Quantitative system pharmacology (QSP) is a discipline that combines computational models of systems biology and systems pharmacology. With the development of high-throughput genomics techniques (genomics, transcriptomics, proteomics, and metabolomics) as well as computer and bioinformatics methods, systems biology and systems pharmacology modeling are widely used to comprehend human biology and disease progression, predict the effectiveness and safety of drug candidates. Due to the advancement of big data and high-quality database, the application of QSP, especially the pre-clinical stage that guides early drug discovery, is increasingly widespread. The traditional drug discovery process takes a long time yet has a low success rate. The early intervention and full participation of QSP in the development of new drugs discovery can form a model-led drug development model to improve the efficiency of drug discovery and scientific appraise, reduce the cost of research and development, and shorten the time to market for new drugs. This article reviews the differences between QSP and other quantitative pharmacology, the problems faced by traditional Chinese medicine research, and the value of QSP in traditional Chinese medicine research, with a view to providing reference and support for the research and development of new traditional Chinese medicine.

Objective: Compound Xuanju has good effects in treating rheumatoid arthritis (RA), but its composition is complex, and its active ingredients and mechanism have not been fully defined. In this study, the active ingredients and mechanism of compound Xuanju for the treatment of RA were explored through network pharmacological methods. Methods: TCMSP and TCMID, Pubmed, CNKI, Wanfang, and VIP databases were used to screen, select active pharmaceutical ingredients and targets; Drugbank disease target screening database, GeneCards database, Therapeutic The Target Database (TTD) database and DisGeNET database were used to collect RA  targets, and OmicShare was used to screen compound Xuanju and RA for common targets and construct a Venn diagram. A protein target database String was used to construct a common target interaction network. OmicShare mapping software builds a "drug-active ingredient-target" network and analyzes their associations. DAVID online software performs gene annotation (GO) and KEGG pathway enrichment analysis on key targets. Results: A total of 73 effective ingredients of compound Xuanju were obtained, and corresponding to 229 targets; 2337 targets for RA. 155 key targets for potential active ingredients of compound Xuanju predicted therapeutic effect of RA, the key targets map 55 active ingredients of compound Xuanju capsules. These targets mainly involve signaling pathways such as Toll-like receptor signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, and TNF signaling pathway acting on RA. Conclusion: Compound Xuanju may via its potential 55 active ingredients act on 155 targets to treat RA through Toll-like receptor signaling pathway, PI3K-Akt signaling pathway, NF-κB signaling pathway, HIF-1 signaling pathway and TNF signaling pathway. This study lays the theoretical basis for the widespread application of compound Xuanju in clinical practice.

Objective: To observe effects of glycolysis on human rheumatoid arthritis Fibroblast-like synoviocytes (HFLS-RA) by inhibiting glycolysis. Methods: Hexokinase inhibitor (3-bromopyruvate, 3-BrPa), 6-phosphofructokinase 1 inhibitor citric acid and pyruvate kinase inhibitor shikonin were applied to HFLS-RA respectively. Cell count 8 Kit detects cell proliferation activity, the activity of hexokinase, 6-phosphofructokinase 1, and pyruvate kinase, as well as the cellular glucose, lactate and ATP content were detected by kits, and the ELISA kit detects the expression of cellular inflammatory factors TNF-α and TGF-β. Results: 10 μg/mL 3-BrPa, 160 μg/mL citric acid and 5 μg/mL shikonin significantly inhibited cell proliferation activity (P<0.001); and significantly inhibited HFLS-RA hexokinase and fructose 6-phosphate Kinase 1 and pyruvate kinase activity; Glucose, lactate and ATP content decreased; TNF-α expression decreased, while TGF-β expression increased. Conclusion: This study explored the changes in glucose metabolism and the expression of inflammatory factors in HFLS-RA by inhibiting the key enzymes of glycolysis, further confirming the important role of glycolysis in HFLS-RA, and laying a theoretical basis for a deep understanding of the pathogenesis of RA.