The potential mechanism of Bupleurum against anxiety was predicted by network pharmacology study and molecular docking.

Bupleurum chinense DC. (Chaihu) is a traditional Chinese medicine (TCM) used in the treatment of anxiety. But the anxiolytic mechanisms of bupleurum are still unclear. Therefore, this unknown is predicted by network pharmacology study with molecular docking in the present study. The components of bupleurum were obtained from the databases. Genes associated with components and disease were also provided by databases. Overlapping genes between components and disease were analyzed. The network of medicine-components-targets-disease was constructed, visualized, and analyzed. Protein-protein interaction (PPI), gene ontology (GO), pathway enrichment (KEGG) and molecular docking were conducted to predict the potential mechanisms of bupleurum on anxiety. A total of 9 bioactive components derived from bupleurum with 80 target genes were involved in anxiety. Neurotransmitter receptor activity, G protein-coupled amine receptor activity, regulation of blood circulation, neuroactive ligand-receptor interaction, calcium signaling pathway and salivary secretion may play significant roles in the anxiolytic of bupleurum. Molecular docking implicated that ACHE and MAOA showed high affinity for stigmasterol. Based on network pharmacology study with molecular docking, multi-component-multi-target-multi-pathway action mode of bupleurum on anxiety was elaborated. Stigmasterol might be the core bioactive component, while ACHE and MAOA might be the core target genes in the pharmacological profile of bupleurum on anxiety.

Exploring the Potential Antidepressant Mechanisms of Pinellia by Using the Network Pharmacology and Molecular Docking.

About 350 million people worldwide suffered from depression, but less than half of the patients received effective and regular treatments. Traditional Chinese Medicine (TCM) such as pinellia has been proven effective for antidepressant treatment with fewer side effects. However, the exact mechanisms remain unclear. Herein, we use the methods of network pharmacology and molecular docking to analyze the effective monomer components of pinellia and reveal the involved signaling pathways to produce antidepressant effects. TCMSP, BATMAN-TCM, and TCMID databases were utilized to analyze the bioactive ingredients and target genes derived from pinellia via the screening the molecular weight (MW), oral bioavailability (OB), blood-brain barrier (BBB) and drug similarity (DL). OMIM, TTD, DisGeNET, GeneCards and DrugBank databases were used to obtain key genes of depression. Then, the networks of protein-protein interaction (PPI) and "medicine-ingredients-targets-pathways" were built. The target signaling pathways were enriched by GO and KEGG by using R language. Furthermore, bioactive ingredients binding of the targets were verified by molecular docking. Nine active monomer ingredients and 96 pivotal gene targets were selected from pinellia. 10,124 disease genes and 87 drug-disease intersecting genes were verified. GO analysis proposed that the receptor activity of neurotransmitter, postsynaptic neurotransmitter, G protein-coupled neurotransmitter, and acetylcholine through the postsynaptic membrane could be modulated by pinellia. KEGG pathway analysis revealed that pinellia influenced depression-related neural tissue interaction, cholinergic synapse, serotonin activated synapse and calcium signaling pathway. Besides, the reliability and accuracy of results obtained from the indirect network pharmacology were validated by molecular docking. The bioactive components of pinellia made significant antidepressant effects by regulating the key target genes/proteins in the pathophysiology of depression.