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

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.

Discovery of potential Q-marker of traditional Chinese medicine based on chemical profiling, chemometrics, network pharmacology, and molecular docking: Centipeda minima as an example.

INTRODUCTION: The characteristics of chemical components or groups of chemical components in traditional Chinese medicines (TCMs) determine their clinical efficacy. Quality markers (Q-markers) is of great significance for standardizing the quality control system of TCM. OBJECTIVES: We aimed to develop a new strategy to discover potential Q-markers of TCM by integrating chemometrics, network pharmacology, and molecular docking, using Centipeda minima (also known as ebushicao [EBSC]) as an example. MATERIALS AND METHODS: First, fingerprints of different batches of EBSC and its counterfeit Arenaria oreophila (also known as zaozhui [ZZ]) were established. Second, chemometric analysis was conducted to determine the influence of varying authenticity/batches of herbs on quality and the chemical markers were screened out. Third, network pharmacology and molecular docking simulations were used to verify the relationship between active ingredients and targets. Lastly, potential Q-markers were selected based on TCM theory. RESULTS: The chemical profiles of EBSC and ZZ were investigated. It was found that different batches of EBSC have differences in chemical composition. Based on our chemometric analysis, chlorogenic acid, rutin, isochlorogenic acid A, quercetin, arnicolide D, and brevilin A were selected as candidate active ingredients. ATIL6, EGFR, CASP3, MYC, HIF1A, and VEGFA were the main targets. Molecular docking was used to verify the binding ability. Based on the concept of Q-marker, arnicolide D and brevilin A were identified as potential Q-markers for EBSC. CONCLUSIONS: Our strategy could be used as a practical approach to discover Q-markers of TCM to evaluate overall chemical consistency.

Comprehensive Quality Evaluation of the Root Bark of Morus alba L. Based on High-Performance Liquid Chromatography Fingerprinting and Chemometric Analyses.

The quality of the root bark of Morus alba L. (SBP) herbs currently circulating in the market is variable. In order to ensure clinical effectiveness, a high-performance liquid chromatography (HPLC) fingerprinting method combined with chemical pattern recognition should be established to control the quality of SBP herbs. The differences of 23 batches of SBP were analyzed by exploratory cluster analysis based on shared fingerprint peak data, and the results indicated that the processing method to remove the cork layer from SBP materials is an important influencing factor on SBP quality. Principal component analysis indicated that SBP samples with the cork layer removed can be clearly distinguished from samples without cork layer removal. The potential chemical markers (kuwanon G, morusin and oxyresveratrol) were screened by partial least squares discriminant analysis. Finally, the contents of the main components were determined, indicating that the processing method of SBP materials can affect content of bioactive ingredients and that cork layer removal leads to a more uniform chemometric profile. The HPLC-based chemometrics approach described here will support the development of quality standards in SBP products.

Comparative Investigation of the Stems, Leaves, Flowers, and Roots of Centipeda Minima Based on Fingerprinting-Multivariate Classification Techniques.

BACKGROUND: Centipeda minima (L.) A. Br. et Aschers, known as Ebushicao (EBSC) in Chinese, has long been used in traditional Chinese medicine for dispelling wind, clearing orifices, detoxification, and swelling. Although the traditional use of EBSC involves the whole plant, during harvesting and processing, separation of the stems, leaves, flowers, and roots often occurs. However, there are few studies on its medicinal parts. OBJECTIVE: A strategy combining high-performance liquid chromatography (HPLC) fingerprinting and multivariate classification techniques are here proposed for the comparison of roots, stems, leaves, and flowers of EBSC. METHOD: The roots, stems, leaves, and flowers of EBSC samples were analyzed and compared based on HPLC fingerprints combined with chemometrics, including hierarchical cluster analysis (HCA), principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and back propagation artificial neural network (BP-ANN). Chemical markers were screened using PLS-DA, and the contents of representative ingredients were determined by an HPLC method. RESULTS: The HCA and PCA provided clear discrimination of roots, stems, leaves, and flowers. Moreover, the PLS-DA model and BP-ANN were established to verify the classification results and showed a greater ability to predict new samples. Four representative chemical markers were screened out, and the content of these markers in flowers and leaves was higher than that in stems and roots, and the difference was significant. CONCLUSIONS: Combining HPLC fingerprinting and multicomponent chemical pattern recognition technology can be used to distinguish different parts of EBSC. The results indicated that brevilin A, quercetin, rutin, and chlorogenic acid, the important active components of EBSC, were mainly present in the leaves and flowers. This is of great significance for the differentiation and identification of the different medicinal parts of EBSC, as well as for the effectiveness of drug usage in clinical practice. HIGHLIGHTS: HP LC was used to quickly obtain chemical for fingerprint analysis. HCA, P CA, P LS-DA were used to visualize the discrimination of roots, stems, leaves and flowers of EBSC. P LS-DA model was established to verify the classification results and obtained the chemical marker. BP-ANN model was used to further improve the discrimination accuracy.