Drug voyager: a computational platform for exploring unintended drug action

BMC Bioinformatics

Research Article

Chihyun Park¹  Taekeon Lee²  Giup Jang²  Youngmi Yoon³  Jaegyoon Ahn⁴  Min Oh⁵ 

[1] Biomedical HPC Technology Research Center, Korean Institute of Science and Technology Information, Daejeon, South Korea;Department of Computer Engineering, Gachon University, Seongnam, South Korea;Department of Computer Engineering, Gachon University, Seongnam, South Korea;Postal Address: Gachon University, 339Ho, Woongji B.D., 1324 Seongnam-daero, 13120, Seongnam-si, South Korea;Department of Computer Science & Engineering, Incheon National University, Incheon, South Korea;Department of Computer Science, Virginia Tech, Blacksburg, VA, USA;

关键词: Drug pathway;    Drug-signaling pathway;    Drug action;    Pharmacodynamics;    Drug repurposing;    Drug repositioning;    Adverse reactions;    Side effects;   

DOI  :  10.1186/s12859-017-1558-3

 received in 2016-10-07, accepted in 2017-02-22,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundThe 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.ResultsWe 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.ConclusionsOur study addresses how we can computationally represent drug-signaling pathways to understand unintended drug actions and to facilitate drug discovery and screening.

【 授权许可】

CC BY   
© The Author(s). 2017