Decoding apoptosis-associated pathways in inflammatory and neurodegenerative diseases: A network pharmacology based drug discovery approach.
PMID 41082993 | DOI 10.1016/j.ejphar.2025.178231 · European journal of pharmacology · 2025 · View on PubMed ↗
Neurodegenerative diseases, particularly Alzheimer's disease (AD), continue to pose significant global health challenges due to their progressive nature, multifactorial etiology, and limited therapeutic options. Contemporary research increasingly emphasizes the potential of phytochemicals derived from medicinal plants in mitigating the complex pathology of such disorders. This study employs a comprehensive network pharmacology approach integrated with molecular docking and simulation techniques to explore the therapeutic potential of bioactive compounds from 12 ethnomedicinal plants, including Ginkgo biloba, Withania somnifera, Curcuma longa, and others. A total of 1218 potential targets were identified via SwissTargetPrediction, which were cross-referenced with Alzheimer's-related genes retrieved from OMIM and GeneCards databases, revealing 479 overlapping targets. Subsequently, protein-protein interaction (PPI) network analysis identified top hub genes including AKT1, CASP3, TNF, and BCL2 as central to disease modulation. Gene ontology and KEGG pathway enrichment analyses highlighted critical biological processes such as apoptosis regulation, inflammatory response, and synaptic signaling. Docking and simulation studies revealed strong binding affinities between selected phytochemicals such as Quercetin, Luteolin, Rosmarinic acid and core targets, suggesting their role in neuroprotection. Particularly, compounds like Quercetin and Rosmarinic acid demonstrated notable interactions with TNF and AKT1, as evidenced by their high docking scores and stable RMSD values. The plant-compound-target-pathway network further consolidated their multi-targeted regulatory potential. Overall, this integrated computational framework provides mechanistic insights into the neurotherapeutic actions of selected natural compounds, paving the way for future experimental validations and drug development strategies targeting Alzheimer's disease.
Validated evidence
| Type | Entity | Source evidence | Confidence | Extractor |
|---|---|---|---|---|
| gene | AKT1 | “Subsequently, protein-protein interaction (PPI) network analysis identified top hub genes including AKT1, CASP3, TNF, and BCL2 as central to disease modulation.” | 0.98 | hgnc_dict_v1 |
| gene | CASP3 | “Subsequently, protein-protein interaction (PPI) network analysis identified top hub genes including AKT1, CASP3, TNF, and BCL2 as central to disease modulation.” | 0.98 | hgnc_dict_v1 |
| gene | TNF | “Subsequently, protein-protein interaction (PPI) network analysis identified top hub genes including AKT1, CASP3, TNF, and BCL2 as central to disease modulation.” | 0.98 | hgnc_dict_v1 |
| gene | BCL2 | “Subsequently, protein-protein interaction (PPI) network analysis identified top hub genes including AKT1, CASP3, TNF, and BCL2 as central to disease modulation.” | 0.98 | hgnc_dict_v1 |
| phenotype | Alzheimer disease | “Neurodegenerative diseases, particularly Alzheimer's disease (AD), continue to pose significant global health challenges due to their progressive nature, multifactorial etiology, and limited therapeutic options.” | 0.93 | phenotype_alias_lexicon_v2 |