Classification and Correlation of St. John's Wort Extracts by Nuclear Magnetic Resonance Spectroscopy, Multivariate Data Analysis and Pharmacological Activity

Abstract

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.

Ethnopharmacology and systems biology: A perfect holistic match

“…In dereplication in studies for new leads from natural sources for drug development the metabolomics approach would be important to rapidly identify known actives. Finally one can also envisage the use of metabolomics approaches in the studies of the activity of medicinal plants (Roos et al, 2004;Mei Wang et al, 2005). By measuring the activity in a living organism (which can be anything from a cell culture, animals to patients) for extracts with different composition, possibly one may identify a compound or a combination of compounds that correlate with the activity.…”

“…The first steps on this route have been made, Roos et al (2004) analyzed different accessions and extracts of St. John's wort by means of NMR and correlated that with activity on the opioid receptor. Wang et al (2005) showed that chamomile ingestion during two weeks resulted in a clearly different NMR-profile of urine metabolites.…”

NMR-based plant metabolomics: where do we stand, where do we go?

“…NMR 1 H NMR has been the predominant profiling method because it is fast and simple, and the technique has been used as a major analytical tool for many applications in plant metabolomics, such as quality control [20], chemotaxonomy (classification [7] and characterization [21,22]), analysis of the equivalence of genetically modified plants [23,24], and interaction with other organisms [25] and the environment [26]. NMR has lower sensitivity than MS so it is particularly useful when high sensitivity is not required, such as in the quality control of medicinal plants [27] and pharmaceutical preparations and tablets [28].…”

Urinary metabolomics study on the protective role of Orthosiphon stamineus in Streptozotocin induced diabetes mellitus in rats via 1H NMR spectroscopy

“…A validated regression model was built in deciding whether the bioactivity of a new set of OS extract could be hypothesized in relation between variables obtained from the regression analysis, without the necessity of having to do the bioassay [32]. The regression model, as represented by the data, was validated based on the coefficient of determination (R2 = 0.825) value, whereby most of the data lie near to the linear regression line of best fit line [32].…”

“…The regression model, as represented by the data, was validated based on the coefficient of determination (R2 = 0.825) value, whereby most of the data lie near to the linear regression line of best fit line [32]. The obtained 1 H NMR data of the plant extracts were used as the X variables and the relative concentration of glucose was used as the Y variable.…”