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基于微流控的 LC 芯片-MS/MS 法测定大鼠血清中氟西汀和去甲氟西汀的含量。

A validated microfluidics-based LC-chip-MS/MS method for the quantitation of fluoxetine and norfluoxetine in rat serum.

机构信息

Laboratory of Analytical Pharmaceutical Chemistry, Department of Pharmacy, CIRM, University of Liège, Belgium.

出版信息

Electrophoresis. 2012 Nov;33(22):3370-9. doi: 10.1002/elps.201200168. Epub 2012 Sep 7.

Abstract

An increasing number of quantitative bioanalyses need to be performed on samples available in limited volumes, such as pharmacokinetic studies on small animals. In this context, microfluidic systems as the LC-chip device coupled to a mass spectrometer combine small sample volume requirements and high sensitivity. In this study, we present the development of a microfluidics-based method for fluoxetine (FLX) and norfluoxetine (NFL) quantitation dedicated to pharmacokinetic investigations in the rat serum. Using the methodology of experimental design, LC parameters were optimised in terms of peak resolution, analysis time, and sensitivity. An SPE method was then developed for serum samples on miniaturised 96-well plates containing a mixed-mode strong cation exchanger that provided very clean extracts with good analyte recovery (≥66.0%). The total SPE-LC-MS/MS process required only 20 μL per sample and the method provided a good sensitivity in a total run time of 12 min. Finally, the developed method for FLX and NFL quantitation in rat serum was fully validated. After having selected the most appropriate regression model on the basis of the accuracy profiles, method selectivity, trueness, precision, accuracy and linearity were demonstrated.

摘要

越来越多的定量生物分析需要在有限体积的样本上进行,例如小动物的药代动力学研究。在这种情况下,将 LC 芯片装置与质谱仪相结合的微流控系统结合了小体积样本的需求和高灵敏度。在本研究中,我们提出了一种基于微流控的方法,用于氟西汀(FLX)和去氟西汀(NFL)的定量分析,专门用于大鼠血清中的药代动力学研究。使用实验设计方法,针对峰分辨率、分析时间和灵敏度对 LC 参数进行了优化。然后,针对微型 96 孔板中的血清样品开发了 SPE 方法,其中含有混合模式强阳离子交换剂,可提供非常干净的提取物,具有良好的分析物回收率(≥66.0%)。整个 SPE-LC-MS/MS 过程仅需每个样品 20 μL,并且该方法在 12 分钟的总运行时间内提供了良好的灵敏度。最后,对大鼠血清中 FLX 和 NFL 定量的开发方法进行了全面验证。在基于准确度概况选择最合适的回归模型之后,证明了方法的选择性、准确度、精密度、准确性和线性。

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