Classification and correlation of St. John's wort extracts by nuclear magnetic resonance spectroscopy, multivariate data analysis and pharmacological activity.

The use of proton NMR spectroscopy allows the analysis of complex multi-component mixtures such as plant extracts by simultaneous quantification of all proton-bearing compounds and consequently all relevant substance classes. Since the spectra obtained are too complicated to be analysed visually, the classification of spectra was carried out using multivariate statistical methods. The spectroscopic data of various extracts of St. John's wort (Hypericum perforatum) samples derived from 4 different accessions extracted with 6 distinct solvents were chemometrically evaluated and calibrated using the partial least square (PLS) algorithm. In a first approach, we found a consistent correlation for the spectroscopic pattern of the extracts and the corresponding IC (50) values derived from non-selective binding to opioid receptors. Consequently, the multivariate data analysis was used to predict the pharmacological efficacy of further St. John's wort extracts on the basis of their proton NMR spectra. In a second approach a PLS 2 model was used to predict the biological activity for eight St. John's wort extracts based on two pharmacological data sets: (i) non-selective binding to opioid receptors and (ii) antagonist effect at corticotrophin-releasing factor type 1 (CRF (1)) receptors. The PLS 2 model confirmed the useful application of the presented approach to assess the quality of medicinal herbs and extracts by spectroscopic analysis derived from bioactivity-related quality parameters.

Thin-layer chromatography and multivariate data analysis of willow bark extracts.

In most cases the pharmacological activity of plant extracts is not assigned to single components and often not all active ingredients are known. Approaches other than those considering single compounds only to analyze plant material have proven helpful for a better characterization of extracts in their entirety. In this study extracts of willow bark are analyzed by high-performance thin-layer chromatography (HPTLC) and two different pharmacological tests [the 2,2'-azobis (2-amidinopropane) dihydrochloride reaction and the xanthine/xanthine oxidase reaction] with the help of multivariate data analysis. Described are two models using the results of the chromatographic study of 22 various extracts of willow bark and their pharmacological properties. The chromatographic data are obtained by a special TLC scanner that enables measurement of HPTLC tracks simultaneously in the range of lambda = 200-400 nm. Additionally, the developed models are used to predict the activity of another three extracts of willow bark demonstrating the quality of the model.