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Ru C, Wen W, Zhong Y. Raman spectroscopy for on-line monitoring of botanical extraction process using convolutional neural network with background subtraction. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 284:121494. [PMID: 35715369 DOI: 10.1016/j.saa.2022.121494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/02/2022] [Accepted: 06/07/2022] [Indexed: 06/15/2023]
Abstract
Aqueous extraction is the most common and cost-effective means of obtaining active ingredients from medicinal plants. However, botanical extracts generally contain high pigment content and complex chemical composition posing a challenge for the process analysis of aqueous extraction. Here, we employed Raman spectroscopy to monitor the physical and chemical properties during the extraction process using convolution neural network (CNN) with background subtraction. Real-time spectra were first preprocessed to eliminate fluorescence background interference. Next, two types of CNN models, the one-dimensional CNN (1D-CNN) based on one preprocessing method, and two-dimensional CNN (2D-CNN) based on a concatenation of differentially pretreated data blocks, were used to receive the preprocessed spectra data. Two case studies were conducted for 1D- and 2D-CNN: the extraction of Aurantii fructus, and the co-extraction of Radix Salvia miltiorrhiza and Rhizoma Ligusticum chuanxiong. Furthermore, partial least squares (PLS) models and sequential preprocessing through orthogonalization (SPORT) models were developed and compared with 1D-CNN and 2D-CNN, respectively. CNN-based methods were superior to other models in terms of prediction accuracy, with 2D-CNN yielding the best results. These results indicated that preprocessing and CNN methods were highly complementary, and could effectively remove the fluorescence effect and artefacts introduced by pretreatment in spectral profile. To the best of our knowledge, this is the first study to demonstrate that a combination of preprocessing and CNN leads to improved prediction performance of analytes when using Raman spectroscopy for online monitoring high-pigmented samples.
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Affiliation(s)
- Chenlei Ru
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China.
| | - Wu Wen
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yi Zhong
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Zhang Boli Intelligent Health Innovation Lab, Hangzhou 311121, China
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Nagy MM, Wang S, Farag MA. Quality analysis and authentication of nutraceuticals using near IR (NIR) spectroscopy: A comprehensive review of novel trends and applications. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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3
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Li T, Yang WZ, Song TX, Liu CJ, Jiang MM. Integrating chemical profiling and network pharmacology analysis based on anti-inflammatory effects for quality control of Scutellaria barbata. PHYTOCHEMICAL ANALYSIS : PCA 2021; 32:1141-1151. [PMID: 33949013 DOI: 10.1002/pca.3055] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/15/2021] [Accepted: 04/08/2021] [Indexed: 06/12/2023]
Abstract
INTRODUCTION With the wide application of Scutellaria barbata D. Don for hepatitis and mastitis, its quality control issues have also received increasing attention. Based on the multi-component and multi-target characteristics of traditional Chinese medicine, there is an urgent need to establish a quality evaluation system. OBJECTIVES This study intends to integrate the "quality-activity-quantification" strategy and establish an activity-related quality control method to ensure the safety and effectiveness of S. barbata. MATERIAL AND METHODS Ultra-high performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (UPLC/IM-QTOF-MS) was used to characterize the chemical components of S. barbata, and network pharmacological analysis was carried out on the identified components. The index components were determined on the basis of comprehensive activity prediction results and content information. At the same time, the contents of 16 batches of S. barbata from different origins were determined. RESULTS A total of 94 compounds were identified according to mass spectrometric data, 12 of which were isolated and structure-confirmed by nuclear magnetic resonance technology. Network pharmacological analysis was applied to predict their key targets and the major pathways mediating their anti-inflammatory effects. On the basis of comprehensive activity prediction and content information, five components were chosen as crucial quality indicators of S. barbata, including scutellarin, scutellarein, luteolin, apigenin, and hispidulin. CONCLUSION In this study, 16 different S. barbata batches were compared, and five quality indicators were determined on the basis of qualitative and activity results. The present study provides useful information for evaluating the quality of S. barbata in different areas, and also provides a new basis for the development of quality evaluation methods.
