Retrospective analysis for valproate screening targets with liquid chromatography–high resolution mass spectrometry with positive electrospray ionization: An omics‐based approach

Determination of Antiepileptics in Biological Samples-A Review

Epilepsy remains a disease that affects many people around the world. With the development of new drugs to treat this condition, the importance of therapeutic drug monitoring continues to rise and remains a challenge for the medical community. This review article explores recent advances in the detection of antiepileptic drugs across various sample types commonly used for drug monitoring, with a focus on their applications and impact. Some of these new methods have proven to be simpler, greener, and faster, making them easier to apply in the context of therapeutic drug monitoring. Additionally, besides the classic use of blood and its derivatives, there has been significant research into the application of alternative matrices due to their ease of sample collection and capacity to reflect drug behavior in blood. These advances have contributed to increasing the efficacy of therapeutic drug monitoring while enhancing its accessibility to the population.

Scalable Analysis of Untargeted LC-HRMS Data by Means of SQL Database Archiving

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used to detect chemicals with a broad range of physiochemical properties in complex biological samples. However, the current data analysis strategies are not sufficiently scalable because of data complexity and amplitude. In this article, we report a novel data analysis strategy for HRMS data founded on structured query language database archiving. A database called ScreenDB was populated with parsed untargeted LC-HRMS data after peak deconvolution from forensic drug screening data. The data were acquired using the same analytical method over 8 years. ScreenDB currently holds data from around 40,000 data files, including forensic cases and quality control samples that can be readily sliced and diced across data layers. Long-term monitoring of system performance, retrospective data analysis for new targets, and identification of alternative analytical targets for poorly ionized analytes are examples of ScreenDB applications. These examples demonstrate that ScreenDB makes a significant improvement to forensic services and that the concept has potential for broad applications for all large-scale biomonitoring projects that rely on untargeted LC-HRMS data.

Metabolomics-Based Mechanistic Insights into Revealing the Adverse Effects of Pesticides on Plants: An Interactive Review

In plant biology, metabolomics is often used to quantitatively assess small molecules, metabolites, and their intermediates in plants. Metabolomics has frequently been applied to detect metabolic alterations in plants exposed to various biotic and abiotic stresses, including pesticides. The widespread use of pesticides and agrochemicals in intensive crop production systems is a serious threat to the functionality and sustainability of agroecosystems. Pesticide accumulation in soil may disrupt soil-plant relationships, thereby posing a pollution risk to agricultural output. Application of metabolomic techniques in the assessment of the biological consequences of pesticides at the molecular level has emerged as a crucial technique in exposome investigations. State-of-the-art metabolomic approaches such as GC-MS, LC-MS/MS UHPLC, UPLC-IMS-QToF, GC/EI/MS, MALDI-TOF MS, and ¹H-HR-MAS NMR, etc., investigating the harmful effects of agricultural pesticides have been reviewed. This updated review seeks to outline the key uses of metabolomics related to the evaluation of the toxicological impacts of pesticides on agronomically important crops in exposome assays as well as bench-scale studies. Overall, this review describes the potential uses of metabolomics as a method for evaluating the safety of agricultural chemicals for regulatory applications. Additionally, the most recent developments in metabolomic tools applied to pesticide toxicology and also the difficulties in utilizing this approach are discussed.

A method for determining valproic acid in human whole blood and urine via gas chromatography-mass spectrometry and small-scale inter-laboratory trial

A simple and cost-effective method for analyzing valproic acid (VPA) in biological samples was developed. VPA was extracted in methyl tertiary-butyl ether (MTBE) and derivatized using trimethylsilyldiazomethane. The MTBE extract was analyzed by gas chromatography-mass spectrometry (GC-MS). The extraction recovery in human whole blood and urine was over 90 %, with good linearity in the range of 1.0 to 250 µg/mL of VPA. The RSD for 2.0, 20, and 200 μg/mL VPA in whole blood ranged from 0.9 to 4.7 % for intra-day and 1.5 to 5.9 % for inter-day. The RSD for 2.0, 20, and 200 μg/mL VPA in urine ranged from 1.9 to 2.6 % for intra-day and 1.2 to 2.9 % for inter-day. As a preliminary cross-validation study, a cross-check was conducted using blinded concentration samples. The results demonstrated that the assay data of the two laboratories were comparable.

