R01AG073474

Mwas+ ? A novel drug repurposing strategy for ADRD prevention - 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.

George Washington University (The)

NOT APPLICABLE

93.866 - Aging Research

National Institute on Aging (NIA) [HHS - NIH]

Washington, District Of Columbia 200520011 United States

Single Zip Code

Translational Bioinformatics Approaches to Advance Drug Repositioning and Combination Therapy Development for Alzheimers Disease (R01 Clinical Trial Optional) (PAR-20-156)

Amendment Since initial award the total obligations have increased 349% from $764,056 to $3,432,711.