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.

Sample Size Requirements of a Pharmaceutical Material Library: A Case in Predicting Direct Compression Tablet Tensile Strength by Latent Variable Modeling.

The material library is an emerging, new data-driven approach for developing pharmaceutical process models. How many materials or samples should be involved in a particular application scenario is unclear, and the impact of sample size on process modeling is worth discussing. In this work, the direct compression process was taken as the research object, and the effects of different sample sizes of material libraries on partial least squares (PLS) modeling in the prediction of tablet tensile strength were investigated. A primary material library comprising 45 materials was built. Then, material subsets containing 5 × i (i = 1, 2, 3, …, 8) materials were sampled from the primary material library. Each subset underwent sampling 1000 times to analyze variations in model fitting performance. Both hierarchical sampling and random sampling were employed and compared, with hierarchical sampling implemented with the help of the tabletability classification index d. For each subset, modeling data were organized, incorporating 18 physical properties and tableting pressure as the independent variables and tablet tensile strength as the dependent variable. A series of chemometric indicators was used to assess model performance and find important materials for model training. It was found that the minimum R2 and RMSE values reached their maximum, and the corresponding values were kept almost unchanged when the sample sizes varied from 20 to 45. When the sample size was smaller than 15, the hierarchical sampling method was more reliable in avoiding low-quality few-shot PLS models than the random sampling method. Two important materials were identified as useful for building an initial material library. Overall, this work demonstrated that as the number of materials increased, the model's reliability improved. It also highlighted the potential for effective few-shot modeling on a small material library by controlling its information richness.

Synergistic effect of Euphorbia kansui stir-fried with vinegar and bile acids on malignant ascites effusion through modulation of gut microbiota.

Background: Toxic Euphorbia kansui (EK) is employed to treat malignant ascites effusion (MAE). EK stir-fried with vinegar (VEK) has been demonstrated to reduce toxicity due to its preserved water-expelling effect. This was demonstrated to be correlated with gut microbiota. Therein, bile acids (BAs) have a bidirectional relationship with the gut microbiota. Therefore, the aim of this study is to explore whether BA-mediated gut microbiota influences the water-expelling effect of VEK against MAE. Methods: The MAE rat model was established by intraperitoneal injection of Walker-256 tumor cells. A reliable simultaneous method for the determination of 15 bile acids in rat feces using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was established and applied to analyze the fecal BAs in rats treated with VEK. The screened BA was then administered to VEK-treated MAE rats. The water-expelling effect was evaluated using histopathological analysis, biochemical examination, inflammatory factors in ascites, urine volume, ascites amount, and intestinal aquaporin expression. The microbial composition was determined using 16S rRNA sequencing, and the contents of bile acids were finally measured. Results: VEK decreased the content of fecal deoxycholic acid (DCA), lithocholic acid (LCA), and taurocholic acid (TCA) while increasing the content of ursodeoxycholic acid (UDCA). VEK alleviated liver, stomach, and intestinal injuries; oxidative damage; and inflammation, which were further ameliorated with UDCA intervention. VEK alleviated MAE by increasing the fecal water content, urine volume, and AQP3 protein expression and decreasing the urine levels of Na+, K+, and Cl-. This was retained with the intervention of UDCA. UDCA and VEK regulated the BA metabolism disorder to a certain extent. Analysis of gut microbiota showed that VEK increased the abundance of Lactobacillus and decreased that of Prevotella_9 in MAE rats. The combined administration of UDCA and VEK showed a better modulation of the microbiota structure than that of VEK alone, and the effect of this administration reached closer to the reference state. Conclusion: The water-expelling effect of VEK did not directly depend on the BA-mediated gut microbiota. However, VEK and BAs had a synergistic effect on malignant ascites effusion through the regulation of the gut microbiota. These results provided a scientific basis for the reasonable usage of VEK and the novel combination treatment strategy of VEK and UDCA.

[Manufacturing classification system for oral solid dosage forms of traditional Chinese medicines (â ¡): classification of tablets disintegration behavior].

