How to perform spectrum-based LC-HR-MS screening for more than 1,000 NPS with HighResNPS consensus fragment ions

Identification of clobromazolam in Australian emergency department intoxications using data-independent high-resolution mass spectrometry and the HighResNPS.com database

The proliferation of novel psychoactive substances (NPSs) continues to challenge toxicology laboratories. In particular, the United Nations Office on Drugs and Crime considers designer benzodiazepines to be a current primary threat among all NPSs. Herein, we report detection of a new emerging designer benzodiazepine, clobromazolam, using high-resolution mass spectrometry and untargeted data acquisition in combination with a "suspect screening" method built from the crowd-sourced HighResNPS.com database. Our laboratory first detected clobromazolam in emergency department presenting intoxications included within the Emerging Drugs Network of Australia-Victoria project in the state of Victoria, Australia, from April 2022 to March 2023. Clobromazolam was the most frequent designer benzodiazepine detected in this cohort (100/993 cases, 10%). No patients reported intentional administration of clobromazolam, although over half reported exposure to alprazolam, which was detected in only 7% of cases. Polydrug use was prevalent (98%), with phenazepam (45%), methylamphetamine (71%) and other benzodiazepines (60%) most frequently co-detected. This is the first case series published in the literature concerning clobromazolam in clinical patients. The identification of clobromazolam in patients presenting to emergency departments in Victoria demonstrates how high-resolution mass spectrometry coupled with the HighResNPS.com database can be a valuable tool to assist toxicology laboratories in keeping abreast of emerging psychoactive drug use.

Workflow to facilitate the detection of new psychoactive substances and drugs of abuse in influent urban wastewater

The complexity around the dynamic markets for new psychoactive substances (NPS) forces researchers to develop and apply innovative analytical strategies to detect and identify them in influent urban wastewater. In this work a comprehensive suspect screening workflow following liquid chromatography - high resolution mass spectrometry analysis was established utilising the open-source InSpectra data processing platform and the HighResNPS library. In total, 278 urban influent wastewater samples from 47 sites in 16 countries were collected to investigate the presence of NPS and other drugs of abuse. A total of 50 compounds were detected in samples from at least one site. Most compounds found were prescription drugs such as gabapentin (detection frequency 79%), codeine (40%) and pregabalin (15%). However, cocaine was the most found illicit drug (83%), in all countries where samples were collected apart from the Republic of Korea and China. Eight NPS were also identified with this protocol: 3-methylmethcathinone 11%), eutylone (6%), etizolam (2%), 3-chloromethcathinone (4%), mitragynine (6%), phenibut (2%), 25I-NBOH (2%) and trimethoxyamphetamine (2%). The latter three have not previously been reported in municipal wastewater samples. The workflow employed allowed the prioritisation of features to be further investigated, reducing processing time and gaining in confidence in their identification.

Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances

The market for illicit drugs has been reshaped by the emergence of more than 1100 new psychoactive substances (NPS) over the past decade, posing a major challenge to the forensic and toxicological laboratories tasked with detecting and identifying them. Tandem mass spectrometry (MS/MS) is the primary method used to screen for NPS within seized materials or biological samples. The most contemporary workflows necessitate labor-intensive and expensive MS/MS reference standards, which may not be available for recently emerged NPS on the illicit market. Here, we present NPS-MS, a deep learning method capable of accurately predicting the MS/MS spectra of known and hypothesized NPS from their chemical structures alone. NPS-MS is trained by transfer learning from a generic MS/MS prediction model on a large data set of MS/MS spectra. We show that this approach enables a more accurate identification of NPS from experimentally acquired MS/MS spectra than any existing method. We demonstrate the application of NPS-MS to identify a novel derivative of phencyclidine (PCP) within an unknown powder seized in Denmark without the use of any reference standards. We anticipate that NPS-MS will allow forensic laboratories to identify more rapidly both known and newly emerging NPS. NPS-MS is available as a web server at https://nps-ms.ca/, which provides MS/MS spectra prediction capabilities for given NPS compounds. Additionally, it offers MS/MS spectra identification against a vast database comprising approximately 8.7 million predicted NPS compounds from DarkNPS and 24.5 million predicted ESI-QToF-MS/MS spectra for these compounds.

