Network Pharmacology-Based Characterization of Mecasin (KCHO-1) as a Multi-Targe...

연구 요약

Network Pharmacology-Based Characterization of Mecasin (KCHO-1) as a Multi-Target Modulator of Neuroinflammatory Pathways in Alzheimer's Disease.

Nutrients 학술지에 발표된 이 연구는 Jo H, Shin J, Lee H 외 연구팀이 수행하였습니다.

이 연구는 'Network Pharmacology-Based Characterization of Mecasin (KCHO-1) as a Multi-Target Modulator of Neuroinflammatory Pathways in Alzheimer's Disease.'에 대한 과학적 분석을 제공합니다.

핵심 내용

Background/Objectives: Mecasin (KCHO-1) is a standardized multi-herb formulation containing diverse bioactive compounds predicted to engage multiple molecular targets. This study applied an integrative network pharmacology approach to explore how Mecasin may interact with Alzheimer's disease (AD)-related molecular networks. Methods: Bioactive constituents from 9 herbs were screened through OASIS and PubChem, and their predicted targets were cross-referenced with 8886 AD-associated genes from GeneCards. Overlapping genes were analyzed using protein-protein interaction mapping, Gene Ontology, and KEGG to identify potential Mecasin-AD core nodes and pathways. Co-expression, co-regulation, and molecular docking analyses were performed to further characterize mechanistic relevance. Results: Network integration identified 6 core genes-AKT1, STAT3, IL6, TNF, EGFR, and IL1B-positioned within signaling pathways related to neuronal survival, inflammatory regulation, and cellular stress responses, including FoxO, JAK-STAT, MAPK, and TNF pathways. Molecular docking suggested that several Mecasin compounds may interact with targets such as AKT1 and TNF. Conclusions: These in silico findings indicate that Mecasin, a multi-component formulation containing numerous phytochemicals that generate broad compound-target associations, may interface with interconnected neuroimmune pathways relevant to AD. While exploratory, the results highlight potential multi-target mechanisms that merit further investigation and provide a systems-level framework to inform future experimental validation.

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