Screening of potential α-glucosidase inhibitors from astragalus membranaceus by affinity ultrafiltration screening coupled with UPLC-ESI-Orbitrap-MS method.

Astragalus 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.

Rapid identification and determination of adulteration in medicinal Arnebiae Radix by combining near infrared spectroscopy with chemometrics.

The medicinal Arnebia Radix (AR) is one of widely-used Chinese herbal medicines (CHMs), usually adulterated with non-medicinal species that seriously compromise the quality of AR and affect patients' health. Detection of these adulterants is usually performed by using expensive and time-consuming analytical instruments. In this study, a rapid, non-destructive, and effective method was proposed to identify and determine the adulteration in the medicinal AR by near-infrared (NIR) spectroscopy coupled with chemometrics. 37 batches of medicinal AR samples originated from Arnebia euchroma (Royle) Johnst., 11 batches of non-medicinal AR samples including Onosma paniculatum Bur. et Franch and Arnebia benthamii (Wall. ex G. Don) Johnston, and 72 batches of adulterated AR samples were characterized by NIR spectroscopy. The data driven-soft independent modeling by class analogy (DD-SIMCA) and partial least squares-discriminant analysis (PLS-DA) were separately used to differentiate the authentic from adulterated AR samples. Then the PLS and support vector machine (SVM) were applied to predict the concentration of the adulteration in the adulterated AR samples, respectively. As a result, the classification accuracies of DD-SIMCA and PLS-DA models were 100% for the calibration set, and 96.7% vs. 100% for the prediction set. Moreover, the relative prediction deviation (RPD) values of PLS models reached 11.38 and 7.75 for quantifying two adulterants species, which were obviously superior to the SVM models. It can be concluded that the NIR spectroscopy coupled with chemometrics is feasible to identify the authentic from adulterated AR samples and quantify the adulteration in adulterated AR samples.

[Research on mechanism of hypolipidemic effect of Massa Medicata Fermentata based on metabolomics].

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.

Comprehensive identifying flavonoids in Citri Reticulatae Pericarpium using a novel strategy based on precursor ions locked and targeted MS/MS analysis.

Citri Reticulatae Pericarpium is a traditional Chinese medicine with extremely high health benefits as well as clinical value. In vivo and in vitro tests have proved that its main active secondary metabolites are flavonoids. However, they have not been comprehensively analyzed up to now mainly due to lack of suitable analysis method. To solve this problem, a novel strategy based on precursor ions locked and targeted MS/MS analysis was proposed. Firstly, the database of the flavonoids previously isolated from Citri Reticulatae Pericarpium was established to obtain the characteristics of their precursor ions. Secondly, after performing the full MS scan of the extract, all compounds in the total ion chromatogram were extracted by Compound Discoverer software. Thirdly, the precursor ions of the flavonoids were locked from the extracted compounds according to their characteristics, forming a precursor ions list. Finally, the precursor ions in the constructed list were performed targeted MS/MS analysis for structures characterization. As a result, total 187 flavonoids were successfully identified, and except for flavones, flavonols as well as dihydroflavones, some chalcones were also characterized from Citri Reticulatae Pericarpium for the first time.

A quality-comprehensive-evaluation-index-based model for evaluating traditional Chinese medicine quality.

BACKGROUND: Evaluating traditional Chinese medicine (TCM) quality is a powerful method to ensure TCM safety. TCM quality evaluation methods primarily include characterization evaluations and separate physical, chemical, and biological evaluations; however, these approaches have limitations. Nevertheless, researchers have recently integrated evaluation methods, advancing the emergence of frontier research tools, such as TCM quality markers (Q-markers). These studies are largely based on biological activity, with weak correlations between the quality indices and quality. However, these TCM quality indices focus on the individual efficacies of single bioactive components and, therefore, do not accurately represent the TCM quality. Conventionally, provenance, place of origin, preparation, and processing are the key attributes influencing TCM quality. In this study, we identified TCM-attribute-based quality indices and developed a comprehensive multiweighted multi-index-based TCM quality composite evaluation index (QCEI) for grading TCM quality. METHODS: The area of origin, number of growth years, and harvest season are considered key TCM quality attributes. In this study, licorice was the model TCM to investigate the quality indicators associated with key factors that are considered to influence TCM quality using multivariate statistical analysis, identify biological-evaluation-based pharmacological activity indicators by network pharmacology, establish real quality indicators, and develop a QCEI-based model for grading TCM quality using a machine learning model. Finally, to determine whether different licorice quality grades differently reduced the inflammatory response, TNF-α and IL-1ß levels were measured in RAW 264.7 cells using ELISA analysis. RESULTS: The 21 quality indices are suitable candidates for establishing a method for grading licorice quality. A computer model was established using SVM analysis to predict the TCM quality composite evaluation index (TCM QCEI). The tenfold cross validation accuracy was 90.26%. Licorice diameter; total flavonoid content; similarities of HPLC chromatogram fingerprints recorded at 250 and 330 nm; contents of liquiritin apioside, liquiritin, glycyrrhizic acid, and liquiritigenin; and pharmacological activity quality index were identified as the key indices for constructing the model for evaluating licorice quality and determining which model contribution rates were proportionally weighted in the model. The ELISA analysis results preliminarily suggest that the inflammatory responses were likely better reduced by premium-grade than by first-class licorice. CONCLUSIONS: In the present study, traditional sensory characterization and modern standardized processes based on production process and pharmacological efficacy evaluation were integrated for use in the assessment of TCM quality. Multidimensional quality evaluation indices were integrated with a machine learning model to identify key quality indices and their corresponding weight coefficients, to establish a multiweighted multi-index and comprehensive quality index, and to construct a QCEI-based model for grading TCM quality. Our results could facilitate and guide the development of TCM quality control research.

