Inferring new drug indications using the complementarity between clinical disease signatures and drug effects

[Display omitted] •We suggest a computational framework to find new uses of existing drugs.•We use the complementarity between clinical disease signatures and clinical drug effects.•The statistical significance of prediction results is supported through two benchmark datasets. Drug repositioning is...

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Bibliographic Details
Published in:Journal of biomedical informatics 2016-02, Vol.59, p.248-257
Main Authors: Jang, Dongjin, Lee, Sejoon, Lee, Jaehyun, Kim, Kiseong, Lee, Doheon
Format: Article
Language:English
Subjects:
Biomedical Research - methods
Clinical disease signatures
Clinical drug effects
Data Mining - methods
Drug repositioning
Drug Repositioning - methods
Drugs
Electronic clinical information
Electronic Health Records
Electronics
Humans
Indication
Mathematical analysis
Mathematical models
Medical Informatics Applications
Signatures
Vectors (mathematics)
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Summary:[Display omitted] •We suggest a computational framework to find new uses of existing drugs.•We use the complementarity between clinical disease signatures and clinical drug effects.•The statistical significance of prediction results is supported through two benchmark datasets. Drug repositioning is the process of finding new indications for existing drugs. Its importance has been dramatically increasing recently due to the enormous increase in new drug discovery cost. However, most of the previous molecular-centered drug repositioning work is not able to reflect the end-point physiological activities of drugs because of the inherent complexity of human physiological systems. Here, we suggest a novel computational framework to make inferences for alternative indications of marketed drugs by using electronic clinical information which reflects the end-point physiological results of drug’s effects on the biological activities of humans. In this work, we use the concept of complementarity between clinical disease signatures and clinical drug effects. With this framework, we establish disease-related clinical variable vectors (clinical disease signature vectors) and drug-related clinical variable vectors (clinical drug effect vectors) by applying two methodologies (i.e., statistical analysis and literature mining). Finally, we assign a repositioning possibility score to each disease–drug pair by the calculation of complementarity (anti-correlation) and association between clinical states (“up” or “down”) of disease signatures and clinical effects (“up”, “down” or “association”) of drugs. A total of 717 clinical variables in the electronic clinical dataset (NHANES), are considered in this study. The statistical significance of our prediction results is supported through two benchmark datasets (Comparative Toxicogenomics Database and Clinical Trials). We discovered not only lots of known relationships between diseases and drugs, but also many hidden disease–drug relationships. For example, glutathione and edetic-acid may be investigated as candidate drugs for asthma treatment. We examined prediction results by using statistical experiments (enrichment verification, hyper-geometric and permutation test P
ISSN:1532-0464
1532-0480
DOI:10.1016/j.jbi.2015.12.003