Development of a single retention time prediction model integrating multiple liquid chromatography systems: Application to new psychoactive substances

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.

Target and suspect screening of psychoactive substances in seizures and oral fluid exploiting retention time prediction and LC-MS/MS analysis

BACKGROUND

Novel psychoactive substances (NPS) are a group of substances, mainly of synthetic origin, characterized by toxicological properties extremely dangerous. The main difficulty in recognizing NPS in seizures and biological samples lies in their dynamic nature, related to the continuous synthesis and introduction on the market of new drugs, often with very similar structures to existing ones. The aim of this study was the creation of a robust and versatile method for the analysis of traditional drugs and NPS in different matrices.

RESULTS

Both target analysis and suspect screening methodologies were developed. The strategy used for suspect screening allowed to collect data through a scheduled multi reaction monitoring (sMRM) survey which triggered the collection of enhanced product ion (EPI) spectra when a compound met information dependent acquisition (IDA) criteria. The retention time of the different drugs, which was crucial to define the sMRM survey scan parameters, was predicted with a Quantitative Structure Retention (Chromatographic) Relationship (QSRR) model by Multiple Linear Regression. The model was validated through the evaluation of training set predictions, an external validation set and a leave-one out strategy; the results showed that the method fit for its purpose. The target method was validated in oral fluid as a testing matrix, with excellent results in term of recovery, accuracy, precision and matrix effect. Finally, the performances of the suspect method were evaluated by analysing a mixture containing 8 reference standards not included in the initial dataset, as well as seizures and real oral fluid samples. Four NPS were putatively identified in the analysed samples.

SIGNIFICANCE

The advantage of the proposed approach is the possibility of quantifying 65 classical drugs of abuse and NPS and, at the same time, detect and putatively identify 146 additional drugs in one single LC-MS/MS run. This is an innovative strategy for multi analyte detection and enables detection of low concentrations of drugs in complex biological matrices such as oral fluid. Considering the highly dynamic drug market, a strength of this strategy is that the analytical method can be kept up to date through the addition of new compounds based on the last drug monitoring bodies alerts without the need of authentic standards.

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.

Cross-column density functional theory-based quantitative structure-retention relationship model development powered by machine learning

Quantitative structure-retention relationship (QSRR) modeling has emerged as an efficient alternative to predict analyte retention times using molecular descriptors. However, most reported QSRR models are column-specific, requiring separate models for each high-performance liquid chromatography (HPLC) system. This study evaluates the potential of machine learning (ML) algorithms and quantum mechanical (QM) descriptors to develop QSRR models that can predict retention times across three different reversed-phase HPLC columns under varying conditions. Four machine learning methods-partial least squares (PLS) regression, ridge regression (RR), random forest (RF), and gradient boosting (GB)-were compared on a dataset of 360 retention times for 15 aromatic analytes. Molecular descriptors were calculated using density functional theory (DFT). Column characteristics like particle size and pore size and experimental conditions like temperature and gradient time were additionally used as descriptors. Results showed that the GB-QSRR model demonstrated the best predictive performance, with Q2 of 0.989 and root mean square error of prediction (RMSEP) of 0.749 min on the test set. Feature analysis revealed that solvation energy (SE), HOMO-LUMO energy gap (∆E HOMO-LUMO), total dipole moment (Mtot), and global hardness (η) are among the most influential predictors for retention time prediction, indicating the significance of electrostatic interactions and hydrophobicity. Our findings underscore the efficiency of ensemble methods, GB and RF models employing non-linear learners, in capturing local variations in retention times across diverse experimental setups. This study emphasizes the potential of cross-column QSRR modeling and highlights the utility of ML models in optimizing chromatographic analysis.

RT-Transformer: retention time prediction for metabolite annotation to assist in metabolite identification

MOTIVATION

Liquid chromatography retention times prediction can assist in metabolite identification, which is a critical task and challenge in nontargeted metabolomics. However, different chromatographic conditions may result in different retention times for the same metabolite. Current retention time prediction methods lack sufficient scalability to transfer from one specific chromatographic method to another.