In this paper, 50 batches of representative traditional Chinese medicine tablets were selected and the disintegration time was examined with the method in Chinese Pharmacopoeia. The disintegration time and disintegration phenomenon were recorded, and the dissolution behaviors of water-soluble and ultraviolet-absorbent components during the disintegration process of tablets were characterized by self-control method. The results revealed that coating type and raw material type influenced the disintegration time of tablets. It was found that only 4% of traditional Chinese medicine tablets had obvious fragmentation during the disintegration process, while 96% of traditional Chinese medicine tablets showed gradual dissolution or dispersion. Furthermore, according to the disintegration speed, disintegration phenomenon, and whether the cumulative dissolution of measured components was > 90% at complete disintegration, a disintegration behavior classification system(DBCS) was created for the regular-release traditional Chinese medicine tablets. As a result, the disintegration behaviors of 50 batches of traditional Chinese medicine tablets were classified into four categories, i.e. ⅠA_2, ⅠB_1, ⅡB_1, and ⅡB_2. traditional Chinese medicine tablets(Class I) with disintegration time ≤ 30 min were defined to be rapid in disintegration, which can be the objective of optimization or improvement of Chinese herbal extract(semi extract) tablets. Different drug release models were used to fit the dissolution curve of traditional Chinese medicine tablets with gradual dissolution or dispersion phenomenon(i.e. Type B tablets). The results showed that the dissolution curves of water-soluble components in the disintegration process conformed to the zero order kinetics and the Ritger-Peppas model. It could be inferred that the disintegration mechanisms of type B tablets were a combination of dissolution controlled and swelling controlled mechanisms. This study contributes to understanding the disintegration behavior of traditional Chinese medicine tablets, and provides a reference for the design and improvement of disintegration performance of traditional Chinese medicine tablets.

[Comparison of alkaloids in Aconiti Kusnezoffii Radix, Aconiti Radix, and Aconiti Lateralis Radix Praeparata based on UHPLC-Q-Exactive Orbitrap MS/MS].

UHPLC-Q-Exactive Orbitrap MS/MS was used to systematically analyze and compare the alkaloids in Aconiti Kusnezoffii Radix, Aconiti Radix, and Aconiti Lateralis Radix Praeparata. After the samples were pretreated in the solid-phase extraction cartridges, 0.1% ammonium hydroxide(A)-acetonitrile(B) was used for gradient elution. The LC-MS method for characterization of alkaloids in the three herbal medicines was established in ESI positive ion mode to collect high resolution MS data of reference substances and samples. On the basis of the information of reference substance cracking behavior, retention time, accurate molecular mass, and related literature, a total of 155 alkaloids were identified in Aconiti Kusnezoffii Radix, Aconiti Radix, and Aconiti Lateralis Radix Prae-parata. Specifically, 130, 127, and 92 alkaloids were identified in Aconiti Kusnezoffii Radix, Aconiti Radix, and Aconiti Lateralis Radix Praeparata, respectively. Monoester alkaloids and amino-alcohol alkaloids were dominant in the three herbal medicines, and the alkaloids in Aconiti Kusnezoffii Radix and Aconiti Radix were similar. This paper can provide a reference for elucidating the pharmacological effects and clinical application differences of the three herbal medicines produced from plants of Aconitum.

The interaction between polyphyllin I and SQLE protein induces hepatotoxicity through SREBP-2/HMGCR/SQLE/LSS pathway.

Polyphyllin I (PPI) and polyphyllin II (PII) are the main active substances in the Paris polyphylla. However, liver toxicity of these compounds has impeded their clinical application and the potential hepatotoxicity mechanisms remain to be elucidated. In this work, we found that PPI and PII exposure could induce significant hepatotoxicity in human liver cell line L-02 and zebrafish in a dose-dependent manner. The results of the proteomic analysis in L-02 cells and transcriptome in zebrafish indicated that the hepatotoxicity of PPI and PII was associated with the cholesterol biosynthetic pathway disorders, which were alleviated by the cholesterol biosynthesis inhibitor lovastatin. Additionally, 3-hydroxy-3-methy-lglutaryl CoA reductase (HMGCR) and squalene epoxidase (SQLE), the two rate-limiting enzymes in the cholesterol synthesis, selected as the potential targets, were confirmed by the molecular docking, the overexpression, and knockdown of HMGCR or SQLE with siRNA. Finally, the pull-down and surface plasmon resonance technology revealed that PPI could directly bind with SQLE but not with HMGCR. Collectively, these data demonstrated that PPI-induced hepatotoxicity resulted from the direct binding with SQLE protein and impaired the sterol-regulatory element binding protein 2/HMGCR/SQLE/lanosterol synthase pathways, thus disturbing the cholesterol biosynthesis pathway. The findings of this research can contribute to a better understanding of the key role of SQLE as a potential target in drug-induced hepatotoxicity and provide a therapeutic strategy for the prevention of drug toxic effects with similar structures in the future.

