Dataset Information

Novel candidate drugs in anti-tumor necrosis factor refractory Crohn's diseases: in silico study for drug repositioning.

ABSTRACT: Biologicals like anti-tumor necrosis factor (TNF) therapy for Crohn's disease (CD) are safe and effective but there is a significant rate of primary and secondary nonresponse in the patients. In this study, we applied a computational approach to discover novel drug therapies for anti-TNF refractory CD in silico. We use a transcriptome dataset (GSE100833) for the anti-TNF refractory CD patients from NCBI GEO. After co-expression analysis, we specifically investigated the extent of protein-protein interactions among genes in clusters based on a protein-protein interaction database, STRING. Pathway analysis was performed using the clEnrich function based on KEGG gene sets. Co-expressed genes in cluster 1, 2, 3, 4, up or down-regulated genes and all differentially expressed genes are highly connected. Among them, cluster 1, which is highly enriched for chemokine signaling, also showed enrichment for cytokine-cytokine receptor interaction and identifies several drugs including cyclosporin with known efficacy in CD. Vorinostat, histone deacetylase inhibitors, and piperlongumine, which is known to have inhibitory effect on activity of NF-?B, were also identified. Some alkaloids were also selected as potential therapeutic drugs. These finding suggest that they might serve as a novel therapeutic option for anti-TNF refractory CD and support the use of public molecular data and computational approaches to discover novel therapeutic options for CD.

SUBMITTER: Kwak MS

PROVIDER: S-EPMC7330029 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Novel candidate drugs in anti-tumor necrosis factor refractory Crohn's diseases: in silico study for drug repositioning.

Kwak Min Seob MS Lee Hun Hee HH Cha Jae Myung JM Shin Hyun Phil HP Jeon Jung Won JW Yoon Jin Young JY

Scientific reports 20200701 1

Biologicals like anti-tumor necrosis factor (TNF) therapy for Crohn's disease (CD) are safe and effective but there is a significant rate of primary and secondary nonresponse in the patients. In this study, we applied a computational approach to discover novel drug therapies for anti-TNF refractory CD in silico. We use a transcriptome dataset (GSE100833) for the anti-TNF refractory CD patients from NCBI GEO. After co-expression analysis, we specifically investigated the extent of protein-protein ...[more]

PMID: 32612148

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Project description:BackgroundBesides binding to proteins, the most recent advances in pharmacogenomics indicate drugs can regulate the expression of non-coding RNAs (ncRNAs). The polypharmacological feature in drugs enables us to find new uses for existing drugs (namely drug repositioning). However, current computational methods for drug repositioning mainly consider proteins as drug targets. Meanwhile, these methods identify only statistical relationships between drugs and diseases. They provide little information about how drug-disease associations are formed at the molecular target level.MethodsHerein, we first comprehensively collect proteins and two categories of ncRNAs as drug targets from public databases to construct drug-target interactions. Experimentally confirmed drug-disease associations are downloaded from an established database. A canonical correlation analysis (CCA) based method is then applied to the two datasets to extract correlated sets of targets and diseases. The correlated sets are regarded as canonical components, and they are used to investigate drug's mechanism of actions. We finally develop a strategy to predict novel drug-disease associations for drug repositioning by combining all the extracted correlated sets.ResultsWe receive 400 canonical components which correlate targets with diseases in our study. We select 4 components for analysis and find some top-ranking diseases in an extracted set might be treated by drugs interfacing with the top-ranking targets in the same set. Experimental results from 10-fold cross-validations show integrating different categories of target information results in better prediction performance than only using proteins or ncRNAs as targets. When compared with 3 state-of-the-art approaches, our method receives the highest AUC value 0.8576. We use our method to predict new indications for 789 drugs and confirm 24 predictions in the top 1 predictions.ConclusionsTo the best of our knowledge, this is the first computational effort which combines both proteins and ncRNAs as drug targets for drug repositioning. Our study provides a biologically relevant interpretation regarding the forming of drug-disease associations, which is useful for guiding future biomedical tests.

Dataset Information

Novel candidate drugs in anti-tumor necrosis factor refractory Crohn's diseases: in silico study for drug repositioning.

Publications

Novel candidate drugs in anti-tumor necrosis factor refractory Crohn's diseases: in silico study for drug repositioning.

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