Trinklein Timothy J, Thapa Malati, Lanphere Lexie A, Frost John A, Koresch Sandra M, Aldstadt Joseph H
Department of Chemistry & Biochemistry, University of Wisconsin Milwaukee, Milwaukee, WI, USA.
Molecular Spectroscopy Group, Thermo Fisher Scientific, Inc., Madison, WI, USA.
Talanta. 2021 Aug 15;231:122355. doi: 10.1016/j.talanta.2021.122355. Epub 2021 Apr 12.
Synthetic cathinones are a class of new psychoactive substances (NPS), an emerging group of analogues to traditional illicit drugs which are functionalized to circumvent legal regulations. The analytical investigation of NPS by traditional methods, such as gas chromatography-mass spectrometry (GC-MS), is challenging because newly emerging NPS may not yet appear in spectral libraries and because of the inability to determine certain positional isomers. Low-field or "benchtop" proton nuclear magnetic resonance spectroscopy (NMR) is an alternative that provides significant qualitative information but is particularly susceptible to matrix interferences. To this end, the development of a Sequential Injection Analysis (SIA) method which uses solid-phase extraction (SPE) to remove interfering matrix components prior to NMR determination is described. Factors including the type of SPE sorbent, column dimensions, and sample loading and elution conditions were examined. Several cathinone simulants (primary, secondary, and tertiary amines), "DEA exempt" cathinone standards, as well as authentic case samples were studied. The selectivity of the SIA-NMR-UV method was investigated against a broad range of "cutting agents" and was found to successfully remove all compounds tested with the exception of other basic drugs (e.g., acetaminophen). The limit of detection and reproducibility of the method were optimized using a Plackett-Burman screening design and Sequential Simplex optimization. Using a UV detector for dual (in series) quantification, the multivariate-optimized method produced a method limit of detection (3σ) for the cathinone simulant Phenylpropanolamine (PPA) of 23 μmol L, and a calibration model, in terms of UV peak area, of Area = 0.19 [PPA, mmol L] - 0.04. The optimized method generated ~2 mL of waste per day, and had a footprint of ~1 m Finally, the multivariate-optimized SIA-NMR-UV method was successfully applied to several more case samples and the cathinones were definitively identified.
合成卡西酮是一类新型精神活性物质（NPS），是传统非法药物的新兴类似物，经过功能化处理以规避法规。用传统方法如气相色谱 - 质谱联用（GC - MS）对NPS进行分析研究具有挑战性，因为新出现的NPS可能尚未出现在光谱库中，且无法确定某些位置异构体。低场或“台式”质子核磁共振光谱（NMR）是一种替代方法，可提供重要的定性信息，但特别容易受到基质干扰。为此，本文描述了一种顺序注射分析（SIA）方法的开发，该方法在NMR测定之前使用固相萃取（SPE）去除干扰基质成分。研究了包括SPE吸附剂类型、柱尺寸以及样品加载和洗脱条件等因素。对几种卡西酮模拟物（伯胺、仲胺和叔胺）、“DEA豁免”卡西酮标准品以及实际案例样品进行了研究。考察了SIA - NMR - UV方法对多种“稀释剂”的选择性，发现除其他碱性药物（如对乙酰氨基酚）外，该方法能成功去除所有测试化合物。使用Plackett - Burman筛选设计和顺序单纯形优化对该方法的检测限和重现性进行了优化。使用紫外检测器进行双（串联）定量，多变量优化方法对卡西酮模拟物苯丙醇胺（PPA）的方法检测限（3σ）为23 μmol/L，以紫外峰面积计的校准模型为：面积 = 0.19 [PPA，mmol/L] - 0.04。优化后的方法每天产生约2 mL废物，占地面积约1 m。最后，多变量优化的SIA - NMR - UV方法成功应用于更多案例样品，并明确鉴定出了卡西酮。
