会研究加（MWAS+）方法。我们的MWA+将采用可解释的创新性 Deep（Machine）Learning，一种强大的人工智能工具，用于噪声，非线性数据。我们将使用退伍军人事务（VA）电子健康记录（EHR）数据> 300万退伍军人≥65岁（54,411名女性； 202,000 非裔美国人），约600种处方药（每种药物≥10,000名退伍军人使用），≥10年的历史和〜200,000 广告/adrd案件。在步骤1（AIM 1）中，我们将进行无假设的探索性案例对照MWA（类似于 GWAS）以识别VA EHR数据中与AD/ADRD相关的药物。 AIM 1中确定的药物将进行审查由一组合理的机械途径和10种药物的专家小组用于假设使用VA EHR数据（AIM 2）和步骤3中的外部验证在步骤2中进行测试（AIM 3）。在 AIM 2和3，我们将使用新的用户设计进行结果盲目的队列研究。边缘结构模型和其他因果推理方法（包括双重推理程序）将用于估计时间 - 这些药物对事件/ADRD的固定（“意图对治疗”）和随时间变化（“处理”）影响。提议项目非常重要，因为它将严格加速对已经批准的药物的识别有很高的潜力被重新利用，以延迟和预防AD/ADRD，这是一种快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 MWAS+研究以前从未用于预防AD/ADRD。此外，VA EHR包含高包括药房填充记录和丰富的表型信息在内的优质临床数据，包括健身和脆弱。该项目的发现将为未来的临床试验提供信息，以改革已批准的AD/ADRD预防药物。