Network Pharmacology Approach to Predict the Mechanisms of Loganin on Alzheimer Disease via Galectin-3 Inhibition

Introduction: Alzheimer's disease (AD) is the world's leading cause of neurolog-ical dysfunction, cognitive decline, and neuronal loss in the elderly. The deposition of beta amyloid (Aβ)-containing plaques, and the formation of tau-containing neurofibrillary tan-gles (NFTs), along with extensive neuroinflammation, are the events that characterize the pathogenesis of AD. The management of this disease remains a significant global challenge, as researchers continuously explore innovative treatment approaches, including the investi-gation of natural products. Loganin is one of the best-known iridoid glycosides. It is named for the Loganiaceae, having first been isolated from the seeds of a member of that plant family, namely those of Strychnos nux-vomica. It has been reported to have anti-inflamma-tory effects in various disease models such as Parkinson's disease and acute pancreatitis. Recent investigations have uncovered the preventive effects against Alzheimer's disease and its associated complications. This study focused on the therapeutic potential of loganin in the management of Alzheimer's disease. Methods: The potential molecular target of loganin was identified using the SwissTarget Prediction platform, and the disease target of Alzheimer's disease was identified using the GeneCards database. STRING, Cytoscape, and CB-Dock 2 were used to construct the net-work pharmacology. results: The Ven diagram assisted to shortlist top 10 genes from 91 overlapping genes, using Cytoscape, namely ALB, TNF, IL6,EGFR, SRC,IL2,HSP90AA1, MMP2,LGALS3 and REN , which was estimated by CytoHubba plug-in. Results: The Venn diagram assisted in shortlisting the top 10 genes from 91 overlapping genes, using Cytoscape, namely ALB, TNF, IL6, EGFR, SRC, IL2, HSP90AA1, MMP2, LGALS3, and REN, which was estimated by the CytoHubba plug-in. The molecular binding score of loganin was determined by using CB-Dock 2 and showed the highest binding energy of -8.7 kcal/mol for the LGALS3 (Galectin-3) protein compared with other proteins. discussion: The molecular binding score of loganin was determined by using CB-dock2 and showed highest biding energy -8.7 Kcal/mol for LGALS3 (Galectin 3) protein compared with other protein. Discussion: The integration of in silico network pharmacology with molecular docking thus provides a robust foundation for understanding the multi-target action of loganin, as it reg-ulates the proteins involved in pathways associated with Alzheimer's disease via Galectin-3, supporting the development of natural therapies for the efficient management of Alzhei-mer's disease. Conclusion: Combined with a healthy lifestyle, such natural interventions may offer a com-plementary strategy to help delay or mitigate Alzheimer’s disease progression.