Mechanism prediction of Zhikepipa Mixture in the treatment of COVID-19 / 西安交通大学学报(医学版)

ABSTRACT

【Objective】 To explore the effect of Zhikepipa Mixture on the treatment of COVID-19 through network pharmacology analysis and molecular docking technology. 【Methods】 First, we performed the network pharmacology method to screen active compounds and targets so as to explore the mixture’s potential mechanisms in the treatment of COVID-19. In line with ADME screening index, like oral bioavailability (OB) ≥30% or drug likeness index (DL) ≥0.18, the active compounds against COVID-19 related targets were selected to construct the 'herb-compound-target’ network. Mechanism prediction of Zhikepipa mixture in the treatment of COVID-19 was analyzed by the interaction of the target sites, the bioinformatic annotation, and the metabolic pathway. Then, we used a molecular docking model to evaluate the binding ability between active compounds and 2019-nCoV (SARS-CoV-2) 3-chymotrypsin-like cysteine protease (3CLpro) receptor-binding domain (PBD ID 6LU7), which was involved in mediating viral replication and transcription functions. 【Results】 The'herb-compound-target’ network showed 34 key compounds and 30 disease targets after overlapping with disease targets. The network topology analysis showed that those selected compounds with higher degree would produce marked anti-inflammatory effects by regulating 30 targets like PTGS1, IL1B, IL6, IL10, CXCL8 and JUN. AGE-RAGE signaling pathway, IL-17 signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway, and MAPK signaling pathway were involved in regulating hepatitis B and diabetic complications. In addition, Folium eriobotryae and Radix stemonae played important roles in the network. The molecular docking results showed that nine compounds were identified with higher docking score rank against 2019-nCoV 3CLpro protease, and most of them were attributed to flavonoids. 【Conclusion】 Zhikepipa Mixture could exhibit both anti-inflammatory and anti-virus actions through multi-component, multi-target, and multi-pathway.