Formononetin for COVID-19

Abstract Objective: Qu Du Qiang Fei 1 Hao Fang (QDQF1) is a novel Chinese herbal medicine formula used to treat coronavirus disease 2019 (COVID-19). However, the pharmacological mechanisms of action of QDQF1 remain unclear. The objective of this study was to identify the effective ingredients and biological targets of QDQF1 for COVID-19 treatment. Materials and Methods: The effective ingredients and mechanisms of action of QDQF1 were analyzed by using network pharmacology methods, which included an analysis of the effective ingredients and corresponding targets, COVID-19-related target acquisition, compound-target network analyses, protein-protein interaction network analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses, and molecular docking studies. Results: In total, 288 effective QDQF1 ingredients were identified. We identified 51 core targets from the 148 targets through an overlap between putative QDQF1 targets and COVID-19-related targets. Six key components, including formononetin, kaempferol, luteolin, naringenin, quercetin, and wogonin were identified through component-target network analyses. GO functional enrichment analysis of the core targets revealed 1296 items, while KEGG pathway enrichment analysis identified 148 signaling pathways. Nine central targets (CCL2, CXCL8, IL1B, IL6, MAPK1, MAPK3, MAPK8, STAT3, and TNF) related to the COVID-19 pathway were identified in the KEGG pathway enrichment analysis. Furthermore, molecular docking analysis suggested that the docking scores of the six key components to the nine central targets were better than those to remdesivir. Conclusions: QDQF1 may regulate multiple immune-and inflammation-related targets to inhibit the progression of severe acute respiratory syndrome coronavirus 2, and thus, may be suitable for the treatment of COVID-19.

To explore the potential mechanism of Gegen Qinlian decoction (GGQL) in the treatment of COVID-19 comorbid with diabetes mellitus (DM) through network pharmacology and molecular docking, and to provide theoretical guidance for clinical transformation research. Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform was used to screen the active compounds and targets of GGQL, the targets of COVID-19 comorbid with DM were searched based on Genecards database. Protein-protein interaction network was constructed using String data platform for the intersection of compounds and disease targets, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis of the intersection targets was performed using DAVID database. Cytoscape software was used to construct the “compound target-pathway (C-T-P)” of GGQL in the treatment of COVID-19 comorbid with DM, the molecular docking platform was used to complete the simulated docking of key compounds and targets. We obtained 141 compounds from GGQL, revealed 127 bioactive compounds and 283 potential targets of GGQL. Quercetin, kaempferol and formononetin in GGQL play a role by modulating the targets (including AR, GSK3B, DPP4, F2, and NOS3). GGQL might affect diverse signaling pathways related to the pathogenesis of coronavirus disease – COVID-19, AGE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, human cytomegalovirus infection and Th17 cell differentiation. Meanwhile, molecular docking showed that the selected GGQL core active components had strong binding activity with the key targets. This study revealed that GGQL play a role in the treatment of COVID-19 comorbid with DM through multi-component, multi-target and multi-pathway mode of action, which provided good theoretical basis for further verification research.