# Computational Methods to Integrate Common Fund Data for Drug Repurposing

> **NIH NIH R03** · YALE UNIVERSITY · 2020 · $335,000

## Abstract

Project Summary
This project will develop novel computational methods to leverage diverse sources of data sets, including the
rich information generated from the NIH Common Fund projects, for drug repurposing, which may dramatically
lower the risk of drug development by skipping early-stage trials, shorten time investment, and cut down capital
investment. With the advancement of high-throughput sequencing and massively parallel technologies, more
and more omics data are available for biomedical research. These genomics, transcriptomics, proteomics,
metabolomics and microbiomics data can help biomedical researchers better understand the complex
biological systems underlying human diseases from different perspectives. For example, genome-wide
association and sequencing studies have successfully identified tens of thousands of variants that are
significantly associated with one or more complex traits. Despite these great successes, the results have not
been fully translated into potential clinical value. The overall goal of this pilot project is to leverage the rich
information generated from the NIH Common Funds projects, in combination of other public data sets, to
explore the feasibility of drug repurposing through novel computational approaches. The ultimate goal of our
project is to develop, implement, and apply a computational framework to integrate data from the Common
Fund projects and other resources to identify potential uses of existing drugs for new indications, and we will
also make our newly developed tools available to the general research community. This will be accomplished
through: [1] further development of a powerful framework proposed by our group to leverage cross-tissue
information in the GTEx data to achieve higher accuracy in imputation of gene expression within each tissue
and combine single-tissue association tests to derive a powerful test for gene-trait association using summary
statistics from genome wide association studies; [2] development of a signature-matching-based drug
repurposing framework with gene expression data from diverse sources (drug perturbation experiments, case
control studies, and patient intervention studies) and GWAS summary statistics; and [3] implementation and
application of the proposed framework to discover candidate drugs for repurposing to diseases in critical need
of drug development, e.g. non-alcoholic steatohepatitis. With the completion of the pilot project, we will be able
to assess the feasibility of the proposed framework for drug repurposing for further developments and
implementations.

## Key facts

- **NIH application ID:** 10111866
- **Project number:** 1R03OD030609-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** HONGYU ZHAO
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $335,000
- **Award type:** 1
- **Project period:** 2020-09-15 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10111866

## Citation

> US National Institutes of Health, RePORTER application 10111866, Computational Methods to Integrate Common Fund Data for Drug Repurposing (1R03OD030609-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10111866. Licensed CC0.

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