Xinyuan Shi

Validation of a NIR quantification method for the determination of chlorogenic acid in Lonicera japonica solution in ethanol precipitation process.

The feasibility of near-infrared spectroscopy (NIRS) for chlorogenic acid content analysis in ethanol precipitation process of water extract of Lonicera japonica was verified in this work. A calibration and validation set was designed for the conception and evaluation of the method adequacy. An experimental protocol was then followed, involving two different NIR instruments for data acquisition. On the basis of this protocol, the model was developed based on partial least squares regression (PLS) and the determination coefficient (R(2)(cal) and R(2)(val)), standard error of calibration and prediction (SEC and SEP) were 0.9962, 0.9955, 111.1 μg/mL and 107.1 μg/mL for Holographic Grating NIR instrument, and 0.9984, 0.9971, 53.6 μg/mL and 83.3 μg/mL for Fourier Transform NIR instrument. However, such above criteria did not clearly demonstrate the models prediction error over each analyzed content range. Consequently, a novel approach based on accuracy profile which allowed the acquisition of the lower limit of quantification (LLOQ) was used to validate the robustness and accuracy of PLS model. The resulting accuracy profile showed that PLS model was able to determine chlorogenic acid content by two NIR systems, whose LLOQ was about 1550 μg/mL. It was concluded that the two NIR systems were suitable for use as Process Analytical Technology (PAT) to understand ethanol precipitation process of water extract of Lonicera japonica.

Multivariate detection limits of on-line NIR model for extraction process of chlorogenic acid from Lonicera japonica

A methodology is proposed to estimate the multivariate detection limits (MDL) of on-line near-infrared (NIR) model in Chinese Herbal Medicines (CHM) system. In this paper, Lonicera japonica was used as an example, and its extraction process was monitored by on-line NIR spectroscopy. Spectra of on-line NIR could be collected by two fiber optic probes designed to transmit NIR radiation by a 2mm-flange. High performance liquid chromatography (HPLC) was used as a reference method to determine the content of chlorogenic acid in the extract solution. Multivariate calibration models were carried out including partial least squares regression (PLS) and interval partial least-squares (iPLS). The result showed improvement of model performance: compared with PLS model, the root mean square errors of prediction (RMSEP) of iPLS model decreased from 0.111mg to 0.068mg, and the R(2) parameter increased from 0.9434 to 0.9801. Furthermore, MDL values were determined by a multivariate method using the type of errors and concentration ranges. The MDL of iPLS model was about 14ppm, which confirmed that on-line NIR spectroscopy had the ability to detect trace amounts of chlorogenic acid in L. japonica. As a result, the application of on-line NIR spectroscopy for monitoring extraction process in CHM could be very encouraging and reliable.

Optimization of Parameter Selection for Partial Least Squares Model Development

In multivariate calibration using a spectral dataset, it is difficult to optimize nonsystematic parameters in a quantitative model, i.e., spectral pretreatment, latent factors and variable selection. In this study, we describe a novel and systematic approach that uses a processing trajectory to select three parameters including different spectral pretreatments, variable importance in the projection (VIP) for variable selection and latent factors in the Partial Least-Square (PLS) model. The root mean square errors of calibration (RMSEC), the root mean square errors of prediction (RMSEP), the ratio of standard error of prediction to standard deviation (RPD), and the determination coefficient of calibration (Rcal2) and validation (Rpre2) were simultaneously assessed to optimize the best modeling path. We used three different near-infrared (NIR) datasets, which illustrated that there was more than one modeling path to ensure good modeling. The PLS model optimizes modeling parameters step-by-step, but the robust model described here demonstrates better efficiency than other published papers.

