ABSTRACT
Spatial distribution uniformity is the critical quality attribute(CQA) of Ginkgo Leaves Tablets, a variety of big brand traditional Chinese medicine. The evaluation of the spatial distribution uniformity of active pharmaceutical ingredients(APIs) in Ginkgo Leaves Tablets is important in ensuring their stable and controllable quality. In this study, hyperspectral imaging technology was used to construct the spatial distribution map of API concentration based on three prediction models, further to realize the visualization research on the spatial distribution uniformity of Ginkgo Leaves Tablets. The region of interest(ROI) was selected from each Ginkgo Leaves Tablet, with length and width of 50 pixels, and a total of 2 500 pixels. Each pixel had 288 spectral channels, and the number of content prediction data could reach 1×10~5 for a single sample. The results of the three models showed that the Partial Least Squares(PLS) model had the highest prediction accuracy, with calibration set determination coefficient R_(pre)~2 of 0.987, prediction set determination coefficient R_(pre)~2 of 0.942, root mean square error of calibration(RMSEC) of 0.160%, and root mean square error of prediction(RMSEP) of 0.588%. The classical least-squares(CLS) model had a greater prediction error, with the RMSEP of 0.867%. Multivariate Curve Resolution-Alternating Least Square(MCR-ALS) model showed the worst predictive ability among the three models, and it couldn't realize content prediction. Based on the prediction results of PLS and CLS models, the spatial distribution map of APIs concentration was obtained through three-dimensional data reconstruction. Furthermore, histogram method was used to evaluate the spatial distribution uniformity of API. The data showed that the spatial distribution of APIs in Ginkgo Leaves Tablets was relatively uniform. The study explored the feasibility of visualization of spatial distribution of Ginkgo Leaves Tablets based on three models. The results showed that PLS model had the highest prediction accuracy, and MCR-ALS model had the lowest prediction accuracy. The research results could provide a new strategy for the visualization method of quality control of Ginkgo Leaves Tablets.
Subject(s)
Calibration , Ginkgo biloba , Least-Squares Analysis , Medicine, Chinese Traditional , Plant Leaves , Quality Control , Spectroscopy, Near-Infrared , TabletsABSTRACT
Identification of critical quality attribute(CQA) is crucial in quality control of Tongren Niuhuang Qingxin Pills(TRNHQXP). In this study, 661 active components in TRNHQXP were selected by liquid chromatography-mass spectrometry(LC-MS) and network pharmacology based on reported data and TCMSP, BATMAN-TCM, and TCMID databases, as well as mass spectrometry data, and 1 413 targets of the active components were obtained through SwissTargetPrediction. The 152 potential targets obtained from the intersection of predicted targets with 456 stroke targets underwent functional enrichment analysis by Metascape. The 27 Chinese medicinals in TRNHQXP were divided into four sets according to efficacies. Thirty-seven key targets in the blood-activating and stasis-resolving set and 41 in the tonifying set were screened out. On the basis of these potential key targets, 137 potential key CQA of TRNHQXP for stroke were reversely predicted. This study revealed the possible mechanism of TRNHQXP in treating stroke and established a modular identification method for the potential CQA of big brand traditional Chinese medicine(TCM) based on efficacies and chemical properties. Consequently, the CQA of TRNHQXP were identified by this method, which has provided a reference for the following experimental studies of CQA.
Subject(s)
Chromatography, Liquid , Drugs, Chinese Herbal , Medicine, Chinese Traditional , Quality ControlABSTRACT
For the field detection problems of critical quality attribute(CQA) of moisture content in traditional Chinese medicine(TCM) manufacturing process, big brand TCM Tongren Niuhuang Qingxin Pills were used as the carrier, to establish a moisture content NIR field detection model with or without cellophane in real world production with use of near infrared(NIR) spectroscopy combined with stoichiometry. With the moisture content determined by drying method as reference value, the partial least square method(PLS) was used to analyze the correlation between the spectrum and the moisture reference value. Then the spectral pretreatment methods were screened and optimized to further improve the accuracy and stability of the model. The results showed that the best quantitative model was developed by the spectral data pretreatment of standard normal variate(SNV) with the latent variable factor number of 2 and 7 of Tongren Niuhuang Qingxin Pills with or without cellophane samples. The prediction coefficient of determination(R_(pre)~2) and standard deviation of prediction(RMSEP) of the model with cellophane samples were 0.765 7 and 0.157 2%; R_(pre)~2 and RMSEP of the model without cellophane samples were 0.772 2 and 0.207 8%. The NIR quantitative models of moisture content of Tongren Niuhuang Qingxin Pills with and without cellophane both showed good predictive performance to realize the rapid, accurate and non-destructive quantitative analysis of moisture content in such pills, and provide a method for the field quality control of the critical chemical attributes of moisture in the manufacturing of big brand TCM.