RESUMEN
Enavogliflozin is a sodium-dependent glucose cotransporter 2 (SGLT2) inhibitor approved for clinical use in South Korea. As SGLT2 inhibitors are a treatment option for patients with diabetes, enavogliflozin is expected to be prescribed in various populations. Physiologically based pharmacokinetic (PBPK) modelling can rationally predict the concentration-time profiles under altered physiological conditions. In previous studies, one of the metabolites (M1) appeared to have a metabolic ratio between 0.20 and 0.25. In this study, PBPK models for enavogliflozin and M1 were developed using published clinical trial data. The PBPK model for enavogliflozin incorporated a non-linear urinary excretion in a mechanistically arranged kidney model and a non-linear formation of M1 in the liver. The PBPK model was evaluated, and the simulated pharmacokinetic characteristics were in a two-fold range from those of the observations. The pharmacokinetic parameters of enavogliflozin were predicted using the PBPK model under pathophysiological conditions. PBPK models for enavogliflozin and M1 were developed and validated, and they seemed useful for logical prediction.
RESUMEN
This study was undertaken to determine whether aqueous blackcurrant extracts (BC) improve glucose metabolism and gut microbiomes in non-obese type 2 diabetic animals fed a high-fat diet and to identify the mechanism involved. Partially pancreatectomized male Sprague-Dawley rats were provided a high-fat diet containing 0% (control), 0.2% (L-BC; low dosage), 0.6% (M-BC; medium dosage), and 1.8% (H-BC; high dosage) blackcurrant extracts; 0.2% metformin (positive-C); plus 1.8%, 1.6%, 1.2%, 0%, and 1.6% dextrin, specifically indigestible dextrin, daily for 8 weeks. Daily blackcurrant extract intakes were equivalent to 100, 300, and 900 mg/kg body weight (bw). After a 2 g glucose or maltose/kg bw challenge, serum glucose and insulin concentrations during peak and final states were obviously lower in the M-BC and H-BC groups than in the control group (p < 0.05). Intraperitoneal insulin tolerance testing showed that M-BC and H-BC improved insulin resistance. Hepatic triglyceride deposition, TNF-α expression, and malondialdehyde contents were lower in the M-BC and H-BC groups than in the control group. Improvements in insulin resistance in the M-BC and H-BC groups were associated with reduced inflammation and oxidative stress (p < 0.05). Hyperglycemic clamp testing showed that insulin secretion capacity increased in the acute phase (2 to 10 min) in the M-BC and H-BC groups and that insulin sensitivity in the hyperglycemic state was greater in these groups than in the control group (p < 0.05). Pancreatic ß-cell mass was greater in the M-BC, H-BC, and positive-C groups than in the control group. Furthermore, ß-cell proliferation appeared to be elevated and apoptosis was suppressed in these three groups (p < 0.05). Serum propionate and butyrate concentrations were higher in the M-BC and H-BC groups than in the control group. BC dose-dependently increased α-diversity of the gut microbiota and predicted the enhancement of oxidative phosphorylation-related microbiome genes and downregulation of carbohydrate digestion and absorption-related genes, as determined by PICRUSt2 analysis. In conclusion, BC enhanced insulin sensitivity and glucose-stimulated insulin secretion, which improved glucose homeostasis, and these improvements were associated with an incremental increase of the α-diversity of gut microbiota and suppressed inflammation and oxidative stress.
RESUMEN
BACKGROUND: Screening test using CA-125 is the most common test for detecting ovarian cancer. However, the level of CA-125 is diverse by variable condition other than ovarian cancer. It has led to misdiagnosis of ovarian cancer. METHODS: In this paper, we explore the 16 serum biomarker for finding alternative biomarker combination to reduce misdiagnosis. For experiment, we use the serum samples that contain 101 cancer and 92 healthy samples. We perform two major tasks: Marker selection and Classification. For optimal marker selection, we use genetic algorithm, random forest, T-test and logistic regression. For classification, we compare linear discriminative analysis, K-nearest neighbor and logistic regression. RESULTS: The final results show that the logistic regression gives high performance for both tasks, and HE4-ELISA, PDGF-AA, Prolactin, TTR is the best biomarker combination for detecting ovarian cancer. CONCLUSIONS: We find the combination which contains TTR and Prolactin gives high performance for cancer detection. Early detection of ovarian cancer can reduce high mortality rates. Finding a combination of multiple biomarkers for diagnostic tests with high sensitivity and specificity is very important.
