Structure Elucidation Of Metabolites Research Articles

Advancing structural elucidation of conjugation drug metabolites in metabolite profiling with novel electron-activated dissociation.

This study focuses on the advantage of using the novel electron-activated dissociation (EAD) technology on the QTOF system for structural elucidation of conjugation metabolites. In drug metabolite identification, conceptual "boxes" are generally used to represent potential sites of modifications, which are proposed based on MS/MS data. Electron-activated dissociation (EAD) provides unique fragmentation patterns, potentially allowing for more precise localization of the metabolic modification sites compared to CID, particularly for conjugations. Known compounds were incubated with rat liver microsomes in the presence of nicotinamide adenine dinucleotide phosphate (NADPH), uridine dihosphate-glucuronic acid (UDPGA), and glutathione. Conjugation metabolites were analyzed using the QTOF system. High-resolution MS/MS spectra were collected using EAD and CID fragmentations along with TOF MS full scan for tested drugs and metabolites. Fragmentation patterns were compared to evaluate their efficiency in structural elucidation. Metabolite profiling identified conjugation metabolites (glucuronides and GSH adducts), using characteristic mass shifts. A comparison of EAD and CID fragmentation revealed EAD-specific fragments for most conjugates. EAD was able to break the relatively stable bonds on parent drug motifs while keeping relatively weak conjugation bonds intact, despite the generally low intensity of EAD. EAD effectively narrowed the conceptual "box" representing modification sites, providing more definitive information on conjugation sites and facilitating the structural elucidation of conjugated metabolites. EAD is a powerful tool for metabolite profiling in drug development, particularly for identifying conjugation sites. EAD-enabled MS/MS spectra offer a greater variety of signature fragments compared to CID, resulting in more comprehensive and unique structural information for metabolic modification analysis. Overall, EAD, complementary to CID, has the potential to narrow down potential modification sites, significantly enhancing the precision of conjugation metabolite structure elucidation.

Molecular Gas-Phase Conformational Ensembles.

Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.

Identification of Xenobiotic Biotransformation Products Using Mass Spectrometry-Based Metabolomics Integrated with a Structural Elucidation Strategy by Assembling Fragment Signatures.

The identification of xenobiotic biotransformation products is crucial for delineating toxicity and carcinogenicity that might be caused by xenobiotic exposures and for establishing monitoring systems for public health. However, the lack of available reference standards and spectral data leads to the generation of multiple candidate structures during identification and reduces the confidence in identification. Here, a UHPLC-HRMS-based metabolomics strategy integrated with a metabolite structure elucidation approach, namely, FragAssembler, was proposed to reduce the number of false-positive structure candidates. biotransformation product candidates were filtered by mass defect filtering (MDF) and multiple-group comparison. FragAssembler assembled fragment signatures from the MS/MS spectra and generated the modified moieties corresponding to the identified biotransformation products. The feasibility of this approach was demonstrated by the three biotransformation products of di(2-ethylhexyl)phthalate (DEHP). Comprehensive identification was carried out, and 24 and 13 biotransformation products of two xenobiotics, DEHP and 4'-Methoxy-α-pyrrolidinopentiophenone (4-MeO-α-PVP), were annotated, respectively. The number of 4-MeO-α-PVP biotransformation product candidates in the FragAssembler calculation results was approximately 2.1 times lower than that generated by BioTransformer 3.0. Our study indicates that the proposed approach has great potential for efficiently and reliably identifying xenobiotic biotransformation products, which is attributed to the fact that FragAssembler eliminates false-positive reactions and chemical structures and distinguishes modified moieties on isomeric biotransformation products. The FragAssembler software and associated tutorial are freely available at https://cosbi.ee.ncku.edu.tw/FragAssembler/ and the source code can be found at https://github.com/YuanChihChen/FragAssembler.

Absorption, Metabolism, and Excretion of ACT-1004-1239, a First-In-Class CXCR7 Antagonist: In Vitro, Preclinical, and Clinical Data.

