A New Strategy for Efficient Retrospective Data Analyses for Designer Benzodiazepines in Large LC-HRMS Datasets

The expanding and dynamic market of new psychoactive substances (NPSs) poses challenges for laboratories worldwide. The retrospective data analysis (RDA) of previously analyzed samples for new targets can be used to investigate analytes missed in the first data analysis. However, RDA has historically been unsuitable for routine evaluation because reprocessing and reevaluating large numbers of forensic samples are highly work- and time-consuming. In this project, we developed an efficient and scalable retrospective data analysis workflow that can easily be tailored and optimized for groups of NPSs. The objectives of the study were to establish a retrospective data analysis workflow for benzodiazepines in whole blood samples and apply it on previously analyzed driving-under-the-influence-of-drugs (DUID) cases. The RDA workflow was based on a training set of hits in ultrahigh-performance liquid chromatography–quadrupole time-of-flight–mass spectrometry (UHPLC-QTOF-MS) data files, corresponding to common benzodiazepines that also had been analyzed with a complementary UHPLC–tandem mass spectrometry (MS/MS) method. Quantitative results in the training set were used as the true condition to evaluate whether a hit in the UHPLC-QTOF-MS data file was true or false positive. The training set was used to evaluate and set filters. The RDA was used to extract information from 47 DBZDs in 13,514 UHPLC-QTOF-MS data files from DUID cases analyzed from 2014 to 2020, with filters on the retention time window, count level, and mass error. Sixteen designer and uncommon benzodiazepines (DBZDs) were detected, where 47 identifications had been confirmed by using complementary methods when the case was open (confirmed positive finding), and 43 targets were not reported when the case was open (tentative positive finding). The most common tentative and confirmed findings were etizolam (n = 26), phenazepam (n = 13), lorazepam (n = 9), and flualprazolam (n = 8). This method efficiently found DBZDs in previously acquired UHPLC-QTOF-MS data files, with only nine false-positive hits. When the standard of an emerging DBZD becomes available, all previously acquired DUID data files can be screened in less than 1 min. Being able to perform a fast and accurate retrospective data analysis across previously acquired data files is a major technological advancement in monitoring NPS abuse

Validation of a method for the targeted analysis of 96 drugs in hair by UPLC–MS/MS

The method presented in this study allows the screening and quantification of 96 drugs, from different groups: opiates, amphetamines, hallucinogens, benzodiazepines, antihistamines, antidepressants, antipsychotics, barbiturates and other sedatives, muscle relaxants, etc. in hair. Drugs are extracted from 10. mg of washed hair in 18. h by a mixture of methanol:acetonitrile:ammonium formate (pH 5.3). Absolute recovery ranged from 70% to 106% for 75% of the analytes. The limits of detection in the low pg/mg range, may allow the detection of single dose drug exposure, with possible application in drug facilitated assaults (DFA); however, chronic use (compliance) can also be examined. The method has been fully validated for the drugs included in the study. The accuracy of the method was demonstrated by the analysis of certified authentic hair samples containing common drugs of abuse. The hair-method has broad potential as the measuring range is wide for the target analytes and new drugs can easily be added to the method due to the versatility of the extraction procedure and chromatographic system. © 2013 Elsevier B.V