RESULTS

Therefore, we present RT-Transformer, a novel deep neural network model coupled with graph attention network and 1D-Transformer, which can predict retention times under any chromatographic methods. First, we obtain a pre-trained model by training RT-Transformer on the large small molecule retention time dataset containing 80 038 molecules, and then transfer the resulting model to different chromatographic methods based on transfer learning. When tested on the small molecule retention time dataset, as other authors did, the average absolute error reached 27.30 after removing not retained molecules. Still, it reached 33.41 when no samples were removed. The pre-trained RT-Transformer was further transferred to 5 datasets corresponding to different chromatographic conditions and fine-tuned. According to the experimental results, RT-Transformer achieves competitive performance compared to state-of-the-art methods. In addition, RT-Transformer was applied to 41 external molecular retention time datasets. Extensive evaluations indicate that RT-Transformer has excellent scalability in predicting retention times for liquid chromatography and improves the accuracy of metabolite identification.

AVAILABILITY AND IMPLEMENTATION

The source code for the model is available at https://github.com/01dadada/RT-Transformer. The web server is available at https://huggingface.co/spaces/Xue-Jun/RT-Transformer.

A supervised machine-learning approach for the efficient development of a multi method (LC-MS) for a large number of drugs and subsets thereof: focus on oral antitumor agents

OBJECTIVES

Accumulating evidence argues for a more widespread use of therapeutic drug monitoring (TDM) to support individualized medicine, especially for therapies where toxicity and efficacy are critical issues, such as in oncology. However, development of TDM assays struggles to keep pace with the rapid introduction of new drugs. Therefore, novel approaches for faster assay development are needed that also allow effortless inclusion of newly approved drugs as well as customization to smaller subsets if scientific or clinical situations require.

METHODS

We applied and evaluated two machine-learning approaches i.e., a regression-based approach and an artificial neural network (ANN) to retention time (RT) prediction for efficient development of a liquid chromatography mass spectrometry (LC-MS) method quantifying 73 oral antitumor drugs (OADs) and five active metabolites. Individual steps included training, evaluation, comparison, and application of the superior approach to RT prediction, followed by stipulation of the optimal gradient.

RESULTS

Both approaches showed excellent results for RT prediction (mean difference ± standard deviation: 2.08 % ± 9.44 % ANN; 1.78 % ± 1.93 % regression-based approach). Using the regression-based approach, the optimum gradient (4.91 % MeOH/min) was predicted with a total run time of 17.92 min. The associated method was fully validated following FDA and EMA guidelines. Exemplary modification and application of the regression-based approach to a subset of 14 uro-oncological agents resulted in a considerably shortened run time of 9.29 min.

CONCLUSIONS

Using a regression-based approach, a multi drug LC-MS assay for RT prediction was efficiently developed, which can be easily expanded to newly approved OADs and customized to smaller subsets if required.

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.

RT-Ensemble Pred: A tool for retention time prediction of metabolites on different LC-MS systems

Liquid chromatography-mass spectrometry (LC-MS) could provide a large amount of information to assist in metabolites identification. Different liquid chromatographic methods (CMs) could produce different retention times to the same metabolite. To predict the retention time of local dataset by online datasets has become a trend, but the datasets downloaded from different databases were differences in quantity levels. And the imbalanced data could produce bad influence in model prediction. Thus, based on quantitative structure-retention relationships (QSRRs), an ensemble model, named RT-Ensemble Pred, has been successfully built to predict retention time of different LC-MS systems in this study. A total of 76, 807 metabolites (76, 909 retention times) have been collected across 9 CMs, and 19 natural products and 1 antifungal drug (20 retention times) have been collected to test the model applicability. An ensemble sampling was applied for the preprocessing procedure to solve the problem of imbalanced data. Based on the ensemble sampling, RT-Ensemble Pred could better utilize online datasets for the prediction of retention time. RT-Ensemble Pred was built based on the online datasets and tested by local dataset. The predictive accuracy of RT-Ensemble Pred was higher than the models without any sampling methods. The results showed that RT-Ensemble Pred could predict the metabolites which was not included in the database and the metabolites which were from new CMs. It could also be used for the prediction of other compounds beside metabolites. Furthermore, a tool of RT-Ensemble Pred was packed and can be freely downloaded at https://gitlab.com/mikic93/rt-ensemble-pred. It provides convenience for the users who need to predict the retention time of metabolites.