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.

A Retrospective Metabolomics Analysis of Gamma-Hydroxybutyrate in Humans: New Potential Markers and Changes in Metabolism Related to GHB Consumption

GHB is an endogenous short-chain organic acid presumably also widely applied as a rape and knock out drug in cases of drug-facilitated crimes or sexual assaults (DFSA). Due to the endogenous nature of GHB and its fast metabolism in vivo, the detection window of exogenous GHB is however narrow, making it challenging to prove use of GHB in DFSA cases. Alternative markers of GHB intake have recently appeared though none has hitherto been validated for forensic use. UHPLC-HRMS based screening of blood samples for drugs of abuse is routinely performed in several forensic laboratories which leaves an enormous amount of unexploited data. Recently we devised a novel metabolomics approach to use archived data from such routine screenings for elucidating both direct metabolites from exogenous compounds, but potentially also regulation of endogenous metabolism and metabolites. In this paper we used UHPLC-HRMS data acquired over a 6-year period from whole blood analysis of 51 drivers driving under the influence of GHB as well as a matched control group. The data were analyzed using a metabolomics approach applying a range of advanced analytical methods such as OPLS-DA, LASSO, random forest, and Pearson correlation to examine the data in depth and demonstrate the feasibility and potential power of the approach. This was done by initially detecting a range of potential biomarkers of GHB consumption, some that previously have been found in controlled GHB studies, as well as several new potential markers not hitherto known. Furthermore, we investigate the impact of GHB intake on human metabolism. In aggregate, we demonstrate the feasibility to extract meaningful information from archived data here exemplified using GHB cases. Hereby we hope to pave the way for more general use of the principle to elucidate human metabolites of e.g. new legal or illegal drugs as well as for applications in more global and large scale metabolomics studies in the future.

Comparison of Comprehensive Screening Results in Postmortem Blood and Brain Tissue by UHPLC-QTOF-MS

Alternative specimens collected during autopsies can be valuable in postmortem toxicology in cases where peripheral blood is not available. The applicability of brain tissue as an alternative matrix for drug screening by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was investigated in this study. Results of the 50 most frequently detected drugs and metabolites of toxicological interest in blood and brain tissue samples from 1,719 autopsy cases were compared. Examination of the results in paired blood and brain tissue samples revealed that the two matrices were in general comparable, as the majority of the 50 analytes were observed in a high number of the examined cases in both blood and brain tissue. This demonstrates the potential of brain tissue as an alternative matrix for drug screening in postmortem toxicology or as a secondary matrix for confirmation.

Metabolomics-driven determination of targets for salicylic acid and ibuprofen in positive electrospray ionization using LC-HRMS

Due to the large number of basic therapeutic and illicit drugs, systematic toxicological analysis has widely been performed with liquid chromatography coupled to mass spectrometry using positive electrospray ionization. However, there exists a smaller number of drugs, typically acidic drugs, which require the use of negative electrospray ionization either via a separate injection or polarity switching. Here, targets relating to salicylic acid and ibuprofen in positive electrospray ionization were determined through a metabolomics-driven retrospective investigation of forensic casework. Samples were previously screened using liquid chromatography coupled with high-resolution mass spectrometry with quantification of target analytes performed using liquid chromatography with tandem mass spectrometry. Of the 1717 whole-blood samples submitted between 2014 and 2019, 48 were positive for salicylic acid (1.1-1400 mg/kg) and 78 for ibuprofen (1-46 mg/kg). Based on the retrospective analysis, 19 and 90 targets were identified for salicylic acid and ibuprofen, respectively. For targets of salicylic acid, the protonated adduct of salicyluric acid ([M+H]⁺ , m/z 196.0605) was present in 89.6% (n = 32) of the salicylic acid positive cases while the [M+HCOOH+CH₃ CN+Ca-H]⁺ adduct (m/z 264.0179) of salicylic acid was present in all positive samples with concentrations above 66 mg/kg salicylic acid. Similarly, the [M + 2Na - H]⁺ adduct (m/z 251.1018) of ibuprofen was present in 98.7% (n = 77) of positive cases and was present in all samples with concentrations above 3 mg/kg ibuprofen.

