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.