Network pharmacology-based study on the mechanism of action for herbal medicines in Alzheimer treatment.

ETHNOPHARMACOLOGICAL RELEVANCE: Alzheimer's disease (AD), as the most common type of dementia, has brought a heavy economic burden to healthcare system around the world. However, currently there is still lack of effective treatment for AD patients. Herbal medicines, featured as multiple herbs, ingredients and targets, have accumulated a great deal of valuable experience in treating AD although the exact molecular mechanisms are still unclear.

MATERIALS AND METHODS: In this investigation, we proposed a network pharmacology-based method, which combined large-scale text-mining, drug-likeness filtering, target prediction and network analysis to decipher the mechanisms of action for the most widely studied medicinal herbs in AD treatment.

RESULTS: The text mining of PubMed resulted in 10 herbs exhibiting significant correlations with AD. Subsequently, after drug-likeness filtering, 1016 compounds were remaining for 10 herbs, followed by structure clustering to sum up chemical scaffolds of herb ingredients. Based on target prediction results performed by our in-house protocol named AlzhCPI, compound-target (C-T) and target-pathway (T-P) networks were constructed to decipher the mechanism of action for anti-AD herbs.

CONCLUSIONS: Overall, this approach provided a novel strategy to explore the mechanisms of herbal medicine from a holistic perspective.