Determination of Free Valproic Acid Concentration in 569 Clinical Samples by LC-MS/MS After Hollow Fiber Centrifugal Ultrafiltration Treatment

OBJECTIVE

To perform therapeutic drug monitoring of total and free plasma valproic acid (VPA) concentrations in clinical samples and to analyze the related factors.

METHODS

The total VPA concentration in plasma was determined by ultrahigh-performance liquid chromatography with precolumn derivatization with α-bromoacetophenone, and the free VPA concentration was determined by liquid chromatography-tandem mass spectrometry after the plasma was treated by hollow fiber centrifugal ultrafiltration. Regression analysis was performed to examine the associations between free plasma VPA, total plasma VPA, and the plasma protein binding rate. The impact of individual situations, outpatient or inpatient factors, and drug combinations on VPA concentrations were examined.

RESULTS

Of the 569 clinical samples, 268 were inpatients and 301 were outpatients, and the total VPA concentration in 138 cases (24.2%) was lower than the effective treatment concentration range; the total and free VPA concentrations in outpatient samples were 11.0% and 26.1% higher than those of inpatients, respectively. There was no linear relationship between the free and total VPA concentrations. The relationship equation between the plasma protein binding rate and free VPA concentrations was as follows: Y = 0.0255X2 - 1.1357X + 97.429 (r = 0.8011). The total and free VPA concentrations were significantly decreased after the coadministration of phenobarbital (83.7% and 64.3% of the control group, P < 0.05) or carbapenem antibiotics (32.0% and 32.7% of the control group, P < 0.05).

CONCLUSIONS

The total VPA concentrations in patients with epilepsy at our hospital was lower than the effective treatment concentration range, which was inadequate for epilepsy control; the total VPA concentrations of outpatients were higher than those of inpatients; as phenobarbital affects VPA metabolism, therapeutic drug monitoring is recommended. Carbapenem antibiotic coadministration with VPA should be avoided because carbapenem antibiotics can lead to the failure of VPA antiepileptic treatment.

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

Database-driven suspect screening has proven to be a useful tool to detect new psychoactive substances (NPS) outside the scope of targeted screening; however, the lack of retention times specific to a liquid chromatography (LC) system can result in a large number of false positives. A singular stream-lined, quantitative structure-retention relationship (QSRR)-based retention time prediction model integrating multiple LC systems with different elution conditions is presented using retention time data (n = 1281) from the online crowd-sourced database, HighResNPS. Modelling was performed using an artificial neural network (ANN), specifically a multi-layer perceptron (MLP), using four molecular descriptors and one-hot encoding of categorical labels. Evaluation of test set predictions (n = 193) yielded coefficient of determination (R²) and mean absolute error (MAE) values of 0.942 and 0.583 min, respectively. The model successfully differentiated between LC systems, predicting 54%, 81% and 97% of the test set within ±0.5, ±1 and ±2 min, respectively. Additionally, retention times for an analyte not previously observed by the model were predicted within ±1 min for each LC system. The developed model can be used to predict retention times for all analytes on HighResNPS for each participating laboratory's LC system to further support suspect screening.

Untargeted Metabolomics in Forensic Toxicology: A New Approach for the Detection of Fentanyl Intake in Urine Samples