[Research progress and maturity assessment of continuous manufacturing of traditional Chinese medicine].

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.

Screening of Potential α-Glucosidase Inhibitors from the Roots and Rhizomes of Panax Ginseng by Affinity Ultrafiltration Screening Coupled with UPLC-ESI-Orbitrap-MS Method.

Panax ginseng was a traditional Chinese medicine with various pharmacological activities and one of its important activities was hypoglycemic activity; therefore, panax ginseng has been used in China as an adjuvant in the treatment of diabetes mellitus. In vivo and in vitro tests have revealed that ginsenosides, which are derived from the roots and rhizomes of panax ginseng have anti-diabetic effects and produce different hypoglycemic mechanisms by acting on some specific molecular targets, such as SGLT1, GLP-1, GLUTs, AMPK, and FOXO1. α-Glucosidase is another important hypoglycemic molecular target, and its inhibitors can inhibit the activity of α-Glucosidase so as to delay the absorption of dietary carbohydrates and finally reduce postprandial blood sugar. However, whether ginsenosides have the hypoglycemic mechanism of inhibiting α-Glucosidase activity, and which ginsenosides exactly attribute to the inhibitory effect as well as the inhibition degree are not clear, which needs to be addressed and systematically studied. To solve this problem, affinity ultrafiltration screening coupled with UPLC-ESI-Orbitrap-MS technology was used to systematically select α-Glucosidase inhibitors from panax ginseng. The ligands were selected through our established effective data process workflow based on systematically analyzing all compounds in the sample and control specimens. As a result, a total of 24 α-Glucosidase inhibitors were selected from panax ginseng, and it was the first time that ginsenosides were systematically studied for the inhibition of α-Glucosidase. Meanwhile, our study revealed that inhibiting α-Glucosidase activity probably was another important mechanism for ginsenosides treating diabetes mellitus. In addition, our established data process workflow can be used to select the active ligands from other natural products using affinity ultrafiltration screening.

Comprehensive Identification of Ginsenosides in the Roots and Rhizomes of Panax ginseng Based on Their Molecular Features-Oriented Precursor Ions Selection and Targeted MS/MS Analysis.

Panax ginseng is widely used in Asian countries and its active constituents-ginsenosides-need to be systematically studied. However, only a small part of ginsenosides have been characterized in the roots and rhizomes of panax ginseng (RRPG) up to date, mainly because of a lack of the fragmentation ions of many more ginsenosides. In order to comprehensively identify ginsenosides in RRPG, molecular features of ginsenosides orienting precursor ions selection and targeted tandem mass spectrometry (MS/MS) analysis strategy were proposed in our study, in which the precursor ions were selected according to the molecular features of ginsenosides irrespective of their peak abundances, and targeted MS/MS analysis was then performed to obtain their fragmentation ions for substance characterization. Using this strategy, a total of 620 ginsenosides were successfully characterized in RRPG, including 309 protopanaxadiol-type ginsenosides, 258 protopanaxatriol-type ginsenosides and 53 oleanane-type ginsenosides. It is worth noting that, except for the known aglycones mass-to-charge ratio (m/z) 459, 475 and 455, twelve other aglycones, including m/z 509, 507, 493, 491, 489, 487, 477, 473, 461, 457, 443 and 441, were first reported in our experiment and they were probably the derivatizations of the protopanaxatriol and protopanaxadiol. Our study will not only help people to improve the cognition of ginsenosides in RRPG, but will also play a guiding and reference role for the isolation and characterization of potentially new ginsenosides from RRPG.

