An UHPLC-HRMS-based metabolite profiling approach to detect potentially anti-inflammatory active compounds in Chinese Lonicera species

In this study, the feasibility of a metabolite profiling approach to detect compounds with potential anti-inflammatory activity from Chinese Lonicera species has been assessed.

Ethanolic leaf extracts from eight Chinese Lonicera species (36 accessions) were phytochemically analyzed by UHPLC-HRMS and pharmacologically tested in four different cellular in-vitro assays related to inflammatory processes (NF-κB- and PPARγ-activation, IL8- and NO- production). Phytochemical and pharmacological datasets were correlated by orthogonal projection to latent structures discriminant analysis (OPLS-DA) and candidate compounds potentially related to pharmacological activity were deduced from the respective S-plots. Overall, 65 candidate compounds from different chemical classes were assigned on the basis of mass spectrometry data, and eight of them (one flavone, three bioflavonoids and four long-chain polyhydroxy fatty acids) were isolated from L. hypoglauca leaves. In a test set of 15 candidate compounds, the activities derived from the OPLS-DA models could be partially verified. Several of the tested compounds indeed showed anti-inflammatory activity, but some of them also negatively influenced cell viability. In summary, UHPLC-HRMS-metabolite profiling in combination with OPLS-DA was a feasible strategy for obtaining candidate compounds potentially involved in pharmacological activity, but it may need further optimization to enhance its accuracy.

Funding

The authors gratefully acknowledge the funding provided by the Austrian Science Fund (FWF):

S 10705 and S 10704 (NFN “Drugs from Nature Targeting Inflammation”)

Publication History

Article published online:
13 December 2021