Molecular Network-Based Identification of Tramadol Metabolites in a Fatal Tramadol Poisoning

Structural elucidation of tramadol, its derivatives, and metabolites using chemical derivatization and liquid chromatography-high-resolution tandem mass spectrometry

RATIONALE

Tramadol (T) is a strong painkiller drug that belongs to the opioid analgesic group. Several accidental intoxication cases after oral administration of T have been reported in the past decade. Tramadol, its derivatives, and metabolites present information-limited mass spectra with one prominent peak representing the amine-containing residue; therefore, their structural determination based on both electron impact mass spectrometry (EI-MS) and ESI-MS/MS spectra could be misleading.

METHODS

A novel analytical method for the structural elucidation of tramadol, its four homologs, and its two main phase I metabolites (N-desmethyltramadol and O-desmethyltramadol) was developed using chemical modification and liquid chromatography-high-resolution tandem mass spectrometry (LC-HR-MS/MS) with Orbitrap technology.

RESULTS

After chemical derivatization, each of the investigated T series exhibited informative mass spectra that enabled better exposition of their structures. The developed method was successfully implemented to explicitly identify the structures of tramadol and its N-desmethyltramadol metabolite in urine samples at low ng/mL levels.

CONCLUSIONS

An efficient derivatization-aided strategy was developed for rapidly elucidating the structure of tramadol-like compounds. The method is intended to assist forensic chemists in better diagnosing T and its analogs and metabolites in clinical or forensic toxicology laboratories.

Feature-Based Molecular Network for New Psychoactive Substance Identification: The Case of Synthetic Cannabinoids in a Seized e-Liquid and Biological Samples

The comprehensive detection of new psychoactive substances, including synthetic cannabinoids along with their associated metabolites in biological samples, remains an analytical challenge. To detect these chemicals, untargeted approaches using appropriate bioinformatic tools such as molecular networks are useful, albeit it necessitates as a prerequisite the identification of a node of interest within the cluster. To illustrate it, we reported in this study the identification of synthetic cannabinoids and some of their metabolites in seized e-liquid, urine, and hair collected from an 18-year-old poisoned patient hospitalized for neuropsychiatric disorders. A comprehensive analysis of the seized e-liquid was performed using gas chromatography coupled with electron ionization mass spectrometry, 1H NMR, and liquid chromatography coupled with high resolution tandem mass spectrometry combined with data processing based on molecular network strategy. It allowed researchers to detect in the e-liquid known synthetic cannabinoids including MDMB-4en-PINACA, EDMB-4en-PINACA, MMB-4en-PINACA, and MDMB-5F-PICA. Compounds corresponding to transesterification of MDMB-4en-PINACA with pentenol, glycerol, and propylene glycol were also identified. Regarding the urine sample of the patient, metabolites of MDMB-4en-PINACA were detected, including MDMB-4en-PINACA butanoic acid, dihydroxylated MDMB-4en-PINACA butanoic acid, and glucurono-conjugated MDMB-4en-PINACA butanoic acid. Hair analysis of the patient allowed the detection of MDMB-4en-PINACA and MDMB-5F-PICA in the two investigated hair segments. This untargeted analysis of seized materials and biological samples demonstrates the utility of the molecular network strategy in identifying closely related compounds and metabolites of synthetic cannabinoids. It also emphasizes the need for developing strategies to anchor molecular networks, especially for new psychoactive substances.

Application of a molecular networking approach using LC-HRMS combined with the MetWork webserver for clinical and forensic toxicology

Backgrounds and aims

In toxicology, LC-HRMS for untargeted screening yields a great deal of high quality spectral data. However, there we lack tools to visualize/organize the MS data. We applied molecular networking (MN) to untargeted screening interpretation. Our aims were to compare theoretical MS libraries obtained in silico with our experimental dataset in patients to broaden its application, and to use the MetWork web application for metabolite identification.

Methods

Samples were analyzed using an LC-HRMS system. For MN, data was generated using MZmine, and analyzed and visualized using MetGem. MetWork annotations were filtered and this file was used for annotation of the previously obtained MN.

Results

155 compounds including drugs found in patients were recorded. Using this dataset, we confirmed in 60 patients intake of tramadol, amitriptyline bromazepam, and cocaine. The results obtained by the reference methods were confirmed by MN approaches. Eighty percent of the compounds were common to both conventional and MN approaches. Using MetWork, metabolites and parent drugs such as amitriptyline, its metabolite nortriptyline and amitriptyline glucuronide phase 2 metabolites were anticipated and proposed as putative annotations.

