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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 岁以上的美国人患有阿尔茨海默病 (AD) 或 AD 相关痴呆 (ADRD)。 AD/ADRD 给患者、家庭和社会带来重大的情感、身体和经济负担。那里无法治愈 AD/ADRD，除了 2021 年 6 月有争议的 aducanumab“加速批准”之外，没有自 2003 年以来，新的症状缓解药物已获得批准，这凸显了预防 AD/ADRD 的必要性。目前，尚无药物可延缓 AD/ADRD 的发作。开发新药或药物的成本高昂重新定位部分开发的用于 AD/ADRD 治疗的药物对于 AD/ADRD 的治疗将更加令人望而却步预防，因为后者需要更大的样本量和更长的随访时间。一种具有成本效益且有效的方法是将超过 20,000 种 FDA 批准的药物重新用于 AD/ADRD 预防。然而，药物的重新利用常常是偶然的。及时、有目的地发现老药的新临床益处需要对具有纵向记录和长期记录的大型综合临床数据库进行系统检查后续工作，使用创新、复杂的混合机器学习和统计工具。此应用程序已为响应 NIA PAR-20-156 而准备，题为“推进药物的转化生物信息学方法” 阿尔茨海默病的重新定位和联合疗法开发”。我们提出了一个三步法全药物关联研究加 (MWAS+) 方法。我们的 MWAS+ 将采用创新的、可解释的深度（机器）学习，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超过 300 万 65 岁以上退伍军人（54,411 名女性；202,000 名退伍军人）的事务 (VA) 电子健康记录 (EHR) 数据非裔美国人），约 600 种处方药（每种药物被 ≥10,000 名退伍军人使用），≥10 年历史和约 200,000 AD/ADRD 病例。在步骤 1（目标 1）中，我们将进行无假设探索性病例对照 MWAS（类似于 GWAS）来识别 VA EHR 数据中与 AD/ADRD 相关的药物。将审查目标 1 中确定的药物由专家小组研究合理的机制途径，并将推荐 10 种药物用于假设在步骤 2 中使用 VA EHR 数据进行测试（目标 2），在步骤 3 中使用 Medicare 数据进行外部验证（目标 3）。在目标 2 和 3，我们将使用新的用户设计进行结果盲队列研究。边际结构模型和其他因果推理方法，包括双鲁棒推理程序，将用于估计时间这些药物对 AD/ADRD 事件的固定（“意向治疗”）和时变（“治疗后”）影响。拟议的该项目非常重要，因为它将大大加快已批准药物的识别速度，具有很大的潜力被重新利用来推迟和预防 AD/ADRD 这一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 MWAS+ 研究以前从未用于 AD/ADRD 预防。此外，VA EHR 包含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。该项目的研究结果将为未来的临床试验提供信息，以重新利用已批准的药物来预防 AD/ADRD。