An effective vacuum assisted extraction (VAE) technique was proposed for the first time and applied to extract bioactive components from Andrographis paniculata. The process was carefully optimized by response surface methodology (RSM). Under the optimized experimental conditions, the best results were obtained using a boiling temperature of 65 °C, 50% ethanol concentration, 16 min of extraction time, one extraction cycles and a 12:1 liquid-solid ratio. Compared with conventional ultrasonic assisted extraction and heat reflux extraction, the VAE technique gave shorter extraction times and remarkable higher extraction efficiency, which indicated that a certain degree of vacuum gave the solvent a better penetration of the solvent into the pores and between the matrix particles, and enhanced the process of mass transfer. The present results demonstrated that VAE is an efficient, simple and fast method for extracting bioactive components from A. paniculata, which shows great potential for becoming an alternative technique for industrial scale-up applications.

To reveal the structural stabilities and transformation mechanism of rhynchophylline (RIN) and isorhynchophylline (IRN), HPLC and UPLC-Q-TOF-MS method were developed for the qualitative and quantitative analysis of the conversion rate. The method was validated for linearity, inter- and intra-day precisions, repeatability and stability. All the quantitative determination method validation results were satisfactory. Under the optimized chromatographic conditions, the effect of various heat temperatures, retention time, and solvent polarities on conversion rate and equilibrium were systematically investigated for the first time. Besides, a model relating the retention yield value and time-temperature was built to predict the t_0.5 and Ea of the conversion rate by the Arrhenius equation. The experimental results proved to be in good accordance with the predicted values. Furthermore, UPLC-Q-TOF-MS analysis was performed to verify the transformation mechanism and provide valuable information for stability analysis of the conversion products.

The method of cell biospecific extraction coupled with UPLC/Q-TOF-MS has been developed as a tool for the screening and identification of potential immunological active components from Andrographis Herba (AH). In our study, a macrophage cell line (RAW264.7) was used to extract cell-combining compounds from the ethanol extract of AH. The cell binding system was then analyzed and identified by UPLC/Q-TOF-MS analysis. Finally, nine compounds, which could combine with macrophages, in an ethanol extract of AH were detected by comparing basic peak intensity (BPI) profiles of macrophages before and after treatment with AH. Then they were identified as Andrographidine E (1), Andrographidine D (2), Neoandrographolide (3), Dehydroandrographolide (4), 5, 7, 2′, 3′-tetramethoxyflavone (5), β-sitosterol (7), 5-hydroxy-7, 2′, 3′-trimethoxyflavone (8) and 5-hydroxy-7, 8, 2′, 3′-tetramethoxyflavone (9), which could classified into five flavonoids, three diterpene lactones, and one sterol. Their structures were recognized by their characteristic fragment ions and fragmentations pattern of diterpene lactones and flavonoids. Additionally, the activity of compounds 3, 4, and 7 was tested in vitro. Results showed that these three compounds could decrease the release of NO (p < 0.01) in macrophages remarkably. Moreover, 3, 4, and 7 showed satisfactory dose-effect relationships and their IC₅₀ values were 9.03, 18.18, and 13.76 μg/mL, respectively. This study is the first reported work on the screening of immunological active components from AH. The potential immunological activity of flavonoids from AH has not been reported previously.

A support vector regression (SVR) method was introduced to improve the robustness and predictability of the design space in the implementation of quality by design (QbD), taking the extraction process of Pueraria lobata as a case study. In this paper, extraction time, number of extraction cycles, and liquid–solid ratio were identified as critical process parameters (CPPs), and the yield of puerarin, total isoflavonoids, and extracta sicca were the critical quality attributes (CQAs). Models between CQAs and CPPs were constructed using both a conventional quadratic polynomial model (QPM) and the SVR algorithm. The results of the two models indicated that the SVR model had better performance, with a higher R² and lower root-mean-square error (RMSE) and mean absolute deviation (MAD) than those of the QPM. Furthermore, the design space was predicted using a grid search technique. The operational range was extraction time, 24–51 min; number of extraction cycles, 3; and liquid–solid ratio, 14–18 mL/g. This study is the first reported work optimizing the design space of the extraction process of P. lobata based on an SVR model. SVR modeling, with its better prediction accuracy and generalization ability, could be a reliable tool for predicting the design space and shows great potential for the quality control of QbD.