The challenges and opportunities of traditional Chinese medicines against COVID-19: a way out from a network perspective
Hua-li Zuo1,2,3,
Yang-Chi-Dung Lin1,2,
Hsi-Yuan Huang1,2,
Xu Wang4,
Yun Tang1,2,
Yuan-jia Hu5,
Xiang-jun Kong5,
Qian-jun Chen6,7,
Yu-zhu Zhang6,7,
Hsiao-Chin Hong1,2,
Jing Li1,2,
Si-yao Hu1,2,
Hsien-Da Huang1,2
1 Warshel Institute for Computational Biology, The Chinese University of Hong Kong-Shenzhen, Shenzhen 518172, China
2 School of Life and Health Sciences, The Chinese University of Hong Kong-Shenzhen, Shenzhen 518172, China
3 School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China
4 School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331, China
5 State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao SAR 999078, China
6 Breast Department, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou 510120, China
7 The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510300, China
Correspondence to: Hsien-Da Huang: huanghsienda@cuhk.edu.cn,
DOI: 10.1038/s41401-021-00645-0
Received: 21 January 2021
Accepted: 10 March 2021
Advance online: 13 April 2021
Abstract
The purpose of this perspective is to provide insights into the current challenges and opportunities of applying network pharmacology (NP) to illustrate the effectiveness of traditional Chinese medicines (TCMs) against the coronavirus disease 2019 (COVID-19). Emerging studies have indicated that the progression of COVID-19 is associated with hematologic and immunologic responses in patients, and TCMs may fight against COVID-19 regarding the two aspects. However, the underlying mechanisms remain largely unclear [1]. This perspective is intended as a brief report derived from our previous experience in investigating the efficacy of TCMs, via conventional reductionism-based research methods, holistic NP, systems biology, or “omics” research, and prevailing big data analysis.
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