Identification of Bletilla striata and related decoction pieces: a data fusion method combining electronic nose, electronic tongue, electronic eye, and high-performance liquid chromatography data.

Introduction: We here describe a new method for distinguishing authentic Bletilla striata from similar decoctions (namely, Gastrodia elata, Polygonatum odoratum, and Bletilla ochracea schltr). Methods: Preliminary identification and analysis of four types of decoction pieces were conducted following the Chinese Pharmacopoeia and local standards. Intelligent sensory data were then collected using an electronic nose, an electronic tongue, and an electronic eye, and chromatography data were obtained via high-performance liquid chromatography (HPLC). Partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and back propagation neural network (BP-NN) models were built using each set of single-source data for authenticity identification (binary classification of B. striata vs. other samples) and for species determination (multi-class sample identification). Features were extracted from all datasets using an unsupervised approach [principal component analysis (PCA)] and a supervised approach (PLS-DA). Mid-level data fusion was then used to combine features from the four datasets and the effects of feature extraction methods on model performance were compared. Results and Discussion: Gas chromatography-ion mobility spectrometry (GC-IMS) showed significant differences in the types and abundances of volatile organic compounds between the four sample types. In authenticity determination, the PLS-DA and SVM models based on fused latent variables (LVs) performed the best, with 100% accuracy in both the calibration and validation sets. In species identification, the PLS-DA model built with fused principal components (PCs) or fused LVs had the best performance, with 100% accuracy in the calibration set and just one misclassification in the validation set. In the PLS-DA and SVM authenticity identification models, fused LVs performed better than fused PCs. Model analysis was used to identify PCs that strongly contributed to accurate sample classification, and a PC factor loading matrix was used to assess the correlation between PCs and the original variables. This study serves as a reference for future efforts to accurately evaluate the quality of Chinese medicine decoction pieces, promoting medicinal formulation safety.

Data fusion of electronic noses and electronic tongues aids in botanical origin identification on imbalanced Codonopsis Radix samples.

Codonopsis Radix (CR) is an edible food and traditional Chinese herb medicine in China. Various varieties of Codonopsis Radix have different tastes. To make the flavor of processed food stable, two kinds of electronic sensory devices, electronic nose and electronic tongue, were used to establish a discrimination model to identify the botanical origin of each sample. The optimal model built on the 88 batches of samples was selected from the models trained with all combination of two pretreatment methods and three classification methods. A comparison were performed on the models trained on the data collected by electronic nose and electronic tongue. The results showed that the model trained on the fused dataset outperformed the models trained separately on the electronic nose data and electronic tongue data. The two preprocessing approaches could improve the prediction performance of all classification methods. Classification and Regression Tree approach performed better than Partial Least Square Discriminant Analysis and Linear Discriminant Analysis in terms of accuracy. But Classification and Regression Tree tends to assign the samples of minority class to the majority class. Meanwhile, Partial Least Square Discriminant Analysis keeps a good balance between the identification requirements of all the two groups of samples. Taking all the results above, the model built using the Partial Least Square Discriminant Analysis method on the fused data after z-score was used to identify the botanical origin of Codonopsis Radix.

Improving natural products identification through molecular features-oriented precursor ion selection and targeted MS/MS analysis: a case study of Zhi-Ke-Yang-Yin capsule.

Chemical substance identification is an indispensable step in research on therapeutic materials based on traditional Chinese medicine and its formulas. The successful characterization of chemical substances mainly relies on high-quality MS/MS spectra. However, to date, relatively few studies have specifically addressed the issues of improving the acquisition of MS/MS spectra of compounds for characterization. The current auto-MS/MS mode, where the precursor ions are selected depending on their signal intensity, encounters a drawback when the sample contains many overlapping signals, leading to compounds with a lower or much lower abundance missing identification. To solve this problem, a strategy in which molecular features oriented precursor ion selection was followed by targeted MS/MS analysis for structure elucidation was proposed. The precursor ions were selected according to their first and second molecular features, namely m/z and retention time, irrespective of their intensities. By performing targeted MS/MS analysis, the MS/MS spectra of many more compounds of interest can be obtained, leading to an improvement in natural product identification. As an example, the chemical substances in the Zhi-Ke-Yang-Yin extract were analyzed using this strategy, and as a result, 431 ingredients were tentatively characterized, including both known and unknown or new compounds.