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Affiliation(s)
- Tong Li
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Wen-Zhi Yang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Tong-Xin Song
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Cheng-Juan Liu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Miao-Miao Jiang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
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Zhou L, Zhang C, Qiu Z, He Y. Information fusion of emerging non-destructive analytical techniques for food quality authentication: A survey. Trends Analyt Chem 2020. [DOI: 10.1016/j.trac.2020.115901] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Li Y, Shen Y, Yao CL, Guo DA. Quality assessment of herbal medicines based on chemical fingerprints combined with chemometrics approach: A review. J Pharm Biomed Anal 2020; 185:113215. [DOI: 10.1016/j.jpba.2020.113215] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 01/08/2020] [Accepted: 02/26/2020] [Indexed: 12/30/2022]
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Yan X, Zhang S, Fu H, Qu H. Combining convolutional neural networks and on-line Raman spectroscopy for monitoring the Cornu Caprae Hircus hydrolysis process. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 226:117589. [PMID: 31634714 DOI: 10.1016/j.saa.2019.117589] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 09/29/2019] [Accepted: 09/30/2019] [Indexed: 06/10/2023]
Abstract
Cornu Caprae Hircus (goat horn, GH) is one of the frequently used medicinal animal horns in traditional Chinese medicine (TCM). Hydrolysis is one of the key steps for GH pretreatment in pharmaceutical manufacturing. However, the physicochemical complexity of the hydrolysis samples imposes a challenge for hydrolysis process analysis and monitoring. In this study, convolutional neural networks (CNNs), one of the most popular deep learning methods, were used to develop quantitative calibration models based on on-line Raman spectroscopy for monitoring the GH hydrolysis process. Partial least squares (PLS) calibration models were also developed for model performance comparison. For CNN modeling, raw Raman spectra were used as inputs and hyperparameters in the CNN structure were optimized. Results show for four of the seven analytes, the optimized CNN models using raw spectra as inputs outperform the optimized PLS models developed with preprocessed spectra. Therefore, compared with the commonly used PLS algorithm, CNN modeling is also a practicable regression method and can be employed for the analytical purpose of this study. Models with better performance are expected to be obtained by improving the CNN model structure and using more effective hyperparameter optimization approaches in further studies. To the best of our knowledge, this is the first reported case study of combining CNNs and on-line Raman spectroscopy for a regression task.
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Affiliation(s)
- Xu Yan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Sheng Zhang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Hao Fu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Haibin Qu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
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Yang Y, Ju Z, Yang Y, Zhang Y, Yang L, Wang Z. Phytochemical analysis of Panax species: a review. J Ginseng Res 2020; 45:1-21. [PMID: 33437152 PMCID: PMC7790905 DOI: 10.1016/j.jgr.2019.12.009] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 12/29/2019] [Accepted: 12/31/2019] [Indexed: 12/22/2022] Open
Abstract
Panax species have gained numerous attentions because of their various biological effects on cardiovascular, kidney, reproductive diseases known for a long time. Recently, advanced analytical methods including thin layer chromatography, high-performance thin layer chromatography, gas chromatography, high-performance liquid chromatography, ultra-high performance liquid chromatography with tandem ultraviolet, diode array detector, evaporative light scattering detector, and mass detector, two-dimensional high-performance liquid chromatography, high speed counter-current chromatography, high speed centrifugal partition chromatography, micellar electrokinetic chromatography, high-performance anion-exchange chromatography, ambient ionization mass spectrometry, molecularly imprinted polymer, enzyme immunoassay, 1H-NMR, and infrared spectroscopy have been used to identify and evaluate chemical constituents in Panax species. Moreover, Soxhlet extraction, heat reflux extraction, ultrasonic extraction, solid phase extraction, microwave-assisted extraction, pressurized liquid extraction, enzyme-assisted extraction, acceleration solvent extraction, matrix solid phase dispersion extraction, and pulsed electric field are discussed. In this review, a total of 219 articles published from 1980 to 2018 are investigated. Panax species including P. notoginseng, P. quinquefolius, sand P. ginseng in the raw and processed forms from different parts, geographical origins, and growing times are studied. Furthermore, the potential biomarkers are screened through the previous articles. It is expected that the review can provide a fundamental for further studies.
