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AbstractAstragalus membranaceus is a traditional Chinese medicine with multiple pharmacological activities. Modern pharmacological research has found that Astragalus membranaceus extract has an inhibitory effect on α-glucosidase, however, which component can inhibit the activity of α-glucosidase and its degree of inhibition are unknown. To address this issue, this study used affinity ultrafiltration screening combined with UPLC-ESI-Orbitrap-MS technology to screen α-glucosidase inhibitors in Astragalus membranaceus. Using affinity ultrafiltration technology, we obtained the active components, and using UPLC-ESI-Orbitrap-MS technology, we quickly analyzed and identified them. As a result, a total of 8 ingredients were selected as α-glucosidase inhibitors. [ABSTRACT FROM AUTHOR]

This paper aims to study the therapeutic effect of Massa Medicata Fermentata on hyperlipidemia model rats and investigate its mechanism of hypolipidemic effect with the help of non-targeted metabolomics. The mixed hyperlipidemia model rats were constructed by giving high-fat chow. After successful modeling, the rats were divided into the model group, pravastatin sodium group(4.4 mg·kg~(-1)), lipotropic group(0.1 g·kg~(-1)), high-dose group(2.4 g·kg~(-1)), medium-dose group(1.2 g·kg~(-1)), and low-dose group(0.6 g·kg~(-1)) of Massa Medicata Fermentata, and they were administered for four weeks once daily. An equal volume of ultrapure water was given to the blank group and model group. Serum lipid level and liver hematoxylin-eosin(HE) staining were used as indicators to estimate the intervention effect of Massa Medicata Fermentata on mixed hyperlipidemia, and the changes in metabolites in plasma of mixed hyperlipidemia model rats were analyzed by non-targeted metabolomics. The mechanism of the hypolipidemic effect of Massa Medicata Fermentata was analyzed through metabolite pathway enrichment. The results showed that compared with the model group, the Massa Medicata Fermentata administration group, especially the high-dose group, could significantly reduce the content of total cholesterol(TC), triglyceride(TG), and low-density lipoprotein cholesterol(LDL-c)(P<0.05 or P<0.01), and liver HE staining revealed that the number of adipocytes in the high-dose group was reduced to some extent. The potential biomarkers obtained by non-targeted metabolomics screening included glycerol 3-phosphate, sphingomyelin, sphingosine 1-phosphate, and deoxyuridine, which were mainly involved in the sphingolipid metabolism process, glycerophospholipid metabolism process, glycerol ester metabolism pathway, and pyrimidine metabolism pathway, totaling four possible metabolic pathways related to lipid metabolism. This study provides a reference for an in-depth investigation of the hypolipidemic mechanism of Massa Medicata Fermentata, which is of great significance for further promoting the clinical application of Massa Medicata Fermentata and increasing the indications.

The pharmaceutical manufacturing model is gradually changing from intermittent manufacturing to continuous manufacturing and intelligent manufacturing. This paper briefly reviewed the supervision and research progress in continuous pharmaceutical manufacturing in China and abroad and described the definition and advantages of continuous pharmaceutical manufacturing. The continuous manufacturing of traditional Chinese medicine(TCM) at the current stage was summarized in the following three terms: the enhancement of the continuity of intermittent manufacturing operations, the integration of continuous equipment to improve physical continuity between units, and the application of advanced process control strategies to improve process continuity. To achieve continuous manufacturing of TCM, the corresponding key technologies, such as material property characterization, process modeling and simulation, process analysis technology, and system integration, were analyzed from the process and equipment, respectively. It was proposed that the continuous manufacturing equipment system should have the characteristics of high speed, high response, and high reliability, "three high(H~3)" for short. Considering the characteristics and current situation of TCM manufacturing, based on the two dimensions of product quality control and production efficiency, a maturity assessment model for continuous manufacturing of TCM, consisting of operation continuity, equipment continuity, process continuity, and quality control continuity, was proposed to provide references for the application of continuous manufacturing technology for TCM. The implementation of continuous manufacturing or the application of key continuous manufacturing technologies in TCM can help to systematically integrate advanced pharmaceutical technology elements and promote the uniformity of TCM quality and the improvement of production efficiency.

