BACKGROUND
Due to the high cost and low success rate in new drug development, systematical drug repositioning methods are exploited to find new indications from existing drugs.
OBJECTIVE
We sought to propose a new computational drug repositioning method to identify repositioning drugs for Parkinson’s disease (PD).
METHODS
We developed a novel heterogeneous network mining repositioning method that constructed a three-layer network of disease, drug, and adverse drug reaction (ADR), 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 (ADRs) are potential intermediaries to reveal relationships between disease and drug.
Drug Repositioning for Parkinson�s Disease Based on Adverse Drug Reactions Detected from Social Media (Preprint)