A method for the sensitive targeted screening of synthetic cannabinoids and opioids in whole blood by LC-QTOF-MS with simultaneous suspect screening using HighResNPS.com

A sensitive method for the qualitative screening of synthetic cannabinoids and opioids in whole blood was developed and validated using alkaline liquid-liquid extraction (LLE) and liquid chromatography-time-of-flight mass spectrometry (LC-QTOF-MS). Estimated limits of detection for validated compounds ranged from 0.03 to 0.29 µg/L (median, 0.04 µg/L) for the 27 opioids and from 0.04 to 0.5 µg/L (median, 0.07 µg/L) for the 23 synthetic cannabinoids. Data processing occurred in two stages; first, a targeted screen was performed using an in-house database containing retention times, accurate masses and MS-MS spectra for 79 cannabinoids and 53 opioids. Suspect screening was then performed using a database downloaded from the crowd sourced NPS data website HighResNPS.com which contains mass, consensus MS-MS data and laboratory-specific predicted retention times for a far greater number of compounds. The method was applied to 61 forensic cases where synthetic cannabinoid or opioid screening was requested by the client or their use was suspected due to case information. CUMYL-PEGACLONE was detected in two cases and etodesnitazine, 5 F-MDMB-PICA, 4-cyano-CUMYL-BUTINACA and carfentanil were detected in one case each. These compounds were within the targeted scope of the method but were also detected through the suspect screening workflow. The method forms a solid base for expansion as more compounds emerge onto the illicit drug market.

Identification of Emerging Novel Psychoactive Substances by Retrospective Analysis of Population-Scale Mass Spectrometry Data Sets

Over the last two decades, hundreds of new psychoactive substances (NPSs), also known as "designer drugs", have emerged on the illicit drug market. The toxic and potentially fatal effects of these compounds oblige laboratories around the world to screen for NPS in seized materials and biological samples, commonly using high-resolution mass spectrometry. However, unambiguous identification of a NPS by mass spectrometry requires comparison to data from analytical reference materials, acquired on the same instrument. The sheer number of NPSs that are available on the illicit market, and the pace at which new compounds are introduced, means that forensic laboratories must make difficult decisions about which reference materials to acquire. Here, we asked whether retrospective suspect screening of population-scale mass spectrometry data could provide a data-driven platform to prioritize emerging NPSs for assay development. We curated a suspect database of precursor and diagnostic fragment ion masses for 83 emerging NPSs and used this database to retrospectively screen mass spectrometry data from 12,727 urine drug screens from one Canadian province. We developed integrative computational strategies to prioritize the most reliable identifications and tracked the frequency of these identifications over a 3 year study period between August 2019 and August 2022. The resulting data were used to guide the acquisition of new reference materials, which were in turn used to validate a subset of the retrospective identifications. Last, we took advantage of matching clinical reports for all 12,727 samples to systematically benchmark the accuracy of our retrospective data analysis approach. Our work opens up new avenues to enable the rapid detection of emerging illicit drugs through large-scale reanalysis of mass spectrometry data.

Liquid Chromatography High Resolution Mass Spectrometry in Forensic Toxicology: What Are the Specifics of Method Development, Validation and Quality Assurance for Comprehensive Screening Approaches?

The use of High Resolution Mass Spectrometry (HRMS) has increased over the past decade in clinical and forensic toxicology, especially for comprehensive screening approaches. Despite this, few guidelines of this field have specifically addressed HRMS issues concerning compound identification, validation, measurement uncertainty and quality assurance. To fully implement this technique, certainly in an era in which the quality demands for laboratories are ever increasing due to various norms (e.g. the International Organization for Standardization's ISO 17025), these specific issues need to be addressed. This manuscript reviews 26 HRMS-based methods for qualitative systematic toxicological analysis (STA) published between 2011 and 2021. Key analytical data such as samples matrices, analytical platforms, numbers of analytes and employed mass spectral reference databases/libraries as well as the studied validation parameters are summarized and discussed. The article further includes a critical review of targeted and untargeted data acquisition approaches, available HRMS reference databases and libraries as well as current guidelines for HRMS data interpretation with a particular focus on identification criteria. Moreover, it provides an overview on current recommendations for the validation and determination measurement uncertainty of qualitative methods. Finally, the article aims to put forward suggestions for method development, compound identification, validation experiments to be performed, and adequate determination of measurement uncertainty for this type of wide-range qualitative HRMS-based methods.

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.

Developments in high-resolution mass spectrometric analyses of new psychoactive substances

The proliferation of new psychoactive substances (NPS) has necessitated the development and improvement of current practices for the detection and identification of known NPS and newly emerging derivatives. High-resolution mass spectrometry (HRMS) is quickly becoming the industry standard for these analyses due to its ability to be operated in data-independent acquisition (DIA) modes, allowing for the collection of large amounts of data and enabling retrospective data interrogation as new information becomes available. The increasing popularity of HRMS has also prompted the exploration of new ways to screen for NPS, including broad-spectrum wastewater analysis to identify usage trends in the community and metabolomic-based approaches to examine the effects of drugs of abuse on endogenous compounds. In this paper, the novel applications of HRMS techniques to the analysis of NPS is reviewed. In particular, the development of innovative data analysis and interpretation approaches is discussed, including the application of machine learning and molecular networking to toxicological analyses.

