Integrating Network Pharmacology and Component Analysis to Study the Potential Mechanisms of Qi-Fu-Yin Decoction in Treating Alzheimer's Disease

Network Pharmacology and Molecular Docking Identify the Potential Mechanism and Therapeutic Role of Scutellaria baicalensis in Alzheimer's Disease

Aim

Scutellaria baicalensis, a traditional Chinese medicinal herb renowned for its anti-inflammatory, antioxidant, and anti-tumor properties, has shown promise in alleviating cognitive impairment associated with Alzheimer's disease. Nonetheless, the exact neuroprotective mechanism of Scutellaria baicalensis against Alzheimer's disease remains unclear. In this study, network pharmacology was employed to explore the possible mechanisms by which Scutellaria baicalensis protects against Alzheimer's disease.

Methods

The active compounds of Scutellaria baicalensis were retrieved from the TCMSP database, and their corresponding targets were identified. Alzheimer's disease-related targets were obtained through searches in the GeneCards and OMIM databases. Cytoscape 3.6.0 software was utilized to construct a regulatory network illustrating the "active ingredient-target" relationships. Subsequently, the target genes affected by Scutellaria baicalensis in the context of Alzheimer's disease were input into the String database to establish a PPI network. GO analysis and KEGG analysis were conducted using the DAVID database to predict the potential pathways associated with these key targets. Following this, the capacity of these active ingredients to bind to core targets was confirmed through molecular docking. In vitro experiments were then carried out for further validation.

Results

A total of 36 active ingredients from Scutellaria baicalensis were screened out, which corresponded to 365 targets. Molecular docking results demonstrated the robust binding abilities of Baicalein, Wogonin, and 5,2'-Dihydroxy-6,7,8-trimethoxyflavone to key target proteins (SRC, PIK3R1, and STAT3). In vitro experiments showed that the active components of Scutellaria baicalensis can inhibit STAT3 expression by downregulating the PIK3R1/SRC pathway in Neuro 2A cells.

Conclusion

In summary, these findings collectively suggest that Scutellaria baicalensis holds promise as a viable treatment option for Alzheimer's disease.

Machine learning in Alzheimer's disease drug discovery and target identification

Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.