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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. Ho...

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Autores principales: Rastegar-Mojarad, Majid, Liu, Hongfang, Nambisan, Priya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4929348/
https://www.ncbi.nlm.nih.gov/pubmed/27311964
http://dx.doi.org/10.2196/resprot.5621
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author Rastegar-Mojarad, Majid
Liu, Hongfang
Nambisan, Priya
author_facet Rastegar-Mojarad, Majid
Liu, Hongfang
Nambisan, Priya
author_sort Rastegar-Mojarad, Majid
collection PubMed
description 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.
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spelling pubmed-49293482016-07-18 Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study Rastegar-Mojarad, Majid Liu, Hongfang Nambisan, Priya JMIR Res Protoc Original Paper 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. JMIR Publications 2016-06-16 /pmc/articles/PMC4929348/ /pubmed/27311964 http://dx.doi.org/10.2196/resprot.5621 Text en ©Majid Rastegar-Mojarad, Hongfang Liu, Priya Nambisan. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 16.06.2016. https://creativecommons.org/licenses/by/2.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/ (https://creativecommons.org/licenses/by/2.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Rastegar-Mojarad, Majid
Liu, Hongfang
Nambisan, Priya
Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study
title Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study
title_full Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study
title_fullStr Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study
title_full_unstemmed Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study
title_short Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study
title_sort using social media data to identify potential candidates for drug repurposing: a feasibility study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4929348/
https://www.ncbi.nlm.nih.gov/pubmed/27311964
http://dx.doi.org/10.2196/resprot.5621
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