Drug repositioning for diabetes based on 'omics' data mining.

Drug repositioning has shorter developmental time, lower cost and less safety risk than traditional drug development process. The current study aims to repurpose marketed drugs and clinical candidates for new indications in diabetes treatment by mining clinical 'omics' data. We analyzed da...

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Main Authors: Ming Zhang, Heng Luo, Zhengrui Xi, Ekaterina Rogaeva
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0126082
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spelling doaj-c3c6ad43512646338e765e7c429063632021-03-03T20:05:15ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01105e012608210.1371/journal.pone.0126082Drug repositioning for diabetes based on 'omics' data mining.Ming ZhangHeng LuoZhengrui XiEkaterina RogaevaDrug repositioning has shorter developmental time, lower cost and less safety risk than traditional drug development process. The current study aims to repurpose marketed drugs and clinical candidates for new indications in diabetes treatment by mining clinical 'omics' data. We analyzed data from genome wide association studies (GWAS), proteomics and metabolomics studies and revealed a total of 992 proteins as potential anti-diabetic targets in human. Information on the drugs that target these 992 proteins was retrieved from the Therapeutic Target Database (TTD) and 108 of these proteins are drug targets with drug projects information. Research and preclinical drug targets were excluded and 35 of the 108 proteins were selected as druggable proteins. Among them, five proteins were known targets for treating diabetes. Based on the pathogenesis knowledge gathered from the OMIM and PubMed databases, 12 protein targets of 58 drugs were found to have a new indication for treating diabetes. CMap (connectivity map) was used to compare the gene expression patterns of cells treated by these 58 drugs and that of cells treated by known anti-diabetic drugs or diabetes risk causing compounds. As a result, 9 drugs were found to have the potential to treat diabetes. Among the 9 drugs, 4 drugs (diflunisal, nabumetone, niflumic acid and valdecoxib) targeting COX2 (prostaglandin G/H synthase 2) were repurposed for treating type 1 diabetes, and 2 drugs (phenoxybenzamine and idazoxan) targeting ADRA2A (Alpha-2A adrenergic receptor) had a new indication for treating type 2 diabetes. These findings indicated that 'omics' data mining based drug repositioning is a potentially powerful tool to discover novel anti-diabetic indications from marketed drugs and clinical candidates. Furthermore, the results of our study could be related to other disorders, such as Alzheimer's disease.https://doi.org/10.1371/journal.pone.0126082
collection DOAJ
language English
format Article
sources DOAJ
author Ming Zhang
Heng Luo
Zhengrui Xi
Ekaterina Rogaeva
spellingShingle Ming Zhang
Heng Luo
Zhengrui Xi
Ekaterina Rogaeva
Drug repositioning for diabetes based on 'omics' data mining.
PLoS ONE
author_facet Ming Zhang
Heng Luo
Zhengrui Xi
Ekaterina Rogaeva
author_sort Ming Zhang
title Drug repositioning for diabetes based on 'omics' data mining.
title_short Drug repositioning for diabetes based on 'omics' data mining.
title_full Drug repositioning for diabetes based on 'omics' data mining.
title_fullStr Drug repositioning for diabetes based on 'omics' data mining.
title_full_unstemmed Drug repositioning for diabetes based on 'omics' data mining.
title_sort drug repositioning for diabetes based on 'omics' data mining.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2015-01-01
description Drug repositioning has shorter developmental time, lower cost and less safety risk than traditional drug development process. The current study aims to repurpose marketed drugs and clinical candidates for new indications in diabetes treatment by mining clinical 'omics' data. We analyzed data from genome wide association studies (GWAS), proteomics and metabolomics studies and revealed a total of 992 proteins as potential anti-diabetic targets in human. Information on the drugs that target these 992 proteins was retrieved from the Therapeutic Target Database (TTD) and 108 of these proteins are drug targets with drug projects information. Research and preclinical drug targets were excluded and 35 of the 108 proteins were selected as druggable proteins. Among them, five proteins were known targets for treating diabetes. Based on the pathogenesis knowledge gathered from the OMIM and PubMed databases, 12 protein targets of 58 drugs were found to have a new indication for treating diabetes. CMap (connectivity map) was used to compare the gene expression patterns of cells treated by these 58 drugs and that of cells treated by known anti-diabetic drugs or diabetes risk causing compounds. As a result, 9 drugs were found to have the potential to treat diabetes. Among the 9 drugs, 4 drugs (diflunisal, nabumetone, niflumic acid and valdecoxib) targeting COX2 (prostaglandin G/H synthase 2) were repurposed for treating type 1 diabetes, and 2 drugs (phenoxybenzamine and idazoxan) targeting ADRA2A (Alpha-2A adrenergic receptor) had a new indication for treating type 2 diabetes. These findings indicated that 'omics' data mining based drug repositioning is a potentially powerful tool to discover novel anti-diabetic indications from marketed drugs and clinical candidates. Furthermore, the results of our study could be related to other disorders, such as Alzheimer's disease.
url https://doi.org/10.1371/journal.pone.0126082
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AT hengluo drugrepositioningfordiabetesbasedonomicsdatamining
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