Combinatorial Chemistry & High Throughput Screening

Author(s): Ziying Qiu, Xiaoran Zhao, Meiqi Liu, Yanan Liu, Lili Sun*, Xiaoliang Ren* and Yanru Deng

DOI: 10.2174/1386207325666220822102014

Identification of the Origin, Authenticity and Quality of Panax Japonicus Based on a Multistrategy Platform

Page: [1375 - 1384] Pages: 10

  • * (Excluding Mailing and Handling)

Abstract

Background: Panax Japonicus (PJ) is a widely used Chinese herbal medicine, functional food and tonic. However, its origin has a great influence on the quality of PJ, and with the increasing demand for PJ, fake and inferior products, such as Panax Stipuleanatus (PS), often appear. The identification of the origin and authenticity of PJ is critical for ensuring the quality, safety and effectiveness of drugs.

Objective: Proposing a strategy to identify the origin, authenticity, and quality of PJ using HPLC fingerprints, chemometrics, and network pharmacology.

Methods: The chromatographic fingerprint method was established to analyze the origin and authenticity of PJ. Multiple chemometric methods were performed to analyze the fingerprints, including a Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), and Counter Propagation Artificial Neural Network (CP-ANN). Finally, the network pharmacology method was used to construct the "active ingredient-target" network, predict and assist in analyzing potential Qmarkers in PJ.

Results: Ward’s method was used for the HCA. The results showed that PJ samples from different origins had significant regional differences and could be accurately distinguished from PS. The PCA classification results are consistent with the HCA classification results, further illustrating the model's accuracy. The CP-ANN model can analyze and predict PJ and PS and accurately obtain PJ and PS chemical markers to identify PJ and PS correctly. The network pharmacology of PJ was constructed, and three PJ Q-markers, namely, ginsenoside Ro, ginsenoside Rb1, and chikusetsu saponin Ⅳa, were identified, which lays a foundation for the establishment of PJ quality standards.

Conclusion: This research provides a feasible platform for the quality evaluation of PJ in the future.

Keywords: Panax japonicus, authentication, Panax stipuleanatus, pattern recognition, chromatographic fingerprints, network pharmacology analysis.

