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Cross-reactivity studies and predictive modeling of "Bath Salts" and other amphetamine-type stimulants with amphetamine screening immunoassays.
Clin Toxicol (Phila) 2013; 51(2):83-91CT

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.

Authors+Show Affiliations

Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural

Language

eng

PubMed ID

23387345

Citation

Petrie, M, et al. "Cross-reactivity Studies and Predictive Modeling of "Bath Salts" and Other Amphetamine-type Stimulants With Amphetamine Screening Immunoassays." Clinical Toxicology (Philadelphia, Pa.), vol. 51, no. 2, 2013, pp. 83-91.
Petrie M, Lynch KL, Ekins S, et al. Cross-reactivity studies and predictive modeling of "Bath Salts" and other amphetamine-type stimulants with amphetamine screening immunoassays. Clin Toxicol (Phila). 2013;51(2):83-91.
Petrie, M., Lynch, K. L., Ekins, S., Chang, J. S., Goetz, R. J., Wu, A. H., & Krasowski, M. D. (2013). Cross-reactivity studies and predictive modeling of "Bath Salts" and other amphetamine-type stimulants with amphetamine screening immunoassays. Clinical Toxicology (Philadelphia, Pa.), 51(2), pp. 83-91. doi:10.3109/15563650.2013.768344.
Petrie M, et al. Cross-reactivity Studies and Predictive Modeling of "Bath Salts" and Other Amphetamine-type Stimulants With Amphetamine Screening Immunoassays. Clin Toxicol (Phila). 2013;51(2):83-91. PubMed PMID: 23387345.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Cross-reactivity studies and predictive modeling of "Bath Salts" and other amphetamine-type stimulants with amphetamine screening immunoassays. AU - Petrie,M, AU - Lynch,K L, AU - Ekins,S, AU - Chang,J S, AU - Goetz,R J, AU - Wu,A H B, AU - Krasowski,M D, PY - 2013/2/8/entrez PY - 2013/2/8/pubmed PY - 2013/4/3/medline SP - 83 EP - 91 JF - Clinical toxicology (Philadelphia, Pa.) JO - Clin Toxicol (Phila) VL - 51 IS - 2 N2 - 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. SN - 1556-9519 UR - https://www.unboundmedicine.com/medline/citation/23387345/Cross_reactivity_studies_and_predictive_modeling_of_"Bath_Salts"_and_other_amphetamine_type_stimulants_with_amphetamine_screening_immunoassays_ L2 - http://www.tandfonline.com/doi/full/10.3109/15563650.2013.768344 DB - PRIME DP - Unbound Medicine ER -