Shengyun Dai

Profiling and identification of (-)-epicatechin metabolites in rats using ultra-high performance liquid chromatography coupled with linear trap-Orbitrap mass spectrometer.

(-)-Epicatechin (EC), an optical antipode of (+)-catechin (C), possesses many potential significant health benefits. However, the in vivo metabolic pathway of EC has not been clarified yet. In this study, an efficient strategy based on ultra-high performance liquid chromatography coupled with a linear ion trap-Orbitrap mass spectrometer was developed to profile and characterize EC metabolites in rat urine, faeces, plasma, and various tissues. Meanwhile, post-acquisition data-mining methods including high-resolution extracted ion chromatogram (HREIC), multiple mass defect filters (MMDFs), and diagnostic product ions (DPIs) were utilized to screen and identify EC metabolites from HR-ESI-MS1 to ESI-MSn stage. Finally, a total of 67 metabolites (including parent drug) were tentatively identified based on standard substances, chromatographic retention times, accurate mass measurement, and relevant drug biotransformation knowledge. The results demonstrated that EC underwent multiple in vivo metabolic reactions including methylation, dehydration, hydrogenation, glucosylation, sulfonation, glucuronidation, ring-cleavage, and their composite reactions. Among them, methylation, dehydration, glucosylation, and their composite reactions were observed only occurring on EC when compared with C. Meanwhile, the distribution of these detected metabolites in various tissues including heart, liver, spleen, lung, kidney, and brain were respectively studied. The results demonstrated that liver and kidney were the most important organs for EC and its metabolites elimination. In conclusion, the newly discovered EC metabolites significantly expanded the understanding on its pharmacological effects and built the foundation for further toxicity and safety studies. Copyright

Identification of Metabolites of 6'-Hydroxy-3,4,5,2',4'-pentamethoxychalcone in Rats by a Combination of Ultra-High-Performance Liquid Chromatography with Linear Ion Trap-Orbitrap Mass Spectrometry Based on Multiple Data Processing Techniques.

In this study, an efficient strategy was established using ultra-high-performance liquid chromatography coupled with linear ion trap-Orbitrap mass spectrometry (UHPLC-LTQ-Orbitrap MS) to profile the in vivo metabolic fate of 6′-hydroxy-3,4,5,2′,4′-pentamethoxychalcone (PTC) in rat urine and feces. The UHPLC-LTQ-Orbitrap method combines the high trapping capacity and MSn scanning function of the linear ion trap along with accurate mass measurements within 5 ppm and a resolving power of up to 30,000 over a wider dynamic range compared to many other mass spectrometers. In order to reduce the potential interferences of endogenous substances, the post-acquisition processing method including high-resolution extracted ion chromatogram (HREIC) and multiple mass defect filters (MMDF) were developed for metabolite detection. As a result, a total of 60 and 35 metabolites were detected in the urine and feces, respectively. The corresponding in vivo reactions such as methylation, hydroxylation, hydrogenation, decarbonylation, demethylation, dehydration, methylation, demethoxylation, sulfate conjugation, glucuronide conjugation, and their composite reactions were all detected in this study. The result on PTC metabolites significantly expanded the understanding of its pharmacological effects, and could be targets for future studies on the important chemical constituents from herbal medicines.

Establishment and reliability evaluation of the design space for HPLC analysis of six alkaloids in Coptis chinensis (Huanglian) using Bayesian approach

Coptis chinensis (Huanglian) is a commonly used traditional Chinese medicine (TCM) herb and alkaloids are the most important chemical constituents in it. In the present study, an isocratic reverse phase high performance liquid chromatography (RP-HPLC) method allowing the separation of six alkaloids in Huanglian was for the first time developed under the quality by design (QbD) principles. First, five chromatographic parameters were identified to construct a Plackett-Burman experimental design. The critical resolution, analysis time, and peak width were responses modeled by multivariate linear regression. The results showed that the percentage of acetonitrile, concentration of sodium dodecyl sulfate, and concentration of potassium phosphate monobasic were statistically significant parameters (P < 0.05). Then, the Box-Behnken experimental design was applied to further evaluate the interactions between the three parameters on selected responses. Full quadratic models were built and used to establish the analytical design space. Moreover, the reliability of design space was estimated by the Bayesian posterior predictive distribution. The optimal separation was predicted at 40% acetonitrile, 1.7 g·mL(-1) of sodium dodecyl sulfate and 0.03 mol·mL(-1) of potassium phosphate monobasic. Finally, the accuracy profile methodology was used to validate the established HPLC method. The results demonstrated that the QbD concept could be efficiently used to develop a robust RP-HPLC analytical method for Huanglian.

