# 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 · 2020 · $395,914

## Abstract

There have been more than 550,000 confirmed cases and over 22,000 deaths for COVID-19, the disease caused
by the virus SARS-CoV-2, in the United States. Older individuals have the declined immune systems and a
higher mortality from COVID-19; furthermore, there are currently no effective antiviral medications against
COVID-19. Drug repurposing, represented as an effective drug discovery strategy from existing drugs, offers
emerging prevention and treatment strategies for COVID-19. SARS-CoV-2 requires host cellular factors for
successful replication during infection. Targeting virus-host protein-protein interactions (PPIs) offers an effective
way for the development of drug repurposing (i.e., hydroxychloroquine (HCQ), melatonin, and indomethacin)
for COVID-19 as demonstrated in our recent study (Cell Discovery 2020). Supported by the NIA R01, our team
are developing and implementing innovative network medicine and systems biology methodologies for drug
repurposing and drug combinations. We showed that HCQ was associated with a decreased risk of coronary
artery disease by reducing the expression of VCAM1 and IL-1β in human aortic endothelial cells. Exogenous
melatonin administration may be of particular benefit to COVID-19 older patients given an aging-related reduction
of endogenous melatonin levels. Therefore, the central unifying hypothesis of this project is that an integrative,
network medicine methodology that quantifies the interplay between the virus-host interactome and drug
targets in the human interactome network will offer highly repurposable drugs and clinically relevant combination
regimens for effective treatment of COVID-19. Aim 1 will test disease module hypothesis for prediction and
validation of repurposable drugs for effective treatment of older individuals with COVID-19. We will utilize a
network-based knowledge graph approach that incorporates not only virus-host interactions from SARS-CoV-2,
but also public drug-target databases, the human protein-protein interactome, along with 24 millions of
publications from PubMed database. Aim 2 will test the hypothesis that combining anti-inflammatory and antiviral
therapeutics for effective treatment of the underlying pulmonary and cardiovascular conditions in older individuals
with COVID-19. We will utilize state-of-the-art pharmacoepidemiologic analyses to validate the clinical efficiency
of drug combinations (i.e., melatonin plus HCQ) in reducing incidence of pulmonary and cardiovascular
conditions (including acute respiratory distress syndrome, pneumonia and lung injury) in older individuals, using
large-scale longitudinal Claims-Electronic Medical Record (EHR) patient databases, along with in vitro
observations in human aortic endothelial cell and pulmonary arterial endothelial cell models. To reduce the
confounding factors from patient databases, we will perform time-to-event pharmacoepidemiologic outcome
analyses using large-scale de-identified patient EHRs from the Cleveland Cli...

## Key facts

- **NIH application ID:** 10146748
- **Project number:** 3R01AG066707-01S1
- **Recipient organization:** CLEVELAND CLINIC LERNER COM-CWRU
- **Principal Investigator:** Feixiong Cheng
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $395,914
- **Award type:** 3
- **Project period:** 2020-04-15 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10146748

## Citation

> US National Institutes of Health, RePORTER application 10146748, Endophenotype Network-based Approaches to Prediction and Population-based Validation of in Silico Drug Repurposing for Alzheimer's Disease (3R01AG066707-01S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10146748. Licensed CC0.

---

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