The misuse of fentanyl, and novel synthetic opioids (NSO) in general, has become a public health emergency, especially in the United States. The detection of NSO is often challenged by the limited diagnostic time frame allowed by urine sampling and the wide range of chemically modified analogues, continuously introduced to the recreational drug market. In this study, an untargeted metabolomics approach was developed to obtain a comprehensive "fingerprint" of any anomalous and specific metabolic pattern potentially related to fentanyl exposure. In recent years, in vitro models of drug metabolism have emerged as important tools to overcome the limited access to positive urine samples and uncertainties related to the substances actually taken, the possible combined drug intake, and the ingested dose. In this study, an in vivo experiment was designed by incubating HepG2 cell lines with either fentanyl or common drugs of abuse, creating a cohort of 96 samples. These samples, together with 81 urine samples including negative controls and positive samples obtained from recent users of either fentanyl or "traditional" drugs, were subjected to untargeted analysis using both UHPLC reverse phase and HILIC chromatography combined with QTOF mass spectrometry. Data independent acquisition was performed by SWATH in order to obtain a comprehensive profile of the urinary metabolome. After extensive processing, the resulting datasets were initially subjected to unsupervised exploration by principal component analysis (PCA), yielding clear separation of the fentanyl positive samples with respect to both controls and samples positive to other drugs. The urine datasets were then systematically investigated by supervised classification models based on soft independent modeling by class analogy (SIMCA) algorithms, with the end goal of identifying fentanyl users. A final single-class SIMCA model based on an RP dataset and five PCs yielded 96% sensitivity and 74% specificity. The distinguishable metabolic patterns produced by fentanyl in comparison to other opioids opens up new perspectives in the interpretation of the biological activity of fentanyl.

Cocaine profiling method retrospectively developed with nontargeted discovery of markers using liquid chromatography with time-of-flight mass spectrometry data

Illicit drug profiling performed by forensic laboratories assists law enforcement agencies through providing information about chemical and/or physical characteristics of seized specimens. In this article, a model was developed for the comparison of seized cocaine based on retrospective analysis of data generated from ultrahigh performance liquid chromatography with time‐of‐flight mass spectrometry (UHPLC‐TOF‐MS) comprehensive drug screening. A nontargeted approach to discover target compounds was employed, which generated 53 potential markers using data from cocaine positive samples. Twelve marker compounds were selected for the development of the final profiling model. The selection included a mixture of commonly used cocaine profiling targets and other cocaine‐related compounds. Combinations of pretreatments and comparison metrics were assessed using receiver operating characteristic curves to determine the combination with the best discrimination between linked and unlinked populations. Using data from 382 linked and 34,519 unlinked distances, a classification model was developed using a combination of the standardization and normalization transformations with Canberra distance, resulting in a linked cut‐off with a 0.5% false positive rate. The present study demonstrates the applicability of retrospectively developing a cocaine profiling model using data generated from UHPLC‐TOF‐MS nontargeted drug screening without pre‐existing information about cocaine impurities. The developed workflow was not specific to cocaine and thus could potentially be applied to any seized drug in which there are both sufficient data and impurities present.

Applications of Metabolomics in Forensic Toxicology and Forensic Medicine

Forensic toxicology and forensic medicine are unique among all other medical fields because of their essential legal impact, especially in civil and criminal cases. New high-throughput technologies, borrowed from chemistry and physics, have proven that metabolomics, the youngest of the “omics sciences”, could be one of the most powerful tools for monitoring changes in forensic disciplines. Metabolomics is a particular method that allows for the measurement of metabolic changes in a multicellular system using two different approaches: targeted and untargeted. Targeted studies are focused on a known number of defined metabolites. Untargeted metabolomics aims to capture all metabolites present in a sample. Different statistical approaches (e.g., uni- or multivariate statistics, machine learning) can be applied to extract useful and important information in both cases. This review aims to describe the role of metabolomics in forensic toxicology and in forensic medicine.

Hyphenated high-resolution mass spectrometry-the "all-in-one" device in analytical toxicology?

This trend article reviews papers with hyphenated high-resolution mass spectrometry (HRMS) approaches applied in analytical toxicology, particularly in clinical and forensic toxicology published since 2016 and referenced in PubMed. The article focuses on the question of whether HRMS has or will become the all-in-one device in these fields as supposed by the increasing number of HRMS presentations at scientific meetings, corresponding original papers, and review articles. Typical examples for the different application fields are discussed such as targeted or untargeted drug screening, quantification, drug metabolism studies, and metabolomics approaches. Considering the reviewed papers, HRMS is currently the only technique that fulfills the criteria of an all-in-one device for the various applications needed in analytical toxicology.

