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在鼠肺中成像药物、代谢物和生物标志物:用于评估药物诱导的脂肪变性的 DESI-MS 策略。

Imaging drugs, metabolites and biomarkers in rodent lung: a DESI MS strategy for the evaluation of drug-induced lipidosis.

机构信息

National Physical Laboratory, Teddington, London, TW11 0LW, UK.

Institute of Pharmaceutical Science, King's College London, London, WC2R 2LS, UK.

出版信息

Anal Bioanal Chem. 2019 Dec;411(30):8023-8032. doi: 10.1007/s00216-019-02151-z. Epub 2019 Nov 27.

Abstract

Within drug development and pre-clinical trials, a common, significant and poorly understood event is the development of drug-induced lipidosis in tissues and cells. In this manuscript, we describe a mass spectrometry imaging strategy, involving repeated analysis of tissue sections by DESI MS, in positive and negative polarities, using MS and MS/MS modes. We present results of the detected distributions of the administered drug, drug metabolites, lipid molecules and a putative marker of lipidosis, di-docosahexaenoyl (22:6)-bis(monoacylglycerol) phosphate (di-22:6-BMP). A range of strategies have previously been reported for detection, isolation and identification of this compound, which is an isomer of di-docosahexaenoic (22:6 n-3) phosphatidylglycerol (di-22:6 PG), a commonly found lipid that acts as a surfactant in lung tissues. We show that MS imaging using MS/MS can be used to differentiate these compounds of identical mass, based upon the different distributions of abundant fragment ions. Registration of images of these fragments, and detected drugs and metabolites, is presented as a new method for studying drug-induced lipidosis in tissues. Graphical abstract.

摘要

在药物开发和临床前试验中,一个常见的、重要的但理解不充分的现象是药物在组织和细胞中诱导脂质沉积。在本文中,我们描述了一种质谱成像策略,涉及使用 DESI MS 在正、负极性下通过 MS 和 MS/MS 模式对组织切片进行重复分析。我们展示了所检测到的给药药物、药物代谢物、脂质分子和脂质沉积标志物二二十二碳六烯酰基(22:6)-双(单酰基甘油)磷酸酯(二-22:6-BMP)的分布结果。先前已经报道了多种用于检测、分离和鉴定该化合物的策略,该化合物是二二十二碳六烯酸(22:6n-3)磷脂酰甘油(二-22:6PG)的异构体,是一种常见的脂质,在肺组织中作为表面活性剂发挥作用。我们表明,基于丰富的碎片离子的不同分布,使用 MS/MS 的 MS 成像可用于区分这些质量相同的化合物。这些片段以及检测到的药物和代谢物的图像注册被提出作为研究组织中药物诱导的脂质沉积的新方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b29/6920235/b000da2b9fa2/216_2019_2151_Figa_HTML.jpg

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