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.

[1]  Taha A. Kass-Hout,et al.  Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter , 2014, Drug Safety.

[2]  Joel Dudley,et al.  Exploiting drug-disease relationships for computational drug repositioning , 2011, Briefings Bioinform..

[3]  Olivier Bodenreider,et al.  The Unified Medical Language System (UMLS): integrating biomedical terminology , 2004, Nucleic Acids Res..

[4]  L. Cardon,et al.  Use of genome-wide association studies for drug repositioning , 2012, Nature Biotechnology.

[5]  Yanli Wang,et al.  PubChem applications in drug discovery: a bibliometric analysis. , 2014, Drug discovery today.

[6]  Simon M Lin,et al.  Opportunities for drug repositioning from phenome-wide association studies , 2015, Nature Biotechnology.

[7]  P Ryan,et al.  Novel Data‐Mining Methodologies for Adverse Drug Event Discovery and Analysis , 2012, Clinical pharmacology and therapeutics.

[8]  Stephen T. C. Wong,et al.  Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. , 2014, Drug discovery today.

[9]  Laetitia Martin-Chanas,et al.  Identify drug repurposing candidates by mining the Protein Data Bank , 2011, Briefings Bioinform..

[10]  Johannes M Freudenberg,et al.  Mining emerging biomedical literature for understanding disease associations in drug discovery. , 2014, Methods in molecular biology.

[11]  Alan R. Aronson,et al.  Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program , 2001, AMIA.

[12]  Paul N. Schofield,et al.  Linking PharmGKB to Phenotype Studies and Animal Models of Disease for Drug Repurposing , 2011, Pacific Symposium on Biocomputing.

[13]  Ying Li,et al.  Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality , 2014, J. Am. Medical Informatics Assoc..

[14]  Van V. Brantner,et al.  Estimating the cost of new drug development: is it really 802 million dollars? , 2006, Health affairs.

[15]  Rong Xu,et al.  Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing , 2013, BMC Bioinformatics.

[16]  Jagruti Patel,et al.  Systematic drug repurposing through text mining. , 2014, Methods in molecular biology.

[17]  E. Tobinick.  The value of drug repositioning in the current pharmaceutical market. , 2009, Drug news & perspectives.

[18]  Simon M. Lin,et al.  Collecting and Analyzing Patient Experiences of Health Care From Social Media , 2015, JMIR research protocols.

[19]  Philip R. O. Payne,et al.  ‘RE:fine drugs’: an interactive dashboard to access drug repurposing opportunities , 2016, Database J. Biol. Databases Curation.

[20]  Jian Yang,et al.  Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts in Health-Related Social Networks , 2010, BioNLP@ACL.

[21]  Ksenia Oguievetskaia,et al.  Computational Fragment-Based Approach at PDB Scale by Protein Local Similarity , 2009, J. Chem. Inf. Model..

[22]  Richard B. Berlin,et al.  Predicting adverse drug events from personal health messages. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[23]  David S. Wishart,et al.  DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs , 2010, Nucleic Acids Res..

[24]  P. Bork,et al.  A side effect resource to capture phenotypic effects of drugs , 2010, Molecular systems biology.

[25]  Sarvnaz Karimi,et al.  Cadec: A corpus of adverse drug event annotations , 2015, J. Biomed. Informatics.

[26]  Christopher C. Yang,et al.  Social media mining for drug safety signal detection , 2012, SHB '12.

[27]  A. Persidis,et al.  Drug repurposing and adverse event prediction using high‐throughput literature analysis , 2011, Wiley interdisciplinary reviews. Systems biology and medicine.

[28]  Yanli Wang,et al.  PubChem BioAssay: 2014 update , 2013, Nucleic Acids Res..