Quality Markers for Astragali Radix and Its Products Based on Process Analysis

Rapid analysis of Radix Astragali using a portable Raman spectrometer with 1064-nm laser excitation and data fusion with PLS-DA

Radix Astragali is a medicinal herb with various physiological activities. There were high similarities among Radix Astragali samples from different regions owing to similarities in their major chemical compositions. Raman spectroscopy is a non-invasive and non-des- tructive technique that can be used in in-situ analysis of herbal samples. Dispersive Raman scattering, excited at 1064 nm, produced minimal fluorescence background and facilitated easy detection of the weak Raman signal. By moving the portable Raman probe point-by- point from the centre of the Radix Astragali sample to the margin, the spectral fingerprints, composed of dozens of Raman spectra representing the entire Radix Astragali samples, were obtained. Principal component analysis and partial least squares discriminant analysis (PLS-DA) were applied to the Radix Astragali spectral data to compare classification results, leading to efficient discrimination between genuine and counterfeit products. Furthermore, based on the PLS-DA model using data fusion combined with different pre- processing methods, the samples from Shanxi Province were separated from those belonging to other habitats. The as-proposed combination method can effectively improve the recognition rate and accuracy of identification of herbal samples, which can be a valuable tool for the identification of genuine medicinal herbs with uneven qualities and various origins.

Identify production area, growth mode, species, and grade of Astragali Radix using metabolomics "big data" and machine learning

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.

Astragaloside in cancer chemoprevention and therapy

ABSTRACT

Tumor chemoprevention and treatment are two approaches aimed at improving the survival of patients with cancers. An ideal anti-tumor drug is that which not only kills tumor cells but also alleviates tumor-causing risk factors, such as precancerous lesions, and prevents tumor recurrence. Chinese herbal monomers are considered to be ideal treatment agents due to their multi-target effects. Astragaloside has been shown to possess tumor chemoprevention, direct anti-tumor, and chemotherapeutic drug sensitization effects. In this paper, we review the effects of astragaloside on tumor prevention and treatment and provide directions for further research.

A Mathematical Kinetic Model and Network Analysis for Multicomponent Dissolution Relationships during the Extraction of Natural Products

Traditional Chinese Medicine (TCM) has a long history and typical ethnic traits. Astragalus and Angelica are used in a natural product called a buyang huanwu decoctionand are considered to function as both food and medicine; such products are called a “homology of medicine and food”. In this study, we examined the complex extraction kinetics that occur during the preparation of the natural product BYHWD. Mathematical tools, including the Laplace transformation and Fick’s law, were used to set up kinetic equations for different components in a model of the decoction. We selected the five most important bioactive ingredients of the BYHWD to find the most important speed control component. The intensity and capacity process parameters of the model were determined. A kinetic model was used to quantitatively analyze the dissolution restriction mechanism among the major components. Further, a component–effect network relationship was established to study the interactions of different components during extraction, considering the integrative effect of TCM compositions. Finally, using network pharmacology, certain network parameters were determined through topological analysis. The results indicate that Astragaloside IV exerts the strongest control over the dissolution rates of other components. The BYHWD has a short average path and a high clustering coefficient. The theoretical and experimental results can be used to quantitatively simulate and optimize TCM extraction processes.

Astragalus mongholicus Bunge (Fabaceae): Bioactive Compounds and Potential Therapeutic Mechanisms Against Alzheimer’s Disease

Astragalus mongholicus Bunge (Fabaceae) (also known as Astragali radix-AR), a widely used herb by Traditional Chinese Medicine practitioners, possesses a wide range of pharmacological effects, and has been used to treat Alzheimer’s disease (AD) historically. Its bioactive compounds are categorized into four families: saponins, flavonoids, polysaccharides, and others. AR’s bioactive compounds are effective in managing AD through a variety of mechanisms, including inhibiting Aβ production, aggregation and tau hyperphosphorylation, protecting neurons against oxidative stress, neuroinflammation and apoptosis, promoting neural stem cell proliferation and differentiation and ameliorating mitochondrial dysfunction. This review aims to shed light upon the chemical constituents of AR and the mechanisms underlying the therapeutic effect of each compound in manging AD. Also presented are clinical studies which reported successful management of AD with AR and other herbs. These will be helpful for drug development and clinical application of AR to treat AD.

Effects of Physical Properties and Processing Methods on Astragaloside IV and Flavonoids Content in Astragali radix

The aim of this study was to investigate the effects of the physical properties (diameter size, powder particle size, composition of bark- and wood-tissue, and turnover rate) and processing methods on the content of active ingredients in Astragali radix (AR), a popular Chinese herbal medicine. The astragaloside IV and flavonoid contents increased with decreasing diameter size. Bark-tissue had significantly higher astragaloside IV and formononetin content than that in the wood-tissue. As a higher proportion of bark-tissue is associated with decreasing diameter, a strong correlation was also shown between bark- to wood-tissue ratio and active ingredients' content. Furthermore, an increase in astragaloside IV content was observed in thin powder as compared to coarse powder ground from the whole root. However, this association between active ingredients' content and powder particle size was abolished when isolating bark- and wood-tissue individually. Moreover, AR stir-frying with refined honey, a typical processing method of AR, increased formononetin content. The turnover rate of active constituents upon decoction ranged from 61.9-81.4%. Assessing the active constituent contents using its physical properties and processing methods allows for a more comprehensive understanding of optimizing and strengthening the therapeutic potentials of AR used in food and herbal supplements.