دورية أكاديمية
Identify production area, growth mode, species, and grade of Astragali Radix using metabolomics "big data" and machine learning.
| العنوان: | Identify production area, growth mode, species, and grade of Astragali Radix using metabolomics "big data" and machine learning. |
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| المؤلفون: | Wu J; Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University, Ministry of Education, Nanjing, 210009, China; Department of Pharmaceutical Analysis, School of Pharmacy, China Pharmaceutical University, Nanjing, 210009, China., Deng S; Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University, Ministry of Education, Nanjing, 210009, China., Yu X; Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University, Ministry of Education, Nanjing, 210009, China., Wu Y; National Institutes for Food and Drug Control, Beijing, 102629, China., Hua X; Department of Traditional Chinese Medicine Testing, Wuxi Center for Drug Safety Control, Wuxi, 214028, China., Zhang Z; Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University, Ministry of Education, Nanjing, 210009, China. Electronic address: zunjianzhangcpu@hotmail.com., Huang Y; Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University, Ministry of Education, Nanjing, 210009, China; Department of Pharmaceutical Analysis, School of Pharmacy, China Pharmaceutical University, Nanjing, 210009, China. Electronic address: huangyin@cpu.edu.cn. |
| المصدر: | Phytomedicine : international journal of phytotherapy and phytopharmacology [Phytomedicine] 2024 Jan; Vol. 123, pp. 155201. Date of Electronic Publication: 2023 Nov 08. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Urban & Fischer Verlag Country of Publication: Germany NLM ID: 9438794 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1618-095X (Electronic) Linking ISSN: 09447113 NLM ISO Abbreviation: Phytomedicine Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Stuttgart : Urban & Fischer Verlag Original Publication: Stuttgart ; New York : G. Fischer, c1994- |
| مواضيع طبية MeSH: | Drugs, Chinese Herbal*/pharmacology , Astragalus Plant*, Chromatography, Liquid ; Chromatography, High Pressure Liquid/methods ; Astragalus propinquus/chemistry ; Tandem Mass Spectrometry/methods ; Alanine |
| مستخلص: | Background: Astragali Radix (AR) is a widely used herbal medicine. The quality of AR is influenced by several key factors, including the production area, growth mode, species, and grade. However, the markers currently used to distinguish these factors primarily focus on secondary metabolites, and their validation on large-scale samples is lacking. Purpose: This study aims to discover reliable markers and develop classification models for identifying the production area, growth mode, species, and grade of AR. Methods: A total of 366 batches of AR crude slices were collected from six provinces in China and divided into learning (n = 191) and validation (n = 175) sets. Three ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) methods were developed and validated for determining 22 primary and 10 secondary metabolites in AR methanol extract. Based on the quantification data, seven machine learning algorithms, such as Nearest Neighbors and Gradient Boosted Trees, were applied to screen the potential markers and build the classification models for identifying the four factors associated with AR quality. Results: Our analysis revealed that secondary metabolites (e.g., astragaloside IV, calycosin-7-O-β-D-glucoside, and ononin) played a crucial role in evaluating AR quality, particularly in identifying the production area and species. Additionally, fatty acids (e.g., behenic acid and lignoceric acid) were vital in determining the growth mode of AR, while amino acids (e.g., alanine and phenylalanine) were helpful in distinguishing different grades. With both primary and secondary metabolites, the Nearest Neighbors algorithm-based model was constructed for identifying each factor of AR, achieving good classification accuracy (>70%) on the validation set. Furthermore, a panel of four metabolites including ononin, astragaloside II, pentadecanoic acid, and alanine, allowed for simultaneous identification of all four factors of AR, offering an accuracy of 86.9%. Conclusion: Our findings highlight the potential of integrating large-scale targeted metabolomics and machine learning approaches to accurately identify the quality-associated factors of AR. This study opens up possibilities for enhancing the evaluation of other herbal medicines through similar methodologies, and further exploration in this area is warranted. Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2023 Elsevier GmbH. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Astragalus; Classification; Quality marker; Targeted metabolomics; UPLC-MS/MS |
| المشرفين على المادة: | 0 (Drugs, Chinese Herbal) OF5P57N2ZX (Alanine) |
| تواريخ الأحداث: | Date Created: 20231117 Date Completed: 20240117 Latest Revision: 20240117 |
| رمز التحديث: | 20240117 |
| DOI: | 10.1016/j.phymed.2023.155201 |
| PMID: | 37976693 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1618-095X |
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| DOI: | 10.1016/j.phymed.2023.155201 |