The effect of Danhong Injection on the endogenous metabolites of rabbit platelets was analyzed by the liquid chromatography-mass spectrometry( LC-MS) based metabonomic approach. Anti-platelet aggregation was detected after Danhong Injection treatment and the changes of platelet metabolites were analyzed by metabonomics. Principal component analysis( PCA) and partial least squares discriminant analysis( PLS-DA) were performed to investigate the effect of Danhong Injection on endogenous metabolites of platelets,characterize the biomarkers,and explore the relevant pathways and the underlying mechanism. As demonstrated by the pharmacodynamic results,Danhong Injection of different doses and concentrations antagonized platelet aggregation in a dose-and concentration-dependent manner. In contrast to the control group,25 differential metabolites such as nicotinic acid,nicotinic acid riboside,and hypoxanthine were screened out after platelets were treated by Danhong Injection. These metabolites,serving as important biomarkers,were mainly enriched in the nicotinic acid-niacinamide metabolic pathway and purine metabolic pathway. This study explored the therapeutic mechanism of Danhong Injection from a microscopic perspective by metabonomics,which is expected to provide a new idea for the investigation of platelet-related mechanisms.

The quality of traditional Chinese medicine tablets is correlated with clinical efficacy and drug safety, and plays a great role in promoting the development of traditional Chinese medicine. However, the existing traditional artificial identification and modern instrument detection in terms of accuracy and timeliness have both advantages and disadvantages. Therefore, how to quickly and accurately identify the quality of traditional Chinese medicine tablets has become a high-profile issue. The purpose of this paper is to explore the feasibility of the application of electronic eye technology in the study of rapid identification of traditional Chinese medicine quality. A total of 80 batches of samples were collected and tested by Fritillariae Cirrhosae Bulbus for traditional empirical identification(M_1) and modern pharmacopeia(M_2). The optical data was collected from electronic eyes, and the chemical metrology was used to establish suitable discrimination models(M_3). Four authenticity and commodity specification models, namely identification analysis(DA), minimum bidirectional support vector machine(LS-SVM), partial minimum two-multiplier analysis(PLS-DA), main component analysis identification analysis(PCA-DA), were established, respectively. The accuracies of the authenticity identification models were 82.5%, 90.0%, 96.2% and 93.8%, while the accuracies of the commodity specification identification models were 89.3%, 96.0%, 90.7% and 97.3%, respectively. The models were well judged, the authenticity identification was based on the final identification model of PLS-DA, and the commodity specification was based on the final identification model of PCA-DA. There was no significant difference between its accuracy and M_1, and the time of determination was much shorter than M_2(P<0.01). Therefore, electronic-eye technology could be used for the rapid identification of the quality of Fritillariae Cirrhosae Bulbus.

Along with the striding of the Chinese medicine(CM) manufacturing toward the Industry 4.0, some digital factories have accumulated lightweight industrial big data, which become part of the enterprise assets. These digital assets possess the possibility of solving the problems within the CM production system, like the Sigma gap and the poverty of manufacturing knowledge. From the holistic perspective, a three-tiered architecture of CM industrial big data is put forward, and it consists of the data integration layer, the data analysis layer and the application scenarios layer. In data integration layer, sensing of CM critical quality attributes is the key technology for big data collection. In data analysis and mining layer, the self-developed iTCM algorithm library and model library are introduced to facilitate the implementation of the model lifecycle methodologies, including process model development, model validation, model configuration and model maintenance. The CM quality transfer structure is closely related with the connection mode of multiple production units. The system modeling technologies, such as the partition-integration modeling method, the expanding modeling method and path modeling method, are key to mapping the structure of real manufacturing system. It is pointed out that advance modeling approaches that combine the first-principles driven and data driven technologies are promising in the future. At last, real-world applications of CM industrial big data in manufacturing of injections, oral solid dosages, and formula particles are presented. It is shown that the industrial big data can help process diagnosis, quality forming mechanism interpretations, real time release testing method development and intelligent product formulation design. As renewable resources, the CM industrial big data enable the manufacturing knowledge accumulation and product quality improvement, laying the foundation of intelligent manufacturing.

