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数据依赖中性丢失 MS3:朝向药物代谢物中 N-氧化物官能团的自动化鉴定。

Data-dependent neutral gain MS3: toward automated identification of the N-oxide functional group in drug metabolites.

机构信息

Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, USA.

出版信息

J Am Soc Mass Spectrom. 2010 Apr;21(4):559-63. doi: 10.1016/j.jasms.2009.12.015. Epub 2010 Jan 7.

Abstract

We report here an automated method for the identification of N-oxide functional groups in drug metabolites by using the combination of liquid chromatography/tandem mass spectrometry (LC/MS(n)) based on ion-molecule reactions and collision-activated dissociation (CAD). Data-dependent acquisition, which has been readily utilized for metabolite characterization using CAD-based methods, is adapted for use with ion-molecule reaction-based tandem mass spectrometry by careful choice of select experimental parameters. Two different experiments utilizing ion-molecule reactions are demonstrated, data-dependent neutral gain MS(3) and data-dependent neutral gain pseudo-MS(3), both of which generate functional group selective mass spectral data in a single experiment and facilitate increased throughput in structural elucidation of unknown mixture components. Initial results have been generated by using an LC/MS(n) method based on ion-molecule reactions developed earlier for the identification of the N-oxide functional group in pharmaceutical samples, a notoriously difficult functional group to identify via CAD alone. Since commercial software and straightforward, external instrument modification are used, these experiments are readily adaptable to the industrial pharmaceutical laboratory.

摘要

我们在这里报告了一种通过使用基于液相色谱/串联质谱(LC/MS(n))的离子-分子反应和碰撞诱导解离(CAD)相结合的方法,自动识别药物代谢物中 N-氧化物官能团的方法。数据依赖型采集已被广泛用于基于 CAD 的代谢物特征描述方法,通过仔细选择选择实验参数,适应了基于离子-分子反应的串联质谱。本文展示了两种利用离子-分子反应的不同实验,即数据依赖型中性增益 MS(3)和数据依赖型中性增益伪 MS(3),这两种方法都可以在单个实验中生成官能团选择性质谱数据,从而提高未知混合物成分结构鉴定的通量。这些实验采用了先前为鉴定药物样品中 N-氧化物官能团而开发的基于离子-分子反应的 LC/MS(n)方法,该方法可以有效地识别单独使用 CAD 难以鉴定的官能团。由于使用了商业软件和简单的外部仪器修改,这些实验很容易适应于工业制药实验室。

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