Bibliometric Analysis of Network Pharmacology in Traditional Chinese Medicine. (15th June 2022)

• Record Type: Journal Article
• Authors: Miao, Runpei; Meng, Qinggang; Wang, Chennan; Yuan, Wenjing
• Other Names: Abdel-Tawab Mona Academic Editor.
• Is Part Of: Evidence-based complementary and alternative medicine. Volume 2022(2022)
• Publication Date: 2022-06-15
• Subjects: Alternative medicine -- Periodicals; 615.505
• DOI: 10.1155/2022/1583773
• Languages: English
• ISSNs: 1741-427X

Abstract

Abstract : Aim . We evaluated the developmental process, research status, and existing challenges of network pharmacology. Moreover, we elucidated the corresponding solutions to improve and develop network pharmacology. Methods . Research data for the current study were retrieved from the Web of Science. The developmental process of network pharmacology was analyzed using HisCite, whereas cooccurrence analysis of countries, institutions, keywords, and references in literature was conducted using CiteSpace. Results . In literature, there was a trend of annual increase of studies on network pharmacology and China was found to be the country with the most published literature on network pharmacology. The main publishing research institutions were universities of traditional Chinese medicine (TCM). The keywords with more research frequency were TCM, mechanisms, molecular docking, and quercetin, among others. Conclusion . Currently, studies on network pharmacology are mainly associated with the exploration of action mechanisms of TCM. The main active ingredient in many Chinese medicines is quercetin. This ingredient may lead to deviation of research results, inability to truly analyze active ingredients, and even mislead the research direction of TCM. Such deviation may be because the database fails to reflect the content and composition changes of Chinese medicinal components. The database does not account for interactions among components, targets, and diseases, and it ignores the different pathological states of the disease. Therefore, network pharmacology should be improved from the databases and research methods.