Conclusion

The workflow increases confidence in toxicological screening by highlighting putative structures in biological matrices in combination with CFM-ID (Competitive Fragmentation Modeling for Metabolite Identification) and MetWork to extend the annotation of potential drugs even without a reference standard.

A combined toxicokinetic and metabolic approach to investigate deschloro-N-ethylketamine exposure in a multidrug user

The use of new psychoactive substances derived from ketamine is rarely reported in France. A chronic GHB, 3-MMC, and methoxetamine consumer presented a loss of consciousness in a chemsex context and was referred to the intensive care unit with a rapid and favorable outcome. To investigate the chemicals responsible for the intoxication, a comprehensive analysis was conducted on the ten plasma samples collected over a 29.5-hour period, urine obtained upon admission, a 2-cm hair strand sample, and a seized crystal. These analyses were performed using liquid chromatography hyphenated to high resolution tandem mass spectrometry operating in targeted and untargeted modes. Additionally, analyses using gas chromatography coupled to mass spectrometry and nuclear magnetic resonance were conducted to probe the composition of the seized crystal. The molecular network-based approach was employed for data processing in non-targeted analyses. It allowed to confirm a multidrug exposure encompassing GHB, methyl-(aminopropyl)benzofuran (MAPB), (aminopropyl)benzofuran (APB), methylmethcathinone, chloromethcathinone, and a new psychoactive substance belonging to the arylcyclohexylamine family namely deschloro-N-ethyl-ketamine (O-PCE). Molecular network analysis facilitated the annotation of 27 O-PCE metabolites, including phase II compounds not previously reported. Plasma kinetics of O-PCE allowed the estimation of the elimination half-life of ∼5 hours. Kinetics of O-PCE metabolites was additionally characterized, possibly useful as surrogate biomarkers of consumption. We also observed marked alterations in lipid metabolism related to poly consumption of drugs. In conclusion, this case report provides a comprehensive analysis of exposure to O-PCE in a multidrug user including kinetic and metabolism data in human.

Analysis of homemade cannabis edibles by UHPLC-HRMS after standard addition method

With recent evolution of cannabis legalization around the world, cannabis edibles are booming, and determining their concentration in Δ9-tetrahydrocannabinol (Δ9-THC), the regulated psychoactive substance, remains a challenge for toxicology laboratories, which must prove whether the product has legal status or not. Cannabinoids are a large family of structurally similar and lipophilic molecules, requiring dedicated pre-analytical methods, as well as efficient chromatographic separation to differentiate cannabinoid isomers which are distinguished by their psychoactive properties and their legal status. Here, we present two independent cases of cannabis edibles, for which we performed analysis of homemade cannabis chocolate cakes and of the resins and herbs used for cooking. Quantitation was carried out with a new developed standard addition method, to avoid matrix effects and matrix-dependent calibration. Extraction by QuEChERs method, followed by targeted and non-targeted analysis by ultra-high performance liquid chromatography hyphenated to high resolution mass spectrometry (UHPLC-HRMS) allowed the identification of several phytocannabinoids, mainly Δ9-tetrahydrocannabinol (Δ9-THC), cannabidiol (CBD) and their acid precursors Δ9-THC acid (THCA) and CBD acid (CBDA). Δ9-THC was identified in significant concentrations (mg/g) in both edibles, even though one was prepared with CBD herb. This work highlights the need to analyze cannabis edibles, as well as the resins and herbs used in their preparation if it is homemade, and it proposes a reliable analytical method for toxicology laboratories.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

A Transversal Approach Combining In Silico, In Vitro and In Vivo Models to Describe the Metabolism of the Receptor Interacting Protein 1 Kinase Inhibitor Sibiriline

Sibiriline is a novel drug inhibiting receptor-interacting protein 1 kinase (RIPK1) and necroptosis, a regulated form of cell death involved in several disease models. In this study, we aimed to investigate the metabolic fate of sibiriline in a cross-sectional manner using an in silico prediction, coupled with in vitro and in vivo experiments. In silico predictions were performed using GLORYx and Biotransformer 3.0 freeware; in vitro incubation was performed on differentiated human HepaRG cells, and in vivo experiments including a pharmacokinetic study were performed on mice treated with sibiriline. HepaRG culture supernatants and mice plasma samples were analyzed with ultra-high-performance liquid chromatography, coupled with tandem mass spectrometry (LC-HRMS/MS). The molecular networking bioinformatics tool applied to LC-HRMS/MS data allowed us to visualize the sibiriline metabolism kinetics. Overall, 14 metabolites, mostly produced by Phase II transformations (glucuronidation and sulfation) were identified. These data provide initial reassurance regarding the toxicology of this new RIPK1 inhibitor, although further studies are required.