Application of Data Fusion in Traditional Chinese Medicine: A Review.

Traditional Chinese medicine is characterized by numerous chemical constituents, complex components, and unpredictable interactions among constituents. Therefore, a single analytical technique is usually unable to obtain comprehensive chemical information. Data fusion is an information processing technology that can improve the accuracy of test results by fusing data from multiple devices, which has a broad application prospect by utilizing chemometrics methods, adopting low-level, mid-level, and high-level data fusion techniques, and establishing final classification or prediction models. This paper summarizes the current status of the application of data fusion strategies based on spectroscopy, mass spectrometry, chromatography, and sensor technologies in traditional Chinese medicine (TCM) in light of the latest research progress of data fusion technology at home and abroad. It also gives an outlook on the development of data fusion technology in TCM analysis to provide references for the research and development of TCM.

Identification of intrinsic hepatotoxic compounds in Polygonum multiflorum Thunb. using machine-learning methods.

ETHNOPHARMACOLOGICAL RELEVANCE: Polygonum multiflorum Thunb. (PM) is a herb, extracts of which have been used as Chinese medicine for years. Although it is believed to be beneficial to the liver, heart, and kidneys, it causes idiosyncratic drug-induced liver injury (DILI). AIM OF THE STUDY: We propose that the intrinsic DILI caused by natural products in PM (NPPM) is an important complementary mechanism to PM-related herb-induced liver injury, and aim to identify the ingredients with high DILI potential by machine learning methods. MATERIALS AND METHODS: One hundred and ninety-seven NPPM were collected from the literature to identify the intrinsic hepatotoxic compounds. Additionally, a DILI-labeled dataset consisting of 2384 compounds was collected and randomly split into training and test sets. A diparametric optimization method was developed to tune the parameters of extended-connectivity fingerprints (ECFPs), Rdkit, and atom-pair fingerprints as well as those of machine-learning (ML) algorithms. Subsequently, K means were employed to cluster the NPPM that were predicted to have a high DILI risk. An in vitro cell-viability assay was performed using HepaRG cells to validate the prediction results. RESULTS: ECFPs with the top 35% of features ranked by the F-value with support vector machine (SVM) yielded the best performance. The optimized SVM model achieved an accuracy of 0.761 and recall value of 0.834 on the test dataset. The silico screening for NPPM resulted in 47 ingredients with high DILI potential, which were clustered into six groups based on the elbow method. A representative subgroup that contained 21 ingredients, of which two dianthrones exhibited the lowest IC50 value (0.7-0.9 µM) and anthraquinones showed moderate toxicity (15-25 µM), was constructed. CONCLUSION: Using ML methods and in vitro screening, two classes of compounds, dianthrones and anthraquinones, were predicted and validated to have a high risk of DILI. The diparametric optimization method used in this study could provide a useful and powerful tool to screen toxicants for large datasets and is available at https://github.com/dreadlesss/Hepatotoxicity_predictor.

A general procedure for establishing composite quality evaluation indices based on key quality attributes of traditional Chinese medicine.

Licorice, a medicinal herb and food flavor ingredient, has been widely used in traditional Chinese medicine (TCM) for the past 4000 years. In this study, we propose a new quality evaluation approach for licorice quality control based on the key quality attributes commonly used in TCM. The high quality of TCM formulations is ensured by verifying the genuine origin and implementing good agricultural and collection practices for each medicinal herb. In our study, the genuine production area, the harvest season, and the number of growth years were considered the key quality attributes of TCM. To ensure the representativeness of our analysis, we obtained a total of 158 licorice sample batches that differed in the number of growth years, the location of the production areas, and the season for harvesting. Initially, the 158 sample batches were subjected to ultra-high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS/MS). A preliminary screen identified 11 licorice compounds related to the three key quality attributes of TCM . An analysis by ultra-high-performance liquid chromatography coupled with triple quadrupole tandem mass spectrometry (UHPLC-TQ-MS/MS) verified the presence of 34 compounds in all licorice samples. These 34 compounds included the 11 compounds related to the three key quality attributes of the samples, along with other bioactive components identified in previous studies. After using UHPLC-TQ-MS/MS to assess the signal peak intensities of the 34 compounds, we selected 17 licorice compounds to establish sample content evaluation indices, which were determined by high-performance liquid chromatography at four different wavelengths in all 158 licorice sample batches. Finally, the screen identified nine compounds that were closely associated with the quality attributes of licorice based on principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Our results suggested that liquiritin and eight other compounds could be used as quality control indicators of licorice, which provided a foundation to establish the TCM quality composite evaluation index (TCM QCEI). In summary, this research concept can serve as a reference for research on quality markers and the evaluation of TCM.

