Prediction of Lycii Cortex Quality Marker Based on Network Pharmacology and Chemometrics Methods
Based on the effectiveness, measurability, and traceability of the quality marker (Q-marker) theory of traditional Chinese medicine, the Q-marker of Lycii Cortex (LC) was preliminarily predicted and analyzed. A UPLC–Q-TOF-MS qualitative analysis method for LC samples was established. A total of 44 L...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | International Journal of Analytical Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2024/1790697 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832557875780648960 |
|---|---|
| author | Xinrui Wang Guotong Li Haoqiang Ding Xiqin Du Lanying Zhang Jingze Zhang Dailin Liu |
| author_facet | Xinrui Wang Guotong Li Haoqiang Ding Xiqin Du Lanying Zhang Jingze Zhang Dailin Liu |
| author_sort | Xinrui Wang |
| collection | DOAJ |
| description | Based on the effectiveness, measurability, and traceability of the quality marker (Q-marker) theory of traditional Chinese medicine, the Q-marker of Lycii Cortex (LC) was preliminarily predicted and analyzed. A UPLC–Q-TOF-MS qualitative analysis method for LC samples was established. A total of 44 LC chemical components, 16 plasma prototype components, 25 urine prototype components, and 27 fecal prototype components were identified. At the same time, the “component–target–disease” network diagram was constructed by network pharmacology to predict the potential active components of LC. A UPLC–MS/MS quantitative analysis method was established to determine the contents of 11 components such as kukoamine A in 35 batches of LC from seven producing areas. Principal component analysis, orthogonal partial least squares discriminant analysis, and other mathematical analysis methods were used to screen the differential components. Based on the comprehensive consideration of the Q-marker traceability, transitivity, specificity, effectiveness, and measurability, kukoamine A and kukoamine B were preliminarily predicted as LC potential Q-markers, and the high-quality producing area was determined to be Chengcheng County, Weinan City, Shaanxi Province. The prediction analysis of the LC Q-marker provides a reference for the comprehensive control of the quality of LC medicinal materials and also lays a foundation for the research and exploration of the substance basis and mechanism of action of LC. |
| format | Article |
| id | doaj-art-161132878f0642c58a97d691efa1abc1 |
| institution | Kabale University |
| issn | 1687-8779 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Analytical Chemistry |
| spelling | doaj-art-161132878f0642c58a97d691efa1abc12025-02-03T01:47:19ZengWileyInternational Journal of Analytical Chemistry1687-87792024-01-01202410.1155/2024/1790697Prediction of Lycii Cortex Quality Marker Based on Network Pharmacology and Chemometrics MethodsXinrui Wang0Guotong Li1Haoqiang Ding2Xiqin Du3Lanying Zhang4Jingze Zhang5Dailin Liu6State Key Laboratory of Component-Based Chinese MedicineState Key Laboratory of Component-Based Chinese MedicineTCM Formula R&D DepartmentState Key Laboratory of Component-Based Chinese MedicineState Key Laboratory of Component-Based Chinese MedicineState Key Laboratory of Component-Based Chinese MedicineState Key Laboratory of Component-Based Chinese MedicineBased on the effectiveness, measurability, and traceability of the quality marker (Q-marker) theory of traditional Chinese medicine, the Q-marker of Lycii Cortex (LC) was preliminarily predicted and analyzed. A UPLC–Q-TOF-MS qualitative analysis method for LC samples was established. A total of 44 LC chemical components, 16 plasma prototype components, 25 urine prototype components, and 27 fecal prototype components were identified. At the same time, the “component–target–disease” network diagram was constructed by network pharmacology to predict the potential active components of LC. A UPLC–MS/MS quantitative analysis method was established to determine the contents of 11 components such as kukoamine A in 35 batches of LC from seven producing areas. Principal component analysis, orthogonal partial least squares discriminant analysis, and other mathematical analysis methods were used to screen the differential components. Based on the comprehensive consideration of the Q-marker traceability, transitivity, specificity, effectiveness, and measurability, kukoamine A and kukoamine B were preliminarily predicted as LC potential Q-markers, and the high-quality producing area was determined to be Chengcheng County, Weinan City, Shaanxi Province. The prediction analysis of the LC Q-marker provides a reference for the comprehensive control of the quality of LC medicinal materials and also lays a foundation for the research and exploration of the substance basis and mechanism of action of LC.http://dx.doi.org/10.1155/2024/1790697 |
| spellingShingle | Xinrui Wang Guotong Li Haoqiang Ding Xiqin Du Lanying Zhang Jingze Zhang Dailin Liu Prediction of Lycii Cortex Quality Marker Based on Network Pharmacology and Chemometrics Methods International Journal of Analytical Chemistry |
| title | Prediction of Lycii Cortex Quality Marker Based on Network Pharmacology and Chemometrics Methods |
| title_full | Prediction of Lycii Cortex Quality Marker Based on Network Pharmacology and Chemometrics Methods |
| title_fullStr | Prediction of Lycii Cortex Quality Marker Based on Network Pharmacology and Chemometrics Methods |
| title_full_unstemmed | Prediction of Lycii Cortex Quality Marker Based on Network Pharmacology and Chemometrics Methods |
| title_short | Prediction of Lycii Cortex Quality Marker Based on Network Pharmacology and Chemometrics Methods |
| title_sort | prediction of lycii cortex quality marker based on network pharmacology and chemometrics methods |
| url | http://dx.doi.org/10.1155/2024/1790697 |
| work_keys_str_mv | AT xinruiwang predictionoflyciicortexqualitymarkerbasedonnetworkpharmacologyandchemometricsmethods AT guotongli predictionoflyciicortexqualitymarkerbasedonnetworkpharmacologyandchemometricsmethods AT haoqiangding predictionoflyciicortexqualitymarkerbasedonnetworkpharmacologyandchemometricsmethods AT xiqindu predictionoflyciicortexqualitymarkerbasedonnetworkpharmacologyandchemometricsmethods AT lanyingzhang predictionoflyciicortexqualitymarkerbasedonnetworkpharmacologyandchemometricsmethods AT jingzezhang predictionoflyciicortexqualitymarkerbasedonnetworkpharmacologyandchemometricsmethods AT dailinliu predictionoflyciicortexqualitymarkerbasedonnetworkpharmacologyandchemometricsmethods |