Comprehensive quality evaluation of Polygoni Orientalis Fructus and its processed product: chemical fingerprinting and simultaneous determination of seven major components coupled with chemometric analyses

Network pharmacology combined with metabolomics to reveal the anti-fibrotic mechanism of Polygoni Orientalis Fructus in CCl4-induced hepatic fibrosis rats

Polygoni Orientalis Fructus (POF), a dried ripe fruit of Polygonum orientale L., is commonly used in China for liver disease treatment. However, its therapeutic mechanism remains unclear. The aim of this study was to elucidate the effects of POF on the regulation of endogenous metabolites and identify its key therapeutic targets in hepatic fibrosis (HF) rats by integrating network pharmacology and metabolomics approaches. First, serum liver indices and histopathological analyses were used to evaluate the therapeutic effects of POF on carbon tetrachloride (CCl4)-induced HF. Subsequently, differential metabolites and potential therapeutic targets of POF were screened using plasma metabolomics and network pharmacology, respectively. The key targets of POF were identified by overlapping differential metabolite-associated targets with the potential targets and validated by molecular docking and ELISA experiments. The results showed that POF effectively alleviated HF in rats. A total of 51 metabolites related to HF were screened, and 24 were associated with POF. 232 potential therapeutic targets were identified by network pharmacology analysis. Finally, six key targets were identified through a combined analysis. Furthermore, molecular docking and ELISA validation revealed that AGXT, PAH, and NOS3 are targets of POF action, while CBS, ALDH2, and ARG1 were identified as potential targets. SIGNIFICANCE: POF is now commonly used in the treatment of liver disease, but its mechanism of action remains unclear. Current studies on metabolomics of liver disease primarily focuse on the interpretation of differential metabolites and related metabolic pathways. This research delves into the intricate details of metabolomics findings via network pharmacology to uncover the targets and pathways of drug action.

Quality assessment of traditional Chinese medicine based on data fusion combined with machine learning: A review

The authenticity and quality of traditional Chinese medicine (TCM) directly impact clinical efficacy and safety. Quality assessment of traditional Chinese medicine (QATCM) is a global concern due to increased demand and shortage of resources. Recently, modern analytical technologies have been extensively investigated and utilized to analyze the chemical composition of TCM. However, a single analytical technique has some limitations, and judging the quality of TCM only from the characteristics of the components is not enough to reflect the overall view of TCM. Thus, the development of multi-source information fusion technology and machine learning (ML) has further improved QATCM. Data information from different analytical instruments can better understand the connection between herbal samples from multiple aspects. This review focuses on the use of data fusion (DF) and ML in QATCM, including chromatography, spectroscopy, and other electronic sensors. The common data structures and DF strategies are introduced, followed by ML methods, including fast-growing deep learning. Finally, DF strategies combined with ML methods are discussed and illustrated for research on applications such as source identification, species identification, and content prediction in TCM. This review demonstrates the validity and accuracy of QATCM-based DF and ML strategies and provides a reference for developing and applying QATCM methods.

Authentication of Herbal Medicines Based on Modern Analytical Technology Combined with Chemometrics Approach: A Review

Since ancient times, herbal medicines (HMs) have been widely popular with consumers as a "natural" drug for health care and disease treatment. With the emergence of problems, such as increasing demand for HMs and shortage of resources, it often occurs the phenomenon of shoddy exceed and mixing the false with the genuine in the market. There is an urgent need to evaluate the quality of HMs to ensure their important role in health care and disease treatment, and to reduce the possibility of threat to human health. Modern analytical technology is can be analyzed for analyzing chemical components of HMs or their preparations. Reflecting complex chemical components' characteristic curves in the analysis sample, and the comprehensive effect of active ingredients of HMs. In this review, modern analytical technology (chromatography, spectroscopy, mass spectrometry), chemometrics methods (unsupervised, supervised) and their advantages, disadvantages, and applicability were introduced and summarized. In addition, the authentication application of modern analytical technology combined with chemometrics methods in four aspects, including origin, processing methods, cultivation methods, and adulteration of HMs have also been discussed and illustrated by a few typical studies. This article offers a general workflow of analytical methods that have been applied for HMs authentication and explains that the accuracy of authentication in favor of the quality assurance of HMs. It was provided reference value for the development and application of modern HMs.

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.

CONCLUSION

Combining HPLC fingerprinting and multi-component 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.

Investigating the medicinal potential, material basis and mechanism of Polygoni Orientalis Fructus based on multi-technology integrated network pharmacology

BACKGROUND

Polygoni Orientalis Fructus (POF) refers to the dried ripe fruit of Polygonum orientale L. which has a long historical application in clinic for treatment of various conditions in China. However, its chemical constituents, pharmacological effects and their coupled correlation have not been intensively investigated.

PURPOSE

In present work, we aimed to elucidate the medicinal material basis, optimum indication and corresponding therapeutic mechanism of POF.

METHODS

The main phytochemical ingredients in POF were characterized by liquid chromatography-mass spectrometry (LC-MS) analysis. The optimum medicinal potential and corresponding molecular mechanism of POF were deduced based on integrated statistic pattern recognition and network pharmacology. The deduced pharmacologic efficacy and mechanism of POF were further validated through in vitro study in free-fatty acid (FFA)-induced LO₂ cells.