Festivals following the easing of COVID-19 restrictions: Prevalence of new psychoactive substances and illicit drugs

The market for illicit drugs and new psychoactive substances (NPS) has grown significantly and people attending festivals have been identified as being at high risk (high extent and frequency of substance use). Traditional public health surveillance data sources have limitations (high costs, long implementation times, and ethical issues) and wastewater-based epidemiology (WBE) can cost-effectively support surveillance efforts. Influent wastewater samples were analyzed for NPS and illicit drug consumption collected during New Year period (from 29-Dec-2021 to 4-Jan-2022) and a summer Festival (from 29-June-2022 to 12-July-2022) in a large city in Spain. Samples were analyzed for phenethylamines, cathinones, opioids, benzodiazepines, plant-based NPS, dissociatives, and the illicit drugs methamphetamine, MDA, MDMA, ketamine, heroin, cocaine, and pseudoephedrine by liquid chromatography mass spectrometry. High consumption rates of specific NPS and established illicit drugs were identified at the peak of each event. Furthermore, a dynamic change in NPS use (presence and absence of substances) was detected over a period of six months. Eleven NPS, including synthetic cathinones, benzodiazepines, plant-based NPS and dissociatives, and seven illicit drugs were found across both the New Year and summer Festival. Statistically significant differences (p < 0.05) were seen for 3-MMC (New Year vs summer Festival), eutylone (New Year vs summer Festival), cocaine (summer Festival vs normal week and summer Festival vs New Year), MDMA (New Year vs normal week and summer Festival vs normal week), heroin (summer Festival vs New Year) and pseudoephedrine (summer Festival vs New Year). This WBE study assessed the prevalence of NPS and illicit drugs at festivals following the reduction of the COVID-19 pandemic restrictions highlighting the high use of specific substances at the peak of each event. This approach identified in a cost-effective and timely manner without any ethical issues the most used drugs and changes in use patterns and, thus, can complement public health information.

Efficient Validation Strategies in Environmental Analytical Chemistry: A Focus on Organic Micropollutants in Water Samples

This article critically reviews analytical method validation and quality control applied to the environmental chemistry field. The review focuses on the determination of organic micropollutants (OMPs), specifically emerging contaminants and pesticides, in the aquatic environment. The analytical technique considered is (gas and liquid) chromatography coupled to mass spectrometry (MS), including high-resolution MS for wide-scope screening purposes. An analysis of current research practices outlined in the literature has been performed, and key issues and analytical challenges are identified and critically discussed. It is worth emphasizing the lack of specific guidelines applied to environmental analytical chemistry and the minimal regulation of OMPs in waters, which greatly affect method development and performance, requirements for method validation, and the subsequent application to samples. Finally, a proposal is made for method validation and data reporting, which can be understood as starting points for further discussion with specialists in environmental analytical chemistry.

Predicting the retention time of Synthetic Cannabinoids using a combinatorial QSAR approach

Background

Abuse of Synthetic Cannabinoids (SCs) has become a serious threat to public health. Due to the various structural and chemical group modified by criminals, their detection is a major challenge in forensic toxicological identification. Therefore, rapid and efficient identification of SCs is important for forensic toxicology and drug bans. The prediction of an analyte's retention time in liquid chromatography is an important index for the qualitative analysis of compounds and can provide informatics solutions for the interpretation of chromatographic data.

Methods

In this study, experimental data from high-resolution mass spectrometry (HRMS) are used to construct a regression model for predicting the retention time of SCs using machine learning methods. The prediction ability of the model is improved by adopting a strategy that combines different descriptors in different independent machine-learning methods.

Results

The best model was obtained with a method that combined Substructure Fingerprint Count and Finger printer features and the support vector regression (SVR) method, as it exhibited an R² value of 0.81 for the validation set and 0.83 for the test set. In addition, 4 new SCs were predicted by the optimized model, with a prediction error within 3%.

Conclusions

Our study provides a model that can predict the retention time of compounds and it can be used as a filter to reduce false-positive candidates when used in combination with LC-HRMS, especially in the absence of reference standards. This can improve the confidence of identification in non-targeted analysis and the reliability of identifying unknown substances.

