Yishu Liu , Xue Li , Chao Chen , Nan Ding , Shiyu Ma , Ming Yang
{"title":"利用网络药理学探索中药配方的相容性规则并发现其中的有效成分","authors":"Yishu Liu , Xue Li , Chao Chen , Nan Ding , Shiyu Ma , Ming Yang","doi":"10.1016/j.chmed.2023.09.008","DOIUrl":null,"url":null,"abstract":"<div><div>Network pharmacology is an interdisciplinary field that utilizes computer science, technology, and biological networks to investigate the intricate interplay among compounds/ingredients, targets, and diseases. Within the realm of traditional Chinese medicine (TCM), network pharmacology serves as a scientific approach to elucidate the compatibility relationships and underlying mechanisms of action in TCM formulas. It facilitates the identification of potential active ingredients within these formulas, providing a comprehensive understanding of their holistic and systematic nature, which aligns with the holistic principles inherent in TCM theory. TCM formulas exhibit complexity due to their multi-component characteristic, involving diverse targets and pathways. Consequently, investigating their material basis and mechanisms becomes challenging. Network pharmacology has emerged as a valuable approach in TCM formula research, leveraging its holistic and systematic advantages. The manuscript aims to provide an overview of the application of network pharmacology in studying TCM formula compatibility rules and explore future research directions. Specifically, we focus on how network pharmacology aids in interpreting TCM pharmacological theories and understanding formula compositions. Additionally, we elucidate the process of utilizing network pharmacology to identify active ingredients within TCM formulas. These findings not only offer novel research models and perspectives for integrating network pharmacology with TCM theory but also present new methodologies for investigating TCM formula compatibility. All in all, network pharmacology has become an indispensable and crucial tool in advancing TCM formula research.</div></div>","PeriodicalId":9916,"journal":{"name":"Chinese Herbal Medicines","volume":"16 4","pages":"Pages 572-588"},"PeriodicalIF":4.7000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploration of compatibility rules and discovery of active ingredients in TCM formulas by network pharmacology\",\"authors\":\"Yishu Liu , Xue Li , Chao Chen , Nan Ding , Shiyu Ma , Ming Yang\",\"doi\":\"10.1016/j.chmed.2023.09.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Network pharmacology is an interdisciplinary field that utilizes computer science, technology, and biological networks to investigate the intricate interplay among compounds/ingredients, targets, and diseases. Within the realm of traditional Chinese medicine (TCM), network pharmacology serves as a scientific approach to elucidate the compatibility relationships and underlying mechanisms of action in TCM formulas. It facilitates the identification of potential active ingredients within these formulas, providing a comprehensive understanding of their holistic and systematic nature, which aligns with the holistic principles inherent in TCM theory. TCM formulas exhibit complexity due to their multi-component characteristic, involving diverse targets and pathways. Consequently, investigating their material basis and mechanisms becomes challenging. Network pharmacology has emerged as a valuable approach in TCM formula research, leveraging its holistic and systematic advantages. The manuscript aims to provide an overview of the application of network pharmacology in studying TCM formula compatibility rules and explore future research directions. Specifically, we focus on how network pharmacology aids in interpreting TCM pharmacological theories and understanding formula compositions. Additionally, we elucidate the process of utilizing network pharmacology to identify active ingredients within TCM formulas. These findings not only offer novel research models and perspectives for integrating network pharmacology with TCM theory but also present new methodologies for investigating TCM formula compatibility. All in all, network pharmacology has become an indispensable and crucial tool in advancing TCM formula research.</div></div>\",\"PeriodicalId\":9916,\"journal\":{\"name\":\"Chinese Herbal Medicines\",\"volume\":\"16 4\",\"pages\":\"Pages 572-588\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chinese Herbal Medicines\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1674638424000327\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MEDICINAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Herbal Medicines","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1674638424000327","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
Exploration of compatibility rules and discovery of active ingredients in TCM formulas by network pharmacology
Network pharmacology is an interdisciplinary field that utilizes computer science, technology, and biological networks to investigate the intricate interplay among compounds/ingredients, targets, and diseases. Within the realm of traditional Chinese medicine (TCM), network pharmacology serves as a scientific approach to elucidate the compatibility relationships and underlying mechanisms of action in TCM formulas. It facilitates the identification of potential active ingredients within these formulas, providing a comprehensive understanding of their holistic and systematic nature, which aligns with the holistic principles inherent in TCM theory. TCM formulas exhibit complexity due to their multi-component characteristic, involving diverse targets and pathways. Consequently, investigating their material basis and mechanisms becomes challenging. Network pharmacology has emerged as a valuable approach in TCM formula research, leveraging its holistic and systematic advantages. The manuscript aims to provide an overview of the application of network pharmacology in studying TCM formula compatibility rules and explore future research directions. Specifically, we focus on how network pharmacology aids in interpreting TCM pharmacological theories and understanding formula compositions. Additionally, we elucidate the process of utilizing network pharmacology to identify active ingredients within TCM formulas. These findings not only offer novel research models and perspectives for integrating network pharmacology with TCM theory but also present new methodologies for investigating TCM formula compatibility. All in all, network pharmacology has become an indispensable and crucial tool in advancing TCM formula research.
期刊介绍:
Chinese Herbal Medicines is intended to disseminate the latest developments and research progress in traditional and herbal medical sciences to researchers, practitioners, academics and administrators worldwide in the field of traditional and herbal medicines. The journal's international coverage ensures that research and progress from all regions of the world are widely included.
CHM is a core journal of Chinese science and technology. The journal entered into the ESCI database in 2017, and then was included in PMC, Scopus and other important international search systems. In 2019, CHM was successfully selected for the “China Science and Technology Journal Excellence Action Plan” project, which has markedly improved its international influence and industry popularity. CHM obtained the first impact factor of 3.8 in Journal Citation Reports (JCR) in 2023.