Identification of hepatoprotective traditional Chinese medicines based on the structure–activity relationship, molecular network, and machine learning techniques

Elafibranor: A promising treatment for alcoholic liver disease, metabolic-associated fatty liver disease, and cholestatic liver disease

Liver diseases pose a significant threat to human health. Although effective therapeutic agents exist for some liver diseases, there remains a critical need for advancements in research to address the gaps in treatment options and improve patient outcomes. This article reviews the assessment of Elafibranor's effects on liver fibrosis and intestinal barrier function in a mouse model of alcoholic liver disease (ALD), as reported by Koizumi et al in the World Journal of Gastroenterology. We summarize the impact and mechanisms of Elafibranor on ALD, metabolic-associated fatty liver disease, and cholestatic liver disease based on current research. We also explore its potential as a dual agonist of PPARα/δ, which is undergoing Phase III clinical trials for metabolic-associated steatohepatitis. Our goal is to stimulate further investigation into Elafibranor's use for preventing and treating these liver diseases and to provide insights for its clinical application.

A novel bioinformatics strategy to uncover the active ingredients and molecular mechanisms of Bai Shao in the treatment of non-alcoholic fatty liver disease

Introduction: As a new discipline, network pharmacology has been widely used to disclose the material basis and mechanism of Traditional Chinese Medicine in recent years. However, numerous researches indicated that the material basis of TCMs identified based on network pharmacology was the mixtures of beneficial and harmful substances rather than the real material basis. In this work, taking the anti-NAFLD (non-alcoholic fatty liver disease) effect of Bai Shao (BS) as a case, we attempted to propose a novel bioinformatics strategy to uncover the material basis and mechanism of TCMs in a precise manner. Methods: In our previous studies, we have done a lot work to explore TCM-induced hepatoprotection. Here, by integrating our previous studies, we developed a novel computational pharmacology method to identify hepatoprotective ingredients from TCMs. Then the developed method was used to discover the material basis and mechanism of Bai Shao against Non-alcoholic fatty liver disease by combining with the techniques of molecular network, microarray data analysis, molecular docking, and molecular dynamics simulation. Finally, literature verification method was utilized to validate the findings. Results: A total of 12 ingredients were found to be associated with the anti-NAFLD effect of BS, including monoterpene glucosides, flavonoids, triterpenes, and phenolic acids. Further analysis found that IL1-β, IL6, and JUN would be the key targets. Interestingly, molecular docking and molecular dynamics simulation analysis showed that there indeed existed strong and stable binding affinity between the active ingredients and the key targets. In addition, a total of 23 NAFLD-related KEGG pathways were enriched. The major biological processes involved by these pathways including inflammation, apoptosis, lipid metabolism, and glucose metabolism. Of note, there was a great deal of evidence available in the literature to support the findings mentioned above, indicating that our method was reliable. Discussion: In summary, the contributions of this work can be summarized as two aspects as follows. Firstly, we systematically elucidated the material basis and mechanism of BS against NAFLD from multiple perspectives. These findings further enhanced the theoretical foundation of BS on NAFLD. Secondly, a novel computational pharmacology research strategy was proposed, which would assist network pharmacology to uncover the scientific connotation TCMs in a more precise manner.

The complete plastome of Campsis radicans (L.) Bureau 1864 and its phylogenetic analysis

Campsis radicans (L.) Bureau 1864, a species of Bignoniaceae, has a widespread paleotropical distribution and is utilized for horticultural and traditional Chinese medicinal purposes. Despite the plant's significance, its genetic diversity must be better understood. In this study, we have successfully assembled and characterized the complete plastome of C. radicans, marking a significant advancement toward comprehending its genetic composition. The plastome is 153,630 bp long and harbors 130 genes, including 86 protein-coding genes, 36 tRNA genes, and eight rRNA genes. Our phylogenomic analysis of the representative species of Bignoniaceae indicated that C. radicans formed a monophyletic sister clade of Campsis with C. grandiflora. These findings are crucial for conserving and utilizing this important plant species. They also highlight the potential for future research into the evolution and preservation of C. radicans, which could be advantageous in pharmaceutical applications.

Machine learning in TCM with natural products and molecules: current status and future perspectives

Traditional Chinese medicine (TCM) has been practiced for thousands of years with clinical efficacy. Natural products and their effective agents such as artemisinin and paclitaxel have saved millions of lives worldwide. Artificial intelligence is being increasingly deployed in TCM. By summarizing the principles and processes of deep learning and traditional machine learning algorithms, analyzing the application of machine learning in TCM, reviewing the results of previous studies, this study proposed a promising future perspective based on the combination of machine learning, TCM theory, chemical compositions of natural products, and computational simulations based on molecules and chemical compositions. In the first place, machine learning will be utilized in the effective chemical components of natural products to target the pathological molecules of the disease which could achieve the purpose of screening the natural products on the basis of the pathological mechanisms they target. In this approach, computational simulations will be used for processing the data for effective chemical components, generating datasets for analyzing features. In the next step, machine learning will be used to analyze the datasets on the basis of TCM theories such as the superposition of syndrome elements. Finally, interdisciplinary natural product-syndrome research will be established by unifying the results of the two steps outlined above, potentially realizing an intelligent artificial intelligence diagnosis and treatment model based on the effective chemical components of natural products under the guidance of TCM theory. This perspective outlines an innovative application of machine learning in the clinical practice of TCM based on the investigation of chemical molecules under the guidance of TCM theory.