Project Details
Description
Project Summary
Alzheimer’s disease (AD) is a major public health crisis with no available cure. Given recent failures of
many AD clinical trials, there is an urgent need for developing effective strategies to identify new AD targets for
disease modeling and new candidates for drug repurposing and development. We propose here a research
project to develop transformative big data analytic approaches in the fields of translational bioinformatics,
machine learning and deep learning to advance drug repurposing for AD. Our overarching goal is to develop
innovative machine learning and deep learning approaches as well as informatics tools and pipelines that
leverage big data in relevant biomedical domains. These big data include large-scale genetic, multi-omics,
imaging, cognitive and other phenotypic data from landmark AD studies, functional interaction data among
drugs, proteins and diseases, pharmacologic perturbation data, electronic health record data, and MarketScan
data. Our proposed computational research is aimed at developing novel translational informatics approaches
to analyze various types of molecular, clinical and other relevant data to identify individual drugs or drug
combinations with favorable efficacy and toxicity profiles as candidates for repositioning against AD or AD-
related dementia (ADRD). To achieve our goal, we have four Aims. Aim 1 is to develop network-based multi-
omics data integration methods to identify genes and pathways as novel targets for AD drug repositioning
research. Aim 2 is to develop informatics strategies to prioritize and evaluate promising candidate targets via
examining their associations with AD biomarkers and phenotypes. Aim 3 is to develop knowledge-driven drug
repurposing methods using network reinforcement and drug scoring to identify AD candidate drugs. Aim 4 is to
prioritize and evaluate the identified candidate drugs for repurposing against AD/ADRD using pharmacologic
perturbation, EHR and MarketScan data. Successful completion of these aims will produce novel translational
big data analytic methods and tools to improve our understanding of the genetic, molecular and neurobiological
mechanisms of AD, facilitate the identification of novel promising targets and drugs for repurposing, and
ultimately have a translational impact on disease treatment and prevention. These advances are fundamental
to the NIA NAPA goal of effectively treating or preventing AD/ADRD by 2025. The resulting methods and tools
are also expected to impact biomedical research in general and benefit public health outcomes.
Status | Finished |
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Effective start/end date | 05/1/21 → 04/30/24 |
Funding
- National Institute on Aging: $761,646.00
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