[Effect of Danhong Injection on platelet metabolites based on metabonomics technology].

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.

[A rapid identification of authenticity and specifications of Chinese medicine Fritillariae Cirrhosae Bulbus based on E-eye technology].

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.

[Key technologies and applications of industrial big data in manufacturing of Chinese medicine].

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.

Highly selective monitoring of in-source fragmentation sapogenin product ions in positive mode enabling group-target ginsenosides profiling and simultaneous identification of seven Panax herbal medicines.

In-source fragmentation of ginsenosides in the positive ESI mode (pISF-G) frequently occurs, which results in little fragment information useful for the structural elucidation. We are aimed to unveil the genesic mechanism and explore its potential significance in quality control of Ginseng and the related compound formulae. By applying six high-resolution mass spectrometers from Agilent, Waters, and Thermo Fisher, we could primarily demonstrate the susceptibility of pISF-G. The ion clusters in the positive full-scan MS1 spectra were generated from the protonated sapogenins by successive elimination of H2O, and showed specificity for ginsenoside classification. Selective ion monitoring (SIM) of the sapogenin product ions could delineate group-target ginsenoside profiles from Ginseng. A high-selectivity characteristic chromatogram (CC) was elaborated for Ginseng, on the Vion™ IMS-QTOF mass spectrometer by IM (ion mobility) separation and quadrupole filtering of four sapogenin fragments (m/z 407.37/CCS 206.24 Å2; m/z 423.36/CCS 211.26 Å2; m/z 439.36/CCS 209.60 Å2; m/z 457.37/CCS 217.81 Å2). Chemometric analysis, based on the CC data of seven Ginseng drugs (P. ginseng, P. quinquefolius, P. notoginseng, Red ginseng, leaf of P. ginseng, P. japonicus, and P. japonicus var. major), disclosed 35 marker compounds. We could readily discriminate among P. ginseng, P. quinquefolius, and P. notoginseng, in 15 different compound formulae by identifying these marker compounds on both the Vion IMS-QTOF and QTrap 4500 mass spectrometers. Conclusively, SIM of the pISF-G sapogenin product ions renders a new concept of CC enabling the group-target profiling of ginsenosides and authentication of Ginseng and the related compound formulae.

Discovering Temporal Patterns in Longitudinal Nontargeted Metabolomics Data via Group and Nuclear Norm Regularized Multivariate Regression.

Temporal associations in longitudinal nontargeted metabolomics data are generally ignored by common pattern recognition methods such as partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). To discover temporal patterns in longitudinal metabolomics, a multitask learning (MTL) method employing structural regularization was proposed. The group regularization term of the proposed MTL method enables the selection of a small number of tentative biomarkers while maintaining high prediction accuracy. Meanwhile, the nuclear norm imposed into the regression coefficient accounts for the interrelationship of the metabolomics data obtained on consecutive time points. The effectiveness of the proposed method was demonstrated by comparison study performed on a metabolomics dataset and a simulating dataset. The results showed that a compact set of tentative biomarkers charactering the whole antipyretic process of Qingkailing injection were selected with the proposed method. In addition, the nuclear norm introduced in the new method could help the group norm to improve the method's recovery ability.

A Novel Framework to Aid the Development of Design Space across Multi-Unit Operation Pharmaceutical Processes-A Case Study of Panax Notoginseng Saponins Immediate Release Tablet.

The fundamental principle of Quality by Design (QbD) is that the product quality should be designed into the process through an upstream approach, rather than be tested in the downstream. The keystone of QbD is process modeling, and thus, to develop a process control strategy based on the development of design space. Multivariate statistical analysis is a very useful tool to support the implementation of QbD in pharmaceutical process development and manufacturing. Nowadays, pharmaceutical process modeling is mainly focused on one-unit operations and system modeling for the development of design space across multi-unit operations is still limited. In this study, a general procedure that gives a holistic view for understanding and controlling the process settings for the entire manufacturing process was investigated. The proposed framework was tested on the Panax Notoginseng Saponins immediate release tablet (PNS IRT) production process. The critical variables and the critical units acting on the process were identified according to the importance of explaining the variability in the multi-block partial least squares path model. This improved understanding of the process by illustrating how the properties of the raw materials, the process parameters in the wet granulation and the compaction and the intermediate properties affect the tablet properties. Furthermore, the design space was developed to compensate for the variability source from the upstream. The results demonstrated that the proposed framework was an important tool to gain understanding and control the multi-unit operation process.

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.

[Online endpoint detection algorithm for blending process of Chinese materia medica].

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.

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.