Quantification and discovery of quality control chemical markers for Ba-Bao-Dan by UPLC-MS/MS combined with chemometrics

Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review

Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.

Quantification and holistic quality evaluation of Wulingzhi extract by UHPLC-Q-Orbitrap-HRMS coupled with chemometric approaches

The excreta of Trogopterus xanthipes ("Wulingzhi" in Chinese, WLZ) is a well-known traditional Chinese medicine. It has been used for centuries to treat amenorrhea, menstruation and postpartum abdominal pain. However, a systematic quality study on WLZ chemical markers has yet to be conducted. This study aimed to establish an ultra-high-performance liquid chromatography coupled with a hybrid quadruple extraction Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap-HRMS) method for the simultaneous quantitative determination of 20 compounds in 53 batches of WLZ; the method rapidly and sensitively determined the 20 plant- or animal-derived compounds. Firstly, the proposed approach was validated to satisfy the method's linearity, detection limits, precision, repeatability, stability and accuracy. Subsequently, multivariate analysis was used to identify correlations between the samples and feed, processing and regions. Finally, this method was used to further identify chemical markers for quality control in combination with chemometrics. This is the first report on pinusolide, betaine, hippuric acid, 4-oxorentinoic acid, 15-methoxypinusolidic acid and 4-oxoisotrentinoin in WLZ; the quality of WLZ became homogeneous after processing with vinegar (V-WLZ). Moreover, we screened for potential component markers, including uridine, allantoin, amentoflavone, hippuric acid, 3,4-dihydroxybenzoic acid, pinusolide, quercetin and kaempferol. These results were practical and efficient for the chemical clarification of WLZ and V-WLZ.

A MS-feature-based medicinal plant database-driven strategy for ingredient identification of Chinese medicine prescriptions

Identification of the individual herbs that constitute the Chinese medicine prescription (CMP) is a key step to control the quality and ensure the efficacy of traditional Chinese medicine (TCM), but also a challenging task for analysts from all over the world. In this study, a MS-feature-based medicinal plant database-driven strategy was proposed for quick and automatic interpretation of CMP ingredients. The single herb database consisting of stable ions of sixty-one common TCM medicinal herbs was first constructed. And then, the data of CMP was imported into a self-built searching program to achieve quick and automatic identification with four steps including level 1 candidate herb screening based on stable ions (step 1), level 2 candidate herb screening based on unique ions (step 2), difficult-to-distinguish herb differentiation (step 3) and results integration (step 4). The identification model was optimized and validated with homemade Shaoyaogancao Decoction, Mahuang Decoction, Banxiaxiexin Decoction, and their related negative prescriptions and homemade fakes. Another nine batches of homemade and commercial CMPs were applied to this new approach and most of composed herbs in the corresponding CMPs were correctly identified. This work provided a promising and universal strategy for the clarification of CMP ingredients.

Screening strategy for predominant phenolic components of digestive enzyme inhibitors in passion fruit peel extracts on simulated gastrointestinal digestion

BACKGROUND

The targeted biological activity of a natural product is often the result of the combined action of multiple functional components. Screening for predominant contributing components of targeting activity is crucial for quality evaluation.

RESULTS

Thirteen and nine phenolic compounds inhibiting α-glucosidase and α-amylase, respectively, were identified in the ethanol extracts of passion fruit peel through liquid chromatography-tandem mass spectrometry and multivariate analysis. Considering the different concentrations of components and their interactions, the role of the semi-inhibitory concentration (IC₅₀ ) in the dose-effect relationship is limited. We proposed the active contribution rate (ACR), which is the ratio of a single component concentration to its IC₅₀ in the whole, to assess the relative activity of each compound. Luteolin, quercetin, and vitexin exhibited a minimum IC₅₀ . Before the simulation of gastrointestinal digestion, quercetin, salicylic acid, and luteolin were identified as the dominant contributors to α-glucosidase inhibition according to ACR, while salicylic acid, 2,3-dihydroxybenzoic acid, and quercetin were identified as dominant contributors to α-amylase inhibition. After simulated digestion, the contents of all polyphenolic compounds decreased by various degrees. Salicylic acid, gentisic acid, and vitexin became the dominant inhibitors of α-glucosidase based on ACR (cumulative 57.96%), while salicylic acid and 2,3-dihydroxybenzoic acid became the dominant inhibitors of α-amylase (cumulative 84.50%).

CONCLUSION

Therefore, the ACR evaluation strategy can provide a quantitative reference for screening the predominant contributor components of a specific activity in complex systems. © 2022 Society of Chemical Industry.