NIR spectroscopy as a process analytical technology (PAT) tool for monitoring and understanding of a hydrolysis process

The use of near infrared spectroscopy was investigated as a process analytical technology to monitor the amino acids concentration profile during hydrolysis process of Cornu Bubali. A protocol was followed, including outlier selection using relationship plot of residuals versus the leverage level, calibration models using interval partial least squares and synergy interval partial least squares (SiPLS). A strategy of four robust root mean square error of predictions (RMSEP) values have been developed to assess calibration models by means of the desirability index. Furthermore, multivariate quantification limits (MQL) values of the optimum model were determined using two types of error. The SiPLS(3) models for L-proline, L-tyrosine, L-valine, L-phenylalanine and L-lysine provided excellent accuracies with RMSEP values of 0.0915 mg/mL, 0.1605 mg/mL, 0.0515 mg/mL, 0.0586 mg/mL and 0.0613 mg/mL, respectively. The MQL ranged from 90 ppm to 810 ppm, which confirmed that these models can be suitable for most applications.

Visualizing excipient composition and homogeneity of Compound Liquorice Tablets by near-infrared chemical imaging

This study demonstrated that near-infrared chemical imaging (NIR-CI) was a promising technology for visualizing the spatial distribution and homogeneity of Compound Liquorice Tablets. The starch distribution (indirectly, plant extraction) could be spatially determined using basic analysis of correlation between analytes (BACRA) method. The correlation coefficients between starch spectrum and spectrum of each sample were greater than 0.95. Depending on the accurate determination of starch distribution, a method to determine homogeneous distribution was proposed by histogram graph. The result demonstrated that starch distribution in sample 3 was relatively heterogeneous according to four statistical parameters. Furthermore, the agglomerates domain in each tablet was detected using score image layers of principal component analysis (PCA) method. Finally, a novel method named Standard Deviation of Macropixel Texture (SDMT) was introduced to detect agglomerates and heterogeneity based on binary image. Every binary image was divided into different sizes length of macropixel and the number of zero values in each macropixel was counted to calculate standard deviation. Additionally, a curve fitting graph was plotted on the relationship between standard deviation and the size length of macropixel. The result demonstrated the inter-tablet heterogeneity of both starch and total compounds distribution, simultaneously, the similarity of starch distribution and the inconsistency of total compounds distribution among intra-tablet were signified according to the value of slope and intercept parameters in the curve.

Development and validation of NIR model using low-concentration calibration range: rapid analysis of Lonicera japonica solution in ethanol precipitation process

A strategy for both low-concentration calibration range and large number of sample sets selection in the development of a PLS model is studied. A novel approach based on accuracy profile validated the accuracy and precision of the PLS model. The strategy was applied to the determination of chlorogenic acid content in Lonicera japonica using ethanol precipitation by near-infrared (NIR) transmission spectroscopy. The results found the determination coefficient (R2), standard errors of calibration and prediction (SEC and SEP) were 0.9648, 71.2 ppm and 74.9 ppm, respectively. The further study showed that PLS model could be used to determine chlorogenic acid content based on accuracy profile, which has a lower limit of quantification (LLOQ) ∼1700 ppm. Analytical properties such as accuracy, precision, range and linearity from validation criteria also demonstrated the feasibility of the strategy using a low-concentration calibration set in the PLS model, paving the way for analyses in Chinese Herbal Medicine (CHM) applications.

Interaction of menthol with mixed-lipid bilayer of stratum corneum: A coarse-grained simulation study

Menthol is a widely used penetration enhancer in clinical medicine due to its high efficiency and relative safety. Although there are many studies focused on the penetration-enhancing activity of menthol, the details of molecular mechanism are rarely involved in the discussion. In this study, we present a series of coarse-grained molecular dynamics simulations to investigate the interaction of menthol with a mixed-lipid bilayer model consisting of ceramides, cholesterol and free fatty acids in a 2:2:1 molar ratio. Taking both the concentration of menthol and temperature into consideration, it was found that a rise in temperature and concentration within a specific range (1-20%) could improve the penetration-enhancing property of menthol and the floppiness of the bilayer. However, at high concentrations (30% and more), menthol completely mixed with the lipids and the membrane can no longer maintain a bilayer structure. Our results elucidates some of the molecular basis for menthols penetration enhancing effects and may provide some assistance for the development and applications of menthol as a penetration enhancer. Furthermore, we establish a method to investigate the penetration enhancement mechanism of traditional Chinese medicine using the mixed-lipid bilayer model of stratum corneum by molecular dynamics simulations.

