Influence of novel GPR119 agonist in combination with metformin and sitagliptin on glycemia, body weight and food intake in rats fed a high-fat diet

BACKGROUND: Metabolic syndrome and obesity are often precursors of type 2 diabetes mellitus (DM), and current recommendations indicate the advisability of early initiation of drug therapy at the stage of prediabetes. Drugs with incretin activity are one of the priority groups for monotherapy of type 2 diabetes in the onset of the disease, and certain drugs are used to treat obesity. GPR119 agonists increase the secretion of endogenous incretins, and their effectiveness in the treatment of type 2 diabetes and obesity in mono- and combination therapy is currently being actively studied. AIM. To evaluate of the effect of administration of a GPR119 receptor agonist, its combination with metformin or sitagliptin on body weight, food intake and glycemia in rats under a high-calorie diet. MATERIALS AND METHODS: The study was conducted on 56 outbred female rats aged 7–8 months and an initial weight of 305–320 g. Compound ZB-16 is a highly active GPR119 receptor agonist (EC50 = 7 nM). For 12 weeks, the animals were kept on a high-fat and carbohydrate diet and at the same time received the compound ZB-16, metformin and sitagliptin, or its combination (ZB-16 + metformin and ZB-16 + sitagliptin). During the experiment, the weight of the animals, the mass of feed eaten, as well as the level of glycemia after 6 hours of fasting and with an oral glucose load were assessed. RESULTS: In animals of the control group that were on a high-calorie and fatty diet for 12 weeks, an increase in body weight, glycemia and a decrease in the rate of glucose utilization were observed. The introduction of the GPR119 agonist (ZB-16) for 12 weeks led to a significant reduction in the amount of food consumed, limited weight gain and prevented the development of carbohydrate metabolism disorders. The addition of sitagliptin and especially metformin to therapy with the GPR119 agonist significantly increased the effectiveness of therapy compared to the control group, which was expressed in the normalization of animal body weight and glycemia (p <0.05). CONCLUSIONS: The introduction of a combination of the GPR119 agonist (compound ZB-16) with metformin and sitagliptin is more effective than monotherapy in terms of weight gain, food intake, and also prevents the development of carbohydrate metabolism disorders in animals when kept on a high-fat and carbohydrate diet

Chemistry and Hypoglycemic Activity of GPR119 Agonist ZB-16

This article is to highlight the chemical properties and primary pharmacology of novel GPR119 agonist ZB-16 and its analogs, which were rejected during the screening. Experiments were performed in vitro (specific activity, metabolism and cell toxicity) and in vivo (hypoglycemic activity and pharmacokinetics). ZB-16 exhibits nanomolar activity (EC50 = 7.3–9.7 nM) on target receptor GPR119 in vitro associated with hypoglycemic activity in vivo. In animals with streptozotocin-nicotinamide induced type 2 diabetes mellitus (STZ-NA T2D) daily oral dose of ZB-16 (1 mg/kg) or sitagliptin (10 mg/kg) for 28 days resulted in the reduction of blood glucose levels. The effects of ZB-16 were comparable to the hypoglycemic action of sitagliptin. ZB-16 demonstrated relatively low plasma exposition, high distribution volume, mild clearance and a prolonged half-life (more than 12 h). The present study demonstrates that the targeted search for selective GPR119 receptor agonists is a well-founded approach for developing novel drugs for the therapy of T2D. Based on the combination of high in vitro activity (compared to competitor standards), a useful ADME profile, distinct hypoglycemic activity which is comparable to the efficacy of sitagliptin in rats with experimental T2D, and the acceptable pharmacokinetic profile, we recommend the ZB-16 compound for further research

ZB-16, a Novel GPR119 Agonist, Relieves the Severity of Streptozotocin–Nicotinamide-Induced Diabetes in Rats

GPR119 is involved in the regulation of incretin and insulin secretion, so the GPR119 agonists have been suggested as novel antidiabetic medications. The purpose of this work was to assess the influence of novel GPR119 agonist ZB-16 on the glucose utilization, insulin, and glucagon-like peptide-1 (GLP-1) secretion and the morphology of pancreas in rats with streptozotocin–nicotinamide-induced diabetes. 45 male Wistar rats were used in the study. The criteria of streptozotocin–nicotinamide-induced diabetes were blood glucose levels of 9–14 mmol/l measured in fasting conditions on the third day since administration of streptozotocin (65 mg/kg) and nicotinamide (230 mg/kg). Animals failed to reach the criteria were excluded from the experiment. The substances were administered per os once per day for 28 days. Measurements included blood glucose monitoring (every 7 days), glucose tolerance test (every 14 days), the assessment of insulin and GLP-1 levels in blood plasma (28 days after beginning), and the results of immunohistochemical staining of pancreas. It was found that ZB-16 (1 mg/kg per os, once a day) decreases the blood glucose levels under fasting conditions and improves the glucose utilization. These changes were associated with the increase in stimulated secretion of GLP-1 and insulin, accompanied by the growth of insulin-positive cells in pancreas. Thus, ZB-16 could be a promising antidiabetic drug for oral administration

Evaluation of Unsupervised Anomaly Detection Techniques in Labelling Epileptic Seizures on Human EEG

Automated labelling of epileptic seizures on electroencephalograms is an essential interdisciplinary task of diagnostics. Traditional machine learning approaches operate in a supervised fashion requiring complex pre-processing procedures that are usually labour intensive and time-consuming. The biggest issue with the analysis of electroencephalograms is the artefacts caused by head movements, eye blinks, and other non-physiological reasons. Similarly to epileptic seizures, artefacts produce rare high-amplitude spikes on electroencephalograms, complicating their separability. We suggest that artefacts and seizures are rare events; therefore, separating them from the rest data seriously reduces information for further processing. Based on the occasional nature of these events and their distinctive pattern, we propose using anomaly detection algorithms for their detection. These algorithms are unsupervised and require minimal pre-processing. In this work, we test the possibility of an anomaly (or outlier) detection algorithm to detect seizures. We compared the state-of-the-art outlier detection algorithms and showed how their performance varied depending on input data. Our results evidence that outlier detection methods can detect all seizures reaching 100% recall, while their precision barely exceeds 30%. However, the small number of seizures means that the algorithm outputs a set of few events that could be quickly classified by an expert. Thus, we believe that outlier detection algorithms could be used for the rapid analysis of electroencephalograms to save the time and effort of experts