Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA.
Clin Toxicol (Phila). 2013 Feb;51(2):83-91. doi: 10.3109/15563650.2013.768344.
The increasing abuse of amphetamine-like compounds presents a challenge for clinicians and clinical laboratories. Although these compounds may be identified by mass spectrometry-based assays, most clinical laboratories use amphetamine immunoassays that have unknown cross-reactivity with novel amphetamine-like drugs. To date, there has been a little systematic study of amphetamine immunoassay cross-reactivity with structurally diverse amphetamine-like drugs or of computational tools to predict cross-reactivity.
Cross-reactivities of 42 amphetamines and amphetamine-like drugs with three amphetamines screening immunoassays (AxSYM(®) Amphetamine/Methamphetamine II, CEDIA(®) amphetamine/Ecstasy, and EMIT(®) II Plus Amphetamines) were determined. Two- and three-dimensional molecular similarity and modeling approaches were evaluated for the ability to predict cross-reactivity using receiver-operator characteristic curve analysis.
Overall, 34%-46% of the drugs tested positive on the immunoassay screens using a concentration of 20,000 ng/mL. The three immunoassays showed differential detection of the various classes of amphetamine-like drugs. Only the CEDIA assay detected piperazines well, while only the EMIT assay cross-reacted with the 2C class. All three immunoassays detected 4-substituted amphetamines. For the AxSYM and EMIT assays, two-dimensional molecular similarity methods that combined similarity to amphetamine/methamphetamine and 3,4-methylenedioxymethampetamine most accurately predicted cross-reactivity. For the CEDIA assay, three-dimensional pharmacophore methods performed best in predicting cross-reactivity. Using the best performing models, cross-reactivities of an additional 261 amphetamine-like compounds were predicted.
Existing amphetamines immunoassays unevenly detect amphetamine-like drugs, particularly in the 2C, piperazine, and β-keto classes. Computational similarity methods perform well in predicting cross-reactivity and can help prioritize testing of additional compounds in the future.
苯丙胺类化合物的滥用不断增加，给临床医生和临床实验室带来了挑战。虽然这些化合物可以通过基于质谱的检测方法来识别，但大多数临床实验室使用的苯丙胺免疫分析法对新型苯丙胺类药物的交叉反应性未知。迄今为止，对于苯丙胺免疫分析法与结构多样的苯丙胺类药物的交叉反应性，或者对于预测交叉反应性的计算工具，还没有进行过系统的研究。
用三种苯丙胺筛选免疫分析法（AxSYM®苯丙胺/甲基苯丙胺 II、CEDIA®苯丙胺/摇头丸和 EMIT®II Plus 苯丙胺）测定了 42 种苯丙胺和苯丙胺类药物与这三种苯丙胺免疫分析法的交叉反应性。采用二维和三维分子相似性和建模方法，通过接受者操作特征曲线分析评估其预测交叉反应性的能力。
总的来说，在使用 20000ng/mL 浓度的免疫分析筛选中，有 34%-46%的药物呈阳性反应。这三种免疫分析法对各种类别的苯丙胺类药物的检测存在差异。只有 CEDIA 检测法能很好地检测哌嗪类药物，而只有 EMIT 检测法与 2C 类药物有交叉反应。这三种免疫分析法都能检测到 4 取代的苯丙胺。对于 AxSYM 和 EMIT 检测法，二维分子相似性方法，即同时结合与苯丙胺/甲基苯丙胺和 3,4-亚甲二氧基苯丙胺的相似性，最能准确预测交叉反应性。对于 CEDIA 检测法，三维药效基团方法在预测交叉反应性方面表现最好。使用表现最好的模型，预测了另外 261 种苯丙胺类化合物的交叉反应性。
现有的苯丙胺免疫分析法对苯丙胺类药物的检测不均匀，特别是对 2C、哌嗪和β-酮类药物。计算相似性方法在预测交叉反应性方面表现良好，可以帮助确定今后优先检测哪些额外的化合物。
