# Endophenotype Network-based Approaches to Prediction and Population-based Validation of in Silico Drug Repurposing for Alzheimer’s Disease

> **NIH NIH R01** · CLEVELAND CLINIC LERNER COM-CWRU · 2024 · $591,820

## Abstract

Although researchers have conducted more than 400 human trials for potential treatments of Alzheimer’s
disease (AD) in the last two decades, the attrition rate is estimated at over 99%. Furthermore, the “one gene,
one drug, one disease” reductionism-informed paradigm overlooks the inherent complexity of the disease and
continues to challenge drug discovery for AD. The predisposition to AD involves a complex, polygenic, and
pleiotropic genetic architecture. Recent studies have suggested that AD often has common underlying
mechanisms, sharing intermediate endophenotypes with many other complex diseases. These
endophenotypes, such as amyloidosis and tauopathy, have essential roles in many neurodegenerative diseases.
Systematic identification and characterization of novel underlying pathogenesis and disease modules, more so
than mutated genes, will serve as a foundation for generating actionable targets as input for drug repurposing
and rational design of combination therapy in AD. Integration of the genome, transcriptome, proteome, and the
human interactome are essential for such identification. Given our preliminary results, we posit that network-
based identification of novel risk genes and endophenotype modules that share degree between amyloid and
tau offer unexpected opportunities for drug therapy in AD comparing to targeting amyloid and tau separately. To
address the underlying hypothesis, we propose to establish an integrated interdisciplinary research plan with
three specific aims. Aim 1 will explore amyloid and tau-mediated endophenotype modules for AD -- We will test
the network module hypothesis for amyloid and tau using our recently developed Bayesian framework that
integrates multi-omics data (i.e., genome-wide association studies [GWAS] loci, single cell sequencing, and
human brain Hi-C data) and the human interactome. Aim 2 will be capable of searching existing drugs and
combination therapies for AD using network proximity approaches -- We will emphasize the uses of network
proximity approaches (i.e., Genome-wide Positioning Systems network [GPSnet]) to identify repurposable drugs
and efficacious combination regimens. This will be accomplished by integrating AD endophenotype module
findings, public drug-target databases, the human interactome, and the large-scale patient longitudinal Claims-
Electronic Medical Record data (over 200 million patients from the MarketScan database). Aim 3 will evaluate
brain penetration and target network engagement for repurposable drugs -- We will use the humanized in
vitro blood-brain barrier, resected brain tissues (ex vivo/in situ), and transgenic AD models (i.e., TgF344-AD rats)
to experimentally evaluate brain penetration and target network engagement. Evaluation will be based upon
network proximity to the AD-related endophenotype modules that are relevant to maximizing efficacy and to
minimizing side effects. The successful completion of this project will offer powerful network methodologies and
b...

## Key facts

- **NIH application ID:** 10769866
- **Project number:** 5R01AG066707-05
- **Recipient organization:** CLEVELAND CLINIC LERNER COM-CWRU
- **Principal Investigator:** Feixiong Cheng
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $591,820
- **Award type:** 5
- **Project period:** 2020-04-15 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10769866

## Citation

> US National Institutes of Health, RePORTER application 10769866, Endophenotype Network-based Approaches to Prediction and Population-based Validation of in Silico Drug Repurposing for Alzheimer’s Disease (5R01AG066707-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10769866. Licensed CC0.

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