Cross-reactivity studies and predictive modeling of “Bath Salts” and other amphetamine-type stimulants with amphetamine screening immunoassays

Abstract

Introduction. 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. Methods. 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. Results: 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. Conclusions. 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.

Keywords:

• Molecular conformations
• Molecular models
• Similarity
• Substance abuse detection

Acknowledgements

SE thanks Accelrys, Inc. for kindly providing Discovery Studio software.

Declaration of Interest

The authors report no declarations of interest. The authors alone are responsible for the content and writing of the paper.

This paper was funded in part by National Institutes of Health grant K08-GM074238 to MDK.