ACT-1004-1239 is a potent, selective, first-in-class CXCR7 antagonist, which shows a favorable preclinical and clinical profile. Here we report the metabolites and the metabolic pathways of ACT-1004-1239 identified using results from in vitro and in vivo studies. Two complementary in vitro studies (incubation with human liver microsomes in the absence/presence of cytochrome P450- [CYP] specific chemical inhibitors and incubation with recombinant CYPs) were conducted to identify CYPs involved in ACT-1004-1239 metabolism. For the in vivo investigations, a microtracer approach was integrated in the first-in-human study to assess mass balance and absorption, distribution, metabolism, and excretion (ADME) characteristics of ACT-1004-1239. Six healthy male subjects received orally 100 mg non-radioactive ACT-1004-1239 together with 1 μCi 14C-ACT-1004-1239. Plasma, urine, and feces samples were collected up to 240 h post-dose and 14C-drug-related material was measured with accelerator mass spectrometry. This technique was also used to construct radiochromatograms of pooled human samples. Metabolite structure elucidation of human-relevant metabolites was performed using high performance liquid chromatography coupled with high resolution mass spectrometry and facilitated by the use of rat samples. CYP3A4 was identified as the major CYP catalyzing the formation of M1 in vitro. In humans, the cumulative recovery from urine and feces was 84.1% of the dose with the majority being eliminated via the feces (69.6%) and the rest via the urine (14.5%). In human plasma, two major circulating metabolites were identified, i.e., M1 and M23. Elimination via M1 was the only elimination pathway that contributed to ≥25% of ACT-1004-1239 elimination. M1 was identified as a secondary amine metabolite following oxidative N-dealkylation of the parent. M23 was identified as a difluorophenyl isoxazole carboxylic acid metabolite following central amide bond hydrolysis of the parent. Other metabolites observed in humans were A1, A2, and A3. Metabolite A1 was identified as an analog of M1 after oxidative defluorination, whereas both, A2 and A3, were identified as a reduced analog of M1 and parent, respectively, after addition of two hydrogen atoms at the isoxazole ring. In conclusion, CYP3A4 contributes to a relevant extent to ACT-1004-1239 disposition and two major circulating metabolites were observed in humans. Clinical Trial Registration: (https://clinicaltrials.gov/ct2/show/NCT03869320) ClinicalTrials.gov Identifier NCT03869320.

(+)-EPOXYDONE: A MAJOR SECONDARY METABOLITE AS ANTIBACTERIAL AGENT FROM Phomopsis sp. TcBt1Bo-6 ISOLATED FROM STEM OF BROTOWALI PLANT (Tinospora crispa)

Isolation, characterization, and bioproduction capacity of (+)-epoxydone produced by Phomopsis sp. TcBt1Bo-6 was performed in this present report. The fungus was cultured in potatoes dextrose broth (PDB) medium. Isolation and purification of (+)-epoxydone using chromatography methods. The metabolite structure elucidation was carried out by using UV, FTIR, 1D/2D-NMR, MS, and optical rotation, and compared with other reports. This fungus has the bioproduction ability of (+)-epoxydone of 189.43 mg/L. The metabolite of (+)-epoxydone has potent as antibacterial and antimycobacterial against E.coli, S.aureus, and M.smegmatis with MIC values of 25, > 50, and 50 (partial) μg/mL, respectively. This study is the new report on the bioproduction of bioactive metabolite (+)-epoxydone as well as the antibacterial and antimycobacterial activities of this compound.

γ-Glutamyl-β-phenylethylamine, a novel α-glucosidase and α-amylase inhibitory compound from Termitomyces robustus, an edible Nigerian mushroom

Termitomyces species are known edible mushrooms in Nigeria, believed to have exceptional culinary and nutraceutical properties. Methanol extract from fruiting bodies of Termitomyces robustus was evaluated for antidiabetic activity using in vitro α-amylase and α-glucosidase assays. The isolation and structural elucidation of metabolites from the T. robustus extract afforded five compounds including a new natural product γ-glutamyl-β-phenylethylamine 3 and four known phenyl derivatives: tryptophan 1, 4-hydroxyphenylacetic acid 2, 4-hydroxyphenylpropionic acid 4, and phenyllactic acid 5. Structures were elucidated from analyses of spectroscopic data (1 D and 2 D NMR, HRESIMS) and all isolated compounds were tested for α-amylase and α-glycosidase inhibitory activity. The in vitro assay established crude extract to possess α- amylase and α-glucosidase inhibition with IC50 of 78.05 µg/mL and 86.10 µg/mL, respectively. The isolated compounds compared favourably with the standard drug, acarbose with IC50 ranging from 6.18-15.08 µg/mL and 18.28-44.63 µg/mL for α-amylase and glucosidase, respectively.