Robust design space development for HPLC analysis of five chemical components in Panax notoginseng saponins

ABSTRACT The present work describes the systematic development of a robust, precise, and rapid reversed-phase liquid chromatography method for the simultaneous analysis of five chemical components in Panax notoginseng saponins (PNS) using quality by design (QbD). The method was developed in two main phases: screening and optimization. During the screening phase, the most suitable stationary phase, column temperature, and flow rate were identified, while the secondary influential parameters, such as the gradient slope, the initial concentration of acetonitrile, and the initial isocratic hold of the gradient elution system were fine-tuned in the later optimization phase. In this phase, a 17-run experiment was used to examine multifactorial effects of these parameters on the critical resolution, analysis time, and peak symmetry. The Monte Carlo simulation method was successfully used to build the chromatographic design space and the process capability index Cp was introduced to evaluate the robustness of the design space. At last, the verification experiment was performed within the working design space by the accuracy profile methodology and model was found to be accurate. A robust combination of separation conditions predicted in the design space was obtained with the gradient slope of 1.6% · min−1, the initial concentration of acetonitrile of 20%, and the initial isocratic hold of 20 min, and the total analysis time was no more than 40 min. The results demonstrated that rapid separation of five components of PNS herbal extract could be achieved on the chromatographic column packed with narrow size particles at backpressures available in ordinary high performance liquid chromatography (HPLC) instruments. GRAPHICAL ABSTRACT

Determination of Geniposide in Gardenia jasminoides Ellis Fruit by Near-Infrared Spectroscopy and Chemometrics

ABSTRACT Near-infrared spectroscopy was used to monitor the end point of the elution process for the purification of Gardenia jasminoides Ellis (gardenia) extract. A partial least square model was built to quantify the concentration of geniposide. A new strategy was coupled with multivariate statistical process control to update the original multivariate calibration model to correct for fluctuations among batch processes and to enhance the robustness of the model. After updating the model twice by the new strategy, the root mean squared error of prediction decreased from 0.505 to 0.350 mg mL−1, and the interval between the end points determined by near-infrared spectroscopy and high-performance liquid chromatography was reduced from 15 to 3 min. Compared with other model updating methods, this strategy significantly improved the prediction capabilities of the near-infrared calibration model with smaller prediction errors while requiring fewer samples in the calibration set.

Evaluation of the value of near infrared (NIR) spectromicroscopy for the analysis of glycyrrizhic acid in licorice

It has been reported that hyperspectral data could be employed to qualitatively elucidate the spatial composition of tablets of Chinese medicinal plants. To gain more insights into this technology, a quantitative profile provided by near infrared (NIR) spectromicroscopy was further studied by determining the glycyrrhizic acid content in licorice, Glycyrrhiza uralensis. Thirty-nine samples from twenty-four different origins were analyzed using NIR spectromicroscopy. Partial least squares, interval partial least square (iPLS), and least squares support vector regression (LS-SVR) methods were used to develop linear and non-linear calibration models, with optimal calibration parameters (number of interval numbers, kernel parameter, etc.) being explored. The root mean square error of prediction (RMSEP) and the coefficient of determination (R(2)) of the iPLS model were 0.717 7% and 0.936 1 in the prediction set, respectively. The RMSEP and R(2) of LS-SVR model were 0.515 5% and 0.951 4 in the prediction set, respectively. These results demonstrated that the glycyrrhizic acid content in licorice could barely be analyzed by NIR spectromicroscopy, suggesting that good quality quantitative data are difficult to obtain from microscopic NIR spectra for complicated Chinese medicinal plant materials.

