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Drug voyager: a computational platform for exploring unintended drug action
BACKGROUND: The dominant paradigm in understanding drug action focuses on the intended therapeutic effects and frequent adverse reactions. However, this approach may limit opportunities to grasp unintended drug actions, which can open up channels to repurpose existing drugs and identify rare adverse...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5329936/ https://www.ncbi.nlm.nih.gov/pubmed/28241745 http://dx.doi.org/10.1186/s12859-017-1558-3 |
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author | Oh, Min Ahn, Jaegyoon Lee, Taekeon Jang, Giup Park, Chihyun Yoon, Youngmi |
author_facet | Oh, Min Ahn, Jaegyoon Lee, Taekeon Jang, Giup Park, Chihyun Yoon, Youngmi |
author_sort | Oh, Min |
collection | PubMed |
description | BACKGROUND: The dominant paradigm in understanding drug action focuses on the intended therapeutic effects and frequent adverse reactions. However, this approach may limit opportunities to grasp unintended drug actions, which can open up channels to repurpose existing drugs and identify rare adverse drug reactions. Advances in systems biology can be exploited to comprehensively understand pharmacodynamic actions, although proper frameworks to represent drug actions are still lacking. RESULTS: We suggest a novel platform to construct a drug-specific pathway in which a molecular-level mechanism of action is formulated based on pharmacologic, pharmacogenomic, transcriptomic, and phenotypic data related to drug response (http://databio.gachon.ac.kr/tools/). In this platform, an adoption of three conceptual levels imitating drug perturbation allows these pathways to be realistically rendered in comparison to those of other models. Furthermore, we propose a new method that exploits functional features of the drug-specific pathways to predict new indications as well as adverse reactions. For therapeutic uses, our predictions significantly overlapped with clinical trials and an up-to-date drug-disease association database. Also, our method outperforms existing methods with regard to classification of active compounds for cancers. For adverse reactions, our predictions were significantly enriched in an independent database derived from the Food and Drug Administration (FDA) Adverse Event Reporting System and meaningfully cover an Adverse Reaction Database provided by Health Canada. Lastly, we discuss several predictions for both therapeutic indications and side-effects through the published literature. CONCLUSIONS: Our study addresses how we can computationally represent drug-signaling pathways to understand unintended drug actions and to facilitate drug discovery and screening. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1558-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5329936 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53299362017-03-03 Drug voyager: a computational platform for exploring unintended drug action Oh, Min Ahn, Jaegyoon Lee, Taekeon Jang, Giup Park, Chihyun Yoon, Youngmi BMC Bioinformatics Research Article BACKGROUND: The dominant paradigm in understanding drug action focuses on the intended therapeutic effects and frequent adverse reactions. However, this approach may limit opportunities to grasp unintended drug actions, which can open up channels to repurpose existing drugs and identify rare adverse drug reactions. Advances in systems biology can be exploited to comprehensively understand pharmacodynamic actions, although proper frameworks to represent drug actions are still lacking. RESULTS: We suggest a novel platform to construct a drug-specific pathway in which a molecular-level mechanism of action is formulated based on pharmacologic, pharmacogenomic, transcriptomic, and phenotypic data related to drug response (http://databio.gachon.ac.kr/tools/). In this platform, an adoption of three conceptual levels imitating drug perturbation allows these pathways to be realistically rendered in comparison to those of other models. Furthermore, we propose a new method that exploits functional features of the drug-specific pathways to predict new indications as well as adverse reactions. For therapeutic uses, our predictions significantly overlapped with clinical trials and an up-to-date drug-disease association database. Also, our method outperforms existing methods with regard to classification of active compounds for cancers. For adverse reactions, our predictions were significantly enriched in an independent database derived from the Food and Drug Administration (FDA) Adverse Event Reporting System and meaningfully cover an Adverse Reaction Database provided by Health Canada. Lastly, we discuss several predictions for both therapeutic indications and side-effects through the published literature. CONCLUSIONS: Our study addresses how we can computationally represent drug-signaling pathways to understand unintended drug actions and to facilitate drug discovery and screening. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1558-3) contains supplementary material, which is available to authorized users. BioMed Central 2017-02-28 /pmc/articles/PMC5329936/ /pubmed/28241745 http://dx.doi.org/10.1186/s12859-017-1558-3 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Oh, Min Ahn, Jaegyoon Lee, Taekeon Jang, Giup Park, Chihyun Yoon, Youngmi Drug voyager: a computational platform for exploring unintended drug action |
title | Drug voyager: a computational platform for exploring unintended drug action |
title_full | Drug voyager: a computational platform for exploring unintended drug action |
title_fullStr | Drug voyager: a computational platform for exploring unintended drug action |
title_full_unstemmed | Drug voyager: a computational platform for exploring unintended drug action |
title_short | Drug voyager: a computational platform for exploring unintended drug action |
title_sort | drug voyager: a computational platform for exploring unintended drug action |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5329936/ https://www.ncbi.nlm.nih.gov/pubmed/28241745 http://dx.doi.org/10.1186/s12859-017-1558-3 |
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