首页> 外文期刊>Clinical Chemistry: Journal of the American Association for Clinical Chemists >Automated mass spectral deconvolution and identification system for GC-MS screening for drugs, poisons, and metabolites in urine.
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Automated mass spectral deconvolution and identification system for GC-MS screening for drugs, poisons, and metabolites in urine.

机译:自动质谱解卷积和鉴定系统,用于GC-MS筛查尿液中的药物,毒物和代谢物。

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BACKGROUND: The challenge in systematic toxicological analysis using gas chromatography and/or liquid chromatography coupled to mass spectrometry is to identify compounds of interest from background noise. The large amount of spectral information collected in one full-scan MS run demands the use of automated evaluation of recorded data files. We evaluated the applicability of the freeware deconvolution software AMDIS (Automated Mass Spectral Deconvolution and Identification System) for GC-MS-based systematic toxicological analysis in urine for increasing the speed of evaluation and automating the daily routine workload. METHODS: We prepared a set of 111 urine samples for GC-MS analysis by acidic hydrolysis, liquid-liquid extraction, and acetylation. After analysis, the resulting data files were evaluated manually by an experienced toxicologist and automatically using AMDIS with deconvolution and identification settings previously optimized for this type of analysis. The results by manual and AMDIS evaluation were then compared. RESULTS: The deconvolution settings for the AMDIS evaluation were successfully optimized to obtain the highest possible number of components. Identification settings were evaluated and chosen for a compromise between most identified targets and general number of hits. With the use of these optimized settings, AMDIS-based data analysis was comparable or even superior to manual evaluation and reduced by half the overall analysis time. CONCLUSIONS: AMDIS proved to be a reliable and powerful tool for daily routine and emergency toxicology. Nevertheless, AMDIS can identify only targets present in the user-defined target library and may therefore not indicate unknown compounds that might be relevant in clinical and forensic toxicology.
机译:背景:使用气相色谱法和/或液相色谱法与质谱联用进行系统毒理学分析的挑战是从背景噪声中鉴定目标化合物。在一次全扫描MS运行中收集的大量光谱信息需要使用对记录数据文件的自动评估。我们评估了免费的反卷积软件AMDIS(自动质谱反卷积和识别系统)在尿液中基于GC-MS的系统毒理学分析的适用性,以提高评估速度并自动执行日常工作量。方法:我们准备了一套111种尿液样品,用于通过酸性水解,液液萃取和乙酰化进行GC-MS分析。分析后,由经验丰富的毒理学家手动评估生成的数据文件,并自动使用AMDIS进行解卷积和识别设置,该设置先前针对此类分析进行了优化。然后比较了人工和AMDIS评估的结果。结果:AMDIS评估的反卷积设置已成功优化,以获取尽可能多的组件。评估并选择了识别设置,以在大多数识别出的目标和一般命中数之间折衷。通过使用这些优化的设置,基于AMDIS的数据分析可媲美甚至优于手动评估,并减少了一半的总体分析时间。结论:AMDIS被证明是日常常规和紧急毒理学的可靠而强大的工具。尽管如此,AMDIS只能识别用户定义的目标库中存在的目标,因此可能无法指示可能与临床和法医毒理学相关的未知化合物。

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