# MWAS+ – A Novel Drug Repurposing Strategy for ADRD Prevention

> **NIH NIH R01** · GEORGE WASHINGTON UNIVERSITY · 2022 · $764,056

## Abstract

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 ...

## Key facts

- **NIH application ID:** 10446705
- **Project number:** 1R01AG073474-01A1
- **Recipient organization:** GEORGE WASHINGTON UNIVERSITY
- **Principal Investigator:** ALI AHMED
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $764,056
- **Award type:** 1
- **Project period:** 2022-08-15 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10446705

## Citation

> US National Institutes of Health, RePORTER application 10446705, MWAS+ – A Novel Drug Repurposing Strategy for ADRD Prevention (1R01AG073474-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10446705. Licensed CC0.

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