# Harnessing Diverse BioInformatic Approaches to Repurpose Drugs for Alzheimers Disease

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $759,291

## Abstract

Abstract
The exploration of genomes, transcriptomes, and proteomes derived from brains with Alzheimer's disease
(AD) by powerful computational tools has the potential of developing new knowledge, including the
identification of pathways and targets that may be involved in the initiation and/or progression of the disease.
The challenge is to find drugs that impact those pathways, and then validate the importance of those pathways
– distinguishing primary disease drivers from secondary events. Repurposing FDA-approved drugs is one
approach to probe potential pathways in proof of concept, and ultimately therapeutic, clinical trials. Here, we
propose to discover and validate hypotheses for drug repurposing in AD through three integrated,
complementary informatics approaches. Specifically, we will apply classical and network aware (prior-loaded)
machine learning approaches to identify pathways and targets altered in AD brains at different stages of
disease progression using data from Accelerating Medicines Partnership-AD available through Synapse (Aim
1); we will use systems pharmacology approaches to discover the target selectivity of lead compounds in
human neuronal and glial cell types using unbiased RNA-seq, proteomic and imaging studies followed by
pathway analysis (Aim 2). Each of these two Aims has two approaches: data-driven, hypothesis-generating
analyses to discern disease-relevant drug signals; and hypothesis-testing in which positive findings from one
approach are evaluated using the other approaches to assess rigor and reproducibility. Moreover, RNA-seq
and proteomic data collected in cultured human CNS cell types following exposure to potential disease drivers
and/or FDA-approved drugs in Aim 2 will be fed back into Aim 1 as CNS-cell type-derived priors to refine the
predictive models. In Aim 3, we will develop new informatics strategies to conduct in-silico drug trials in EHR
data with “prospective” outcomes to validate hypotheses based on the omics data sets and extant literature,
using two big data sets: the UK 20 year CPRD longitudinal records of 20M National Health Service patients,
and the RPDR Database (based at Partners Healthcare) with 6 M individuals followed for over 20 years. This
integrated informatics program compensates for the limitations of each individual informatics approach to
promote discovery and critical evaluation of “lead compounds” for known and novel AD pathways. To execute
this strategy, we have assembled a multi-site, multi-disciplinary team with expertise ranging from clinical care
to computer science and systems pharmacology. Some of the team members are AD experts and others bring
an outsider's perspective. Finally, as a deliverable, we will create open-source data packages to release all the
supporting evidence, software, and data with provenance in accordance with FAIR (findable, accessible,
interoperable and reproducible) standards through Synapse and the AlzDataLens platform developed at MGH
(Aim 4). T...

## Key facts

- **NIH application ID:** 9974450
- **Project number:** 5R01AG058063-03
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** MARK W ALBERS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $759,291
- **Award type:** 5
- **Project period:** 2018-09-30 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9974450

## Citation

> US National Institutes of Health, RePORTER application 9974450, Harnessing Diverse BioInformatic Approaches to Repurpose Drugs for Alzheimers Disease (5R01AG058063-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9974450. Licensed CC0.

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