Plant-derived bioactive compounds produced by Streptomyces variabilis LCP18 associated with Litsea cubeba (Lour.) Pers as potential target to combat human pathogenic bacteria and human cancer cell lines.

To date, endophytic actinomycetes have been well-documented as great producers of novel antibiotics and important pharmaceutical leads. The present study aimed to evaluate potent bioactivities of metabolites synthesized by the strain LCP18 residing in the Vietnamese medicinal plant Litsea cubeba (Lour.) Pers towards human pathogenic bacteria and human cancer cell lines. Endophytic actinomycete strain LCP18 showed considerable inhibition against seven bacterial pathogens and three human tumor cell lines and was identified as species Streptomyces variabilis. Strain S. variabilis LCP18 was phenotypically resistant to fosfomycin, trimethoprim-sulfamethoxazole, dalacin, cefoxitin, rifampicin, and fusidic acid and harbored the two antibiotic biosynthetic genes such as PKS-II and NRPS. Further purification and structural elucidation of metabolites from the LCP18 extract revealed five plant-derived bioactive compounds including isopcrunetin, genistein, daidzein, syringic acid, and daucosterol. Among those, isoprunetin, genistein, and daidzein exhibited antibacterial activity against Salmonella typhimurium ATCC 14,028 and methicillin-resistant Staphylococcus epidermidis ATCC 35,984 with the MIC values ranging from 16 to 128µg/ml. These plant-derived compounds also exhibited cytotoxic effects against human lung cancer cell line A549 with IC50 values of less than 46μM. These findings indicated that endophytic S. variabilis LCP18 can be an alternative producer of plant-derived compounds which significantly show potential applications in combating bacterial infections and inhibition against lung cancer cell lines.

Evidence of human metabolites of omeprazole and its structure elucidation by using HPLC-MS

The aim of current study was to identify human phase I metabolites of omeprazole using mass spectrometry. An in-vitro assay was performed to generate metabolites by human liver enzymes. Fragmentation pathway of omeprazole was studied for the structure elucidation of metabolite by MS/MS and MS3 method using previously detected fragments. The base peak of omeprazole with m/z 198 was used for a precursor ion scan to detect possible metabolites containing m/z 198 as a fragment. Additional metabolites should be detected by the loss of a typical neutral group using m/z 148 for the offset of a neutral loss scan. The detected precursor ions (m/z 316, 330, 332, 346, 362) were analyzed by enhanced product ion scan in order to receive a mass spectrum for the comparison with the fragmentation of omeprazole. The three reactions (oxidation, reduction of the Sulphur and demethylation) were observed as phase I reactions.

Metabolite identification using an ion mobility enhanced data‐independent acquisition strategy and automated data processing

Liquid chromatography/mass spectrometry is an essential tool for efficient and reliable quantitative and qualitative analysis and underpins much of contemporary drug metabolism and pharmacokinetics. Data-independent acquisition methods such as MSE have reduced the potential to miss metabolites, but do not formally generate quadrupole-resolved product ion spectra. The addition of ion mobility separation to these approaches, for example, in High-Definition MSE (HDMSE ) has the potential to reduce the time needed to set up an experiment and maximize the chance that all metabolites present can be resolved and characterized. We compared High-Definition Data-Dependent Acquisition (HD-DDA), MSE and HDMSE approaches using automated software processing with Mass-MetaSite and WebMetabase. Metabolite identification was performed on incubations of glucagon-like peptide-1 (7-37) (GLP-1) and verapamil hydrochloride. The HD-DDA, MSE and HDMSE experiments were conducted on a Waters ACQUITY UPLC I-Class LC system with a VION IMS quadrupole time-of-flight (QTOF) mass spectrometer operating under UNIFI control. All acquired data were processed using MassMetaSite able to read data from UNIFI 1.9.4. WebMetabase was used to review the detected chromatographic peaks and the spectral data interpretations. A comparison of outcomes obtained for MSE and HDMSE data demonstrated that the same structures were proposed for metabolites of both verapamil and GLP-1. The ratio of structurally matched to mismatched product ions found by MassMetaSite was slightly greater for HDMSE than for MSE , and HD-DDA, thus improving confidence in the structures proposed through the addition of ion mobility based data acquisitions. CONCLUSIONS: HDMSE data acquisition is an effective approach for the elucidation of metabolite structures for both small molecules and peptides, with excellent accuracy and quality, requiring minimal tailoring for the compound under investigation.

