Quantitative Analysis of Drugs in a Mimetic Tissue Model Using Nano-DESI on a Triple Quadrupole Mass Spectrometer

化学 三级四极质谱仪 质谱法 分析化学(期刊) 分光计 四极杆质量分析仪 四极 四极飞行时间 混合质谱仪 纳米- 色谱法 定量分析(化学) 选择性反应监测 串联质谱法 原子物理学 化学工程 光学 物理 工程类
作者
Alyssa M. Moore,Andrew P. Bowman,Syeda Nazifa Wali,Miranda R. Weigand,David S. Wagner,Junhai Yang,Julia Laskin
出处
期刊:Journal of the American Society for Mass Spectrometry [American Chemical Society]
标识
DOI:10.1021/jasms.4c00345
摘要

Mass spectrometry is a powerful analytical technique used at every stage of the pharmaceutical research process. A specialized branch of this method, mass spectrometry imaging (MSI), has emerged as an important tool for determining the spatial distribution of drugs in biological samples. Despite the importance of MSI, its quantitative capabilities are still limited due to the complexity of biological samples and the lack of separation prior to analysis. This makes the simultaneous quantification and visualization of analytes challenging. Several techniques have been developed to address this challenge and enable quantitative MSI. One such approach is the mimetic tissue model, which involves the incorporation of an analyte of interest into tissue homogenates at several concentrations. A calibration curve that accounts for signal suppression by the complex biological matrix is then created by measuring the signal of the analyte in the series of tissue homogenates. Herein, we use the mimetic tissue model on a triple quadrupole mass spectrometer (QqQ) in multiple reaction monitoring mode to demonstrate the quantitative abilities of nanospray desorption electrospray ionization (nano-DESI) and compare these results with those obtained using atmospheric pressure matrix-assisted laser desorption/ionization (AP-MALDI). For the tested compounds, our findings indicate that nano-DESI achieves lower standard deviations than AP-MALDI, resulting in superior limits of detection for the studied analytes. Additionally, we discuss the limitations of the mimetic tissue model in the quantification of certain analytes and the challenges involved with the implementation of the model.
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