Aim
To develop a retrospective data analysis (RDA) workflow tailored to benzodiazepines and use this to identify designer and uncommon benzodiazepines (DBZD) in blood samples from driving-under-the-influence-of-drugs (DUID) cases analyzed from 2014 to 2020.

Method
The RDA workflow was developed from a training set of hits in ultra-high performance liquid chromatography–quadrupole time-of-flight–mass spectrometry (UHPLC-QTOF-MS) data files, corresponding to 13 common benzodiazepines that also had been analyzed with a complementary UHPLC- tandem mass spectrometry (MS/MS) method. Raw data from all UHPLC-QTOF-MS data files were available from a structured query language (SQL) data archive. 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 for the RDA. The RDA was finally used to screen for 47 DBZD in 13514 UHPLC-QTOF-MS data files from DUID cases analysed from 2014 to 2020, with filters on retention time window, count level, and mass error.

Results
Filters for retention time and mass filters was narrower and the count limit lower than what is used for our in-house drug screening method. Additional filters for diagnostic fragment ions, isotopic pattern, and residual precursor ion in high-energy spectra were evaluated, but not used in the final RDA. Extracting hits from 13514 LC-HRMS data files with simple count, mass, and retention time filters for 47 compounds was executed in less than one minute. Sixteen DBZD were detected in the UHPLC-QTOF-MS data files. 47 identifications had been confirmed by complementary methods when the cases were open (confirmed positive finding), and 43 targets were not reported, when the cases were open (tentative positive finding). Metabolites were not considered targets, once their parent drug was detected. 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 DBZD in previously acquired UHPLC-QTOF-MS data files, with only 9 false positive hits. Retention time is a required filter in the RDA workflow and therefore relies on availability of the standard or a clean seizure. Alternatively, a workflow relying on diagnostic fragment ions could allow the search for emerging DBZD. Using predicted or online sourced analytical data, for ranking of hits, was not necessary considering the low number of false positive hits. The sensitivity and selectivity can be attributed to the distinguishable chemical nature of benzodiazepines. The RDA workflow can easily be adjusted to other groups of NPS, but would require a new training set and other filter settings. The three most frequently detected DBZD in this study have all been placed under international control. The prevalence of detected DBZD from 2014-2020 remains very low in DUID samples in Eastern Denmark when comparing with conventional benzodiazepines.

Conclusion
The overall framework presented in this study to tailor RDA workflows to specific groups of compounds from historic data of similar compounds We developed an efficient and scalable retrospective data analysis workflow for DBZD that can easily be tailored and optimized for other groups of NPS.