[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.

[Evaluation and classification of dissolution behavior and capability of Chinese medicine granules based on an inline turbidity sensor].

In this paper, the inline turbidity sensor technology was used to quantify the turbidity of the solution during the dissolution of Chinese medicine granules. The probe measurement position and the magnetic stirring speed were optimized. As a result, the stirring speed was 400 r·min~(-1), and the probe position was at 1/4 of the diameter of the beaker. The measurement results were accurate and reliable. Totally 105 batches of commercially available Chinese medicine granules were collected and dissolved according to the requirements of the Chinese Pharmacopoeia. At the time point of 5 min, 57 batches of granules were completely dissolved, and the corresponding turbidity values ranged between 0-70 FTU; 32 batches of granules showed a slight turbidity, and the corresponding turbidity values ranged between 70-350 FTU; 14 batches of granule solution were turbid, and the corresponding turbidity values ranged between 350-2 000 FTU; two batches of granule solution were heavily turbid, and the corresponding turbidity values were >2 000 FTU. Among the above results, the number of batches in line with the pharmacopoeia dissolution requirement was 84.76%, and the dissolution of some granules still needed to be improved. The turbidity sensor recorded the change curve of turbidity value over time(solubility behavior curve). The degree of important of disintegration and dissolution during the dissolution process showed disintegration > dissolution, disintegration≈dissolution, disintegration < dissolution. The dissolution behavior of the granules can be classified into three categories. The analysis of the mechanism in the process of granule solubility provides a basis for product process improvement.

[Mechanism of "herb soaking with exact amount of water" during moistening process of ginseng based on needle pressure sensor].

In this study, the texture analyzer acupuncture pressure sensor was used to objectively characterize the "herb soaking with exact amount of water" for moistening process of ginseng. The single factor rotation experiment was used to investigate the effects of puncture speed, puncture depth and puncture site on puncture force and work. According to ginseng processing method in Chinese Pharmacopoeia, ginseng medicinal materials with diameters of about 1 cm and 2 cm were selected, and puncture experiments were carried out at the set measurement time to determine the hardness, work and water absorption of the ginseng moistening process. The endpoint threshold for the ginseng softening process was determined and verified. To reflect the actual internal conditions of the ginseng softening process, the puncture depth was preferably 70%, and the puncture speed was 30 mm·min~(-1). In the ginseng moistening process, the softening hardness and the puncture work were in accordance with the first-order kinetic equation y=a×exp(-k×x). The 0 h initial hardness a of 1 cm and 2 cm ginseng herbs were 289.8 N and 1 227 N, and the rate constants K were 0.149 4 N·h~(-1) and 0.100 7 N·h~(-1), respectively. After the ginseng was completely softened, the force required for puncture was 10 N, which can be used as the standard for "drug penetration". At this time, the water absorption rate of ginseng was 70%-100%. The softening time of ginseng with a diameter of 1 cm was about 20-22 h, and the softening time of ginseng with a diameter of 2 cm was about 40-46 h. A needle-type pressure sensor was used to accurately determine the end point of the softening process of ginseng and reduce the loss of active ingredients. The study results provide reference for the softening process kinetics and the process intelligent monitoring of other dried roots and rhizomes.

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.

[UHPLC-HRMS metabonomics to study effect of sulfur-fumigated Ophiopogonis Radix on endogenous metabolites in rats].

