Preparation of Tenuifolin from Polygala senega L. Root Using a Hydrolytic Continuous Flow System under High-Temperature, High-Pressure Conditions.

An improved process for preparing tenuifolin (presenegenin 3-ß-d-glucopyranoside) from the root of Polygala senega L. was developed. A crude saponin mixture extracted from P. senega was subjected to hydrolysis, and the reactivity of compounds in the extract was controlled by utilizing the combination of a flow reactor and experimental design. In addition, column chromatography with HP 20, a synthetic polystyrenic adsorbent, allowed the gram-scale preparation of tenuifolin in a continuous manner with fewer steps. This approach shortens the total time required for gram-scale preparation from 16 to 5 h in a continuous manner while improving the yield from 0.59% to 2.08% (w/w).

Improved quantitative analysis of tenuifolin using hydrolytic continuous-flow system to build prediction models for its content based on near-infrared spectroscopy.

This study used two types of analyses and statistical calculations on powdered samples of Polygala root (PR) and Senega root (SR): (1) determination of saponin content by an independently developed quantitative analysis of tenuifolin content using a flow reactor, and (2) near-infrared spectroscopy (NIR) using crude drug powders as direct samples for metabolic profiling. Furthermore, a prediction model for tenuifolin content was developed and validated using multivariate analysis based on the results of (1) and (2). The goal of this study was to develop a rapid analytical method utilizing the saponin content and explore the possibility of quality control through a wide-area survey of crude drugs using NIR spectroscopy. Consequently, various parameters and appropriate wavelengths were examined in the regression analysis, and a model with a reasonable contribution rate and prediction accuracy was successfully developed. In this case, the wavenumber contributing to the model was consistent with that of tenuifolin, confirming that this model was based on saponin content. In this series of analyses, we have succeeded in developing a model that can quickly estimate saponin content without post-processing and have demonstrated a brief way to perform quality control of crude drugs in the clinical field and on the market.