Prediction of new disease indications for approved drugs by computational methods has been based largely on the genomics signatures of drugs and diseases. We propose a method for drug repositioning that uses the clinical signatures extracted from over 13 years of electronic medical records from a tertiary hospital, including >9.4 M laboratory tests from >530,000 patients, in addition to diverse genomics signatures. Cross-validation using over 17,000 known drug-disease associations shows this approach outperforms various predictive models based on genomics signatures and a well-known "guilt-by-association" method. Interestingly, the prediction suggests that terbutaline sulfate, which is widely used for asthma, is a promising candidate for amyotrophic lateral sclerosis for which there are few therapeutic options. In vivo tests using zebrafish models found that terbutaline sulfate prevents defects in axons and neuromuscular junction degeneration in a dose-dependent manner. A therapeutic potential of terbutaline sulfate was also observed when axonal and neuromuscular junction degeneration have already occurred in zebrafish model. Cotreatment with a β2-adrenergic receptor antagonist, butoxamine, suggests that the effect of terbutaline is mediated by activation of β2-adrenergic receptors.
Bibliographical noteFunding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2012R1A1A3019523, 2010-0022887) and Lucile Packard Foundation for Children’s Health and National Institute of General Medical Sciences (R01 GM079719) of USA. This research was supported by the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2011-0019233). This work was also supported by NRF grant funded by the Korea government (MEST) (No: 2012-0000995). One of the corresponding authors, KiYoung Lee, was diagnosed with pancreatic cancer and lost his battle with the disease before this manuscript could be published. We wish to honor his great contribution to computational biology and systems medicine fields.
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