化学
色谱法
生物分析
肟
药代动力学
萃取(化学)
人血浆
乙酰胆碱酯酶
高效液相色谱法
生物流体
液相色谱-质谱法
串联质谱法
治疗药物监测
定量分析(化学)
准确度和精密度
样品制备
作者
Katie A. Walker,Justin N. Vignola,C. Linn Cadieux,Robert C. diTargiani
出处
期刊:Chromatographia
[Springer Science+Business Media]
日期:2025-11-17
卷期号:89 (1): 25-34
标识
DOI:10.1007/s10337-025-04455-y
摘要
Abstract Oxime reactivators are a part of the standard treatment for chemical warfare nerve agent exposure. Evaluating oxime candidates of interest in biological samples requires analytical detection methods, but oximes as a class of compounds have historically been difficult to isolate, detect, and analyze using conventional analytical techniques. Our lab previously developed novel extraction and liquid chromatography-tandem mass spectrometry (LC–MS/MS) methods to detect and quantitate 2-PAM, HI-6, HLö-7, and MMB-4 in human acetylcholinesterase knock-in, mouse carboxylesterase knock-out (KIKO) mouse plasma. These methods were validated to meet the Food and Drug Administration bioanalytical method validation requirements under Good Laboratory Practice conditions and then were utilized to analyze pharmacokinetic samples from KIKO mice. However, the future utility of these methods has been limited by the lack of reproducibly filled liquid chromatography (LC) columns. Herein we present an alternative LC column for the analysis of these oximes. Previously validated assay methods for oxime extraction from KIKO mouse plasma were evaluated for performance when analyzed by the new LC–MS/MS methodologies. Assays were evaluated for sensitivity, linearity, precision, accuracy, selectivity, and specificity. The results demonstrate that for 2-PAM, HI-6, and HLö-7, there was no change in assay performance when analyzed with the new LC–MS/MS methodologies. However, while MMB-4 was able to be retained on the new column, the sensitivity and linear range were not maintained from the original method. This study evaluates the potential of this alternative column to address inter-lot variability challenges, ensuring the robustness of oxime analytical methods.
科研通智能强力驱动
Strongly Powered by AbleSci AI