Drug metabolite identification using ultrahigh-performance liquid chromatography-ultraviolet spectroscopy and parallelized scans on a tribrid Orbitrap mass spectrometer.

To capture all metabolites in metabolite identification studies, MS/MS information is required in both positive and negative ionization mode, usually involving several sample injections to gain all information about samples. A high-resolution and high mass accuracy quadrupole/linear trap/Orbitrap tribrid instrument was used to gain this information in a novel single injection 'capture-all' approach to metabolite identification. Diclofenac, a model compound, was incubated in human and rat hepatocytes. These incubated samples were run using an ultrahigh-performance liquid chromatography/ultraviolet (UHPLC-UV) system coupled to a Thermo Fusion tribrid mass spectrometer. Five parallel scans were used: positive and negative ion full scan, data-dependent MS/MS, both high energy dissociation and collision-induced dissociation, and data-independent all ion fragmentation (AIF) spectra were collected in positive and negative ion mode. Nine metabolites were identified; a metabolite observed in the UV trace, but not positive ion full scan MS, was detected in the same sample injection by negative ion full scan MS. This was identified as a sulphate metabolite, and the corresponding negative ion AIF allowed for some structural elucidation. The use of a photo-diode array (PDA) detector allowed for spectral assessment in case of changes in absorbance spectra, and the subsequent semi-quantification of metabolites. This method provided good-quality MS/MS data across the m/z range in both positive and negative ion mode. The addition of both negative ion full scan MS and negative ion MS/MS allowed for the detection and structural elucidation of metabolites not observed in positive ion mode. The use of the PDA detector allowed for the semi-quantification of metabolites.

MolFind2: A Protocol for Acquiring and Integrating MS3 Data to Improve In Silico Chemical Structure Elucidation for Metabolomics.

Structure elucidation of metabolites (<1000Da) in biofluids is extremely challenging due to the diversity and complexity of chemical structure space. Generally, due to lack of reference tandem mass data (MS2), in silico fragmenters are used to rank candidates acquired from chemical databases as a function on how well they explain an experimental collision-induced dissociation spectrum. However, multistage fragmentation data (i.e., MS3) have not been adequately utilized in current metabolomics structure elucidation pipelines. To address this shortcoming, here we describe an experimental (nontargeted direct infusion ion mobility-mass spectrometry-based) and computational workflow to acquire and utilize multistage mass (MS3) spectrometry data for database-assisted structure elucidation.

SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information.

Mass spectrometry is a predominant experimental technique in metabolomics and related fields, but metabolite structural elucidation remains highly challenging. We report SIRIUS 4 (https://bio.informatik.uni-jena.de/sirius/), which provides a fast computational approach for molecular structure identification. SIRIUS 4 integrates CSI:FingerID for searching in molecular structure databases. Using SIRIUS 4, we achieved identification rates of more than 70% on challenging metabolomics datasets.

Optimization of stepped collision energies of hcd in orbitrap mass spectrometry for metabolite structure elucidation

Optimization of stepped collision energies of hcd in orbitrap mass spectrometry for metabolite structure elucidation

Metabolic changes associated with papillary thyroid carcinoma: A nuclear magnetic resonance-based metabolomics study.

