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 IC50 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 (CRF1) 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.
Hypericum perforatum
- Clusiaceae - NMR spectroscopy - chemometrics - partial least squares algorithm -
non-selective receptor binding - antagonist effect at CRF1 receptors