Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study.
BACKGROUND
Drug repurposing (defined as discovering new indications for existing drugs) could play a significant role in drug development, especially considering the declining success rates of developing novel drugs. Typically, new indications for existing medications are identified by accident. However, new technologies and a large number of available resources enable the development of systematic approaches to identify and validate drug-repurposing candidates. Patients today report their experiences with medications on social media and reveal side effects as well as beneficial effects of those medications.


OBJECTIVE
Our aim was to assess the feasibility of using patient reviews from social media to identify potential candidates for drug repurposing.


METHODS
We retrieved patient reviews of 180 medications from an online forum, WebMD. Using dictionary-based and machine learning approaches, we identified disease names in the reviews. Several publicly available resources were used to exclude comments containing known indications and adverse drug effects. After manually reviewing some of the remaining comments, we implemented a rule-based system to identify beneficial effects.


RESULTS
The dictionary-based system and machine learning system identified 2178 and 6171 disease names respectively in 64,616 patient comments. We provided a list of 10 common patterns that patients used to report any beneficial effects or uses of medication. After manually reviewing the comments tagged by our rule-based system, we identified five potential drug repurposing candidates.


CONCLUSIONS
To our knowledge, this is the first study to consider using social media data to identify drug-repurposing candidates. We found that even a rule-based system, with a limited number of rules, could identify beneficial effect mentions in patient comments. Our preliminary study shows that social media has the potential to be used in drug repurposing.
Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, United States. rastegar.83@gmail.com.
Majid
http://orcid.org/0000-0001-6962-3554
M
Rastegar-Mojarad
Hongfang
http://orcid.org/0000-0003-2570-3741
H
Liu
Priya
http://orcid.org/0000-0003-4918-2813
P
Nambisan
drug repurposing; natural language processing; patient comments; social media