Analysis of Bitter Almonds and Processed Products Based on HPLC-Fingerprints and Chemometry

In the processing field, there is a saying that "seed drugs be stir-fried". Bitter almond (BA) is a kind of seed Chinese medicine. BA need be used after being fried. To distinguish raw bitter almonds (RBA) from processed products and prove the rationality of "seed drugs be stir-fried", we analyzed the RBA and five processed products (scalded bitter almonds, fried bitter almonds, honey fried bitter almonds, bran fried bitter almonds, bitter almonds cream) using RP-HPLC fingerprints and chemometric methods. The similarity between RBA and processed products was 0.733∼0.995. Hierarchically clustered heatmap was used to evaluate the changes in components. Principal component analysis (PCA) was used for classification, and all samples are distinguished according to RBA and five processing methods. Six chemical markers were obtained by partial least squares discriminant analysis (PLS-DA). The content and degradation rate of amygdalin and β-glucosidase activity were determined. Compared with RBA, the content and degradation rate of amygdalin, and β-glucosidase activity were increased in bitter almonds cream. The content and degradation rate were decreased, and β-glucosidase was inactivated in other processed products. The above results showed that stir-frying had the best effect. The results showed that processing can ensure the stability of RBA quality, and the saying "seed drugs be stir-fried" is reasonable.

Deep learning and machine intelligence: New computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of Traditional Chinese Medicine

It has been increasingly accepted that Multi-Ingredient-Based interventions provide advantages over single-target therapy for complex diseases. With the growing development of Traditional Chinese Medicine (TCM) and continually being refined of a holistic view, "multi-target" and "multi-pathway" integration characteristics of which are being accepted. However, its effector substances, efficacy targets, especially the combination rules and mechanisms remain unclear, and more powerful strategies to interpret the synergy are urgently needed. Artificial intelligence (AI) and computer vision lead to a rapidly expanding in many fields, including diagnosis and treatment of TCM. AI technology significantly improves the reliability and accuracy of diagnostics, target screening, and new drug research. While all AI techniques are capable of matching models to biological big data, the specific methods are complex and varied. Retrieves literature by the keywords such as "artificial intelligence", "machine learning", "deep learning", "traditional Chinese medicine" and "Chinese medicine". Search the application of computer algorithms of TCM between 2000 and 2021 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Elsevier and Springer. This review concentrates on the application of computational in herb quality evaluation, drug target discovery, optimized compatibility and medical diagnoses of TCM. We describe the characteristics of biological data for which different AI techniques are applicable, and discuss some of the best data mining methods and the problems faced by deep learning and machine learning methods applied to Chinese medicine.

Exploring potential mechanism of ciwujia tablets for insomnia by UPLC-Q-TOF-MS/MS, network pharmacology, and experimental validation

Insomnia, whether chronic or intermittent, is a common central nervous system disease. Ciwujia Tablet (CWT) is a well-known traditional Chinese medicine (TCM) made from the extract of Eleutherococcus senticosus (Rupr. & Maxim.) Maxim. This medication is commonly used for treating insomnia in China, but the lack of in-depth research focused on the chemical ingredients of CWT creates a gap in knowledge regarding its effective constituents against insomnia. Considering that the therapeutic material basis, targets, and pathways related to this drug have not been fully investigated by scholars in the field, the focus of this study is on identifying the chemical ingredients or structural characteristics of CWT by the UPLC-Q-TOF-MS/MS technique. Besides, concepts of network pharmacology were also used to investigate the targets and pathways of CWT. An insomnia rat model was established by intraperitoneal injection of p-chlorophenylalanine, and the results were verified through various experiments. A total of 46 ingredients were identified in CWT, such as eleutheroside B, eleutheroside E, isofraxidin, and chlorogenic acid. Among them, 17 ingredients with good solubility, favorable gastrointestinal absorption, and high bioavailability were selected for network pharmacological analysis. It was concluded that CWT participated in the regulation of neurotransmitter levels, modulation of ion transport, neurotransmitter receptor activity, synaptic transmission, dopaminergic transmission and other essential processes. Results from the animal experiments showed that CWT can increase the content of inhibitory neurotransmitters 5-HT and GABA in the brain, reduce the synthesis of excitatory escalating transmitters DA and NE, shorten the sleep latency and prolong the sleep duration of insomnia rats. Furthermore, CWT could significantly alleviate the symptoms of insomnia in model rats. Identifying the chemical ingredients of CWT in this experiment is of great significance for exploring its potential curative effects, which provides a solid basis for further understanding the therapeutic value of this medication.

A comprehensive strategy for quality evaluation of Wushe Zhiyang Pills by integrating UPLC-DAD fingerprint and multi-ingredients rapid quantitation with UPLC-MS/MS technology

Wushe Zhiyang Pills (WZP), a classical traditional Chinese medicine (TCM) formula, has been extensively used for the treatment of chronic urticaria and other relevant dermatologic diseases. In this study, a holistic method combining ultra-performance liquid chromatography coupled with diode array detector (UPLC-DAD) fingerprint and multi-components quantitative analysis was developed and validated for quality evaluation of WZP. As a result, a total of 34 characteristic peaks were screened to assess the chemical similarities of 16 batches of WZP samples. By coupling with a hybrid linear ion trap (LTQ)-Orbitrap mass spectrometer, 163 compounds were identified or tentatively identified in WZP. Furthermore, a rapid quantitative analysis method based on ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) technique was optimized and validated for simultaneously determination of 16 chemical markers within 13 min in WZP. The developed UPLC-MS/MS approach was successfully employed for analysis of 16 batches of WZP samples. The proposed comprehensive method combining holistic chemical profile with notable target compounds has proved to be suitable for the systematical quality evaluation of WZP, which provides a feasible and efficient strategy to monitor the overall quality consistency of TCM formulae.