# CICADA: clinical informatics and computational approaches for drug-repositioning of AD/ADRD

> **NIH NIH R56** · UNIVERSITY OF PENNSYLVANIA · 2022 · $754,268

## Abstract

Project Summary
This proposal seeks support for developing advanced clinical informatics and computational
approaches for drug-repositioning for Alzheimer's disease (AD) and related dementias (ADRD).
The proposed project directly addresses the areas of emphasis in PAR-20-156 to “develop
computational methods such as artificial intelligence/machine learning to investigate new uses
of FDA-approved drugs or candidate drugs from failed Phase II/Phase III clinical trials through
analysis of multimodal data.”
The overarching goals of this proposal are to develop novel clinical informatics and
computational approaches for drug repositioning of AD/ADRD. Specifically, we will develop
statistical methods and ontology technology to extract drug-repositioning signals from
multidimensional data (e.g., pharmacy-linked genetic data and biobank data, historical trials,
and EHR data). The proposed framework is novel because it integrates advanced statistical
inference procedures with semantic technology for data-driven and reproducible drug
repositioning for AD/ADRD. We have three aims:
We have three specific aims:
Aim 1: Develop signal detection methods using multi-modal data (pharmacy-linked
genetic data, genetic and electronic health record (EHR) data, and BioBank data).
Aim 2: Evaluate the efficacy and safety of candidate drugs via historical trials and EHR
data.
Aim 3: Develop novel semantic and natural language processing (NLP) methods for
Knowledge Graph (KG) construction.
The success of this project will lead to novel computational methods, KG, and software for
facilitating drug repositioning for AD/ADRD based on multimodal data. If successful, the
proposed method could identify novel drug repositioning signals and generate novel hypotheses
for prevention and treatment intervention of treat AD/ADRD. Our project holds the promise of
identifying novel drug repositioning signals. This project is novel for integrating evidence
synthesis methods with signal detection methods using advanced multimodal modeling,
and it is potentially transformative for advancing prevention and treatment for AD/ADRD.

## Key facts

- **NIH application ID:** 10490346
- **Project number:** 5R56AG074604-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Yong Chen
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $754,268
- **Award type:** 5
- **Project period:** 2021-09-30 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10490346

## Citation

> US National Institutes of Health, RePORTER application 10490346, CICADA: clinical informatics and computational approaches for drug-repositioning of AD/ADRD (5R56AG074604-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10490346. Licensed CC0.

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