Abstract：
Systematic retrospective processing of previously analysed biological samples has been proven to be a valuable tool in the search for new drugs (e.g. new psychoactive substances (NPS)) and for quality assessment in clinical and forensic toxicology. In a previous study, we developed a strategy for retrospective data-analysis using a personalized library of synthetic cannabinoids, designer benzodiazepines and synthetic opioids obtained from the crowdsourced database HighResNPS (https://highresnps.com). In this study, the same strategy was employed for the compounds within the groups of NPS that were not previously included such as synthetic cathinones, phenethylamines, aminoindanes, arylalkylamines, piperazine derivates, piperidines, pyrrolidines, indolalkylamines and arylcyclohexylamines. Synthetic opioids and designer benzodiazepines, which were not part of the previous study, were also included. To enhance the effectiveness of the retrospective analysis, a predicted retention time was included for all entries. Data files from the analysis of 2186 forensic post mortem samples with an Agilent Technologies 6540 ultra-high pressure liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) performed in the laboratory from January 2014 to December 2021 were retrospectively processed with the up-to-date library. Tentative findings were classified in two groups: The findings where MS/MS data was acquired for library match (category 1) and the less certain findings where such data lacked (category 2). Five compounds of category 1 (three synthetic cathinones and two indolalkylamines) were identified in 12 samples. Only one of the findings, 4-MEAPP (4-methyl-α-ethylaminopentiophenone), was deemed plausible after reviewing case information. As many as 501 presumably positive category 2 findings were detected. Using the predicted retention time as an additional criterion the number was significantly reduced but still too high for a manual review. This work has demonstrated that the strategy developed in the previous study can be applied to other NPS groups. However, it is important to note the limitations such a method may have in detecting compounds at very low concentrations.
摘要：
对先前分析的生物样品进行系统的回顾性处理已被证明是寻找新药（例如新的精神活性物质（NPS））以及临床和法医毒理学质量评估的宝贵工具。在之前的研究中,我们开发了一种使用个性化合成大麻素库进行回顾性数据分析的策略，从众包数据库HighResNPS（https://highresnps.com）获得的设计师苯二氮卓类药物和合成阿片类药物。在这项研究中,相同的策略用于以前未包括的NPS组内的化合物，例如合成卡西酮，苯乙胺，氨基茚满,芳烷基胺，哌嗪衍生物，哌啶,吡咯烷,吲哚烷基胺和芳基环己基胺。合成阿片类药物和设计师苯二氮卓类药物，这不是之前研究的一部分，也包括在内。为加强回顾性分析的成效,所有条目均包括预测的保留时间.从2014年1月至2021年12月，在实验室中使用AgilentTechnologies6540超高压液相色谱四极杆飞行时间质谱（UHPLC-QTOF-MS）对2186个法医验尸样本进行分析的数据文件进行了回顾性处理与最新的库。初步发现分为两组：获得MS/MS数据以进行文库匹配的发现（类别1）和缺乏此类数据的不太确定的发现（类别2）。在12个样品中鉴定出5个1类化合物(3个合成卡西酮和2个吲哚烷基胺)。只有一个发现,4-MEAPP（4-甲基-α-乙基氨基苯甲酮），在审查案件信息后被认为是合理的。检测到多达501个可能是2类阳性的发现。使用预测的保留时间作为附加标准,该数量显著减少,但对于手动检查来说仍然太高。这项工作表明，先前研究中开发的策略可以应用于其他NPS组。然而,重要的是要注意这种方法在检测非常低浓度的化合物时可能存在的局限性。