Investigation of the distributional homogeneity on chlorpheniramine maleate tablets using NIR-CI

Homogeneity is the basic element of pharmaceutical analysis. Distributional Homogeneity Index (DHI) was proposed to assess the distributional homogeneity of commercial chlorpheniramine maleate (CPM) tablets. Furthermore, the divergence value of DHI value from expectation DHI (value = 1) was calculated to obtain the CPM distributional homogeneity. The distribution of commercial CPM tablets from six brands was successfully visualized using near infrared chemical imaging (NIR-CI) coupled with characteristic wavenumber method and binary image. Besides, content homogeneity of CPM was obtained through calculating the proportion of white region in the binary image. The result demonstrated that the distributional homogeneity of brand 4 was to be the best among all the brands, following by brand 2, brand 3, brand 5, brand 6 and brand 1. Furthermore, the sequence of the content uniformity was different from the distributional homogeneity, which demonstrated that content uniformity could not represent the distributional homogeneity. This work was a significant method guideline to assess the distributional homogeneity in pharmaceutical field.

Novelty application of multi-omics correlation in the discrimination of sulfur-fumigation and non-sulfur-fumigation Ophiopogonis Radix

A rapid and sensitive approach to differentiate sulfur-fumigated (SF) Ophiopogonis Radix based on Multi-Omics Correlation Analysis (MOCA) strategy was first established. It was characterized by multiple data-acquisition methods (NIR, HPLC, and UHPLC-HRMS) based metabonomics and multivariate statistical analysis methods. As a result, SF and non-sulfur fumigated (NSF) Ophiopogonis Radix samples were efficaciously discriminated. Moreover, based on the acquired HRMS data, 38 sulfur-containing discriminatory markers were eventually characterized, whose NIR absorption could be in close correlation with the discriminatory NIR wavebands (5000–5200 cm−1) screened by NIR metabonomics coupled with SiPLS and 2D-COS methods. This results were also validated from multiple perspectives, including metabonomics analysis based on the discriminatory markers and the simulation of SF ophiopogonin D and Ophiopogonis Radix sample. In conclusion, our results first revealed the intrinsic mechanism of discriminatory NIR wavebands by means of UHPLC-HRMS analysis. Meanwhile, the established MOCA strategy also provided a promising NIR based differential method for SF Ophiopogonis Radix, which could be exemplary for future researches on rapid discrimination of other SF Chinese herbal medicines.

Latent variable modeling to analyze the effects of process parameters on the dissolution of paracetamol tablet

ABSTRACT The dissolution is one of the critical quality attributes (CQAs) of oral solid dosage forms because it relates to the absorption of drug. In this paper, the influence of raw materials, granules and process parameters on the dissolution of paracetamol tablet was analyzed using latent variable modeling methods. The variability in raw materials and granules was understood based on the principle component analysis (PCA), respectively. A multi-block partial least squares (MBPLS) model was used to determine the critical factors affecting the dissolution. The results showed that the binder amount, the post granulation time, the API content in granule, the fill depth and the punch tip separation distance were the critical factors with variable importance in the projection (VIP) values larger than 1. The importance of each unit of the whole process was also ranked using the block importance in the projection (BIP) index. It was concluded that latent variable models (LVMs) were very useful tools to extract information from the available data and improve the understanding on dissolution behavior of paracetamol tablet. The obtained LVMs were also helpful to propose the process design space and to design control strategies in the further research.

Metabolomics data fusion between near infrared spectroscopy and high-resolution mass spectrometry: A synergetic approach to boost performance or induce confusion

In general, data fusion can improve the classification performance of the model, but little attention is paid to the influence of the data fusion on the spatial distribution of the modeling samples. In this paper, the effect of data fusion on sample spatial distribution was studied through integrating NIR data and UHPLC-HRMS data for sulfur-fumigated Chinese herb medicine. Twelve samples collected from four different geographical origins were sulfur fumigated in the lab, and then metabolomics analysis was conducted using NIR and UHPLC-LTQ-Orbitrap mass spectrometer. First of all, the discriminating power of each technique was respectively examined based on PCA analysis. Secondly, combining NIR and UHPLC-HRMS data sets together with or without variable selection was parallelly compared. The results demonstrated that the discriminable ability was remarkably improved after data fusion, indicating data fusion could visualize variable selection and enhance group separation. Samples in the margin between two classes of samples may increase the experience error but has positive effect on the separation direction. Besides, an interesting feature extraction could obtain better discriminable effect than common data fusion. This study firstly provided a new path to employ a comprehensive analytical approach for discriminating SF Chinese herb medicines to simultaneously benefit from the advantages of several technologies.