Multiscale study on the interaction mechanism between ginsenoside biosurfactant and saikosaponin a

Ginsenoside is an important class of saponin biosurfactant that is derived from ginseng. The interactions between ginsenoside Ro, Rb1, and Rg1 with saikosaponin a (SSa) were explored using multiscale methods. The order of interaction strength was found to be Ro>Rb1>Rg1. Ro markedly increased the solubility of SSa; however, Rb1 could only disperse SSa solid in aqueous medium. No significant interaction was observed between Rg1 and SSa. Ro formed vesicles in aqueous medium while Rb1 and Rg1 formed spherical micelles. The differences in the available surface area of the aggregates appear to have some influence on the interactions between ginsenoside and SSa. However, more important effects are related to their chemical structures and interaction energy. According to the molecular simulation results, glucuronic acid linked to Ro molecules significantly reduced the potential energy through its strong electrical attraction to SSa, which contributed greatly to the strong compatibility between them. The greater number of sugars in Rb1, as compared to Rg1, created more binding sites with SSa, thus resulting in stronger interaction between Rb1 with SSa than between Rg1 and SSa. Spherical and worm-like micelles were found to be formed by Rb1 and SSa molecules. This was different from Ro and SSa, which formed vesicles. The formation of worm-like micelles was through the fusion and modification of small spherical micelles. These results may guide in expanding the applications of ginsenoside.

Interactions of Borneol with DPPC Phospholipid Membranes: A Molecular Dynamics Simulation Study

Borneol, known as a “guide” drug in traditional Chinese medicine, is widely used as a natural penetration enhancer in modern clinical applications. Despite a large number of experimental studies on borneol’s penetration enhancing effect, the molecular basis of its action on bio-membranes is still unclear. We carried out a series of coarse-grained molecular dynamics simulations with the borneol concentration ranging from 3.31% to 54.59% (v/v, lipid-free basis) to study the interactions of borneol with aDPPC(1,2-dipalmitoylsn-glycero-3-phosphatidylcholine) bilayer membrane, and the temperature effects were also considered. At concentrations below 21.89%, borneol’s presence only caused DPPC bilayer thinning and an increase in fluidity; A rise in temperature could promote the diffusing progress of borneol. When the concentration was 21.89% or above, inverted micelle-like structures were formed within the bilayer interior, which led to increased bilayer thickness, and an optimum temperature was found for the interaction of borneol with the DPPC bilayer membrane. These findings revealed that the choice of optimal concentration and temperature is critical for a given application in which borneol is used as a penetration enhancer. Our results not only clarify some molecular basis for borneol’s penetration enhancing effects, but also provide some guidance for the development and applications of new preparations containing borneol.

Solubilization of saikosaponin a by ginsenoside Ro biosurfactant in aqueous solution: mesoscopic simulation.

Ginsenoside Ro (Ro), a natural anionic biosurfactant derived from ginseng, has been found to markedly increase the solubility of saikosaponin a (SSa), which is the active ingredient of Radix Bupleuri. SSa is minimally soluble in water. To determine the mechanism by which Ro solubilizes SSa, the self-assembly behavior of Ro and the phase behavior of blended Ro and SSa systems were studied by mesoscopic dynamics (MesoDyn) and dissipative particle dynamics (DPD) simulations. The simulation results show that Ro can form vesicles via the closure of oblate membranes. At low concentrations, SSa molecules are solubilized in the palisade layer of the Ro vesicles. At high concentrations, they interact with Ro molecules to form mixed vesicles with Ro adsorbing on the surfaces of the vesicles. The evaluation of the SSa solubilization process reveals that, at low concentrations, Ro aggregates preferentially to form vesicles, which then absorb SSa into themselves. However, at high concentrations, SSa first self-aggregates and then dissolves. This is because the solubilization behavior of Ro shifts the precipitation-dissolution equilibrium in the direction of dissolution. These results of the simulations are consistent with those of transmission electron microscopy (TEM) and dynamic light scattering (DLS).