dTGS: Method for Effective Components Identification from Traditional Chinese Medicine Formula and Mechanism Analysis

The Integrated Study on the Chemical Profiling to Explore the Constituents and Mechanism of Traditional Chinese Medicine Preparation Huatuo Jiuxin Pills Based on UPLC-Q-TOF/MSE and Network Pharmacology

Huatuo Jiuxin Pills (HJP), a traditional Chinese medicine (TCM) preparation, has been widely used to treat Cardiovascular Diseases (CVDs) for more than 20 years. However, there were still gaps in the study of chemical components and potential pharmacological effects in the HJP. In this study, ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MSE) combined with network pharmacology was used to comprehensively explore the chemical components in HJP and explore its potential active compounds and the mechanism for the treatment of CVDs. A total of 117 compounds, mainly including saponins, cholic acids, and bufadienolides, were rapidly identified and characterized. Simultaneously, the fragmentation mode and characteristic ion analysis of different types of representative compounds were carried out. Network pharmacology results showed that the more important active ingredients mainly include 5β‐hydroxybufotalin, 19 oxo‐cinobufagin, bufarenogin, etc. While, the main targets were PIK3CA, MAPK1, VEGFA and so on. Importantly, HJP has therapeutic effects on CVDs by acting on endocrine resistance, PI3K-Akt signaling pathway, HIF-1 signaling pathway, etc. In addition, molecular docking results showed that the core active ingredients with higher degrees in HJP have a strong affinity with the core targets of CVDs. The current work fills the gap in the chemical substance basis of HJP, and also facilitates a better understanding of the effective components, therapeutic targets, and signaling pathways of HJP in the treatment of CVDs.

Chinese Medicinal Herbs Targeting the Gut-Liver Axis and Adipose Tissue-Liver Axis for Non-Alcoholic Fatty Liver Disease Treatments: The Ancient Wisdom and Modern Science

Non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases worldwide. The pathogenesis of NAFLD is complex. Frontline western medicines only ameliorate the symptoms of NAFLD. On the contrary, the uniqueness of Chinese medicine in its interpretation of NAFLD and the holistic therapeutic approach lead to a promising therapeutic efficacy. Recent studies reveal that the gut-liver axis and adipose tissue-liver axis play important roles in the development of NAFLD. Interestingly, with advanced technology, many herbal formulae are found to target the gut-liver axis and adipose tissue-liver axis and resolve the inflammation in NAFLD. This is the first review summarizes the current findings on the Chinese herbal formulae that target the two axes in NAFLD treatment. This review not only demonstrates how the ancient wisdom of Chinese medicine is being interpreted by modern pharmacological studies, but also provides valuable information for the further development of the herbal-based treatment for NAFLD.

A Computational Toxicology Approach to Screen the Hepatotoxic Ingredients in Traditional Chinese Medicines: Polygonum multiflorum Thunb as a Case Study

In recent years, liver injury induced by Traditional Chinese Medicines (TCMs) has gained increasing attention worldwide. Assessing the hepatotoxicity of compounds in TCMs is essential and inevitable for both doctors and regulatory agencies. However, there has been no effective method to screen the hepatotoxic ingredients in TCMs available until now. In the present study, we initially built a large scale dataset of drug-induced liver injuries (DILIs). Then, 13 types of molecular fingerprints/descriptors and eight machine learning algorithms were utilized to develop single classifiers for DILI, which resulted in 5416 single classifiers. Next, the NaiveBayes algorithm was adopted to integrate the best single classifier of each machine learning algorithm, by which we attempted to build a combined classifier. The accuracy, sensitivity, specificity, and area under the curve of the combined classifier were 72.798, 0.732, 0.724, and 0.793, respectively. Compared to several prior studies, the combined classifier provided better performance both in cross validation and external validation. In our prior study, we developed a herb-hepatotoxic ingredient network and a herb-induced liver injury (HILI) dataset based on pre-clinical evidence published in the scientific literature. Herein, by combining that and the combined classifier developed in this work, we proposed the first instance of a computational toxicology to screen the hepatotoxic ingredients in TCMs. Then Polygonum multiflorum Thunb (PmT) was used as a case to investigate the reliability of the approach proposed. Consequently, a total of 25 ingredients in PmT were identified as hepatotoxicants. The results were highly consistent with records in the literature, indicating that our computational toxicology approach is reliable and effective for the screening of hepatotoxic ingredients in Pmt. The combined classifier developed in this work can be used to assess the hepatotoxic risk of both natural compounds and synthetic drugs. The computational toxicology approach presented in this work will assist with screening the hepatotoxic ingredients in TCMs, which will further lay the foundation for exploring the hepatotoxic mechanisms of TCMs. In addition, the method proposed in this work can be applied to research focused on other adverse effects of TCMs/synthetic drugs.