The project was launched to analyze the effects of sulfur-fumigated Ophiopogonis Radix on endogenous metabolites in rats by metabonomics. The preparation method of sulfur-fumigated Ophiopogonis Radix in laboratory was established. Then the blood samples of SD rats in blank group,Ophiopogonis Radix extract group and sulfur-fumigated Ophiopogonis Radix extract group were investigated by UHPLC-Q-Exactive. The differential metabolites were screened and identified by PCA(principal component analysis),OPLSDA(orthogonal partial least squares discriminant analysis) and variable importance projection(VIP),and the metabolic pathways were analyzed. Finally,a total of 15 potential biomarkers were identified. Compared with the samples of Ophiopogonis Radix extract group,sulfur-fumigated Ophiopogonis Radix mainly affected the biosynthesis and metabolism of amino acids in normal rats. Its mechanism may be related to the biosynthesis of phenylalanine,tyrosine,tryptophan and aminoacyl-tRNA as well as the metabolism of phenylalanine and tryptophan. Based on UHPLC-HRMS metabonomics,this paper discussed the effects of sulfur-fumigated Ophiopogonis Radix on endogenous metabolites in rats,which provided an idea for the metabolic study of other sulfur-fumigated traditional Chinese medicines.

A compression behavior classification system of pharmaceutical powders for accelerating direct compression tablet formulation design.

In this paper, a compression behavior classification system (CBCS) for direct compression (DC) pharmaceutical powders is presented. Seven descriptors from a series of compression models for powder compressibility, compactibility and tabletability analysis were included in the CBCS. A new tabletability index d was proposed to differentiate three categories of tensile strength (TS) vs. pressure relationships, and its physical meaning was explained thoroughly. 130 materials containing diverse pharmaceutical excipients and natural product powders (NPPs) were fully characterized and were compiled into an in-house developed material library, in which 70 materials with potential DC applications were used to justify the effectiveness of the CBCS. Principle component analysis (PCA) was used to uncover the latent structure of compression variables. Moreover, partial least squares (PLS) regression models are established in prediction of both tablet TS and solid fraction (SF) based on the raw materials' physical characteristics, the compression behavior indices and the compression force. The obtained scores and loadings are used to group the materials and the compression variables, respectively. Different categories of tabletability for DC powders were clearly clustered along two orthogonal directions pointing to the index d and the compression force. Finally, a multi-objective design space was identified under the latent variable space, summarizing the operationally possible region for both material properties and compression pressure required in DC tablet formulation design.

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.

Optimization of PLS modeling parameters via quality by design concept for Gardenia jasminoides Ellis using online NIR sensor.

This paper discussed the process parameters optimization of partial least-square (PLS) modeling according to quality by design (QbD) concept. D-optimal design and online near-infrared (NIR) sensor were proposed to analysis the Geniposide in Gardenia jasminoides Ellis using above process parameters to achieve robustness PLS model. Four critical model parameters (CMPs) were identified to construct a D-optimal design, which included the selection of sample set, spectra pre-processing, latent variables and variable selection methods. NIR sensor dataset was obtained under a pilot scale system. The D-optimal design optimization strategy resulted in a robust PLS model with the optimal parameters, 1/2 samples for calibration sets through Baseline spectra pre-processing with SiPLS-selecting variables under 8 factors. The critical evaluation attributes (CEAs) of PLS model were recommended as follows: the RMSEC and Rcal2 of the calibration set were 0.005901 and 0.9983. The RMSEP and Rpre2 of the validation set were 0.02002 and 0.9845. The multivariate detection limit (MDL) was 1.143 × 10-3. Therefore, design space of CMPs which affected CEAs of PLS model was established. The result demonstrated that the proposed method was beneficial for the robustness of PLS model, which also showed a significant guideline for the design and development of PLS model.

Optimal Selection of Incoming Materials from the Inventory for Achieving the Target Drug Release Profile of High Drug Load Sustained-Release Matrix Tablet.

In the pharmaceutical process, raw material (including APIs and excipients) variability can be delivered to the final product, and lead to batch-to-batch and lot-to-lot variances in its quality, finally impacting the efficacy of the drug. In this paper, the Panax notoginseng saponins (PNS) sustained-release matrix tablet was taken as the model formulation. Hydroxypropyl methylcellulose with the viscosity of 4000 mPa·s (HPMCK4M) from different vendors and batches were collected and their physical properties were characterized by the SeDeM methodology. The in-vitro dissolution profiles of active pharmaceutical ingredients (APIs) from matrix tablets made up of different batches HPMC K4M displayed significant variations. Multi-block partial least squares (MB-PLS) modeling results further demonstrated that physical properties of excipients played dominant roles in the drug release. In order to achieve the target drug release profile with respect to those far from the criteria, the optimal selection method of incoming materials from the available was established and validated. This study provided novel insights into the control of the input variability of the process and amplified the application of the SeDeM expert system, emphasizing the importance of the physical information of the raw materials in the drug manufacturing process.

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.

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.