Human Metabolome Changes after a Single Dose of 3,4-Methylenedioxymethamphetamine (MDMA) with Special Focus on Steroid Metabolism and Inflammation Processes

Boxler, Martina I.; Streun, Gabriel L; Liechti, Matthias E.; Schmid, Yasmin; Kræmer, Thomas; Steuer, Andrea E.

doi:10.1021/acs.jproteome.8b00438

Cited by 21 publications

(31 citation statements)

References 45 publications

(89 reference statements)

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“…Analytical performance was monitored over each whole batch through regular analysis of SST and pooled samples. Mean coefficient of variation (CV) of the peak area of all SST compounds for the four methods were 7.5 +/− 1.5 for HSST ESI + , 14 +/− 4.3 for HSST ESI − , 33+/− 18 for HILIC ESI + , and 16 +/− 8.8 for HILIC ESI − , respectively and were overall comparable to former studies . Pooled samples' CVs were higher when calculated for all detected features.…”

Section: Resultssupporting

confidence: 78%

“…The data were further log transformed and auto‐scaling (mean‐centering and division by the standard deviation of each variable) was applied. With this, uniform distribution of the data was achieved, as also described previously …”

Section: Resultsmentioning

confidence: 96%

“…Two different chromatographic systems – reversed phase (HSST) and HILIC – in both positive and negative ionization mode were chosen to cover as many small molecules as possible. The applied screening method has been extensively examined recently on analyte selection, detectability and sensitivity and was already successfully applied to other untargeted and targeted studies of the metabolome . For peak picking and deconvolution, measurements were only performed in TOF‐MS mode, while additional runs were performed after IDA MS/MS acquisition for identification.…”

Section: Resultsmentioning

confidence: 99%

“…The applied screening method has been extensively examined recently on analyte selection, detectability and sensitivity 20 and was already successfully applied to other untargeted and targeted studies of the metabolome. [26][27][28] For peak picking and deconvolution,…”

Section: Analytical Proceduresmentioning

confidence: 99%

“…With this, uniform distribution of the data was achieved, as also described previously. 27,28 Features showing significant differences in paired t-test analysis were initially considered of particular interest for further identification. Additionally, features showing the highest variable importance parameters (VIPs) scores per method (first 15 each) determined in PLS-DA analysis were additionally considered.…”

Section: Targeted Analysis Of Ghb Ghb-glucuronide and Ghb-sulfatementioning

confidence: 99%

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Identification of new urinary gamma‐hydroxybutyric acid markers applying untargeted metabolomics analysis following placebo‐controlled administration to humans

Steuer

Raeber

Boxler

et al. 2019

Drug Testing and Analysis

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Gamma‐hydroxybutyrate (GHB) is a short‐chain fatty acid that occurs naturally in the mammalian brain and is prescribed as a medication against narcolepsy or used as a drug of abuse. Particularly, its use as a knock‐out drug in cases of drug‐facilitated crimes is of major importance in forensic toxicology. Because of its rapid metabolism and resulting narrow detection windows (<12 hours in urine), detection of GHB remains challenging. Thus, there is an urgent call for new markers to improve the reliable detection of GHB use. In the framework of a randomized, placebo‐controlled, crossover study in 20 healthy male volunteers, urine samples obtained 4.5 hours post‐administration were submitted to untargeted mass spectrometry [MS, quadrupole time of flight (QTOF)] analysis to identify possible new markers of GHB intake. MS data from four different analytical methods (reversed phase and hydrophilic interaction liquid chromatography; positive and negative electrospray ionization) were filtered for significantly changed features applying univariate and multivariate statistics. From the resulting 42 compounds of interest, 8 were finally identified including conjugates of GHB with carnitine, glutamate, and glycine as well as the endogenous compounds glycolate and succinylcarnitine. While GHB conjugates were only detectable in the GHB, but not in the placebo group, glycolate and succinylcarnitine were present in both groups albeit significantly increased through GHB intake. Untargeted metabolomics proved as a suitable tool for the non‐hypothesis driven identification of new GHB markers. However, more studies on actual concentrations, detection windows, and stability will be necessary to assess the suitability of these markers for routine application.

