Data Mining and Network Pharmacology on Tibetan Medicine Prescriptions for the Prevention and Treatment of COVID-19

In this paper, the compatibility rules and drug combinations of Tibetan medicine prescriptions are explored, and the possible anti-epidemic mechanism is analyzed from the perspectives of biological network and signaling pathway, so as to provide reference for scientifically elaborating the potential value of ancient Tibetan medicine in preventing epidemic diseases. Association rules and clustering analysis are used for Tibetan drug mining. A total of 18 prescriptions involving 113 Tibetan medicinal materials were included, and 26 high-frequency Tibetan medicinal materials with the statistical frequency ⩾3 are included, most of which are medicinal materials for clearing heat, detoxicating, eliminating plague. Thirteen potential drug combinations are obtained through association rule analysis. The KMean clustering and hierarchical clustering were used for clustering analysis to obtain five drug clusters, and the "Bamusaeconcretiosilicea - Carthami Flos" combination was selected for network pharmacology research by comparing the two methods. After drug target and pathway analysis, "Bamusaeconcretiosilicea - Carthami Flos" through MAPK cascade, response to oxygen level, reactive oxygen species metabolism process, PI3K-Akt signaling pathway, NF- κ B signaling pathway, cytokine-cytokine Receptor interaction and calcium signaling pathways have certain feasibility for the treatment of immune disease fever in three aspects: immune response, inflammatory response, and oxidative stress.