Papillary thyroid carcinoma (PTC) is the most common thyroid cancer. Nuclear magnetic resonance (NMR)‑based metabolomic technique is the gold standard in metabolite structural elucidation, and can provide different coverage of information compared with other metabolomic techniques. Here, we firstly conducted NMR based metabolomics study regarding detailed metabolic changes especially metabolic pathway changes related to PTC pathogenesis. 1HNMR-based metabolomic technique was adopted in conju-nction with multivariate analysis to analyze matched tumor and normal thyroid tissues obtained from 16patients. The results were further annotated with Kyoto Encyclopedia of Genes and Genomes(KEGG), and Human Metabolome Database, and then were analyzed using modules of pathway analysis and enrichment analysis of MetaboAnalyst3.0. Based on the analytical techniques, we established the models of principal component analysis(PCA), partial least squares-discriminant analysis(PLS-DA), and orthogonal partial least-squares discriminant analysis(OPLS‑DA) which could discriminate PTC from normal thyroid tissue, and found 15robust differentiated metabolites from two OPLS-DA models. We identified 8 KEGG pathways and 3pathways of small molecular pathway database which were significantly related to PTC by using pathway analysis and enrichment analysis, respectively, through which we identified metabolisms related to PTC including branched chain amino acid metabolism (leucine and valine), other amino acid metabolism (glycine and taurine), glycolysis (lactate), tricarboxylic acid cycle (citrate), choline metabolism (choline, ethanolamine and glycerolphosphocholine) and lipid metabolism (very-low‑density lipoprotein and low-density lipoprotein). In conclusion, the PTC was characterized with increased glycolysis and inhibited tricarboxylic acid cycle, increased oncogenic amino acids as well as abnormal choline and lipid metabolism. The findings in this study provide new insights into detailed metabolic changes of PTC, and hold great potential in the treatment of PTC.

Structural elucidation of koumine metabolites by accurate mass measurements using high-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry.

Koumine is one of the major components of total alkaloids from Gelsemium. Koumine possesses a variety of interesting pharmacological effects, including anti-tumor, anti-inflammatory, and anxiolytic activities. It might be a promising lead drug because of its pharmacological activities and mild toxicity. However, little information is available on the metabolism of koumine. A rapid and accurate high-performance liquid chromatography/quadrupole-time-of-flight (HPLC/QqTOF) mass spectrometry method was applied to characterize koumine metabolites. Multiple scans of koumine metabolites, which were formed in rat liver S9, were automatically performed simultaneously through auto MS/MS mode acquisition in only a 30-min analysis. The structural elucidation of these metabolites was performed by comparing their changes in accurate molecular masses and product ions with those of the parent drug or metabolites. As a result, a total of eleven metabolites of koumine were identified, of which nine new metabolites were found. The present results showed that the N-demethylenation, hydrogenation and the oxidation were the three main metabolic pathways of koumine. This was the first investigation of in vitro metabolism of koumine in rat liver S9 using a sensitive and specific HPLC/QqTOF-MS method. The possible metabolic pathways of koumine were tentatively proposed based on the structural elucidations of these metabolites. This work may be useful in the in vivo metabolism of koumine in animals and humans. Copyright © 2016 John Wiley & Sons, Ltd.

In vivo metabolic investigation of silodosin using UHPLC-QTOF-MS/MS and in silico toxicological screening of its metabolites.

Silodosin (SLD) is a novel α1-adrenoceptor antagonist which has shown promising clinical efficacy and safety in patients with benign prostatic hyperplasia (BPH). However, lack of information about metabolism of SLD prompted us to investigate metabolic fate of SLD in rats. To identify in vivo metabolites of SLD, urine, feces and plasma were collected from Sprague-Dawley rats after its oral administration. The samples were prepared using an optimized sample preparation approach involving protein precipitation followed by solid-phase extraction and then subjected to LC/HR-MS/MS analysis. A total of 13 phase I and six phase II metabolites of SLD have been identified in rat urine which includes hydroxylated, N-dealkylated, dehydrogenated, oxidative, glucosylated, glucuronide and N-sulphated metabolites, which are also observed in feces. In plasma, only dehydrogenated, N-dealkylated and unchanged SLD are observed. The structure elucidation of metabolites was done by fragmentation in MS/MS in combination with HRMS data. The potential toxicity profile of SLD and its metabolites were predicted using TOPKAT software and most of the metabolites were proposed to show a certain degree of skin sensitization and occular irritancy. Copyright © 2016 John Wiley & Sons, Ltd.

Linking Biosynthetic Gene Clusters to their Metabolites via Pathway- Targeted Molecular Networking.

