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Integrating gene expression and clinical data to identify drug repurposing candidates for hyperlipidemia and hypertension.

ABSTRACT: Discovering novel uses for existing drugs, through drug repurposing, can reduce the time, costs, and risk of failure associated with new drug development. However, prioritizing drug repurposing candidates for downstream studies remains challenging. Here, we present a high-throughput approach to identify and validate drug repurposing candidates. This approach integrates human gene expression, drug perturbation, and clinical data from publicly available resources. We apply this approach to find drug repurposing candidates for two diseases, hyperlipidemia and hypertension. We screen >21,000 compounds and replicate ten approved drugs. We also identify 25 (seven for hyperlipidemia, eighteen for hypertension) drugs approved for other indications with therapeutic effects on clinically relevant biomarkers. For five of these drugs, the therapeutic effects are replicated in the All of Us Research Program database. We anticipate our approach will enable researchers to integrate multiple publicly available datasets to identify high priority drug repurposing opportunities for human diseases.

SUBMITTER: Wu P

PROVIDER: S-EPMC8748496 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Integrating gene expression and clinical data to identify drug repurposing candidates for hyperlipidemia and hypertension.

Wu Patrick P Feng QiPing Q Kerchberger Vern Eric VE Nelson Scott D SD Chen Qingxia Q Li Bingshan B Edwards Todd L TL Cox Nancy J NJ Phillips Elizabeth J EJ Stein C Michael CM Roden Dan M DM Denny Joshua C JC Wei Wei-Qi WQ

Nature communications 20220110 1

Discovering novel uses for existing drugs, through drug repurposing, can reduce the time, costs, and risk of failure associated with new drug development. However, prioritizing drug repurposing candidates for downstream studies remains challenging. Here, we present a high-throughput approach to identify and validate drug repurposing candidates. This approach integrates human gene expression, drug perturbation, and clinical data from publicly available resources. We apply this approach to find dr ...[more]

PMID: 35013250

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Project description:BackgroundCystic fibrosis (CF) is a fatal genetic disorder caused by mutations in the CF transmembrane conductance regulator (CFTR) gene that primarily affects the lungs and the digestive system, and the current drug treatment is mainly able to alleviate symptoms. To improve disease management for CF, we considered the repurposing of approved drugs and hypothesized that specific microRNA (miRNA) transcription factors (TF) gene networks can be used to generate feed-forward loops (FFLs), thus providing treatment opportunities on the basis of disease specific FFLs.MethodsComprehensive database searches revealed significantly enriched TFs and miRNAs in CF and CFTR gene networks. The target genes were validated using ChIPBase and by employing a consensus approach of diverse algorithms to predict miRNA gene targets. STRING analysis confirmed protein-protein interactions (PPIs) among network partners and motif searches defined composite FFLs. Using information extracted from SM2miR and Pharmaco-miR, an in silico drug repurposing pipeline was established based on the regulation of miRNA/TFs in CF/CFTR networks.ResultsIn human airway epithelium, a total of 15 composite FFLs were constructed based on CFTR specific miRNA/TF gene networks. Importantly, nine of them were confirmed in patient samples and CF epithelial cells lines, and STRING PPI analysis provided evidence that the targets interacted with each other. Functional analysis revealed that ubiquitin-mediated proteolysis and protein processing in the endoplasmic reticulum dominate the composite FFLs, whose major functions are folding, sorting, and degradation. Given that the mutated CFTR gene disrupts the function of the chloride channel, the constructed FFLs address mechanistic aspects of the disease and, among 48 repurposing drug candidates, 26 were confirmed with literature reports and/or existing clinical trials relevant to the treatment of CF patients.ConclusionThe construction of FFLs identified promising drug repurposing candidates for CF and the developed strategy may be applied to other diseases as well.

Dataset Information

Integrating gene expression and clinical data to identify drug repurposing candidates for hyperlipidemia and hypertension.

Publications

Integrating gene expression and clinical data to identify drug repurposing candidates for hyperlipidemia and hypertension.

Similar Datasets

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