MWAS+ – A Novel Drug Repurposing Strategy for ADRD Prevention

MWAS — 预防 ADRD 的新型药物再利用策略

• 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
• 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' s Disease; Alzheimer' 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

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）。