Unravelling the occurrence of trace contaminants in surface waters using semi-quantitative suspected non-target screening analyses

Several classes of anthropogenic chemicals such as pesticides and pharmaceuticals are frequently used in human-related life activities and are discharged into the aquatic environment. These compounds can exert an unknown effect on aquatic life and humans if the water is used for human consumption. Thus, unravelling their occurrence in the aquatic system is crucial for the well-being of life and monitoring purposes. To this end, we used nanoflow-liquid and ion-exchange chromatography hyphenated with orbitrap high-resolution tandem mass spectrometry to detect several thousands of features (chemical entities) in surface water. Later, the features were narrowed down to a few focused lists using a stepwise filtering strategy, for which the structural elucidation was made. Accordingly, the chemical structure was confirmed for 83 compounds from different application areas, mainly being pharmaceuticals, pesticides, and other multiple application industrial compounds and xenobiotic degradation products. The compounds with the highest concentration were lamotrigine (27.6 μg/L), valsartan (14.4 μg/L), and ibuprofen (12.7 μg/L). Some compounds such as prosulfocarb, fluopyram, and tris(3-chloropropyl) phosphate were found to be the most abundant and widespread contaminants. Of the 32 sampling sites, nearly half of the sites (47%) contained more than 30 different compounds. Two sampling sites were far more contaminated than other sites based on the estimated concentration and the number of identified contaminants they contained. Our triplicate analysis revealed a low relative standard deviation between replicates, advocating for the added value in analysing more sampling sites instead of sample repetition. Overall, our study elucidated the occurrence of organic contaminants from a variety of sources in the aquatic environment. Furthermore, our findings highlighted the role of suspected non-target screening in exposing a snapshot of the chemical composition of surface water and the localized possible contamination sources.

Retention Time Prediction with Message-Passing Neural Networks

Retention time prediction, facilitated by advances in machine learning, has become a useful tool in untargeted LC-MS applications. State-of-the-art approaches include graph neural networks and 1D-convolutional neural networks that are trained on the METLIN small molecule retention time dataset (SMRT). These approaches demonstrate accurate predictions comparable with the experimental error for the training set. The weak point of retention time prediction approaches is the transfer of predictions to various systems. The accuracy of this step depends both on the method of mapping and on the accuracy of the general model trained on SMRT. Therefore, improvements to both parts of prediction workflows may lead to improved compound annotations. Here, we evaluate capabilities of message-passing neural networks (MPNN) that have demonstrated outstanding performance on many chemical tasks to accurately predict retention times. The model was initially trained on SMRT, providing mean and median absolute cross-validation errors of 32 and 16 s, respectively. The pretrained MPNN was further fine-tuned on five publicly available small reversed-phase retention sets in a transfer learning mode and demonstrated up to 30% improvement of prediction accuracy for these sets compared with the state-of-the-art methods. We demonstrated that filtering isomeric candidates by predicted retention with the thresholds obtained from ROC curves eliminates up to 50% of false identities.

A cluster of 25B-NBOH poisonings following exposure to powder sold as lysergic acid diethylamide (LSD)

INTRODUCTION

25B-NBOH is a synthetic hallucinogen closely related to the "NBOMe" family of N-substituted 2C phenethylamine derivatives. There have been no published reports documenting the clinical toxicity of NBOH derivatives.

CASE SERIES

Five patients presented to the Emergency Department (ED) with altered conscious state following exposure to powder sold as "powdered LSD" at a party. A 24-year-old male who ingested the powder developed mydriasis, tachycardia, hypertension, and severe agitation requiring parenteral sedation. A 22-year-old male who insufflated the powder developed status epilepticus requiring intubation. Both patients developed acute kidney injury and one had rhabdomyolysis. In both cases, blood analysis detected 25-NBOH and no other illicit/licit drugs. Three other patients developed mild hallucinations. Hyperthermia was not documented in any case.

DISCUSSION

Exposure to 25B-NBOH in a powdered form produced sympathomimetic toxicity, including hallucinations. Insufflation of 25B-NBOH led to rapid onset of status epilepticus in one case. Toxicity in all cases resolved within 12 h. Despite in vitro evidence of 5-HT_2A receptor agonism, hyperthermia was not observed. Potent hallucinogens are often delivered via blotter paper to avoid excessive dosing. The severe clinical toxicity documented in these cases highlights the potential for development of adverse health effects with exposure to apparent small volumes of potent sympathomimetics.

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.