RESUMEN
A pharmacological network of "component/target/pathway" for Rhizoma coptidis against type 2 diabetes (T2D) was established by network-pharmacology, and the active components of Rhizoma coptidis and its mechanism were explored. A literature-based and database study of the components of Rhizoma coptidis was carried out and screened by ADME parameters. The targets of Rhizoma coptidis were predicted by the ligand similarity method. Related pathways were analyzed with databases, and software was used to construct a "component/target/path" network. The mechanism was further confirmed by GEO database with R software. A total of 12 active components were screened from Rhizoma coptidis, involving 57 targets including MAPK1, STAT3, INSR, and 38 signaling pathways were associated with T2D. Related signaling pathways included essential pathways for T2D such as insulin resistance, and pathways that had indirect effect on T2D. It was suggested that Rhizoma coptidis may exert its effects against T2D through multi-component, multi-target, and multi-pathway forms.
Asunto(s)
Biología Computacional/métodos , Diabetes Mellitus Tipo 2/metabolismo , Medicamentos Herbarios Chinos/química , Redes Reguladoras de Genes/efectos de los fármacos , Fitoquímicos/farmacología , Diabetes Mellitus Tipo 2/tratamiento farmacológico , Evaluación Preclínica de Medicamentos , Humanos , Resistencia a la Insulina , Fitoquímicos/aislamiento & purificación , Transducción de Señal/efectos de los fármacosRESUMEN
Albiziae Flos (AF) has been experimentally proven to have an antidepressant effect. However, due to the complexity of botanical ingredients, the exact pharmacological mechanism of action of AF in depression has not been completely deciphered. This study used the network pharmacology method to construct a component-target-pathway network to explore the active components and potential mechanisms of action of AF. The methods included collection and screening of chemical components, prediction of depression-associated targets of the active components, gene enrichment, and network construction and analysis. Quercetin and 4 other active components were found to exert antidepressant effects mainly via monoaminergic neurotransmitters and cAMP signaling and neuroactive ligand-receptor interaction pathways. DRD2, HTR1A, and SLC6A4 were identified as important targets of the studied bioactive components of AF. This network pharmacology analysis provides guidance for further study of the antidepressant mechanism of AF.