Near-infrared Spectroscopy as a Process Analytical Technology Tool for Monitoring the Parching Process of Traditional Chinese Medicine Based on Two Kinds of Chemical Indicators

Advanced applications of mass spectrometry imaging technology in quality control and safety assessments of traditional Chinese medicines

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese medicines (TCMs) have made great contributions to the prevention and treatment of human diseases in China, and especially in cases of COVID-19. However, due to quality problems, the lack of standards, and the diversity of dosage forms, adverse reactions to TCMs often occur. Moreover, the composition of TCMs makes them extremely challenging to extract and isolate, complicating studies of toxicity mechanisms.

AIM OF THE REVIEW

The aim of this paper is therefore to summarize the advanced applications of mass spectrometry imaging (MSI) technology in the quality control, safety evaluations, and determination of toxicity mechanisms of TCMs.

MATERIALS AND METHODS

Relevant studies from the literature have been collected from scientific databases, such as "PubMed", "Scifinder", "Elsevier", "Google Scholar" using the keywords "MSI", "traditional Chinese medicines", "quality control", "metabolomics", and "mechanism".

RESULTS

MSI is a new analytical imaging technology that can detect and image the metabolic changes of multiple components of TCMs in plants and animals in a high throughput manner. Compared to other chemical analysis methods, such as liquid chromatography-mass spectrometry (LC-MS), this method does not require the complex extraction and separation of TCMs, and is fast, has high sensitivity, is label-free, and can be performed in high-throughput. Combined with chemometrics methods, MSI can be quickly and easily used for quality screening of TCMs. In addition, this technology can be used to further focus on potential biomarkers and explore the therapeutic/toxic mechanisms of TCMs.

CONCLUSIONS

As a new type of analysis method, MSI has unique advantages to metabolic analysis, quality control, and mechanisms of action explorations of TCMs, and contributes to the establishment of quality standards to explore the safety and toxicology of TCMs.

Quantification of “Cold-Hot” Medicinal Properties of Chinese Medicines Based on Primary Metabolites and Fisher’s Analysis

Background

Chinese medicinal properties (CMP) are an important part of the basic theory of traditional Chinese medicines (TCMs). Quantitative research on the properties of TCMs is of great significance to deepen the understanding and application of the theory of drug properties and promoting the modernization of TCMs. However, these studies are limited to strong subjectivity or distinguish different drug properties based on certain indicators since CMP studies are diverse.

Objective

To realize quantitative comparison of same medicinal properties of different Chinese medicines.

Method

To solve the above problem, we proposed and explored quantification of Chinese medicinal properties (QMP) and the quantification value of medicinal properties “R”. The correlation between primary metabolites and “cold-hot” medicinal properties was explored on the premise of material basis of Chinese herbal medicines and Fisher's analysis. Based on indicators related to “cold-hot” medicinal properties, we utilized quantitative values “R” to characterize the strength or weakness of “cold-hot” medicinal properties.

Results

According to QMP, the same medicinal properties were quantified and compared by quantification value of medicinal properties that expressed by alphabet “R”. The general theoretical formula of “R” deduced is R = (‖l‖ × cos θ)/‖L‖ = ∑_i=1ⁿj_ip_i/∑_i=1ⁿp_i², in which n ≥ 1. In the light of formula of “R” and indicators related to “cold-hot” medicinal properties, we got “R” value of “cold-cool” and “warm-hot” medicinal properties. “R” values of “cold-cool” medicinal properties of Phellodendri chinensis cortex, Coptidis rhizoma, and Menthae haplocalycis herba were 0.63, 1.00, and 0.49, respectively. The result showed that Coptidis rhizoma is the most “cold-cool”, followed by Phellodendri chinensis cortex, with Menthae haplocalycis herba is the weakest in the three Chinese medicines, consistent with cognition of TCM theory.

Conclusion

QMP has certain guiding significance for the quantification of “cold and hot” drug properties. “R” is feasible to realize the quantitative comparison of the same drug properties of different traditional Chinese medicine, which is helpful to promote process of modern Chinese medicine construction.

Discovery and identification of proangiogenic chemical markers from Gastrodiae Rhizoma based on zebrafish model and metabolomics approach

INTRODUCTION

Angiogenesis is closely related to a variety of diseases, and therapies based on angiogenesis are intensely investigated. Studies have shown that the use of Gastrodiae Rhizoma (GR, Gastrodia elata) can benefit the treatment of ischemic cardiovascular diseases and atherosclerosis by stimulating angiogenesis.

OBJECTIVE

This study tested the angiogenesis effects of a group of chemical markers isolated from GR.

MATERIAL AND METHODS

Zebrafish model was used to evaluate angiogenesis by setting four groups: blank control group, model group, positive control group and treatment group (0.1, 1, and 100 μg/mL RGP). The Gray correlation analysis (GCA) was implemented to calculate the correlation coefficients of each compound between the peak area in liquid chromatography time-of-flight mass spectrometry (LC-TOF-MS) and the bioactivity, the top ten components with the correlation degree > 0.9 were listed.

RESULTS AND DISCUSSION

The optimum final concentration of GR on proangiogenesis effect was determined to be 100 μg/mL. Ten compounds, including gastrodin, parishin E, stigmasterol, p-hydroxybenzyl alcohol, citric acid, etc., were identified to have high correlation coefficients with proangiogenic activity. Furthermore, the network pharmacologic analysis of these compounds revealed that the compounds systematically regulate the formation of new blood vessels via networked vital targets and signalling pathways.

CONCLUSION

GR can promote the growth of blood vessels, ten chemical components discovered contribute to this proangiogenesis activity. These chemical markers of GR thus provide a foundation for further studies on medicinal substances and quality evaluation of GR, also providing a scientific basis for modern interpretation of the processing theory of traditional Chinese medicine.

Combining convolutional neural networks and on-line Raman spectroscopy for monitoring the Cornu Caprae Hircus hydrolysis process

Cornu Caprae Hircus (goat horn, GH) is one of the frequently used medicinal animal horns in traditional Chinese medicine (TCM). Hydrolysis is one of the key steps for GH pretreatment in pharmaceutical manufacturing. However, the physicochemical complexity of the hydrolysis samples imposes a challenge for hydrolysis process analysis and monitoring. In this study, convolutional neural networks (CNNs), one of the most popular deep learning methods, were used to develop quantitative calibration models based on on-line Raman spectroscopy for monitoring the GH hydrolysis process. Partial least squares (PLS) calibration models were also developed for model performance comparison. For CNN modeling, raw Raman spectra were used as inputs and hyperparameters in the CNN structure were optimized. Results show for four of the seven analytes, the optimized CNN models using raw spectra as inputs outperform the optimized PLS models developed with preprocessed spectra. Therefore, compared with the commonly used PLS algorithm, CNN modeling is also a practicable regression method and can be employed for the analytical purpose of this study. Models with better performance are expected to be obtained by improving the CNN model structure and using more effective hyperparameter optimization approaches in further studies. To the best of our knowledge, this is the first reported case study of combining CNNs and on-line Raman spectroscopy for a regression task.