Graphical abstract

Interpol review of toxicology 2016-2019

This review paper covers the forensic-relevant literature in toxicology from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20Review%20.Papers%202019.pdf.

In vitro and in vivo metabolism and detection of 3-HO-PCP, a synthetic phencyclidine, in human samples and pooled human hepatocytes using high resolution mass spectrometry

The new psychoactive substance (NPS) 3-HO-PCP, a phencyclidine (PCP) analog, was detected in a law enforcement seizure and in forensic samples in Denmark. Compared with PCP, 3-HO-PCP is known to be a more potent dissociative NPS, but no toxicokinetic investigations of 3-HO-PCP are yet available. Therefore, 3-HO-PCP was quantified in in vivo samples, and the following were investigated: plasma protein binding, in vitro and in vivo metabolites, and metabolic targets. All samples were separated by liquid chromatography and analyzed by mass spectrometry. The unbound fraction in plasma was determined as 0.72 ± 0.09. After in vitro incubation with pooled human hepatocytes, four metabolites were identified: a piperidine-hydroxyl-and piperidine ring opened N-dealkyl-COOH metabolite, and O-glucuronidated- and O-sulfate-conjugated metabolites. In vivo, depending on the sample and sample preparation, fewer metabolites were detected, as the O-sulfate-conjugated metabolite was not detected. The N-dealkylated-COOH metabolite was the main metabolite in the deconjugated urine sample. in vivo analytical targets in blood and brain samples were 3-HO-PCP and the O-glucuronidated metabolite, with 3-HO-PCP having the highest relative signal intensity. The drug levels of 3-HO-PCP quantified in blood were 0.013 and 0.095 mg/kg in a living and a deceased subject, respectively. The 3-HO-PCP concentrations in deconjugated urine in a sample from a living subject and in post-mortem brain were 7.8 and 0.16 mg/kg, respectively. The post mortem results showed a 1.5-fold higher concentration of 3-HO-PCP in the brain tissue than in the post mortem blood sample.

Metabolomic Strategies in Biomarker Research-New Approach for Indirect Identification of Drug Consumption and Sample Manipulation in Clinical and Forensic Toxicology?

Drug of abuse (DOA) consumption is a growing problem worldwide, particularly with increasing numbers of new psychoactive substances (NPS) entering the drug market. Generally, little information on their adverse effects and toxicity are available. The direct detection and identification of NPS is an analytical challenge due to their ephemerality on the drug scene. An approach that does not directly focus on the structural detection of an analyte or its metabolites, would be beneficial for this complex analytical scenario and the development of alternative screening methods could help to provide fast response on suspected NPS consumption. A metabolomics approach might represent such an alternative strategy for the identification of biomarkers for different questions in DOA testing. Metabolomics is the monitoring of changes in small (endogenous) molecules (<1,000 Da) in response to a certain stimulus, e.g., DOA consumption. For this review, a literature search targeting "metabolomics" and different DOAs or NPS was conducted. Thereby, different applications of metabolomic strategies in biomarker research for DOA identification were identified: (a) as an additional tool for metabolism studies bearing the major advantage that particularly a priori unknown or unexpected metabolites can be identified; and (b) for identification of endogenous biomarker or metabolite patterns, e.g., for synthetic cannabinoids or also to indirectly detect urine manipulation attempts by chemical adulteration or replacement with artificial urine samples. The majority of the currently available literature in that field, however, deals with metabolomic studies for DOAs to better assess their acute or chronic effects or to find biomarkers for drug addiction and tolerance. Certain changes in endogenous compounds are detected for all studied DOAs, but often similar compounds/pathways are influenced. When evaluating these studies with regard to possible biomarkers for drug consumption, the observed changes appear, albeit statistically significant, too small to reliably work as biomarker for drug consumption. Further, different drugs were shown to affect the same pathways. In conclusion, metabolomic approaches possess potential for detection of biomarkers indicating drug consumption. More studies, including more sensitive targeted analyses, multi-variant statistical models or deep-learning approaches are needed to fully explore the potential of omics science in DOA testing.