Identification of anti-inflammatory components in Sinomenii Caulis based on spectrum-effect relationship and chemometric methods.

The present study aimed to identify the anti-inflammatory components in Sinomenii Caulis (SC) based on spectrum-effect relationship and chemometric methods. A phytochemical investigation of SC extract was performed firstly and afforded eleven potential bioactive compounds. The HPLC fingerprints of 19 batches of SC samples were evaluated by the chemometric methods such as similarity analysis (SA) and hierarchical clustering analysis (HCA). The anti-inflammatory effects of these samples were determined by inhibition of Nitric Oxide (NO) production. Partial least squares regression (PLSR) and artificial neural network (ANN) were used to explore the spectrum-effect relationship of SC. The results indicated that there was a close correlation between chemical fingerprint and anti-inflammatory activity of SC, and peaks 8, 9, 12, 13, 14, 16, 19 and 22 might be potential anti-inflammatory compounds in SC. The verification experiments by testing individual compounds and a combination of them indicated that sinomenine (P8), magnoflorine (P13), menisperine (P16) and stepharanine (P19) were the major anti-inflammatory compounds in SC. Collectively, the present study established the spectrum-effect relationship mode of SC and discovered the anti-inflammatory compounds in SC, which could be used for exploration of bioactive components and quality control of herbal medicines.
(Copyright © 2019. Published by Elsevier B.V.)