Drug voyager: a computational platform for exploring unintended drug action
| dc.contributor.author | Oh, Min | en |
| dc.contributor.author | Ahn, Jaegyoon | en |
| dc.contributor.author | Lee, Taekeon | en |
| dc.contributor.author | Jang, Giup | en |
| dc.contributor.author | Park, Chihyun | en |
| dc.contributor.author | Yoon, Youngmi | en |
| dc.contributor.department | Computer Science | en |
| dc.date.accessioned | 2017-08-03T20:00:14Z | en |
| dc.date.available | 2017-08-03T20:00:14Z | en |
| dc.date.issued | 2017-02-28 | en |
| dc.date.updated | 2017-08-03T10:58:48Z | en |
| dc.description.abstract | 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. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | BMC Bioinformatics. 2017 Feb 28;18(1):131 | en |
| dc.identifier.doi | https://doi.org/10.1186/s12859-017-1558-3 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/78636 | en |
| dc.language.iso | en | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.holder | The Author(s) | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.title | Drug voyager: a computational platform for exploring unintended drug action | en |
| dc.title.serial | BMC Bioinformatics | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |