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Alzheimer’s disease (AD) is an increasing concern in human health. Despite significant research, highly effective drugs to treat AD are lacking. The present study describes the text mining process to identify drug candidates from a traditional Chinese medicine (TCM) database, along with associated protein target mechanisms. We carried out text mining to identify literatures that referenced both AD and TCM and focused on identifying compounds and protein targets of interest. After targeting one potential TCM candidate, corresponding protein-protein interaction (PPI) networks were assembled in STRING to decipher the most possible mechanism of action. This was followed by validation using Western blot and co-immunoprecipitation in an AD cell model. The text mining strategy using a vast amount of AD-related literature and the TCM database identified curcumin, whose major component was ferulic acid (FA). This was used as a key candidate compound for further study. Using the top calculated interaction score in STRING, BACE1 and MMP2 were implicated in the activity of FA in AD. Exposure of SHSY5Y-APP cells to FA resulted in the decrease in expression levels of BACE-1 and APP, while the expression of MMP-2 and MMP-9 increased in a dose-dependent manner. This suggests that FA induced BACE1 and MMP2 pathways maybe novel potential mechanisms involved in AD. The text mining of literature and TCM database related to AD suggested FA as a promising TCM ingredient for the treatment of AD. Potential mechanisms interconnected and integrated with Aβ aggregation inhibition and extracellular matrix remodeling underlying the activity of FA were identified using  in vitro  studies.

Application of Ferulic Acid for Alzheimer’s Disease: Combination of Text Mining and Experimental Validation