# Combine Genomics and Symptoms Data Driven Models to Discover Synergistic Combinatory Therapies for Alzheimer's Disease

> **NIH NIH R56** · WASHINGTON UNIVERSITY · 2020 · $500,322

## Abstract

Project Summary
In 2018, an estimated 5.7 million people have Alzheimer's Disease (AD) or a related dementia in the U.S., with
related healthcare costs of ~$277 billion1. However, there is no cure yet for AD. One major challenge is that the
complicated pathogenesis of AD remains unclear, though >42 genes/loci have been associated with AD2,3.
These genes are not actionable or druggable yet for AD management2. Over 240 drugs were tested in clinical
trials but no new drugs have been approved for AD since 20031,4. The failure of these drugs is likely, in part,
due to the limited efficacy of single agents to treat AD that is a genetically complex, multifactorial disease2, i.e.,
robust molecular signaling crosstalks among multi-pathways2,5,6,7,8,9, as well as complicated niche factors, e.g.,
oxidative stress10,11,12,13, and inflammation14,15,16, leading to neuron de-generation. Therefore, combination
therapies eliminating these niche factors, and disrupting the dysfunctional signaling pathways and cross-talks,
can be more effective than single agents for in AD patients.
The goal of this study is to fill the gap of accelerating repositioning of combination therapies for AD using
following novel genomics and symptoms data-driven models seamlessly integrating well designed iPSC Aβ AD
models. The Washington University Charles F. and Joanne Knight Alzheimer's Disease Research Center
(Knight-ADRC), has generated comprehensive omics data for a large group of AD samples. We propose to (in
Aim 1) uncover core signaling pathways and crosstalks of ApoE4 genotype-specific AD subtypes via a novel
signaling convergence network model, and consequently to discover synergistic Signaling Network Disruption
drug combinations (SNDdc) via novel drug prediction models integrating heterogenous pharmacogenomics
datasets. On the other hand, we propose to (in Aim 2) discover potential Neuron Protective drug combinations
(NPdc) using electronic health records (EHR), available in BJC HealthCare system (includes 14 academic and
community hospitals in Missouri and Illinois), of patients with brain injury diseases, especially Traumatic Brain
Injury (TBI), via a novel high-order poly-pharmacy efficacy and safety model. We hypothesize that acute brain
damage in TBI will share the aforementioned key AD-related niche factors. Also because TBI patients often
require multiple drugs daily (high-order poly-pharmacy use), we propose that TBI provides an appropriate
model to study synergy and interactions of combination therapies that can ameliorate acute brain injury, and
thus suggest potentially neuron protective combinations. Combinations in SNDdc and NPdc provide
candidates for novel and effective AD treatment. To filter the false positives, we will (in Aim 3) utilize pooled
CRISPR functional genomics and iPSC neurodegeneration model to identify key signaling genes, and validate
combination therapies with ApoE4 genotype-specific iPSC Aβ models. Our new models represent a
potential breakth...

## Key facts

- **NIH application ID:** 10228346
- **Project number:** 1R56AG065352-01A1
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Fuhai Li
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $500,322
- **Award type:** 1
- **Project period:** 2020-09-15 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10228346

## Citation

> US National Institutes of Health, RePORTER application 10228346, Combine Genomics and Symptoms Data Driven Models to Discover Synergistic Combinatory Therapies for Alzheimer's Disease (1R56AG065352-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10228346. Licensed CC0.

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