Therapeutic mechanism of Convolvulus pluricaulis against Alzheimer's disease: Network pharmacology and molecular dynamics approach

Alzheimer disease (AD) is a type of dementia most prevalent in the elderly. A perennial herb, Convolvulus pluricaulis Choisy (C. pluricaulis) is used as a traditional folk medicine to treat AD and other neurodegenerative diseases, but the mechanism of action for its treatment has not been determined. This study attempts to clarify the mechanism of AD by investigating the phytochemical constituents of C. pluricaulis and their effect on it. In order to screen the active components of C. pluricaulis, values of oral bioavailability (OB) >= 30 % and Drug Like (DL) >= 0.18 was used. STRING and the Cytoscape platform have been used to construct an interactive network of targets and compounds. The network has been analyzed by Cytohubba, whereas GO functionality, KEGG pathways and metabolic pathway enrichment were performed using SRplot. The efficient targets were identified via Maximal Clique Centrality (MCC) and Degree using CytoHubba. The possible correlation between amyloid ,8-protein (A,8), Tau pathology and identified efficient targets was determined via Alzdata database, and finally the core targets were analyzed. Molecular docking analysis and molecular dynamics (MD) simulation was further used in order to determine the binding affinity between the active ingredients and the core targets. Through a network pharmacological approach, we identified key genes in cytoscape. C. pluricaulis targets 30 signaling pathways and diseases associated with AD. Based on the overall results of the present study, it was found that all 4 bioactive compounds were identified to target GSK3,8, and proteinligand complex was shown to be stable for 100 ns in a molecular dynamics simulation and exhibited relativistic conformational dynamics on the trajectory. Thus, the present study suggests that modulating GSK3,8 with natural compounds is an attractive approach in treating AD and other neurodegenerative diseases after required validation. (c) 2024 SAAB. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.