Ropinirole metabolite mimics a new psychoactive substance (4-HO-MET) in LC-MS/MS

Liquid chromatography tandem mass spectrometry (LC-MS/MS) is often regarded as a highly reliable methodology for confirmatory testing in analytical toxicology, especially for detection of new psychoactive substances (NPS) by clinical and forensic laboratories. However, false positives still do occur and erroneous reporting can have substantial legal implications. In this study, we investigated into the mechanism behind a clinically implausible, but apparently analytically sound, finding of a NPS (4-hydroxy-N-methyl-N-ethyltryptamine; 4-HO-MET) in a urine specimen for toxicology screening by LC-MS/MS. We discovered that a ropinirole metabolite (N-despropyl-ropinirole) was the culprit of interference as it shares high structural similarities with 4-HO-MET. The chemical similarities eluded various rigorous regulatory guidelines for compound identification utilizing computer-aided spectral library matching. After careful scrutiny of the mass spectra and comparison with a reference specimen, the compound was correctly identified. Our findings emphasize the important synergy between scientists and pathologists in considering the clinical context, especially drug history, in clinical and forensic toxicology analysis on biological specimens. Mass spectra should be reviewed for relative ion ratios in case of doubt. Understanding drug metabolism is essential for troubleshooting and result interpretation.

In silico prediction, LC-HRMS/MS analysis, and targeted/untargeted data-mining workflow for the profiling of phenylfentanyl in vitro metabolites

Illicit fentanyl and analogues have been involved in many fatalities and cases of intoxication across the United States over the last decade, and are becoming a health concern in Europe. New potent analogues emerge onto the drug market every year to circumvent analytical detection and legislation, and little pharmacological/toxicological data are available when the substances first appear. However, pharmacokinetic data are crucial to determine specific biomarkers of consumption in clinical and forensic settings, considering the low active doses and the rapid metabolism of fentanyl analogues. Phenylfentanyl is a novel analogue that was first detected in seized material in 2017, and little is currently known about this substance and its metabolism. We studied phenylfentanyl metabolic fate using in silico predictions with GLORYx freeware, human hepatocyte incubations, and liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS). We applied a specific targeted/untargeted workflow using data-mining software to allow the rapid and partially automated screening of LC-HRMS/MS raw data. Approximately 90,000 substances were initially individuated after 3-h incubation with hepatocytes, and 115 substances were automatically selected for a manual check by the operators. Finally, 13 metabolites, mostly produced by N-dealkylation, amide hydrolysis, oxidation, and combinations thereof, were identified. We suggest phenylnorfentanyl as the main biological marker of phenylfentanyl use, and we proposed the inclusion of its fragmentation pattern in mzCloud and HighResNPS online libraries. Other major metabolites include N-Phenyl-1-(2-phenylethyl)-4-piperidinamine (4-ANPP), 1-(2-phenylethyl)-4-piperidinol, and other non-specific metabolites. Phase II transformations were infrequent, and the hydrolysis of the biological samples is not required to increase the detection capability of non-conjugated metabolites. The overall workflow is easily adaptable for the metabolite profiling of other novel psychoactive substances.

Sensitive Screening of New Psychoactive Substances in Serum Using Liquid-Chromatography Quadrupole Time-of-Flight Mass Spectrometry

Analysis of new psychoactive substances (NPS) still pose a challenge for many institutions due to the number of available substances and the constantly changing drug market. Both new and well-known substances keep appearing and disappearing on the market, making it hard to adapt analytical methods in a timely manner. In this study we developed a qualitative screening approach for serum samples by means of liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Samples were measured in data-dependent auto-MS/MS mode and identified by fragment spectra comparison, retention time and accurate mass. Approximately 500 NPS, including 195 synthetic cannabinoids, 180 stimulants, 86 hallucinogens, 26 benzodiazepines and 7 others were investigated. Serum samples were fortified to 1 ng/mL and 10 ng/mL concentrations to estimate approximate limits of identification. Samples were extracted using solid-phase extraction with non-endcapped C18 material and elution in two consecutive steps. Benzodiazepines were eluted in the first step, while substances of other NPS subclasses were distributed among both extracts. To determine limits of identification, both extracts were combined. 96 % (470/492) of investigated NPS were detected in 10 ng/mL samples and 88 % (432/492) were detected in 1 ng/mL samples. Stimulants stood out with higher limits of identification, possibly due to instability of certain methcathinone derivatives. However, considering relevant blood concentrations, the method provided sufficient sensitivity for stimulants as well as other NPS subclasses. Data-dependent acquisition was proven to provide high sensitivity and reliability when combined with an information-dependent preferred list, without losing its untargeted operation principle. Summarizing, the developed method fulfilled its purpose as a sensitive untargeted screening for serum samples and allows uncomplicated expansion of the spectral library to include thousands of targets.