RESULTS

Total 30 main phytochemical ingredients were identified in POF in which 18 ingredients were screened to yield 277 potential targets. Based on analyzing the quantitative data matrix of drug-disease targets by statistic pattern recognition, non-alcoholic fatty liver disease (NAFLD) was screened as the optimum indication of POF from 23 candidate diseases. Promising action targets (PPARG, IL6, TNF, IL1B, IKBKB, RELA, etc.) and signaling pathways (AMPK signaling pathway, NF-κB signaling pathway, etc.) were screened and refined to elucidate the therapeutic mechanism of POF against NAFLD based on network pharmacology. In vitro study demonstrated that POF effectively alleviated FFA-induced steatosis, oxidative stress, mitochondrial dysfunction and inflammation, and these beneficial effects were attributed to the activation of AMPK signaling pathway and suppression of NF-κB signaling pathway.

CONCLUSION

POF could be exploited as a promising phytotherapy in the treatment of NAFLD.

Study on the Fingerprints and Quality Evaluation of Angelica Sinensis Radix by HPLC Coupled With Chemometrics Based on Traditional Decoction Process of ACPTCM

Objective

To establish a HPLC fingerprints evaluation method for Angelica Sinensis Radix (ASR) based on traditional decoction process of Ancient Classical Prescriptions of Traditional Chinese Medicine (ACPTCM).

Methods

The fingerprints of 10 batches of ASR were further evaluated by chemometrics methods. The similarity analyzed with "Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine 2004A," and hierarchical clustering analysis (HCA) and principal component analysis (PCA) were performed by SPSS (version 22.0, SPSS Inc., Chicago, IL, USA).

Results

There were 12 common peaks, and the similarity degrees of 10 batches of samples were more than 0.923 and showed that all the samples from different origins were of good consistency. The samples were divided into 4 clusters by HCA. The results of PCA showed that the 3 factors were chosen, the quality of samples could be evaluated basically. The comprehensive score results show that the ASR with Lot.Nos.DG-18007, DG-18008 in Weiyuan County, Gansu and DG-18009 produced in Minle County, Gansu Province rank among the top 3 in all samples.

Conclusions

These results demonstrated that the combination of HPLC chromatographic fingerprint and chemometrics offers an efficient and reliable approach for quality evaluation of ASR from different sources as Ancient Classical Prescriptions ingredients.

Comparative Analysis of Free Amino Acids and Nucleosides in Different Varieties of Mume Fructus Based on Simultaneous Determination and Multivariate Statistical Analyses

Mume Fructus (MF) contains a variety of organic acids, free amino acids, and nucleoside components, and studies have not yet analyzed the relationship between the components of free amino acids and nucleosides with the varieties of MF. A rapid and sensitive method was established for simultaneous determination of 21 free amino acids and 9 nucleosides in MF by ultrafast liquid chromatography-mass spectrometry. The analysis was carried out on a Waters XBridge Amide column (100 mm × 2.1 mm, 3.5 μm) with elution by the mobile phase of 0.2% aqueous formic acid (A) and 0.2% formic acid acetonitrile (B) at a flow rate of 0.2 mL/min with 1 μL per injection. The column temperature was maintained at 30°C. The target compounds were analyzed by the positive ion multiple reaction monitoring (MRM) mode. The comprehensive evaluation of the samples was carried out by principal component analysis (PCA) and technique for order preference by similarity to an ideal solution (TOPSIS) analysis. Results showed the method could simultaneously determine 30 components in MF. The content of total analytes in six mainstream varieties was different, exhibited the order Nangao > Daqingmei > Zhaoshuimei > Yanmei > Shishengme > Baimei, and aspartic acid and adenosine were the most abundant amino acid and nucleoside. PCA and OPLS-DA could easily distinguish the samples, and 11 components could be chemical markers of sample classification. TOPSIS implied that the quality of Nangao and Daqingmei was superior to the other varieties. The results could provide a reliable basis for quality evaluation and utilisation of medicinal and edible MF.

Capillary electrophoresis fingerprints combined with Linear Quantitative Profiling Method to monitor the quality consistency and predict the antioxidant activity of Alkaloids of Sophora flavescens

Alkaloids of Sophora flavescens (ASF) has various pharmacological effects, and it is widely used in clinical practice. The aim of this research was to develop an environmentally friendly methodology that enables not only identification but also the quality consistency assessment of Alkaloids of Sophora flavescens. A background electrolyte composed of 50 mmol/L sodium tetraborate solution, 500 mmol/L boric acid and 1.2 mmol/L citric acid was used to conduct the fingerprint analysis coupled with quantitative determination of three components. Linear quantitative profiling method was used for comprehensive quality discrimination of the test samples from both qualitative and quantitative perspectives, and the 27 batches of samples were well differentiated. In addition, the fingerprint-efficacy relationship between chemical components and antioxidant activity in vitro was established using partial least squares regression model, which provided important medicinal efficacy information for quality control.