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Predicting unintended effects of drugs based on off-target tissue effects
KTH, Centres, Science for Life Laboratory, SciLifeLab. Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, South Korea.ORCID iD: 0000-0002-6428-5936
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2016 (English)In: Biochemical and Biophysical Research Communications - BBRC, ISSN 0006-291X, E-ISSN 1090-2104, Vol. 469, no 3, 399-404 p.Article in journal (Refereed) PublishedText
Abstract [en]

Unintended effects of drugs can be caused by various mechanisms. Conventional analysis of unintended effects has focused on the target proteins of drugs. However, an interaction with off-target tissues of a drug might be one of the unintended effect-related mechanisms. We propose two processes to predict a drug’s unintended effects by off-target tissue effects: 1) identification of a drug’s off-target tissue and; 2) tissue protein - symptom relation identification (tissue protein - symptom matrix). Using this method, we predicted that 1,177 (10.7%) side-effects were related to off-target tissue effects in 11,041 known side-effects. Off-target tissues and unintended effects of successful repositioning drugs were also predicted. The effectiveness of relations of the proposed tissue protein - symptom matrix were evaluated by using the literature mining method. We predicted unintended effects of drugs as well as those effect-related off-target tissues. By using our prediction, we are able to reduce drug side-effects on off-target tissues and provide a chance to identify new indications of drugs of interest.

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 469, no 3, 399-404 p.
Keyword [en]
Unintended effect, Off-target tissue effect, Tissue protein-symptom matrix, Side-effect prediction, Drug repositioning
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-181437DOI: 10.1016/j.bbrc.2015.11.095ISI: 000369352800011PubMedID: 26626077ScopusID: 2-s2.0-84953635142OAI: oai:DiVA.org:kth-181437DiVA: diva2:900435
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20160204. QC 20160304

Available from: 2016-02-04 Created: 2016-02-02 Last updated: 2016-03-04Bibliographically approved

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Lee, Sunjae
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