Houston Cocktail: Driving under influence of hydrocodone, alprazolam, and carisoprodol

Compounds identified in an illicit generic Xanax tablet are the result of a failed synthesis of alprazolam

Unintended compounds produced by inexperienced clandestine chemists may present a challenge in laboratories tasked with their identification. In March 2020, an anonymously submitted tablet purchased as a generic form of Xanax was analyzed by Erowid's DrugsData.org. The gas chromatography-mass spectrometry (GC-MS) results publicly released online indicated several unidentified compounds due to a lack of database references at that time. Elucidation by our group indicated the presence of several structurally related compounds that were linked to a failed synthesis of alprazolam. For this case study, a published procedure for the synthesis of alprazolam starting with the chloroacetylation of 2-amino-5-chlorobenzophenone was identified as a potential source of this failure. The procedure was reproduced to identify pitfalls of the methodology and examine its possible link to the illicit tablet. Reaction outcomes were analyzed via GC-MS and compared to the tablet submission data. The major compound in this submission, N-(2-benzoyl-4-chlorophenyl)-2-chloroacetamide, along with several related byproducts were successfully reproduced indicating that the tablet contents potentially stem from a failure to synthesize alprazolam.

Detection and Quantification of Alprazolam Added to Long Drinks by Near Infrared Spectroscopy and Chemometrics

In the present work, a fast, relatively cheap, and green analytical strategy to identify and quantify the fraudulent (or voluntary) addition of a drug (alprazolam, the API of Xanax^®) to an alcoholic drink of large consumption, namely gin and tonic, was developed using coupling near-infrared spectroscopy (NIR) and chemometrics. The approach used was both qualitative and quantitative as models were built that would allow for highlighting the presence of alprazolam with high accuracy, and to quantify its concentration with, in many cases, an acceptable error. Classification models built using partial least squares discriminant analysis (PLS-DA) allowed for identifying whether a drink was spiked or not with the drug, with a prediction accuracy in the validation phase often higher than 90%. On the other hand, calibration models established through the use of partial least squares (PLS) regression allowed for quantifying the drug added with errors of the order of 2–5 mg/L.