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Analysis of potential role of Chinese classic prescriptions in treatment of COVID-19 based on TCMATCOV platform #MMPMID32726008
Tang X; Tong L; Guo FF; Tang SH; Yang HJ
Zhongguo Zhong Yao Za Zhi 2020[Jul]; 45 (13): 3028-3034 PMID32726008show ga
With the global outbreak of coronavirus disease 2019(COVID-19), screening of effective drugs has became the emphasis of research today; furthermore, screening of Chinese classic prescriptions has became one of the directions for drug development. This study analyzed the application of classic prescriptions in the diagnosis and treatment schemes based on the Diagnosis and Treatment Schemes for Coronavirus Disease at the country, provincial and municipal levels, and further explored its disrobing effect on COVID-19 disease severe phase network, and selected representative prescriptions for core target screening and gene enrichment analysis, so as to reveal its mechanism of action. Among them, 13 prescriptions were found to be used for 10 times or more, including Maxing Shigan Tang, Yinqiao San, Shengjiang San, Dayuan Drink, Xuanbai Chengqi Decoction. In addition, the COVID-19 efficacy prediction analysis platform(TCMATCOV platform) was used to calculate the network disturbances of the Chinese classic prescriptions involved. Based on the prediction results, 68 classic prescriptions were assessed on the COVID-19 disease network robustness disturbance. The average disturbance scores for the interaction confidence scores were ranked to be 0.4, 0.5, and 0.6 from the highest to the lowest. There were 7 prescriptions with a score of 17 or more, and 50 prescriptions with a score of 13 or more. Among them, the top three prescriptions were Ganlu Xiaodu Dan(18.19), Lengxiao Wan(17.74), and Maxing Shigan Tang(17.62). After further mining the action targets of these three prescriptions, it was found that COVID-19 disease-specific factors Ccl2, IL10, IL6 and TNF were all the targets of three prescriptions. Through the enrichment analysis of the biological processes of the core targets, it was found that the three prescriptions may prevent the development of the disease by affecting cell-to-cell adhesion, cytokine-mediated signaling pathway, and chronic inflammatory responses to COVID-19 at the severe phase. This study showed that the TCMATCOV platform could evaluate the disturbance effect of different prescriptions on the COVID-19 disease network, and predict potential effectiveness based on the robustness of drug-interfered pneumonia disease networks, so as to provide a reference for further experiments or clinical verification.
Zhongguo+Zhong+Yao+Za+Zhi 2020 ; 45 (13): 3028-3034
