# Systematic Alzheimer's disease drug repositioning (SMART) based on bioinformatics-guided phenotype screening and image-omics

> **NIH NIH R01** · METHODIST HOSPITAL RESEARCH INSTITUTE · 2021 · $689,643

## Abstract

PROJECT SUMMARY
Given the complexity of Alzheimer's Disease (AD) pathogenesis and the associated co-morbid conditions, both
the “depth” and the “width” of currently available drug repurposing solutions need to be improved in order to
deliver effective AD therapeutic solutions. The depth of a drug-repurposing project refers to the level of
understanding of disease mechanism and drug-target interactions across a wide searching space for the
combination of dosage and treatment time. Achieving depth requires a reliable AD model system that
comprehensively recapitulates AD pathogenesis in a human brain-like environment, and sophisticated
transcriptomic profiles, which can reveal molecular-level changes underlying disease-reversing phenotypes
across multiple treatment conditions. The width of a therapy search relies on the efficacy of predicting and
validating effects of candidate compounds from an enormous search space. Width can be achieved from novel
computational algorithms connecting –omics changes with phenotypic changes, thus guiding the search with
improved knowledge on mechanisms and avoiding exhaustive testing of every available drug.
Integrating the systems medicine and drug repositioning expertise of the Wong Lab at the Houston Methodist
Research Institute of Houston Methodist Hospital with the Alzheimer's biology expertise of the Kim and Tanzi
labs at Massachusetts General Hospital, we propose a SysteMatic Alzheimer's disease drug ReposiTioning
(SMART) framework based on bioinformatics-guided phenotype screening. Reformatting a novel three-
dimensional human neural stem cell culture model of AD (a.k.a. Alzheimer's in a dish) developed in the Kim
and Tanzi labs for high content screening, the Wong lab screened 2,640 known drugs and bioactive
compounds and obtained a panel of 38 primary hits that strongly inhibit β-amyloid-driven p-tau accumulation.
We hypothesize that iteratively running relatively small screens with our novel 3D cell model and applying
systematic artificial intelligence modeling to the transcriptomic profiles of the screening hits will allow us to: 1)
quickly obtain a panel of robust novel drug candidates for AD, and 2) gain an in-depth understanding of
disease mechanisms from those repositioned drug candidates, which will subsequently improve the success
rate of predicting novel hits.
Using the primary 38 hits as a starting point, the SMART computational modules will update the existing
NeuriteIQ software package to quantify the image data from high content screening; it will also incorporate
publicly available big data transcriptomic profiles to predict candidate compounds inducing similar pathway
changes as those original compounds, effectively expanding the search width to tens of thousands of
compounds while only requiring functional validation of less than 100 drug candidates. The validated
predictions will, in turn, add to the panel of known hits that will launch the next round of computational
predictions and exper...

## Key facts

- **NIH application ID:** 10173590
- **Project number:** 5R01AG057635-04
- **Recipient organization:** METHODIST HOSPITAL RESEARCH INSTITUTE
- **Principal Investigator:** STEPHEN TC WONG
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $689,643
- **Award type:** 5
- **Project period:** 2018-09-15 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10173590

## Citation

> US National Institutes of Health, RePORTER application 10173590, Systematic Alzheimer's disease drug repositioning (SMART) based on bioinformatics-guided phenotype screening and image-omics (5R01AG057635-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10173590. Licensed CC0.

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