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Comparison of Two Immunoassay Screening Methods and a LC-MS/MS in Detecting Traditional and Designer Benzodiazepines in Urine.

ABSTRACT: Sensitive and specific immunoassay screening methods for the detection of benzodiazepines in urine represent an important prerequisite for routine analysis in clinical and forensic toxicology. Moreover, emerging designer benzodiazepines force labs to keep their methodologies updated, in order to evaluate the reliability of the immunochemical techniques. This study aimed at evaluating the sensitivity and specificity of two different immunoassay methods for the detection of benzodiazepines in urine, through a comparison with the results obtained by a newly developed liquid chromatographic tandem mass spectrometric (LC-MS/MS) procedure. A cohort of authentic urine samples (N = 501) were processed, before and after a hydrolysis procedure, through two immunoassays and an LC-MS/MS method. The LC-MS/MS target procedure was optimized for monitoring 25 different molecules, among traditional and designer benzodiazepines, including some metabolites. At least one of the monitored substances was detected in 100 out of the 501 samples. A good specificity was observed for the two immunoassays (>0.99), independently of the cut-offs and the sample hydrolysis. The new kit demonstrated a fairly higher sensitivity, always higher than 0.90; in particular, a high cross-reactivity of the new immunoassay was observed for samples that tested positive for lorazepam and 7-aminoclonazepam. The two immunoassays appeared adequate to monitor not only traditional benzodiazepines but also new designer ones.

SUBMITTER: Rossi B

PROVIDER: S-EPMC8746686 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.

Dataset Information

Comparison of Two Immunoassay Screening Methods and a LC-MS/MS in Detecting Traditional and Designer Benzodiazepines in Urine.

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