Comparison of Three Untargeted Data Processing Workflows for Evaluating LC-HRMS Metabolomics Data

Software-assisted automated detection and identification of "unknown" analogues of benzodiazepines in liquid chromatography mass spectrometry analysis

RATIONALE

Benzodiazepines (BZDs) construct a large group of psychoactive drugs acting as depressants of the central nervous system (CNS) and used in medicine as sedatives and anxiolytic and antiepileptic agents. The illicit use of these materials is a worldwide problem, and for many years, part of the benzodiazepines have been abused as rape drugs. For example, flunitrazepam (Rohypnol) is most commonly linked by media reports to drug-facilitated sexual assaults, more commonly referred to as "date rape." Furthermore, there are growing concerns for other misuses of these drugs. Over the last few years, there was an increase in the number, type, and availability of new psychoactive substances (NPS) belonging to the benzodiazepine group, challenging standard forensic labs to fully identify the chemical structure of new, unknown benzodiazepines.

METHODS

This work demonstrates a new application of the automated tool for the detection and identification of benzodiazepine analogues using high-resolution-accurate-mass LC-MS analysis, followed by "Compound Discoverer" (CD) software data processing, to automatically detect various benzodiazepine analogues by picking peaks and compare them to in silico calculated modifications made on a predefined basic backbone. Subsequently, a complete structural elucidation for the proposed molecular formula is obtained by MS/MS data analysis of the suspected component.

RESULTS

This method was found to be useful for the automated detection and putative identification of a series of nine "unknown" benzodiazepine analogues, at concentrations in the low ng/mL range.

CONCLUSIONS

We hereby present a general demonstration of this powerful tool for the forensic community in the detection and identification of hazardous unknown compounds.

Chloroplastic ascorbate modifies plant metabolism and may act as a metabolite signal regardless of oxidative stress

Ascorbate (Asc) is a major plant metabolite that plays crucial roles in various processes, from reactive oxygen scavenging to epigenetic regulation. However, to what extent and how Asc modulates metabolism is largely unknown. We investigated the consequences of chloroplastic and total cellular Asc deficiencies by studying chloroplastic Asc transporter mutant lines lacking PHOSPHATE TRANSPORTER 4; 4 and the Asc-deficient vtc2-4 mutant of Arabidopsis (Arabidopsis thaliana). Under regular growth conditions, both Asc deficiencies caused minor alterations in photosynthesis, with no apparent signs of oxidative damage. In contrast, metabolomics analysis revealed global and largely overlapping alterations in the metabolome profiles of both Asc-deficient mutants, suggesting that chloroplastic Asc modulates plant metabolism. We observed significant alterations in amino acid metabolism, particularly in arginine metabolism, activation of nucleotide salvage pathways, and changes in secondary metabolism. In addition, proteome-wide analysis of thermostability revealed that Asc may interact with enzymes involved in arginine metabolism, the Calvin-Benson cycle, and several photosynthetic electron transport components. Overall, our results suggest that, independent of oxidative stress, chloroplastic Asc modulates the activity of diverse metabolic pathways in vascular plants and may act as an internal metabolite signal.

Impact of three different peak picking software tools on the quality of untargeted metabolomics data

Data quality and control parameters are becoming more important in metabolomics. For peak picking, open-source or commercial solutions are used. Other publications consider different software solutions or data acquisition types for peak picking, a combination, including proposed and new quality parameters for the process of peak picking, does not exist. This study tries to examine the performance of three different software in terms of reproducibility and quality of their output while also considering new quality parameters to gain a better understanding of resulting feature lists in metabolomics data. We saw best recovery of spiked analytes in MS-DIAL. Reproducibility over multiple projects was good among all software. The total number of features found was consistent for DDA and full scan acquisition in MS-DIAL but full scan data leading to considerably more features in MZmine and Progenesis Qi. Feature linearity proved to be a good quality parameter. Features in MS-DIAL and MZmine, showed good linearity while Progenesis Qi produced large variation, especially in full scan data. Peak width proved to be a very powerful filtering criteria revealing many features in MZmine and Progenesis Qi to be of questionable peak width. Additionally, full scan data appears to produce a disproportionally higher number of short features. This parameter is not yet available in MS-DIAL. Finally, the manual classification of true positive features proved MS-DIAL to perform significantly better in DDA data (62 % true positive) than the two other software in either mode. We showed that currently popular solutions MS-DIAL and MZmine perform well in targeted analysis of spiked analytes as well as in classic untargeted analysis. The commercially available solution Progenesis Qi does not hold any advantage over the two in terms of quality parameters, of which we proposed peak width as a new parameter and showed that already proposed parameters such as feature linearity in samples of increasing concentration are advisable to use.