Graphical Abstract

[1]
Deng, L.L.; Yuan, D.; Zhou, Z.Y.; Wan, J.Z.; Zhang, C.C.; Liu, C.Q.; Dun, Y.Y.; Zhao, H.X.; Zhao, B.; Yang, Y.J.; Wang, T. Saponins from Panax japonicus attenuate age-related neuroinflammation via regulation of the mitogen-activated protein kinase and nuclear factor kappa B signaling pathways. Neural Regen. Res., 2017, 12(11), 1877-1884.
[http://dx.doi.org/10.4103/1673-5374.219047] [PMID: 29239335]
[2]
Chinese pharmacopoeia commission. Chinese Pharmacopoeia (part one); China Medical Science and Technology Press: Beijing, 2020, p. 322.
[3]
Ouyang, L.; Xiang, D.; Wu, X.; Xang, D. Progress in research on chemical constituents and pharmacological activities of Panax japonicus. Chin. Herb. Med., 2010, 41(06), 1023-1027.
[4]
Wu, Q.; Chen, P.; Zhang, Q. Advances in research of chemical constituents, pharmacological activities and analytical methods of Panax japonicus. Asia Pac. Trad. Med., 2016, 12(6), 46-54.
[5]
Dun, Y.; Yuan, D. Research progress on chemical constituents of Panax japonicus. Shizhen J. Trad. Chinese Med. Res., 2006, (10), 1909-1911. [J]
[6]
Liu, J.; Liu, Y.; Klaassen, C.D. The effect of Chinese hepatoprotective medicines on experimental liver injury in mice. J. Ethnopharmacol., 1994, 42(3), 183-191.
[http://dx.doi.org/10.1016/0378-8741(94)90084-1] [PMID: 7934088]
[7]
Borrelli, F.; Izzo, A.A. The plant kingdom as a source of anti-ulcer remedies. Phytother. Res., 2000, 14(8), 581-591.
[http://dx.doi.org/10.1002/1099-1573(200012)14:8<581:AID-PTR776>3.0.CO;2-S] [PMID: 11113992]
[8]
Ruan, B.; Wang, R.; Yang, Y.; Wang, D.; Wang, J.; Zhang, C.; Yuan, D.; Zhou, Z.; Wang, T. Improved effects of saponins from Panax japonicus on decline of cognitive function in natural aging rats via NLRP3 inflammasome pathway. Zhongguo Zhongyao Zazhi, 2019, 44(2), 344-349.
[http://dx.doi.org/10.19540/j.cnki.cjcmm.20180921.001]
[9]
Yuan, D.; Zuo, R.; Zhang, C. Effects of total saponins of Panax japonicus on human leukemic HL-60 cells. Zhong Xi Yi Jie He Xue Bao, 2007, 5(5), 570-572.
[http://dx.doi.org/10.3736/jcim20070519] [PMID: 17854562]
[10]
Wang, J.; Wang, D.; Zhou, Z.; Zhang, X.; Zhang, C.; He, Y.; Liu, C.; Yuan, C.; Yuan, D.; Wang, T. Saponins from Panax japonicus alleviate HFD-induced impaired behaviors through inhibiting NLRP3 inflammasome to upregulate AMPA receptors. Neurochem. Int., 2021, 148, 105098.
[http://dx.doi.org/10.1016/j.neuint.2021.105098] [PMID: 34129896]
[11]
Zhao, H.; He, Y.; Yuan, D. Zhang, Ch Research advances on Panax japonicas and its approximation varieties in Tujia nationality. Agric. Sci. Technol., 2010, 11(1), 126-129.
[http://dx.doi.org/10.16175/j.cnki.1009-4229.2010.01.031]
[12]
Wang, Z.; Li, Y. Geographical distribution and growth pattern of Panax stipuleanatus, an anti - cancer plant. Shizhen J. Trad. Chinese Med. Res., 2018, 29(11), 2742-2745.
[13]
Li, Y.; Shen, Y.; Yao, C.L.; Guo, D.A. Quality assessment of herbal medicines based on chemical fingerprints combined with chemometrics approach: A review. J. Pharm. Biomed. Anal., 2020, 185, 113215.
[http://dx.doi.org/10.1016/j.jpba.2020.113215] [PMID: 32199327]
[14]
Chen, J.; Yang, R.; Zhang, Q.; Wang, J.; Wei, F.; Ma, Sh. Specific chromatograms of Glycyrrhiza uralensis Fisch. flavonoids in different growth years by HPLC coupled with chemometric analysis. Chung Kuo Yao Hsueh Tsa Chih, 2020, 55(17), 1415-1420.
[15]
Chen, J.; Zhang, Q.; Zhao, S. Quality evaluation of Glycyrrhiza uralensis Fisch.in different harvest periods based on combina-tive methods of HPLC specific chromatogram,multi-component assay,and chemometrics. Chung Kuo Yao Hsueh Tsa Chih, 2020, 55, 1540-1547.
[16]
Zhao, X.; Liu, R.; Feng, H.; Mao, W.; Wang, Y.; Cao, F.; Zhang, L. Quality control of Platycodon grandiflorum based on chemometrics method and HPLC fingerprint. Natural product. Res. Dev., 2020, 32(09), 1491-1498.
[17]
Tao, X.; Gong, H.; Xie, C.; Zhang, J.; Li, J.; Geng, X.; Liu, Q.; Lei, J. Quality evaluation of Dioscorea zingiberensis from different origins based on UPLC fingerprint and chemometrics. Chin. Herb. Med., 2021, 52(01), 227-233.