Blending process, which is an essential part of the pharmaceutical preparation, has a direct influence on the homogeneity and stability of solid dosage forms. With the official release of Guidance for Industry PAT, online process analysis techniques have been more and more reported in the applications in blending process, but the research on endpoint detection algorithm is still in the initial stage. By progressively increasing the window size of moving block standard deviation (MBSD), a novel endpoint detection algorithm was proposed to extend the plain MBSD from off-line scenario to online scenario and used to determine the endpoint in the blending process of Chinese medicine dispensing granules. By online learning of window size tuning, the status changes of the materials in blending process were reflected in the calculation of standard deviation in a real-time manner. The proposed method was separately tested in the blending processes of dextrin and three other extracts of traditional Chinese medicine. All of the results have shown that as compared with traditional MBSD method, the window size changes according to the proposed MBSD method (progressively increasing the window size) could more clearly reflect the status changes of the materials in blending process, so it is suitable for online application., Competing Interests: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose., (Copyright© by the Chinese Pharmaceutical Association.)

Feature selection can improve the interpretation of the modeling variables to a certain extent by selecting variables from the complex spectra backgrounds. However, the improvement of models interpretation does not mean that the modeling variables have the exact physical or chemical significance. In this paper, We explore the relation between the chemical characteristics of target components and the spectrum variables selected with 3 kinds of variables selection methods which are moving window partial least squares regression (mwPLS), synergy interval partial least squares regression (siPLS) and competitive adaptive re-weighted sampling (CARS), and compare the interpretation difference of the variables selected with the above variables selection methods. The results show that the variables selected with mwPLS accord with ν(φ)C=C of liquiritin and δCH3 or δCH2 of glycyrrhizin, which are the obvious spectra differences between the flavonoids and saponins in Radix Glycyrrhizae, and the variables selected with siPLS are the characteristic intervals combinations of the flavonoids or saponins in Radix Glycyrrhizae, which is the combination of ν(ø)C=C, ν(ø)C-O, ν(ø)C-H of flavonoids or the combination of νC-O vC-H, νO-H of saponins while the variables selected with CARS can better accord with most of the characteristic peaks from 1000 to 4000 cm(-1) of liquiritin or glycyrrhizin in Radix Glycyrrhizae, and the predict performance of the infrared quantitative model established on the spectroscopic variables selected with CARS can be improved. Therefore, most of the variables selected with CARS can be interpreted by the characteristic peaks in the infrared characteristic region of the target components, which is beneficial to improve the interpretation of the quantitative model.

The appropriate algorithm for calibration set selection was one of the key technologies for a good NIR quantitative model. There are different algorithms for calibration set selection, such as Random Sampling (RS) algorithm, Conventional Selection (CS) algorithm, Kennard-Stone(KS) algorithm and Sample set Portioning based on joint x-y distance (SPXY) algorithm, et al. However, there lack systematic comparisons between two algorithms of the above algorithms. The NIR quantitative models to determine the asiaticoside content in Centella total glucosides were established in the present paper, of which 7 indexes were classified and selected, and the effects of CS algorithm, KS algorithm and SPXY algorithm for calibration set selection on the accuracy and robustness of NIR quantitative models were investigated. The accuracy indexes of NIR quantitative models with calibration set selected by SPXY algorithm were significantly different from that with calibration set selected by CS algorithm or KS algorithm, while the robustness indexes, such as RMSECV and |RMSEP-RMSEC|, were not significantly different. Therefore, SPXY algorithm for calibration set selection could improve the predicative accuracy of NIR quantitative models to determine asiaticoside content in Centella total glucosides, and have no significant effect on the robustness of the models, which provides a reference to determine the appropriate algorithm for calibration set selection when NIR quantitative models are established for the solid system of traditional Chinese medcine.

Portable near infrared spectrometer combined with multi-class support vector machines was used to discriminate wolfberry fruit of different geographic regions. Data pre-processing methods were explored before modeling with the identification rate as indicator. To eliminate the influence of sample subset partitioning on model performance, multiple modeling and predicting were conducted and the statistical result of identification rate was utilized to assess model performance of different acquisition sites. The results showed that SVM model with raw spectra after pretreatment of second derivative and Savitzky-Golay filter smoothing showed the best predicative ability. And the model of every acquisition site except for site 5 exhibited good stability and prediction ability and its median and average of identification rate of external validation were all greater than 97%. It was suggested that surface NIR spectra of wolfberry fruit was applicable to accurate identification of geographic region, and portable near infrared spectrometer could act as an effective means of monitoring the quality of Chinese herbal medicine in circulation.