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Prediction Analysis of Quality Markers of Rhododendron molle G. Don Based on Chemical Pattern Recognition and Network Pharmacology
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Wenya MEI1, Junbao YU1, Ting DENG1, Xiangdan LIU1, 2, 3, Qiaozhen TONG1, 2, 3, Zhihui WANG1, 2, Xiaorong LIU1, 2, *, Ribao ZHOU1, 2, 3, *
Chinese Pharmaceutical Journal | 2024, 59(21) : 2030 - 2041
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Chinese Pharmaceutical Journal | 2024, 59(21): 2030-2041
Prediction Analysis of Quality Markers of Rhododendron molle G. Don Based on Chemical Pattern Recognition and Network Pharmacology
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Wenya MEI1, Junbao YU1, Ting DENG1, Xiangdan LIU1, 2, 3, Qiaozhen TONG1, 2, 3, Zhihui WANG1, 2, Xiaorong LIU1, 2, *, Ribao ZHOU1, 2, 3, *
Affiliations
  • 1 School of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, China
  • 2 Key Research Laboratory of Germplasm Resources and Standardized Planting of Genuine Regional Medicinal Materials Produced in Hunan Province, Changsha 410208, China
  • 3 Key Laboratory of Modern Research of Traditional Chinese Medicine, Education Department of Hunan Province, Changsha 410208, China
Published: 2024-11-08 doi: 10.11669/cpj.2024.21.006
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OBJECTIVE To screen the quality markers (Q-marker) of Rhododendron molle based on HPLC-ELSD and network pharmacology, compare the contents of quality markers in different parts, different flowering periods and different producing areas of R. molle, and explore the potential medicinal value of R.molle and speculate its possible mechanism of action. METHODS HPLC-ELSD was used to establish the fingerprint of R. molle, and hierarchical clustering analysis (HCA), principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were carried out. The network diagram of ' component-target-pathway-pharmacological effect-efficacy ' was constructed by network pharmacology method, and the disease prediction was carried out. The quality markers of R. molle were screened and quantitatively analyzed. RESULTS In this study, the fingerprints of 11 batches of Rhododendron molle were established, and total of 10 common peaks were calibrated. Four differential components were obtained after analysis. It was predicted that rhodojaponin-Ⅲ, rhodojaponin-Ⅱ, hyperoside and quercitrin were candidate components of quality markers of R. molle and network pharmacology analysis was performed. It involved 84 target proteins, including ATK1, TNF, INS, etc., acting on 159 signaling pathways, and had potential therapeutic effects on tumors, autoimmune diseases, and cardiovascular diseases. The results of component content determination showed that the flowering of Rhododendron molle might have better curative effect, and the S10 origin was the best. CONCLUSION The quality markers of R. molle are predicted by chemical pattern recognition, fingerprint and network pharmacology. It is proved that R. molle could treat diseases through multiple targets and multiple pathways, which provides a reference for improving the establishment of quality control standards of R. molle and further exploring the mechanism of disease treatment.

Rhododendron molle G.Don  /  finger print  /  quality marker  /  chemical pattern recognition method  /  network pharmacology
Wenya MEI, Junbao YU, Ting DENG, Xiangdan LIU, Qiaozhen TONG, Zhihui WANG, Xiaorong LIU, Ribao ZHOU. Prediction Analysis of Quality Markers of Rhododendron molle G. Don Based on Chemical Pattern Recognition and Network Pharmacology[J]. Chinese Pharmaceutical Journal, 2024 , 59 (21) : 2030 -2041 . DOI: 10.11669/cpj.2024.21.006
Year 2024 volume 59 Issue 21
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59
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Article Info
doi: 10.11669/cpj.2024.21.006
  • Receive Date:2024-05-13
  • Online Date:2025-11-16
  • Published:2024-11-08
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  • Received:2024-05-13
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Affiliations
    1 School of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, China
    2 Key Research Laboratory of Germplasm Resources and Standardized Planting of Genuine Regional Medicinal Materials Produced in Hunan Province, Changsha 410208, China
    3 Key Laboratory of Modern Research of Traditional Chinese Medicine, Education Department of Hunan Province, Changsha 410208, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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