The connection of microbial biosynthetic gene clusters to the small molecule metabolites they encode is central to the discovery and characterization of new metabolic pathways with ecological and pharmacological potential. With increasing microbial genome sequence information being deposited into publicly available databases, it is clear that microbes have the coding capacity for many more biologically active small molecules than previously realized. Of increasing interest are the small molecules encoded by the human microbiome, as these metabolites likely mediate a variety of currently uncharacterized human-microbe interactions that influence health and disease. In this mini-review, we describe the ongoing biosynthetic, structural, and functional characterizations of the genotoxic colibactin pathway in gut bacteria as a thematic example of linking biosynthetic gene clusters to their metabolites. We also highlight other natural products that are produced through analogous biosynthetic logic and comment on some current disconnects between bioinformatics predictions and experimental structural characterizations. Lastly, we describe the use of pathway-targeted molecular networking as a tool to characterize secondary metabolic pathways within complex metabolomes and to aid in downstream metabolite structural elucidation efforts.

Software-aided cytochrome P450 reaction phenotyping and kinetic analysis in early drug discovery.

Cytochrome P450 (CYP450) reaction phenotyping (CRP) and kinetic studies are essential in early drug discovery to determine which metabolic enzymes react with new drug entities. A new semi-automated computer-assisted workflow for CRP is introduced in this work. This workflow provides not only information regarding parent disappearance, but also metabolite identification and relative metabolite formation rates for kinetic analysis. Time-course experiments based on incubating six probe substrates (dextromethorphan, imipramine, buspirone, midazolam, ethoxyresorufin and diclofenac) with recombinant human enzymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) and human liver microsomes (HLM) were performed. Liquid chromatography/high-resolution mass spectrometry (LC/HRMS) analysis was conducted with an internal standard to obtain high-resolution full-scan and MS/MS data. Data were analyzed using Mass-MetaSite software. A server application (WebMetabase) was used for data visualization and review. CRP experiments were performed, and the data were analyzed using a software-aided approach. This automated-evaluation approach led to (1) the detection of the CYP450 enzymes responsible for both substrate depletion and metabolite formation, (2) the identification of specific biotransformations, (3) the elucidation of metabolite structures based on MS/MS fragment analysis, and (4) the determination of the initial relative formation rates of major metabolites by CYP450 enzymes. This largely automated workflow enabled the efficient analysis of HRMS data, allowing rapid evaluation of the involvement of the main CYP450 enzymes in the metabolism of new molecules during drug discovery.

In vitro, in vivo and in silico metabolic profiling of α-pyrrolidinopentiothiophenone, a novel thiophene stimulant.

Little or no pharmacological or toxicological data are available for novel psychoactive substances when they first emerge, making their identification and interpretation in biological matrices challenging. A new synthetic cathinone, α-pyrrolidinopentiothiophenone (α-PVT), was incubated with hepatocytes and samples were analyzed using liquid chromatography coupled to a Q Exactive™ Orbitrap mass spectrometer. Authentic urine specimens from suspected α-PVT cases were also analyzed. Scans were data mined with Compound Discoverer™ for identification and structural elucidation of metabolites. Seven α-PVT metabolites were identified in hepatocyte incubations, and in the authentic urine samples, also with an additional monohydroxylated product and a glucuronide of low intensity. α-PVT dihydroxypyrrolidinyl, α-PVT 2-ketopyrrolidinyl, α-PVT hydroxythiophenyl and α-PVT thiophenol had the most intense in vivo signals.

High resolution mass spectrometry for structural identification of metabolites in metabolomics

High resolution mass spectrometry (HRMS) is increasingly used to produce metabolomics data. Thanks to its high mass resolution and mass measurement accuracy, it is also very useful for metabolite identification. Nevertheless, a rigorous methodology is required. This manuscript describes different steps involved in the structural elucidation of metabolites and demonstrates the utility of HRMS for such purpose. After a brief overview of HRMS performances in terms of mass measurement accuracy, peak resolution, isotopic clusters/patterns and the instrumentation used, the first section is devoted to the data processing generally performed to reduce the data set size. Based on the mass accuracy measurements, different post-acquisition data processing procedures have been developed for complex mixture analysis and can be used in metabolomics. The second section describes protocols used to process putative metabolite annotations or identifications with HRMS data, based on elemental composition determined from accurately measured m/z value and mass spectral databases. Non-classical approaches are also proposed for tentative structure elucidation of unknown metabolites. Finally, limitations of the proposed workflow for metabolite structure elucidation are discussed and possible improvements are proposed.