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Affiliation(s)
- Yuangui Yang
- The MOE Key Laboratory for Standardization of Chinese Medicines and the SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, China
| | - Zhengcai Ju
- The MOE Key Laboratory for Standardization of Chinese Medicines and the SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, China
| | - Yingbo Yang
- The MOE Key Laboratory for Standardization of Chinese Medicines and the SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, China
| | - Yanhai Zhang
- The MOE Key Laboratory for Standardization of Chinese Medicines and the SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, China
| | - Li Yang
- The MOE Key Laboratory for Standardization of Chinese Medicines and the SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, China.,Shanghai R&D Center for Standardization of Chinese Medicines, China
| | - Zhengtao Wang
- The MOE Key Laboratory for Standardization of Chinese Medicines and the SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, China.,Shanghai R&D Center for Standardization of Chinese Medicines, China
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Yan X, Fu H, Zhang S, Qu H. Combining convolutional neural networks and in-line near-infrared spectroscopy for real-time monitoring of the chromatographic elution process in commercial production of notoginseng total saponins. J Sep Sci 2019; 43:663-670. [PMID: 31674130 DOI: 10.1002/jssc.201900874] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 10/17/2019] [Accepted: 10/25/2019] [Indexed: 11/10/2022]
Abstract
The chromatographic elution process is a key step in the production of notoginseng total saponins. Due to quality variability of loading samples and resin capacity decreasing over cycle time, saponins, especially the five main saponins of notoginseng total saponins, need to be monitored in real time during the elution process. In this study, convolutional neural networks, one of the most popular deep learning methods, were used to develop quantitative calibration models based on in-line near-infrared spectroscopy for notoginsenoside R1 , ginsenosides Rg1 , Re, Rb1 and Rd, and their sum concentration, with root mean square error of prediction values of 0.87, 2.76, 0.60, 1.57, 0.28, and 4.99 mg/mL, respectively. Partial least squares calibration models were also developed for model performance comparison. Results show predicted concentration profiles outputted by both the convolutional neural network models and partial least squares models show agreements with the real trends defined by reference measurements, and can be used for elution process monitoring and endpoint determination. To the best of our knowledge, this is the first reported case study of combining convolutional neural networks and in-line near-infrared spectroscopy for monitoring of the chromatographic elution process in commercial production of botanical drug products.
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Affiliation(s)
- Xu Yan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, P. R. China
| | - Hao Fu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, P. R. China
| | - Sheng Zhang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, P. R. China
| | - Haibin Qu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, P. R. China
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Identification of high-risk patients for ADR induced by traditional Chinese medicine injection: a nested case-control study. Sci Rep 2019; 9:16721. [PMID: 31723184 PMCID: PMC6853959 DOI: 10.1038/s41598-019-53267-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 10/30/2019] [Indexed: 02/06/2023] Open
Abstract
The adverse drug reaction (ADR) of traditional Chinese medicine injection (TCMI) has become one of the major concerns of public health in China. There are significant advantages for developing methods to improve the use of TCMI in routine clinical practice. The method of predicting TCMI-induced ADR was illustrated using a nested case-control study in 123 cases and 123 controls. The partial least squares regression (PLSR) models, which mapped the influence of basic characteristics and routine examinations to ADR, were established to predict the risk of ADR. The software was devised to provide an easy-to-use tool for clinic application. The effectiveness of the method was evaluated through its application to new patients with 95.7% accuracy of cases and 91.3% accuracy of controls. By using the method, the patients at high-risk could be conveniently, efficiently and economically recognized without any extra financial burden for additional examination. This study provides a novel insight into individualized management of the patients who will use TCMI.
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Development and validation of in-line near-infrared spectroscopy based analytical method for commercial production of a botanical drug product. J Pharm Biomed Anal 2019; 174:674-682. [DOI: 10.1016/j.jpba.2019.06.046] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 06/25/2019] [Accepted: 06/29/2019] [Indexed: 11/21/2022]
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11
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Wu L, Su Y, Yu H, Qian X, Zhang X, Wang Q, Kuang H, Cheng G. Rapid Determination of Saponins in the Honey-Fried Processing of Rhizoma Cimicifugae by Near Infrared Diffuse Reflectance Spectroscopy. Molecules 2018; 23:E1617. [PMID: 29970842 PMCID: PMC6100369 DOI: 10.3390/molecules23071617] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Revised: 06/28/2018] [Accepted: 06/29/2018] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE A model of Near Infrared Diffuse Reflectance Spectroscopy (NIR-DRS) was established for the first time to determine the content of Shengmaxinside I in the honey-fried processing of Rhizoma Cimicifugae. METHODS Shengmaxinside I content was determined by high-performance liquid chromatography (HPLC), and the data of the honey-fried processing of Rhizoma Cimicifugae samples from different batches of different origins by NIR-DRS were collected by TQ Analyst 8.0. Partial Least Squares (PLS) analysis was used to establish a near-infrared quantitative model. RESULTS The determination coefficient R² was 0.9878. The Cross-Validation Root Mean Square Error (RMSECV) was 0.0193%, validating the model with a validation set. The Root Mean Square Error of Prediction (RMSEP) was 0.1064%. The ratio of the standard deviation for the validation samples to the standard error of prediction (RPD) was 5.5130. CONCLUSION This method is convenient and efficient, and the experimentally established model has good prediction ability, and can be used for the rapid determination of Shengmaxinside I content in the honey-fried processing of Rhizoma Cimicifugae.
