Gegen Qinlian Decoction (GQD), a traditional Chinese medicine (TCM) formula, has long been used for the treatment of common metabolic diseases, including type 2 diabetes mellitus. However, the main limitation of its wider application is ingredient complexity of this formula. Thus, it is critically important to identify the major active ingredients of GQD and to illustrate mechanisms underlying its action. Here, we compared the effects of GQD and berberine, a hypothetical key active pharmaceutical ingredient of GQD, on a diabetic rat model by comprehensive analyses of gut microbiota, short-chain fatty acids, proinflammatory cytokines, and ileum transcriptomics. Our results show that berberine and GQD had similar effects on lowering blood glucose levels, modulating gut microbiota, inducing ileal gene expression, as well as relieving systemic and local inflammation. As expected, both berberine and GQD treatment significantly altered the overall gut microbiota structure and enriched many butyrate-producing bacteria, including Faecalibacterium and Roseburia, thereby attenuating intestinal inflammation and lowering glucose. Levels of short-chain fatty acids in rat feces were also significantly elevated after treatment with berberine or GQD. Moreover, concentration of serum proinflammatory cytokines and expression of immune-related genes, including Nfkb1, Stat1, and Ifnrg1, in pancreatic islets were significantly reduced after treatment. Our study demonstrates that the main effects of GQD can be attributed to berberine via modulating gut microbiota. The strategy employed would facilitate further standardization and widespread application of TCM in many diseases.

Berberine (BBR), extracted from the traditional medicinal plant Coptis chinensis Franch., has been widely used for the treatment of type 2 diabetes mellitus (T2DM) and its complications. To determine the potential pharmacological mechanisms underlying BBR therapeutic effect on T2DM and its complications by in silico network pharmacology and experimental in vivo validation. A predictive network depicting the relationship between BBR and T2DM was designed based on information collected from several databases, namely STITCH, CHEMBL, PharmMapper, TTD, Drugbank, and PharmGKB. Identified overlapping targets related to both BBR and T2DM were crossed with information on biological processes (BPs) and molecular/signaling pathways using the DAVID platform and Cytoscape software. Three candidate targets identified with the BBR-T2DM network (RXRA, KCNQ1 and NR3C1) were evaluated in the C57BL/6J mouse model of T2DM. The mice were treated with BBR or metformin for 10 weeks. Weight, fasting blood glucose (FBG), oral glucose tolerance, and expression levels of the three targets were evaluated. A total of 31 targets of BBR that were also related to T2DM were identified, of which 14 had already been reported in previous studies. Furthermore, these 31 overlapping targets were enriched in 21 related BPs and 18 pathways involved in T2DM treatment. The identified BP-target-pathway network revealed the underlying mechanisms of BBR antidiabetic activity were mediated by core targets such as RXRA, KCNQ1, and NR3C1. In vivo experiments further confirmed that treatment with BBR significantly reduced weight and FBG and alleviated insulin resistance in T2DM mice. Moreover, BBR treatment promoted RXRA expression, whereas it reduced KCNQ1 and NR3C1 expression in the liver. Using network pharmacology and a T2DM mouse model, this study revealed that BBR can effectively prevent T2DM symptoms through vital targets and multiple signaling pathways. Network pharmacology provides an efficient, time-saving approach for therapeutic research and the development of new drugs.

Diabetic kidney disease (DKD) is a serious complication associated with diabetes mellitus and can cause end-stage renal disease (ESRD). Traditional Chinese medicine (TCM) is widely used in China to treat DKD, and in particular microalbuminuria and macroalbuminuria. This study will address the efficacy and safety of Shenzhuo Formula (SZF), a frequently prescribed TCM, in DKD patients with macroalbuminuria. This study is a 24-week, randomized, multi-center, double-blinded, double-dummy, controlled, clinical trial that will include 120 DKD patients aged 18 to 80 years old with a 24-h urinary protein (24-h UP) level of between 0.5 g and 3 g and serum creatinine (SCr) ≤ 133 μmol/L (1.5 mg/dL) and compare SZF to irbesartan. The 24-h UP change from baseline to week 24 will represent the primary endpoint with secondary endpoints including SCr, estimated glomerular filtration rate (eGFR), TCM symptoms, urinary albumin excretion rate (UAER), etc. Safety assessments will also be evaluated. This study will provide initial evidence regarding the efficacy and safety of SZF relative to irbesartan in the treatment of DKD patients with macroalbuminuria. Chinese Clinical Trial Registry, ID: ChiCTR-ICR-15006311 . Registered on 15 April 2015.

Epidermal growth factor receptor (EGFR) mutations are the most common oncogenic driver in non-small cell lung cancer (NSCLC). Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are widely used in the treatment of lung cancer, especially in the first-line treatment of advanced NSCLC, and EGFR-TKIs monotherapy has achieved better efficacy and tolerability compared with standard chemotherapy. However, acquired resistance to EGFR-TKIs and associated adverse events pose a significant obstacle to targeted lung cancer therapy. Therefore, there is an urgent need to seek effective interventions to overcome these limitations. Natural medicines have shown potential therapeutic advantages in reversing acquired resistance to EGFR-TKIs and reducing adverse events, bringing new options and directions for EGFR-TKIs combination therapy. In this paper, we systematically demonstrated the resistance mechanism of EGFR-TKIs, the clinical strategy of each generation of EGFR-TKIs in the synergistic treatment of NSCLC, the treatment-related adverse events of EGFR-TKIs, and the potential role of traditional Chinese medicine in overcoming the resistance and adverse reactions of EGFR-TKIs. Herbs and active compounds have the potential to act synergistically through multiple pathways and multiple mechanisms of overall regulation, combined with targeted therapy, and are expected to be an innovative model for NSCLC treatment.

Background. Shenzhuo formula (SZF) is a traditional Chinese medicine (TCM) prescription which has significant therapeutic effects on diabetic kidney disease (DKD). However, its mechanism remains unknown. Therefore, this study aimed to explore the underlying anti-DKD mechanism of SZF. Methods. The active ingredients and targets of SZF were obtained by searching TCMSP, TCMID, SwissTargetPrediction, HIT, and literature. The DKD target was identified from TTD, DrugBank, and DisGeNet. The potential targets were obtained and PPI network were built after mapping SZF targets and DKD targets. The key targets were screened out by network topology and the SZF-key targets-DKD network was constructed by Cytoscape. GO analysis and KEGG pathway enrichment analysis were performed by using DAVID, and the results were visualized by Omicshare Tools. Results. We obtained 182 potential targets and 30 key targets. Furthermore, a SZF-key targets-DKD network topological analysis showed that active ingredients like M51, M21, M5, M71, and M28 and targets like EGFR, MMP9, MAPK8, PIK3CA, and STAT3 might play important roles in the process of SZF treating in DKD. GO analysis results showed that targets were mainly involved in positive regulation of transcription from RNA polymerase II promoter, inflammatory response, lipopolysaccharide-mediated signaling pathway, and other biological processes. KEGG showed that DKD-related pathways like TNF signaling pathway and PI3K-Akt signaling pathway were at the top of the list. Conclusion. This research reveals the potential pharmacological targets of SZF in the treatment of DKD through network pharmacology and lays a foundation for further studies.