Department of Experimental and Clinical Toxicology, Institute of Experimental and Clinical Pharmacology and Toxicology, Saarland University, 66421, Homburg, Saar, Germany.
Anal Bioanal Chem. 2011 Apr;400(1):79-88. doi: 10.1007/s00216-010-4398-9. Epub 2010 Nov 16.
In contrast to GC-MS libraries, currently available LC-MS libraries for toxicological detection contain besides parent drugs only some main metabolites limiting their applicability for urine screening. Therefore, a metabolite-based LC-MS(n) screening procedure was developed and exemplified for antidepressants. The library was built up with MS(2) and MS(3) wideband spectra using an LXQ linear ion trap with electrospray ionization in the positive mode and full-scan information-dependent acquisition. Pure substance spectra were recorded in methanolic solution and metabolite spectra in urine from rats after administration of the corresponding drugs. After identification, the metabolite spectra were added to the library. Various drugs and metabolites could be sufficiently separated. Recovery, process efficiency, matrix effects, and limits of detection for selected drugs were determined using protein precipitation. Automatic data evaluation was performed using ToxID and SmileMS software. The library consists of over 700 parent compounds including 45 antidepressants, over 1,600 metabolites, and artifacts. Protein precipitation led to sufficient results for sample preparation. ToxID and SmileMS were both suitable for target screening with some pros and cons. In our study, only SmileMS was suitable for untargeted screening being not limited to precursor selection. The LC-MS(n) method was suitable for urine screening as exemplified for antidepressants. It also allowed detecting unknown compounds based on known fragment structures. As ion suppression can never be excluded, it is advantageous to have several targets per drug. Furthermore, the detection of metabolites confirms the body passage. The presented LC-MS(n) method complements established GC-MS or LC-MS procedures in the authors' lab.
与 GC-MS 库相反，目前可用于毒理学检测的 LC-MS 库除了主要代谢物外，仅包含一些母药，这限制了它们在尿液筛选中的应用。因此，开发了一种基于代谢物的 LC-MS(n)筛选程序，并以抗抑郁药为例进行了说明。该库使用带有电喷雾电离正模式的 LXQ 线性离子阱，通过 MS(2) 和 MS(3) 宽带光谱构建，并采用全扫描信息相关采集。在甲醇溶液中记录纯物质光谱，并在大鼠给予相应药物后记录尿液中的代谢物光谱。鉴定后，将代谢物光谱添加到库中。各种药物和代谢物可以充分分离。使用蛋白沉淀法测定了选定药物的回收率、过程效率、基质效应和检测限。使用 ToxID 和 SmileMS 软件自动进行数据评估。该库包含超过 700 种母体化合物，包括 45 种抗抑郁药、超过 1600 种代谢物和人工制品。蛋白沉淀法足以满足样品制备的要求。ToxID 和 SmileMS 都适合用于目标筛选，但各有优缺点。在我们的研究中，只有 SmileMS 适合于非靶向筛选，不受前体选择的限制。该 LC-MS(n)方法适用于以抗抑郁药为例的尿液筛选。它还可以基于已知的片段结构检测未知化合物。由于离子抑制永远无法排除，因此每种药物有多个目标是有利的。此外，代谢物的检测证实了身体的通道。所提出的 LC-MS(n)方法补充了作者实验室中已建立的 GC-MS 或 LC-MS 程序。
