# Advancing Drug Repositioning for Alzheimer’s Disease using Real-world Data

> **NIH NIH R56** · UNIVERSITY OF FLORIDA · 2021 · $798,718

## Abstract

Project Summary:
Alzheimer’s disease (AD) and AD-related dementias (ADRD) is the 6th leading cause of death affecting about
5.7 million Americans. Generally, one in five women and one in ten men are expected to develop AD/ADRD;
and the number of people living with AD/ADRD is expected to grow to 14 million in the next two decades. The
quality of life of AD/ADRD patients is gradually diminished and caring for AD/ADRD patients imposes
tremendous emotional and financial burden on family caregivers, communities, and healthcare systems.
However, up until now, there is no cure and not even effective treatment for AD/ADRD patients, probably due
to the complex mechanisms involved in the pathogenesis of AD/ADRD. As drug development is becoming
increasingly expensive and time-consuming (with estimated cost from $648 million8 to $2.5 billion9 and an
average of 9-12 years for new drugs), drug repurposing, aiming to discover new uses of existing drugs, is one
potential solution to speed up the drug development for AD/ADRD. However, previous attempts on drug
repurposing for AD/ADRD based on omics data have not been successful so far, indicating that animal models
may not translate to humans as readily as hoped. New methods that can speed up drug development for
AD/ADRD are needed.
In this study, we propose to detect drugs that can be potentially repurposed for AD/ADRD using 4 unique EHR
data sets. This study will address the critical challenges of EHR-based drug repurposing including incomplete
patient’s information and misclassification error associated bias. Aim 1 will focus on a drug repurposing
knowledgebase for AD/ADRD, natural language processing methods to extract risk factors from clinical
narratives, and phenotyping algorithms to accurately identify MCI and AD/ADRD patients to support the patient
cohort construction. In Aim 2, we will develop drug repurposing methods that account for the high-dimensional
of risk factors and misclassification error associated bias and apply them to detect drug repurposing signals
using large collections of EHRs from (1) the OneFlorida network (2) the Cerner Health Facts database, (3)
EHR from physician practice at University of Texas Health Science Center at Houston, and (4) EHR data from
the University of Pennsylvania. In Aim 3, we propose to validate the top-ranked signals through a prospective
cohort study. We will recruit patients and routinely collect detailed pragmatic information and genotypes to
validate the efficacy of the identified drug signals. The success of our study will: (1) produce a knowledgebase
with timely updated risk factors, biomarkers, genotypes, and drug signals for AD/ADRD, (2) develop an open-
source drug repurposing package - RAIDER (Repurposing Alzheimer Impacting Drugs using Electronic health
Records) for AD/ADRD, and (3) generate drug repurposing signals validated in a prospective cohort study,
which will inform the design of future large-scale national trials for AD/ADRD.

## Key facts

- **NIH application ID:** 10330045
- **Project number:** 1R56AG069880-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Jiang Bian
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $798,718
- **Award type:** 1
- **Project period:** 2021-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10330045

## Citation

> US National Institutes of Health, RePORTER application 10330045, Advancing Drug Repositioning for Alzheimer’s Disease using Real-world Data (1R56AG069880-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10330045. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*