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Section: Resultssupporting

confidence: 78%

Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Section: Analytical Proceduresmentioning

confidence: 99%

Section: Targeted Analysis Of Ghb Ghb-glucuronide and Ghb-sulfatementioning

confidence: 99%

See 3 more Smart Citations

Identification of new urinary gamma‐hydroxybutyric acid markers applying untargeted metabolomics analysis following placebo‐controlled administration to humans

Steuer

Raeber

Boxler

et al. 2019

Drug Testing and Analysis

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A machine learning approach for handling big data produced by high resolution mass spectrometry after data independent acquisition of small molecules – Proof of concept study using an artificial neural network for sample classification

Streun

Elmiger

Dobay

et al. 2020

Drug Testing and Analysis

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Liquid chromatography coupled to high‐resolution mass spectrometry (HRMS) enables data independent acquisition (DIA) and untargeted screening. However, to avoid the handling of the resulting large dataset, most laboratories in that field still use targeted screening methods, which offer good sensitivity and specificity but are limited to known compounds. The promising field of machine learning offers new possibilities such as artificial neural networks that can be trained to classify large amounts of data. In this proof of concept study, we exemplify such a machine learning approach for raw HRMS‐DIA data files. We evaluated a machine learning model using training, validation, and test sets of solvent and whole blood samples containing drugs (of abuse) common in forensic toxicology. For that purpose, different platforms were used. With a feedforward neural network model architecture, a category prediction (blank sample vs. drug containing sample) was aimed for. With the applied machine learning approaches, the sensitivity and specificity, of the validation and test set, for the prediction of sample classes were in a suitable range for an actual use in a (routine) laboratory (e.g. workplace drug testing). In conclusion, this proof of concept study clearly demonstrated the huge potential of machine learning in the analysis of HRMS‐DIA data.

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Untargeted metabolomics approaches to improve casework in clinical and forensic toxicology—“Where are we standing and where are we heading?”

Steuer

Brockbals

Kræmer

2021

WIREs Forensic Science

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Clinical toxicology (CT) and forensic toxicology (FT) represent two disciplines of toxicology dedicated to the qualitative and quantitative analysis and respective interpretation of alcohol, drugs of abuse (DOA), over the counter (OTC), and prescription drugs or poisons, mainly in humans. Analytical challenges, such as the transient occurrence of new psychoactive substances (NPS) on the (il)legal drug market, insufficiently long detection windows of some DOAs, or the lack of objective measures to unambiguously proof sample manipulation as well as interpretative issues, for instance, the difficulty to discriminate occasional users from chronic/addictive ones still require further evaluation and research, though. Metabolomics is a rather new research field, aiming for qualitative, and quantitative analysis of endogenous compounds (small molecules with a molecular weight < 1000 Da) in an organism to identify changes related to a certain stimulus, for example, a drug intake. Over the last years, metabolomics has been established as a valuable tool in different disciplines, including very recently CT and FT. Analytical techniques usually applied include nuclear magnetic resonance and majorly hyphenated high-resolution mass spectrometry (HR-MS), similar to the instrumentation used in CT and FT. We will summarize practical considerations for each step of a typical untargeted metabolome workflow (experimental set-up, sample collection and storage, analysis, data processing, statistics, and identification) in CT and FT, provide an update on the current applications of untargeted metabolome profiling and will critically discuss its benefits or lack thereof within the particular field.

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Human Metabolome Changes after a Single Dose of 3,4-Methylenedioxymethamphetamine (MDMA) with Special Focus on Steroid Metabolism and Inflammation Processes

Cited by 21 publications

References 45 publications

Identification of new urinary gamma‐hydroxybutyric acid markers applying untargeted metabolomics analysis following placebo‐controlled administration to humans

Identification of new urinary gamma‐hydroxybutyric acid markers applying untargeted metabolomics analysis following placebo‐controlled administration to humans

A machine learning approach for handling big data produced by high resolution mass spectrometry after data independent acquisition of small molecules – Proof of concept study using an artificial neural network for sample classification

Untargeted metabolomics approaches to improve casework in clinical and forensic toxicology—“Where are we standing and where are we heading?”

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