Sensitive screening of synthetic cannabinoids using liquid chromatography quadrupole time-of-flight mass spectrometry after solid phase extraction

Analysis of synthetic cannabinoids still poses a challenge for many institutions due to the number of available substances and the constantly changing drug market. Both new and well-known substances keep appearing and disappearing on the market, making it hard to adapt analytical methods in a timely manner. In this study, we developed a qualitative screening approach for synthetic cannabinoids and their metabolites by means of liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Samples were measured in data-dependent auto-MS/MS mode and identified by fragment spectra, retention time and accurate mass. Two established solid phase extractions were compared using fortified serum and urine samples. Mixes of 199 synthetic cannabinoids and 110 metabolites were used in 1- and 10-ng/ml concentrations. Up to 93% of synthetic cannabinoids and 74% of metabolites were detected in fortified 1-ng/ml samples. From February 2018 to October 2020, we analyzed 1492 cases, of which 73 cases were positive for synthetic cannabinoids or metabolites. 5F-MDMB-PICA, 4F-MDMB-BINACA, MDMB-4en-PINACA, and 4F-MDMB-BICA were most frequently detected. Hydrolysis metabolites were detected in many blood samples, providing a longer detection window. Quantification was conducted via liquid chromatography triple quadrupole mass spectrometry after liquid-liquid extraction. Concentrations were mostly close to 1 ng/ml in blood samples. LC-QTOF-MS was able to detect substances above trace quantities (< 0.1 ng/ml) in most cases, therefore fulfilling its purpose as a sensitive general screening approach. Expansion of the screening library was uncomplicated and enables future additions for up to thousands of targets.

Comprehensive UHPLC-HR-MSE screening workflow optimized for use in routine laboratory medicine: Four workflows in one analytical method

A comprehensive HR-MS screening can be used to identify thousands of drugs from a single analysis, which makes it a valuable tool for broad-scope component-resolved toxicological analysis. However, it is common practice in clinical toxicology to perform restricted data analysis to avoid examining and/or reporting data not requested for examination. In this study, a HR-MS screening workflow was developed to allow a comprehensive toxicological evaluation, but also restricted and levelled data analysis to fit in a clinical setting. Following precipitation and reconstitution, samples were injected on an UHPLC-HR-MS and data were analyzed with the data processing software UNIFI. Analytical validation of 38 selected drugs of abuse (DoA), included determination of matrix effect, recovery, process efficiency, and limit of identification (LOI). The method was tested on 49 authentic samples and matrix-matched ranges of calibrators for 95 drugs. The LOI ranged from 0.3 to 1426.7 ng mL^-1 for most analytes which was within expected concentration range for authentic samples with THC-COOH (>1722.0 ng mL^-1) and morphine (1426.7 ng mL^-1) as notable exceptions. Four individual screening workflows were developed: 1) a targeted workflow to serve as orthogonal identification of the 38 selected DOAs from another in-house method, 2) a general toxicology workflow, 3) an extended toxicology workflow including new psychoactive substances (NPS), and 4) a workflow for NPS based on the online HighResNPS library. Our study presents a comprehensive LC-HR-MS toxicology screening method optimized for laboratory medicine. The workflow allows for levelled data reviewing when requested without compromising the ability to perform full toxicological analyses.

Investigation of Biotransformation Products of p-Methoxymethylamphetamine and Dihydromephedrone in Wastewater by High-Resolution Mass Spectrometry

There is a paucity of information on biotransformation and stability of new psychoactive substances (NPS) in wastewater. Moreover, the fate of NPS and their transformation products (TPs) in wastewater treatment plants is not well understood. In this study, batch reactors seeded with activated sludge were set up to evaluate biotic, abiotic, and sorption losses of p-methoxymethylamphetamine (PMMA) and dihydromephedrone (DHM) and identify TPs formed during these processes. Detection and identification of all compounds was performed with target and suspect screening approaches using liquid chromatography quadrupole-time-of-flight mass spectrometry. Influent and effluent 24 h composite wastewater samples were collected from Athens from 2014 to 2020. High elimination rates were found for PMMA (80%) and DHM (97%) after a seven-day experiment and degradation appeared to be related to biological activity in the active bioreactor. Ten TPs were identified and the main reactions were O- and N-demethylation, oxidation, and hydroxylation. Some TPs were reported for the first time and some were confirmed by reference standards. Identification of some TPs was enhanced by the use of an in-house retention time prediction model. Mephedrone and some of its previously reported human metabolites were formed from DHM incubation. Retrospective analysis showed that PMMA was the most frequently detected compound.