A systematic literature review and classification of knowledge discovery in traditional medicine

INTRODUCTION AND OBJECTIVE

Despite the importance of machine learning methods application in traditional medicine there is a no systematic literature review and a classification for this field. This is the first comprehensive literature review of the application of data mining methods in traditional medicine.

METHOD

We reviewed 5 database between 2000 to 2017 based on the Kitchenham systematic review methodology. 502 articles were identified and reviewed for their relevance to application of machine learning methods in traditional medicine, 42 selected papers were classified and categorized on four dimension; 1) application domain of data mining techniques in traditional medicine; 2) the data mining methods most frequently used in traditional medicine; 3) main strength and limitation of data mining techniques in traditional medicine; 4) the performance evaluation methods in data mining methods in traditional medicine.

RESULT

The result obtained showed that main application domain of data mining techniques in traditional medicine was related to syndrome differentiation. Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) were recognized as being the methods most frequently applied in traditional medicine. Furthermore, each data mining techniques has its own strength and limitations when applied in traditional medicine. Single scaler methods were frequently used for performance evaluation of data mining methods.

CONCLUSION

Machine learning methods have become an important research field in traditional medicine. Our research provides information about this methods by examining the related articles.

Effect of catalpol on remyelination through experimental autoimmune encephalomyelitis acting to promote Olig1 and Olig2 expressions in mice

BACKGROUND

Multiple sclerosis (MS) as an autoimmune disorder is a common disease occurring in central nervous system (CNS) and the remyelination plays a pivotal role in the alleviating neurological impairment in the MS. Catalpol, an effective component extracted from the Chinese herb Radix Rehmanniae, which has been proved protective in cerebral diseases.

METHODS

To determine the protective effects and mechanisms of Catalpol on MS, the mice with experimental autoimmune encephalomyelitis (EAE) were induced by myelin oligodendrocyte glycoprotein (MOG) 35-55, as a model for human MS. Th17 cells were counted by flow cytometric (FCM). The expressions of nerve-glial antigen (NG) 2 and myelin basic protein (MBP) were measured by immunohistochemical staining. Olig1+ and Olig2+/BrdU+ cells were counted by immunofluorescence. Olig1 and Olig2 gene expressions were detected by real-time fluorescent quantitative reverse transcription (qRT) -PCR.

RESULTS

The results showed that Catalpol improved neurological function, reduced inflammatory cell infiltration and demyelination. It could decrease Th17 cells in the peripheral blood. It increased the protein expressions of NG2 and MBP in mice brains, up-regulated markedly protein and gene expressions of Olig1 and Olig2 in terms of timing, site and targets.

CONCLUSIONS

These data demonstrated that Catalpol had a strong neuroprotective effect on EAE mice. Catalpol also plays a role in remyelination by promoting the expressions of Olig1 and Olig2 transcription factors.

ITPI: Initial Transcription Process-Based Identification Method of Bioactive Components in Traditional Chinese Medicine Formula

Identification of bioactive components is an important area of research in traditional Chinese medicine (TCM) formula. The reported identification methods only consider the interaction between the components and the target proteins, which is not sufficient to explain the influence of TCM on the gene expression. Here, we propose the Initial Transcription Process-based Identification (ITPI) method for the discovery of bioactive components that influence transcription factors (TFs). In this method, genome-wide chip detection technology was used to identify differentially expressed genes (DEGs). The TFs of DEGs were derived from GeneCards. The components influencing the TFs were derived from STITCH. The bioactive components in the formula were identified by evaluating the molecular similarity between the components in formula and the components that influence the TF of DEGs. Using the formula of Tian-Zhu-San (TZS) as an example, the reliability and limitation of ITPI were examined and 16 bioactive components that influence TFs were identified.

Using Bioinformatics Approach to Explore the Pharmacological Mechanisms of Multiple Ingredients in Shuang-Huang-Lian

Due to the proved clinical efficacy, Shuang-Huang-Lian (SHL) has developed a variety of dosage forms. However, the in-depth research on targets and pharmacological mechanisms of SHL preparations was scarce. In the presented study, the bioinformatics approaches were adopted to integrate relevant data and biological information. As a result, a PPI network was built and the common topological parameters were characterized. The results suggested that the PPI network of SHL exhibited a scale-free property and modular architecture. The drug target network of SHL was structured with 21 functional modules. According to certain modules and pharmacological effects distribution, an antitumor effect and potential drug targets were predicted. A biological network which contained 26 subnetworks was constructed to elucidate the antipneumonia mechanism of SHL. We also extracted the subnetwork to explicitly display the pathway where one effective component acts on the pneumonia related targets. In conclusions, a bioinformatics approach was established for exploring the drug targets, pharmacological activity distribution, effective components of SHL, and its mechanism of antipneumonia. Above all, we identified the effective components and disclosed the mechanism of SHL from the view of system.