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Affiliation(s)
- Lun Wu
- Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin 150040, China.
| | - Yang Su
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Key Laboratory of Medicinal Materials, Chinese Academy of Sciences, Harbin 150040, China.
| | - Haoran Yu
- Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin 150040, China.
| | - Xiuhui Qian
- Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin 150040, China.
| | - Xueting Zhang
- Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin 150040, China.
| | - Qiuhong Wang
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510000, China.
| | - Haixue Kuang
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Key Laboratory of Medicinal Materials, Chinese Academy of Sciences, Harbin 150040, China.
| | - Genhong Cheng
- Faculty of Microbiology and Immunogenetics, University of California, Los Angeles, CA 90095, USA.
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Bai G, Zhang T, Hou Y, Ding G, Jiang M, Luo G. From quality markers to data mining and intelligence assessment: A smart quality-evaluation strategy for traditional Chinese medicine based on quality markers. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2018; 44:109-116. [PMID: 29426601 DOI: 10.1016/j.phymed.2018.01.017] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 12/20/2017] [Accepted: 01/20/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND The quality of traditional Chinese medicine (TCM) forms the foundation of its clinical efficacy. The standardization of TCM is the most important task of TCM modernization. In recent years, there has been great progress in the quality control of TCM. However, there are still many issues related to the current quality standards, and it is difficult to objectively evaluate and effectively control the quality of TCM. PURPOSE To face these challenge, we summarized the current quality marker (Q-marker) research based on its characteristics and benefits, and proposed a reasonable and intelligentized quality evaluation strategy for the development and application of Q-markers. METHODS Ultra-performance liquid chromatography-quadrupole/time-of-flight with partial least squares-discriminant analysis was suggested to screen the chemical markers from Chinese medicinal materials (CMM), and a bioactive-guided evaluation method was used to select the Q-markers. Near-infrared spectroscopy (NIRS), based on the distinctive wavenumber zones or points from the Q-markers, was developed for its determination. Then, artificial intelligence algorithms were used to clarify the complex relationship between the Q-markers and their integral functions. Internet and mobile communication technology helped us to perform remote analysis and determine the information feedback of test samples. CHAPTERS The quality control research, evaluation, standard establishment and quality control of TCM must be based on the systematic analysis of Q-markers to study and describe the material basis of TCM efficacy, define the chemical markers in the plant body, and understand the process of herb drug acquisition, change and transmission laws affecting metabolism and exposure. Based on the advantages of chemometrics, new sensor technologies, including infrared spectroscopy, hyperspectral imaging, chemical imaging, electronic nose and electronic tongue, have become increasingly important in the quality evaluation of CMM. Inspired by the concept of Q-marker, the quantitation can be achieved with the help of artificial intelligence, and these subtle differences can be discovered, allowing the quantitative analysis by NIRS and providing a quick and easy detection method for CMM quality evaluations. CONCLUSION The concept of Q-markers focused on unique CMM differences, dynamic changes and their transmission and traceability to establish an overall quality control and traceability system. Based on the basic attributes, an integration model and artificial intelligence research path was proposed, with the hope of providing new ideas and perspectives for the TCM quality management.
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Affiliation(s)
- Gang Bai
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300350, People's Republic of China.
