A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus
Drug repositioning is a strategy for identifying new applications of an existing drug that has been previously proven to be safe. Based on several examples of drug repositioning, we aimed to determine the methodologies and relevant steps associated with drug repositioning that should be pursued in t...
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| Format: | Article |
| Language: | English |
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Korean Endocrine Society
2022-04-01
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| Series: | Endocrinology and Metabolism |
| Subjects: | |
| Online Access: | http://www.e-enm.org/upload/pdf/enm-2022-1404.pdf |
| _version_ | 1811302868435599360 |
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| author | Suehyun Lee Seongwoo Jeon Hun-Sung Kim |
| author_facet | Suehyun Lee Seongwoo Jeon Hun-Sung Kim |
| author_sort | Suehyun Lee |
| collection | DOAJ |
| description | Drug repositioning is a strategy for identifying new applications of an existing drug that has been previously proven to be safe. Based on several examples of drug repositioning, we aimed to determine the methodologies and relevant steps associated with drug repositioning that should be pursued in the future. Reports on drug repositioning, retrieved from PubMed from January 2011 to December 2020, were classified based on an analysis of the methodology and reviewed by experts. Among various drug repositioning methods, the network-based approach was the most common (38.0%, 186/490 cases), followed by machine learning/deep learning-based (34.3%, 168/490 cases), text mining-based (7.1%, 35/490 cases), semantic-based (5.3%, 26/490 cases), and others (15.3%, 75/490 cases). Although drug repositioning offers several advantages, its implementation is curtailed by the need for prior, conclusive clinical proof. This approach requires the construction of various databases, and a deep understanding of the process underlying repositioning is quintessential. An in-depth understanding of drug repositioning could reduce the time, cost, and risks inherent to early drug development, providing reliable scientific evidence. Furthermore, regarding patient safety, drug repurposing might allow the discovery of new relationships between drugs and diseases. |
| first_indexed | 2024-04-13T07:37:05Z |
| format | Article |
| id | doaj.art-7aa22bfe25494dc6a0c16a1da55d8594 |
| institution | Directory Open Access Journal |
| issn | 2093-596X 2093-5978 |
| language | English |
| last_indexed | 2024-04-13T07:37:05Z |
| publishDate | 2022-04-01 |
| publisher | Korean Endocrine Society |
| record_format | Article |
| series | Endocrinology and Metabolism |
| spelling | doaj.art-7aa22bfe25494dc6a0c16a1da55d85942022-12-22T02:56:07ZengKorean Endocrine SocietyEndocrinology and Metabolism2093-596X2093-59782022-04-0137219520710.3803/EnM.2022.14042283A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes MellitusSuehyun Lee0Seongwoo Jeon1Hun-Sung Kim2 Department of Biomedical Informatics, Konyang University College of Medicine, Daejeon, Korea Health Care Data Science Center, Konyang University Hospital, Daejeon, Korea Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, KoreaDrug repositioning is a strategy for identifying new applications of an existing drug that has been previously proven to be safe. Based on several examples of drug repositioning, we aimed to determine the methodologies and relevant steps associated with drug repositioning that should be pursued in the future. Reports on drug repositioning, retrieved from PubMed from January 2011 to December 2020, were classified based on an analysis of the methodology and reviewed by experts. Among various drug repositioning methods, the network-based approach was the most common (38.0%, 186/490 cases), followed by machine learning/deep learning-based (34.3%, 168/490 cases), text mining-based (7.1%, 35/490 cases), semantic-based (5.3%, 26/490 cases), and others (15.3%, 75/490 cases). Although drug repositioning offers several advantages, its implementation is curtailed by the need for prior, conclusive clinical proof. This approach requires the construction of various databases, and a deep understanding of the process underlying repositioning is quintessential. An in-depth understanding of drug repositioning could reduce the time, cost, and risks inherent to early drug development, providing reliable scientific evidence. Furthermore, regarding patient safety, drug repurposing might allow the discovery of new relationships between drugs and diseases.http://www.e-enm.org/upload/pdf/enm-2022-1404.pdfdrug repositioningsemanticsmachine learningreal-world datadata science |
| spellingShingle | Suehyun Lee Seongwoo Jeon Hun-Sung Kim A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus Endocrinology and Metabolism drug repositioning semantics machine learning real-world data data science |
| title | A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus |
| title_full | A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus |
| title_fullStr | A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus |
| title_full_unstemmed | A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus |
| title_short | A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus |
| title_sort | study on methodologies of drug repositioning using biomedical big data a focus on diabetes mellitus |
| topic | drug repositioning semantics machine learning real-world data data science |
| url | http://www.e-enm.org/upload/pdf/enm-2022-1404.pdf |
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