Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Dec 13, 2017
Open Peer Review Period: Dec 14, 2017 - Mar 22, 2018
Date Accepted: Jun 18, 2018
(closed for review but you can still tweet)
Drug Repositioning to Accelerate Drug Development Using Social Media Data: Computational Study on Parkinson Disease
ABSTRACT
Background:
Due to the high cost and low success rate in new drug development, systematic drug repositioning methods are exploited to find new indications for existing drugs.
Objective:
We sought to propose a new computational drug repositioning method to identify repositioning drugs for Parkinson disease (PD).
Methods:
We developed a novel heterogeneous network mining repositioning method that constructed a 3-layer network of disease, drug, and adverse drug reaction and involved user-generated data from online health communities to identify potential candidate drugs for PD.
Results:
We identified 44 non-Parkinson drugs by using the proposed approach, with data collected from both pharmaceutical databases and online health communities. Based on the further literature analysis, we found literature evidence for 28 drugs.
Conclusions:
In summary, the proposed heterogeneous network mining repositioning approach is promising for identifying repositioning candidates for PD. It shows that adverse drug reactions are potential intermediaries to reveal relationships between disease and drug.
Citation
Per the author's request the PDF is not available.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.