Mechanisms of main components in Curcuma longa L. on hepatic fibrosis based on network pharmacology and molecular docking: A review

Abstract

Background: Hepatic fibrosis is a great concern in public health. While effective drugs for its treatment are lacking, Curcuma longa L. (CL) has been reported as a promising therapeutic. We aimed to uncover the core components and mechanisms of CL against hepatic fibrosis via a network pharmacology approach. Methods: The main components of CL were obtained and screened. While targets of components and disease were respectively collected using SwissTargetPrediction and online databases, common targets were assessed. A protein–protein interaction (PPI) network was constructed, and core targets were identified. GO and KEGG pathway enrichment analyses were performed, and molecular docking was conducted to validate the binding of core components in CL on predicted core targets. Results: Nine main components from CL based on high-performance liquid chromatography (HPLC) and 63 anti-fibrosis targets were identified, and a PPI network and a component target-disease target network were constructed. Apigenin, quercetin, demethoxycurcumin, and curcumin are likely to become key phenolic-based components and curcuminoids for the treatment of hepatic fibrosis, respectively. KEGG pathway enrichment analysis revealed that the HIF-1 signaling pathway (hsa04066) was most significantly enriched. Considering core targets of the PPI network and a network of the common targets and pathways enriched, AKT1, MAPK1, EGFR, MTOR, and SRC may be the core potential targets of CL against hepatic fibrosis. Molecular docking was carried out to verify the binding of above core components to core targets. Conclusions: The therapeutic effect of CL on hepatic fibrosis may be attributed to multi-components, multi-targets, and multi-pathways.

Isoliquiritigenin Inhibits the Growth of Colorectal Cancer Cells through the ESR2/PI3K/AKT Signalling Pathway

“…Network pharmacology is one of the research strategies for developing new drugs as it helps to expand the space of available targets for drugs. It has been widely used in the research of new medicines, the prediction of mechanisms of drug action and the prediction of disease therapeutic targets [ 37 , 38 , 39 ]. Our study predicted the targets of ISL for the prevention and treatment of CRC through network pharmacological analysis.…”