title: Cross-reactivity studies and predictive modeling of "Bath Salts" and other amphetamine-type stimulants with amphetamine screening immunoassays.
abstract: BACKGROUND
  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: Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA.
authors: M
authors: M
authors: Petrie
authors: K L
authors: KL
authors: Lynch
authors: S
authors: S
authors: Ekins
authors: J S
authors: JS
authors: Chang
authors: R J
authors: RJ
authors: Goetz
authors: A H B
authors: AH
authors: Wu
authors: M D
authors: MD
authors: Krasowski
mesh_terms: D000662:Amphetamines / Q000032:analysis* / Q000276:immunology; D000918:Antibody Specificity; D019540:Area Under Curve; D000697:Central Nervous System Stimulants / Q000032:analysis* / Q000276:immunology; D003429:Cross Reactions; D018360:Crystallography, X-Ray; D013287:Illicit Drugs; D007118:Immunoassay / Q000379:methods*; D008956:Models, Chemical; D008958:Models, Molecular; D008968:Molecular Conformation; D011237:Predictive Value of Tests; D012372:ROC Curve; D015813:Substance Abuse Detection
chemical_list: D000662:Amphetamines; D000697:Central Nervous System Stimulants; D013287:Illicit Drugs