Pan Meiru, Rasmussen Brian Schou, Dalsgaard Petur Weihe, Mollerup Christian Brinch, Nielsen Marie Katrine Klose, Nedahl Michael, Linnet Kristian, Mardal Marie
Department of Forensic Medicine, University of Copenhagen, Copenhagen, Denmark.
Department of Pharmacy, The Arctic University of Norway, Tromsø, Norway.
Front Chem. 2022 May 19;10:868532. doi: 10.3389/fchem.2022.868532. eCollection 2022.
The expanding and dynamic market of new psychoactive substances (NPSs) poses challenges for laboratories worldwide. The retrospective data analysis (RDA) of previously analyzed samples for new targets can be used to investigate analytes missed in the first data analysis. However, RDA has historically been unsuitable for routine evaluation because reprocessing and reevaluating large numbers of forensic samples are highly work- and time-consuming. In this project, we developed an efficient and scalable retrospective data analysis workflow that can easily be tailored and optimized for groups of NPSs. The objectives of the study were to establish a retrospective data analysis workflow for benzodiazepines in whole blood samples and apply it on previously analyzed driving-under-the-influence-of-drugs (DUID) cases. The RDA workflow was based on a training set of hits in ultrahigh-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry (UHPLC-QTOF-MS) data files, corresponding to common benzodiazepines that also had been analyzed with a complementary UHPLC-tandem mass spectrometry (MS/MS) method. Quantitative results in the training set were used as the true condition to evaluate whether a hit in the UHPLC-QTOF-MS data file was true or false positive. The training set was used to evaluate and set filters. The RDA was used to extract information from 47 DBZDs in 13,514 UHPLC-QTOF-MS data files from DUID cases analyzed from 2014 to 2020, with filters on the retention time window, count level, and mass error. Sixteen designer and uncommon benzodiazepines (DBZDs) were detected, where 47 identifications had been confirmed by using complementary methods when the case was open (confirmed positive finding), and 43 targets were not reported when the case was open (tentative positive finding). The most common tentative and confirmed findings were etizolam ( = 26), phenazepam ( = 13), lorazepam ( = 9), and flualprazolam ( = 8). This method efficiently found DBZDs in previously acquired UHPLC-QTOF-MS data files, with only nine false-positive hits. When the standard of an emerging DBZD becomes available, all previously acquired DUID data files can be screened in less than 1 min. Being able to perform a fast and accurate retrospective data analysis across previously acquired data files is a major technological advancement in monitoring NPS abuse.
新型精神活性物质（NPS）不断扩张且动态变化的市场给全球各地的实验室带来了挑战。对先前分析过的样本进行新目标的回顾性数据分析（RDA），可用于调查首次数据分析中遗漏的分析物。然而，由于对大量法医样本进行重新处理和重新评估非常耗费人力和时间，RDA在历史上一直不适合常规评估。在本项目中，我们开发了一种高效且可扩展的回顾性数据分析工作流程，该流程可轻松针对NPS类别进行定制和优化。本研究的目的是建立全血样本中苯二氮䓬类药物的回顾性数据分析工作流程，并将其应用于先前分析过的药物影响下驾驶（DUID）案件。RDA工作流程基于超高效液相色谱 - 四极杆飞行时间质谱（UHPLC - QTOF - MS）数据文件中的一组命中训练集，这些命中对应于常见的苯二氮䓬类药物，同时也采用了互补的超高效液相色谱 - 串联质谱（MS/MS）方法进行分析。训练集中的定量结果用作真实条件，以评估UHPLC - QTOF - MS数据文件中的命中是真阳性还是假阳性。训练集用于评估和设置过滤条件。RDA用于从2014年至2020年分析的DUID案件的13514个UHPLC - QTOF - MS数据文件中提取47种苯二氮䓬类药物的信息，并设置了保留时间窗口、计数水平和质量误差的过滤条件。检测到16种设计型和不常见的苯二氮䓬类药物（DBZD），其中47种鉴定在案件调查时已通过互补方法得到确认（确认阳性结果），43个目标在案件调查时未报告（暂定阳性结果）。最常见的暂定和确认结果是依替唑仑（ = 26）、非那西泮（ = 13）、劳拉西泮（ = 9）和氟硝西泮（ = 8）。该方法在先前获取的UHPLC - QTOF - MS数据文件中有效地发现了DBZD，仅有9例假阳性命中。当新型DBZD的标准可用时，所有先前获取的DUID数据文件可在不到1分钟内进行筛查。能够对先前获取的数据文件进行快速准确的回顾性数据分析是监测NPS滥用方面的一项重大技术进步。
