Abstract

Despite liquid chromatography–high-resolution tandem mass spectrometry (MS²) enables untargeted acquisition, data processing in toxicological screenings is almost invariably performed in targeted mode. We developed a computational approach based on open source chemometrics software that, starting from a suspected synthetic cannabinoid (SC) determined formula, searches for isomers in different new psychoactive substances web databases, predicts retention time (RT) and high-resolution MS² spectrum, and compares them with the unknown providing a rank-ordered candidates list. R was applied on 105 SC measured data to develop and validate a multiple linear regression quantitative structure–activity relationship model predicting RT. Competitive Fragmentation Modeling for Metabolite Identification (CFM-ID) freeware was used to predict/compare spectra with Jaccard similarity index. Data-dependent acquisition was performed with an Agilent Infinity 1290 LC-6550 iFunnel Q-TOF MS with ZORBAX Eclipse-Plus C18 (100 × 2.1 mm²/1.8 µm) in water/acetonitrile/ammonium formate gradient. Ability of the combined RT/MS² prediction to identify unknowns was evaluated on SC standards (with leave-one-out from the RT model) and on unexpected SC encountered in real cases. RT prediction reduced the number of isomers retrieved from a group of new psychoactive substances web databases to one-third (2,792 ± 3,358→845 ± 983) and differentiated between SC isomers when spectra were not selective (4F-MDMB-BUTINACA, 4F-MDMB-BUTINACA 2ʹ-indazole isomer) or unavailable (4CN-Cumyl-B7AICA, 4CN-Cumyl-BUTINACA). When comparing 30/40 eV measured spectra of 99 SC against RT-selected, CFM-ID predicted spectra of isomers, the right candidate ranked 1st on median and 4th on average; 54% and 88% of times the right match ranked 1st or within the first 5 matches, respectively. To our knowledge, this is the first case of extensive chemometrics application to toxicological screening. In most cases, presumptive identification (being based on computation, it requires further information for confirmation) of unexpected SC was achieved without reference measured information. This method is currently the closest possible to true unbiased/untargeted screening. The bottleneck of the method is the processing time required to predict mass spectra (ca. 30–35 s/compound using a 64-bit 2.50-GHz Intel^® Core™ i5-7200U CPU). However, strategies can be implemented to reduce prediction processing time.

中文翻译：

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LC-QTOF-MS无参考色谱保留/质谱图信息的合成大麻素的假定鉴定。二。预测和识别未知的高分辨率产物离子质谱的计算方法的评估

摘要

尽管使用液相色谱-高分辨率串联质谱（MS ²）可以进行非靶向采集，但毒理学筛查中的数据处理几乎总是以靶向模式进行。我们基于开源化学计量学软件开发了一种计算方法，该方法从可疑的合成大麻素（SC）确定的公式开始，在不同的新型精神活性物质网络数据库中搜索异构体，预测保留时间（RT）和高分辨率MS ²光谱，并将其与未知数进行比较，以提供按顺序排列的候选者列表。[R将其用于105个SC测量数据，以开发和验证预测RT的多元线性回归定量结构-活性关系模型。代谢物鉴定的竞争性片段化建模（CFM-ID）免费软件用于预测/比较具有Jaccard相似性指数的光谱。数据依赖的采集是通过在水/乙腈/甲酸铵梯度中使用带有ZORBAX Eclipse-Plus C18（100×2.1 mm ² /1.8 µm）的Agilent Infinity 1290 LC-6550 iFunnel Q-TOF MS进行的。组合式RT / MS的能力²在SC标准上（对RT模型保留了一个遗漏）和在实际情况下遇到的意外SC，对识别未知物的预测进行了评估。RT预测将从一组新的精神活性物质网络数据库中检索到的异构体数量减少到三分之一（2,792±3,358→845±983），并在光谱不具有选择性时区分SC异构体（4F-MDMB-BUTINACA，4F-MDMB -BUTINACA 2′-吲唑异构体）或不可用（4CN-Cumyl-B7AICA，4CN-Cumyl-BUTINACA）。当将99 SC的30/40 eV测量光谱与RT选择的CFM-ID预测的异构体光谱进行比较时，正确的候选物的中位数排名第1，平均排名第4；正确比赛的54％和88％分别排在第一名或前五场比赛之内。据我们所知，这是化学计量学广泛应用于毒理学筛查的第一个案例。在大多数情况下，无需参考测量信息就可以实现对意外SC的推定识别（基于计算，它需要更多信息以进行确认）。目前，该方法是最接近真正的无偏/无靶筛选的方法。该方法的瓶颈是预测质谱所需的处理时间（CA。30-35秒/使用一个64位的2.50-GHz英特尔化合物^®睿™i5-7200U CPU）。但是，可以实施一些策略来减少预测处理时间。