Ke Li1, Junfang Li2, Jin Su3, Xuefeng Xiao4, Xiujuan Peng5, Feng Liu5, Defeng Li3, Yi Zhang3, Tao Chong6, Haiyu Xu7, Changxiao Liu8, Hongjun Yang3. 1. Key Laboratory of UWB & THz of Shandong Academy of Sciences, Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, PR China. 2. Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, PR China; Tianjin University of Traditional Chinese Medicine, Tianjin 300193, PR China. 3. Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, PR China. 4. Tianjin University of Traditional Chinese Medicine, Tianjin 300193, PR China. 5. Shaanxi Institute of International Trade & Commerce, Xianyang 712046, PR China. 6. Wangjing Hospital of China Academy of Chinese Medical Sciences, Beijing, PR China. 7. Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, PR China; Shaanxi Institute of International Trade & Commerce, Xianyang 712046, PR China. Electronic address: hyxu@icmm.ac.cn. 8. Shaanxi Institute of International Trade & Commerce, Xianyang 712046, PR China; State Key Laboratory of Drug Delivery Technology and Pharmacokinetics, Tianjin Institute of Pharmaceutical Research, Tianjin 300193, PR China.
Abstract
BACKGROUND: The quality evaluation of traditional Chinese medicine (TCM) formulations is needed to guarantee the safety and efficacy. In our laboratory, we established interaction rules between chemical quality control and biological activity evaluations to study Yuanhu Zhitong tablets (YZTs). Moreover, a quality marker (Q-marker) has recently been proposed as a new concept in the quality control of TCM. However, no appropriate methods are available for the identification of Q-markers from the complex TCM systems. PURPOSE: We aimed to use an integrative pharmacological (IP) approach to further identify Q-markers from YZTs through the integration of multidisciplinary knowledge. In addition, data mining was used to determine the correlation between multiple constituents of this TCM and its bioactivity to improve quality control. METHODS: The IP approach was used to identify the active constituents of YZTs and elucidate the molecular mechanisms by integrating chemical and biosynthetic analyses, drug metabolism, and network pharmacology. Data mining methods including grey relational analysis (GRA) and least squares support vector machine (LS-SVM) regression techniques, were used to establish the correlations among the constituents and efficacy, and dose efficacy in multiple dimensions. RESULTS: Seven constituents (tetrahydropalmatine, α-allocryptopine, protopine, corydaline, imperatorin, isoimperatorin, and byakangelicin) were identified as Q-markers of YZT using IP based on their high abundance, specific presence in the individual herbal constituents and the product, appropriate drug-like properties, and critical contribution to the bioactivity of the mixture of YZT constituents. Moreover, three Q-markers (protopine, α-allocryptopine, and corydaline) were highly correlated with the multiple bioactivities of the YZTs, as found using data mining. Finally, three constituents (tetrahydropalmatine, corydaline, and imperatorin) were chosen as minimum combinations that both distinguished the authentic components from false products and indicated the intensity of bioactivity to improve the quality control of YZTs. CONCLUSIONS: Tetrahydropalmatine, imperatorin, and corydaline could be used as minimum combinations to effectively control the quality of YZTs.
BACKGROUND: The quality evaluation of traditional Chinese medicine (TCM) formulations is needed to guarantee the safety and efficacy. In our laboratory, we established interaction rules between chemical quality control and biological activity evaluations to study Yuanhu Zhitong tablets (YZTs). Moreover, a quality marker (Q-marker) has recently been proposed as a new concept in the quality control of TCM. However, no appropriate methods are available for the identification of Q-markers from the complex TCM systems. PURPOSE: We aimed to use an integrative pharmacological (IP) approach to further identify Q-markers from YZTs through the integration of multidisciplinary knowledge. In addition, data mining was used to determine the correlation between multiple constituents of this TCM and its bioactivity to improve quality control. METHODS: The IP approach was used to identify the active constituents of YZTs and elucidate the molecular mechanisms by integrating chemical and biosynthetic analyses, drug metabolism, and network pharmacology. Data mining methods including grey relational analysis (GRA) and least squares support vector machine (LS-SVM) regression techniques, were used to establish the correlations among the constituents and efficacy, and dose efficacy in multiple dimensions. RESULTS: Seven constituents (tetrahydropalmatine, α-allocryptopine, protopine, corydaline, imperatorin, isoimperatorin, and byakangelicin) were identified as Q-markers of YZT using IP based on their high abundance, specific presence in the individual herbal constituents and the product, appropriate drug-like properties, and critical contribution to the bioactivity of the mixture of YZT constituents. Moreover, three Q-markers (protopine, α-allocryptopine, and corydaline) were highly correlated with the multiple bioactivities of the YZTs, as found using data mining. Finally, three constituents (tetrahydropalmatine, corydaline, and imperatorin) were chosen as minimum combinations that both distinguished the authentic components from false products and indicated the intensity of bioactivity to improve the quality control of YZTs. CONCLUSIONS:Tetrahydropalmatine, imperatorin, and corydaline could be used as minimum combinations to effectively control the quality of YZTs.