Assessment of anti-inflammatory properties of extracts from Honeysuckle (Lonicera sp. L., Caprifoliaceae) by ATR-FTIR spectroscopy

Publication date: 1 December 2017
Source:Talanta, Volume 175
Author(s): R. Nikzad-Langerodi, S. Ortmann, E.M. Pferschy-Wenzig, V. Bochkov, Y.M. Zhao, J.H. Miao, J. Saukel, A. Ladurner, E.H. Heiss, V.M. Dirsch, R. Bauer, A.G. Atanasov
Inflammation is a hallmark of some of today's most life-threatening diseases such as arteriosclerosis, cancer, diabetes and Alzheimer's disease. Herbal medicines (HMs) are re-emerging resources in the fight against these conditions and for many of them, anti-inflammatory activity has been demonstrated. However, several aspects of HMs such as their multi-component character, natural variability and pharmacodynamic interactions (e.g. synergism) hamper identification of their bioactive constituents and thus the development of appropriate quality control (QC) workflows. In this study, we investigated the potential use of Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy as a tool to rapidly and non-destructively assess different anti-inflammatory properties of ethanolic extracts from various species of the Genus Lonicera (Caprifoliaceae). Reference measurements for multivariate calibration comprised in vitro bioactivity of crude extracts towards four key players of inflammation: Nitric oxide (NO), interleukin 8 (IL-8), peroxisome proliferator-activated receptor β/δ (PPAR β/δ), and nuclear factor kappa-light-chain-enhancer of activated B-cells (NF-κB). Multivariate analysis of variance (MANOVA) revealed a statistically significant, quantitative pattern-activity relationship between the extracts' ATR-FTIR spectra and their ability to modulate these targets in the corresponding cell models. Ensemble orthogonal partial least squares (OPLS) discriminant models were established for the identification of extracts exhibiting high and low activity with respect to their potential to suppress NO and IL-8 production. Predictions made on an independent test set revealed good generalizability of the models with overall sensitivity and specificity of 80% and 100%, respectively. Partial least squares (PLS) regression models were successfully established to predict the extracts' ability to suppress NO production and NF-κB activity with root mean squared errors of cross-validation (RMSECV) of 8.7% and 0.05-fold activity, respectively.

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