| | - Tiejun Zhang
- Department of Traditional Chinese Medicine, State Key Laboratory of Drug Delivery and Pharmacokinetics, Tianjin Institute of Pharmaceutical Research, Tianjin 300193, People's Republic of China
| | - Yuanyuan Hou
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300350, People's Republic of China
| | - Guoyu Ding
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300350, People's Republic of China
| | - Min Jiang
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300350, People's Republic of China
| | - Guoan Luo
- Analysis Center, Tsinghua University, Room 139, Building of Life Science, Beijing 100084, People's Republic of China
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A Bioactive Chemical Markers Based Strategy for Quality Assessment of Botanical Drugs: Xuesaitong Injection as a Case Study. Sci Rep 2017; 7:2410. [PMID: 28546540 PMCID: PMC5445085 DOI: 10.1038/s41598-017-02305-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 04/10/2017] [Indexed: 01/01/2023] Open
Abstract
Current chemical markers based quality assessment methods largely fail to reflect intrinsic chemical complexity and multiple mechanisms of action of botanical drugs (BD). The development of novel quality markers is greatly needed. Here we propose bioactive chemical markers (BCM), defined as a group of chemo-markers that exhibit similar pharmacological activities comparable to the whole BD, which can therefore be used to effectively assess the quality of BD. As a proof-of-concept, a BCM-based strategy was developed and applied to Xuesaitong Injection (XST) for assessing the efficacy and consistency of different batches. Firstly, systemic characterization of chemical profile of XST revealed a total number of 97 compounds. Secondly, notoginsenoside R1, ginsenoside Rg1, Re, Rb1 and Rd were identified as BCM of XST on treating cardiovascular and cerebrovascular diseases according to Adjusted Efficacy Score following an in vivo validation. Analytical method for quantification of BCM was then developed to ensure the efficacy of XST. Finally, chemical fingerprinting was developed and used to evaluate the batch-to-batch consistency. Our present case study on XST demonstrates that BCM-based strategy offers a rational approach for quality assessment of BD and provides a workflow for chemistry, manufacturing, and controls (CMC) study of BD required by regulatory authority.
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Yang W, Qiao X, Li K, Fan J, Bo T, Guo DA, Ye M. Identification and differentiation of Panax ginseng, Panax quinquefolium, and Panax notoginseng by monitoring multiple diagnostic chemical markers. Acta Pharm Sin B 2016; 6:568-575. [PMID: 27818924 PMCID: PMC5071635 DOI: 10.1016/j.apsb.2016.05.005] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 04/26/2016] [Accepted: 05/27/2016] [Indexed: 02/03/2023] Open
Abstract
To differentiate traditional Chinese medicines (TCM) derived from congeneric species in TCM compound preparations is usually challenging. The roots of Panax ginseng (PG), Panax quinquefolium (PQ) and Panax notoginseng (PN) are used as popular TCM. They contain similar triterpenoid saponins (ginsenosides) as the major bioactive constituents. Thus far, only a few chemical markers have been discovered to differentiate these three species. Herein we present a multiple marker detection approach to effectively differentiate the three Panax species, and to identify them in compound preparations. Firstly, 85 batches of crude drug samples (including 32 PG, 30 PQ, and 23 PN) were analyzed by monitoring 40 major ginsenosides in the extracted ion chromatograms (EICs) using a validated LC–MS fingerprinting method. Secondly, the samples were clustered into different groups by pattern recognition chemometric approaches using PLS-DA and OPLS-DA models, and 17 diagnostic chemical markers were discovered. Aside from the previously known Rf and p-F11, ginsenoside Rs1 could be a new marker to differentiate PG from PQ. Finally, the above multiple chemical markers were used to identify the Panax species in 60 batches of TCM compound preparations.
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Affiliation(s)
- Wenzhi Yang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Xue Qiao
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Kai Li
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Jingran Fan
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Tao Bo
- Agilent Technologies, Beijing 100102, China
| | - De-an Guo
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- Corresponding author. Tel.: +86 21 2023 1000x2221; fax: +86 21 50272789.
| | - Min Ye
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
- Corresponding author. Tel./fax: +86 10 8280 2024.
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15
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Hung HY, Wu TS. Recent progress on the traditional Chinese medicines that regulate the blood. J Food Drug Anal 2016; 24:221-238. [PMID: 28911575 PMCID: PMC9339571 DOI: 10.1016/j.jfda.2015.10.009] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Revised: 10/13/2015] [Accepted: 10/29/2015] [Indexed: 01/12/2023] Open
Abstract
In traditional Chinese medicine, the herbs that regulate blood play a vital role. Here, nine herbs including Typhae Pollen, Notoginseng Root, Common Bletilla Tuber, India Madder Root and Rhizome, Chinese Arborvitae Twig, Lignum Dalbergiae Oderiferae, Chuanxiong Rhizoma, Corydalis Tuber, and Motherwort Herb were selected and reviewed for their recent studies on anti-tumor, anti-inflammatory and cardiovascular effects. Besides, the analytical methods developed to qualify or quantify the active compounds of the herbs are also summarized.
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Affiliation(s)
- Hsin-Yi Hung
- School of Pharmacy, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
| | - Tian-Shung Wu
- School of Pharmacy, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan; Department of Pharmacy, Tajen University, Pingtung 907, Taiwan.
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