[18]
Sun, L.L.; Wang, M.; Zhang, H.J.; Liu, Y.N.; Ren, X.L.; Deng, Y.R.; Qi, A.D. Comprehensive analysis of Polygoni Multiflori Radix of different geographical origins using ultra-high-performance liquid chromatography fingerprints and multivariate chemometric methods. J. Food Drug Anal., 2018, 26(1), 90-99.
[http://dx.doi.org/10.1016/j.jfda.2016.11.009] [PMID: 29389593]
[19]
Sun, L.; Wang, M.; Liu, Y.; Zhang, H.; Liu, Y.; Ren, X.; Deng, Y. Discrimination of Polygoni Multiflori radix and Cynanchi Auriculati radix using ultra-high performance liquid chromatography fingerprints and chemical pattern recognition. Biomed. Chromatogr., 2018, 32(2), e4050.
[http://dx.doi.org/10.1002/bmc.4050] [PMID: 28722757]
[20]
Zhang, T.; Bai, G.; Liu, Ch. The concept, core theory and research methods of Chinese medicine quality markers. Chinese J. Pharm., 2019, 54(02), 187-196.
[21]
Deng, H. Difference between cluster analysis and discriminant analysis. Wuhan Academic Journal, 2006, (1), 29-31. [J]
[22]
Kang, L.; Liu, X.; Kang, L. Application and consideration of multivariate statistical analysis in quality control of traditional. Chin. Med., 2017, 2(Z2), 105-107.
[23]
Yi, T.; Chen, Q.; He, X.; So, S.; Lo, Y.; Fan, L.; Xu, J.; Tang, Y.; Zhang, J.; Zhao, Z.; Chen, H. Chemical quantification and antioxidant assay of four active components in Ficus hirta root using UPLC-PAD-MS fingerprinting combined with cluster analysis. Chem. Cent. J., 2013, 7(1), 115.
[http://dx.doi.org/10.1186/1752-153X-7-115] [PMID: 23835498]
[24]
Chen, Q.L.; Zhu, L.; Tang, Y.N.; Kwan, H.Y.; Zhao, Z.Z.; Chen, H.B.; Yi, T. Comparative evaluation of chemical profiles of three representative ‘snow lotus’ herbs by UPLC-DAD-QTOF-MS combined with principal component and hierarchical cluster analyses. Drug Test. Anal., 2017, 9(8), 1105-1115.
[http://dx.doi.org/10.1002/dta.2123] [PMID: 27764538]
[25]
Liu, J.; Chen, X.; Zou, Y. Progress on chemical pattern recognition in traditional Chinese medicines by multidimensional information of metabolic fingerprinting analysis. Zhongguo Zhongyao Zazhi, 2012, 37(8), 1081-1088.
[PMID: 22779354]
[26]
Xu, L.; Shao, X. Methods of Chemometrics, 2nd ed; Science Press: Beijing, 2004, p. 130.
[27]
Otto Matthias. Chemometrics: Statistics and computer application in analytical chemical, 3rd ed; Matthias Otto: Germany, 1999.
[28]
Ballabio, D.; Consonni, V.; Todeschini, R. The kohonen and CP-ANN toolbox: A collection of MATLAB modules for self organizing maps and counterpropagation artificial neural networks. Chemom. Intell. Lab. Syst., 2009, 98(2), 115-122.
[http://dx.doi.org/10.1016/j.chemolab.2009.05.007]
[29]
Ballabio, D.; Vasighi, M.; Filzmoser, P. Effects of supervised Self Organising Maps parameters on classification performance. Anal. Chim. Acta, 2013, 765, 45-53.
[http://dx.doi.org/10.1016/j.aca.2012.12.027]
[30]
Sun, L.; Yang, J.; Wang, M.; Zhang, H.; Liu, Y.; Ren, X.; Qi, A. Combination of counterpropagation artificial neural networks and antioxidant activities for comprehensive evaluation of associated-extraction efficiency of various cyclodextrins in the traditional Chinese formula Xue-Zhi-Ning. J. Pharm. Biomed. Anal., 2015, 115, 580-586.
[http://dx.doi.org/10.1016/j.jpba.2015.08.006] [PMID: 26322951]
[31]
Hopkins, A.L. Network pharmacology: The next paradigm in drug discovery. Nat. Chem. Biol., 2008, 4(11), 682-690.
[http://dx.doi.org/10.1038/nchembio.118] [PMID: 18936753]
[32]
Li, S.; Zhang, B. Traditional Chinese medicine network pharmacology: Theory, methodology and application. Chin. J. Nat. Med., 2013, 11(2), 110-120.
[http://dx.doi.org/10.1016/S1875-5364(13)60037-0] [PMID: 23787177]
[33]
Xu, M.; Li, Z.; Yang, L.; Zhai, W.; Wei, N.; Zhang, Q.; Chao, B.; Huang, S.; Cui, H. Elucidation of the mechanisms and molecular targets of Sanhuang Xiexin decoction for type 2 diabetes mellitus based on network pharmacology. BioMed Res. Int., 2020, 2020, 5848497.
[http://dx.doi.org/10.1155/2020/5848497] [PMID: 32851081]