Selective DYRK1A Inhibitor for the Treatment of Type 1 Diabetes: Discovery of 6-Azaindole Derivative GNF2133.
用于治疗 1 型糖尿病的选择性 DYRK1A 抑制剂: 6-氮杂吲哚衍生物 gnf2133 的发现
- 作者列表："Liu YA","Jin Q","Zou Y","Ding Q","Yan S","Wang Z","Hao X","Nguyen B","Zhang X","Pan J","Mo T","Jacobsen K","Lam T","Wu TY","Petrassi HM","Bursulaya B","DiDonato M","Gordon WP","Liu B","Baaten J","Hill R","Nguyen-Tran V","Qiu M","Zhang YQ","Kamireddy A","Espinola S","Deaton L","Ha S","Harb G","Jia Y","Li J","Shen W","Schumacher AM","Colman K","Glynne R","Pan S","McNamara P","Laffitte B","Meeusen S","Molteni V","Loren J
:Autoimmune deficiency and destruction in either β-cell mass or function can cause insufficient insulin levels and, as a result, hyperglycemia and diabetes. Thus, promoting β-cell proliferation could be one approach toward diabetes intervention. In this report we describe the discovery of a potent and selective DYRK1A inhibitor GNF2133, which was identified through optimization of a 6-azaindole screening hit. In vitro, GNF2133 is able to proliferate both rodent and human β-cells. In vivo, GNF2133 demonstrated significant dose-dependent glucose disposal capacity and insulin secretion in response to glucose-potentiated arginine-induced insulin secretion (GPAIS) challenge in rat insulin promoter and diphtheria toxin A (RIP-DTA) mice. The work described here provides new avenues to disease altering therapeutic interventions in the treatment of type 1 diabetes (T1D).
: Β 细胞质量或功能的自身免疫缺陷和破坏可导致胰岛素水平不足，结果导致高血糖和糖尿病。因此，促进 β 细胞增殖可能是糖尿病干预的一种方法。在本报告中，我们描述了一种强效、选择性 DYRK1A 抑制剂 GNF2133 的发现，该抑制剂是通过优化 6-氮杂吲哚筛选 hit 确定的。在体外，GNF2133 能够增殖啮齿类动物和人 β 细胞。在体内，GNF2133 对葡萄糖增强的精氨酸诱导的胰岛素分泌 (GPAIS) 表现出显著的剂量依赖性葡萄糖处置能力和胰岛素分泌大鼠胰岛素启动子和白喉毒素 A (RIP-DTA) 小鼠的挑战。这里描述的工作为 1 型糖尿病 (T1D) 治疗中的疾病改变治疗干预提供了新的途径。
METHODS:Aims We aimed to develop a prediction model based on clinical and biochemical variables for gestational diabetes mellitus (GDM) based on the 2013 World Health Organization (WHO) criteria. Methods A total of 1843 women from a Belgian multi-centric prospective cohort study underwent universal screening for GDM. Using multivariable logistic regression analysis, a model to predict GDM was developed based on variables from early pregnancy. The performance of the model was assessed by receiver-operating characteristic (AUC) analysis. To account for over-optimism, an eightfold cross-validation was performed. The accuracy was compared with two validated models (van Leeuwen and Teede). Results A history with a first degree relative with diabetes, a history of smoking before pregnancy, a history of GDM, Asian origin, age, height and BMI were independent predictors for GDM with an AUC of 0.72 [95% confidence interval (CI) 0.69–0.76)]; after cross-validation, the AUC was 0.68 (95% CI 0.64–0.72). Adding biochemical variables, a history of a first degree relative with diabetes, a history of GDM, non-Caucasian origin, age, height, weight, fasting plasma glucose, triglycerides and HbA_1c were independent predictors for GDM, with an AUC of the model of 0.76 (95% CI 0.72–0.79); after cross-validation, the AUC was 0.72 (95% CI 0.66–0.78), compared to an AUC of 0.67 (95% CI 0.63–0.71) using the van Leeuwen model and an AUC of 0.66 (95% CI 0.62–0.70) using the Teede model. Conclusions A model based on easy to use variables in early pregnancy has a moderate accuracy to predict GDM based on the 2013 WHO criteria.
METHODS:Leveraging the availability of nationwide electronic health records from over 500,000 pregnancies in Israel, a machine-learning approach offers an alternative means of predicting gestational diabetes at high accuracy in the early stages of pregnancy. Gestational diabetes mellitus (GDM) poses increased risk of short- and long-term complications for mother and offspring^ 1 – 4 . GDM is typically diagnosed at 24–28 weeks of gestation, but earlier detection is desirable as this may prevent or considerably reduce the risk of adverse pregnancy outcomes^ 5 , 6 . Here we used a machine-learning approach to predict GDM on retrospective data of 588,622 pregnancies in Israel for which comprehensive electronic health records were available. Our models predict GDM with high accuracy even at pregnancy initiation (area under the receiver operating curve (auROC) = 0.85), substantially outperforming a baseline risk score (auROC = 0.68). We validated our results on both a future validation set and a geographical validation set from the most populated city in Israel, Jerusalem, thereby emulating real-world performance. Interrogating our model, we uncovered previously unreported risk factors, including results of previous pregnancy glucose challenge tests. Finally, we devised a simpler model based on just nine questions that a patient could answer, with only a modest reduction in accuracy (auROC = 0.80). Overall, our models may allow early-stage intervention in high-risk women, as well as a cost-effective screening approach that could avoid the need for glucose tolerance tests by identifying low-risk women. Future prospective studies and studies on additional populations are needed to assess the real-world clinical utility of the model.
METHODS::Repurposing of currently approved medications is an attractive option for the development of novel treatment strategies against physiological and infectious diseases. The antidiabetic sulfonylurea glyburide has demonstrated off-target capacity to inhibit activation of the NLRP3 inflammasome in a variety of disease models, including vaginal candidiasis, caused primarily by the fungal pathogen Candida albicans Therefore, we sought to determine which of the currently approved sulfonylurea drugs prevent the release of interleukin 1β (IL-1β), a major inflammasome effector, during C. albicans challenge of the human macrophage-like THP1 cell line. Findings revealed that the second-generation antidiabetics (glyburide, glisoxepide, gliquidone, and glimepiride), which exhibit greater antidiabetic efficacy than prior iterations, demonstrated anti-inflammatory effects with various degrees of potency as determined by calculation of 50% inhibitory concentrations (IC50s). These same compounds were also effective in reducing IL-1β release during noninfectious inflammasome activation (e.g., induced by lipopolysaccharide [LPS] plus ATP), suggesting that their anti-inflammatory activity is not specific to C. albicans challenge. Moreover, treatment with sulfonylurea drugs did not impact C. albicans growth and filamentation or THP1 viability. Finally, the use of ECE1 and Candidalysin deletion mutants, along with isogenic NLRP3-/- cells, demonstrated that both Candidalysin and NLRP3 are required for IL-1β secretion, further confirming that sulfonylureas suppress inflammasome signaling. Moreover, challenge of THP1 cells with synthetic Candidalysin peptide demonstrated that this toxin is sufficient to activate the inflammasome. Treatment with the experimental inflammasome inhibitor MCC950 led to similar blockade of IL-1β release, suggesting that Candidalysin-mediated inflammasome activation can be inhibited independently of potassium efflux. Together, these results demonstrate that the second-generation antidiabetic sulfonylureas retain anti-inflammatory activity and may be considered for repurposing against immunopathological diseases, including vaginal candidiasis.