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Automated two-step manufacturing of [11C]glyburide radiopharmaceutical for PET imaging in humans.

自动两步制造 [11C] 格列本脲放射性药物,用于人体 PET 成像。

  • 影响因子:2.23
  • DOI:10.1016/j.nucmedbio.2019.12.008
  • 作者列表:"Caillé F","Gervais P","Auvity S","Coulon C","Marie S","Tournier N","Kuhnast B
  • 发表时间:2020-01-07
Abstract

INTRODUCTION:Glyburide is an approved anti-diabetes drug binding to the sulfonylurea receptors-1 (SUR-1) and substrate of solute carrier (SLC) transporters, which can be isotopically radiolabelled with carbon-11 for PET imaging. The aim of this work is to present an original and reproducible automated radiosynthesis of [11C]glyburide and a full European Pharmacopeia 9.7 compliant quality control to use [11C]glyburide in PET imaging clinical trials. METHODS:Different conditions were explored to afford non-radioactive glyburide by one or two-step methylation. These experiments were monitored by UPLC-MS. The optimized process was applied to the automated radiosynthesis of [11C]glyburide using a TRACERlab® FX C Pro. A complete quality control according to Pharmacopeia guidelines was realized. RESULTS:One-step methylation revealed regioselectivity issues as methylation occurred preferentially on the sulfonylurea moiety. Two-step approach by methylation followed by reaction with cyclohexyl isocyanate afforded glyburide without formation of methylated side products. Ready-to-inject [11C]glyburide was obtained in 5% non-decay corrected radiochemical yield and 110 ± 20 GBq/μmol molar activity within 40 min (n = 8). [11C]Glyburide quality control was compliant with the Pharmacopeia requirements. CONCLUSIONS:We have described a highly reproducible and automated two-step radiosynthesis of [11C]glyburide which was qualified as a radiopharmaceutical for human injection. This whole manufacturing process is currently being used to conduct a clinical trial to elucidate the hepatic transport of drugs. ADVANCES IN KNOWLEDGE:Compared to previously reported radiosynthesis of [11C]glyburide, this work provides an original and reproducible approach which can be transferred to any PET centre interested in using this radiotracer for preclinical or clinical imaging. IMPLICATION FOR PATIENT CARE:This work provides a method to manufacture [11C]glyburide for human PET imaging. This radiopharmaceutical could be used to elucidate the role of transporters in drug exposure of different organs or to monitor brain recovery after central nervous system (CNS) injuries.

摘要

简介: 格列本脲是一种被批准的抗糖尿病药物,与磺酰脲受体-1 (SUR-1) 和溶质载体 (SLC) 转运体的底物结合, 可以用碳-11 进行同位素放射性标记,用于 PET 成像。这项工作的目的是提出一种原始的、可重复的 [11C] 格列本脲自动化放射合成方法和一种完全符合欧洲药典 9.7 标准的质量控制方法,以在 PET 成像临床试验中使用 [11C] 格列本脲。 方法: 通过一步或两步甲基化,探索不同条件以提供非放射性格列本脲。通过 UPLC-MS 监测这些实验。使用 TRACERlab 将优化的工艺应用于 [11C] 格列本脲的自动放射合成®FX C Pro.根据药典指南实现了完整的质量控制。 结果: 一步甲基化揭示了区域选择性问题,因为甲基化优先发生在磺酰脲基团上。两步法通过甲基化,然后与环己基异氰酸酯反应,得到格列本脲而不形成甲基化副产物。以 5% 的非衰变校正放射化学产率和 110 ± 20 gbq/μ mol 摩尔活性在 40 min 内获得准备注入 [11C] 格列本脲 (n = 8)。[11C] 格列本脲质量控制符合药典要求。 结论: 我们描述了一种高度可重复性和自动化的 [11C] 格列本脲两步放射合成方法,该方法有资格作为人体注射放射性药物。这整个制造过程目前正在用于进行临床试验,以阐明药物的肝脏转运。 知识进步: 与以前报道的 [11C] 格列本脲放射合成相比, 这项工作提供了一种原创和可重复的方法,可以转移到任何有兴趣使用这种放射性示踪剂进行临床前或临床成像的 PET 中心。 对患者护理的启示: 这项工作提供了一种制造 [11C] 格列本脲用于人体 PET 成像的方法。该放射性药物可用于阐明转运蛋白在不同器官药物暴露中的作用或监测中枢神经系统 (CNS) 损伤后的脑恢复。

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影响因子:2.86
发表时间:2020-01-08
来源期刊:Acta Diabetologica
DOI:10.1007/s00592-019-01469-5
作者列表:["Benhalima, Katrien","Crombrugge, Paul","Moyson, Carolien","Verhaeghe, Johan","Vandeginste, Sofie","Verlaenen, Hilde","Vercammen, Chris","Maes, Toon","Dufraimont, Els","Block, Christophe","Jacquemyn, Yves","Mekahli, Farah","Clippel, Katrien","Den Bruel, Annick","Loccufier, Anne","Laenen, Annouschka","Minschart, Caro","Devlieger, Roland","Mathieu, Chantal"]

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.

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翻译标题与摘要 下载文献
影响因子:19.14
发表时间:2020-01-01
来源期刊:Nature Medicine
DOI:10.1038/s41591-019-0724-8
作者列表:["Artzi, Nitzan Shalom","Shilo, Smadar","Hadar, Eran","Rossman, Hagai","Barbash-Hazan, Shiri","Ben-Haroush, Avi","Balicer, Ran D.","Feldman, Becca","Wiznitzer, Arnon","Segal, Eran"]

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.

关键词: 暂无
翻译标题与摘要 下载文献
影响因子:4.34
发表时间:2020-01-27
DOI:10.1128/AAC.01777-19
作者列表:["Lowes DJ","Hevener KE","Peters BM"]

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.

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