# Predictive Networks-based in-silico approach for Precision Medicine-repurposing for Alzheimer's Disease

> **NIH NIH R56** · UNIVERSITY OF ARIZONA · 2020 · $777,659

## Abstract

Project Summary
Alzheimer's disease is the most common form of Dementia estimated to affect 36 million people worldwide.
This number is expected to rise to 115 million by 2050 unless an effective therapeutic is developed. Recently,
NIA organized large-scale efforts, through AMP-/M2OVE-AD consortia, has generated the richest genotype,
genomic and clinical data, which enabled an unprecedented opportunity to explore the enormous complexity of
AD pathogenesis. On the other hand, through all failed clinic trials, we learned that an efficacious treatment
would need to target multiple aspects of the disease and be directed towards several pathogenic processes in
AD. Moreover, patients with different sex and risk factor will respond differently to the same treatment due to
distinct pathological mechanisms, therefore, it became extremely critical to develop patient-specific therapeutic
targets and precision medicine for each patient sub-group. However, despite tremendous interests in
advancing therapy and drug development for AD, there is a paucity of advanced bioinformatics approaches
available to guide the effective and efficient development of drugs and de-risk investment in these expensive
therapeutic approaches. We respond to the PAR (PAR-17-032) with the goals 1) to apply novel computational
systems biology approach, i.e. top-down and bottom-up predictive network for short), to analyze the existing
rich genetics, genomics, proteomics, metabolomics, and clinical datasets in AMP-AD and other datasets in AD
and 2) to build network models and to predict therapeutic targets of single-cell type and multi-cell cross-talk
pathways contributing to the onset and progression of AD pathology; 3) to stratify patients into sub-groups
according to Sex, APOE and disease-stage (whenever clinical data available) and to predict therapeutic
targets for each sub-group of patients towards precision medicine (drug repurposing) in AD; 4) to use novel in-
silico prediction pipeline to prioritize therapeutic targets; 5) to repurpose FDA-approved, investigational, and
experimental drugs binding to prioritized therapeutic targets through (known) on-targets and/or (predicted by
docking) off-targets; 6) to in-silico evaluate repurposed drugs: efficacy, toxicity, mechanism, transability
through BBB; 7) to evaluate prioritized drug/combination using in-vitro and in-vivo AD models.

## Key facts

- **NIH application ID:** 10017130
- **Project number:** 5R56AG062620-02
- **Recipient organization:** UNIVERSITY OF ARIZONA
- **Principal Investigator:** Rui Chang
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $777,659
- **Award type:** 5
- **Project period:** 2019-09-15 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10017130

## Citation

> US National Institutes of Health, RePORTER application 10017130, Predictive Networks-based in-silico approach for Precision Medicine-repurposing for Alzheimer's Disease (5R56AG062620-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10017130. Licensed CC0.

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