A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry

Feature detection plays a crucial role in non-target screening (NTS), requiring careful selection of algorithm parameters to minimize false positive (FP) features. In this study, a stochastic approach was employed to optimize the parameter settings of feature detection algorithms used in processing high-resolution mass spectrometry data. This approach was demonstrated using four open-source algorithms (OpenMS, SAFD, XCMS, and KPIC2) within the patRoon software platform for processing extracts from drinking water samples spiked with 46 per- and polyfluoroalkyl substances (PFAS). The designed method is based on a stochastic strategy involving random sampling from variable space and the use of Pearson correlation to assess the impact of each parameter on the number of detected suspect analytes. Using our approach, the optimized parameters led to improvement in the algorithm performance by increasing suspect hits in case of SAFD and XCMS, and reducing the total number of detected features (i.e., minimizing FP) for OpenMS. These improvements were further validated on three different drinking water samples as test dataset. The optimized parameters resulted in a lower false discovery rate (FDR%) compared to the default parameters, effectively increasing the detection of true positive features. This work also highlights the necessity of algorithm parameter optimization prior to starting the NTS to reduce the complexity of such datasets.

Impact of four different extraction methods and three different reconstitution solvents on the untargeted metabolomics analysis of human and rat urine samples

Unsuitable sample preparation may result in loss of important analytes and consequently affect the outcome of untargeted metabolomics. Due to species differences, different sample preparations may be required within the same biological matrix. The study aimed to compare the in-house sample preparation method for urine with methods from literature and to investigate the transferability of sample preparation from human urine to rat urine. A total of 12 different conditions for protein precipitation were tested, combining four different extraction solvents and three different reconstitution solvents using an untargeted liquid-chromatography high resolution mass spectrometry (LC-HRMS) metabolomics analysis. Evaluation was done based on the impact on feature count, their detectability, as well as the reproducibility of selected compounds. Results showed that a combination of methanol as extraction and acetonitrile/water (75/25) as reconstitution solvent provided improved results at least regarding the total feature count. Additionally, it was found that a higher amount of methanol was most suitable for extraction of rat urine among the tested conditions. In comparison, human urine requires significantly less volume of extraction solvent. Overall, it is recommended to systematically optimize both, the extraction method, and the reconstitution solvent for the used biofluid and the individual analytical settings.

Integrated non-targeted metabolomics and transcriptomics reveals the browning mechanism of scraped ginger (Zingiber officinale Rosc.)

Ginger (Zingiber officinale Rosc.) possesses a rich nutritional profile, making it a valuable ingredient for a wide range of culinary applications. After removing its outer skin, ginger can be effectively utilized in the production of pickles and other processed food products. However, following scraping, ginger undergoes a series of physiological and biochemical changes during storage, which can impact its subsequent development and utilization in food. Thus, the current study aimed to investigate the browning mechanism of scraped ginger using non-targeted metabolomics and transcriptomics. The findings revealed 149 shared differential metabolites and 639 shared differential genes among freshly scraped ginger, ginger browned for 5 days, and ginger browned for 15 days. These metabolites and genes are primarily enriched in stilbenes, diarylheptane, and gingerol biosynthesis, phenylpropanoid biosynthesis, and tyrosine metabolism. Through the combined regulation of these pathways, the levels of phenolic components (such as chlorogenic acid and ferulic acid) and the ginger indicator component (6-gingerol) decreased, whereas promoting an increase in the content of coniferaldehyde and curcumin. Additionally, the activities of polyphenol oxidase (PPO) and peroxidase (POD) were significantly increased (p-adjust <0.05). This study hypothesized that chlorogenic and ferulic acid undergo polymerization under the catalysis of PPO and POD, thereby exacerbating the lignification of scraped ginger. These findings offer a theoretical foundation for understanding the browning mechanism of ginger after scraping. PRACTICAL APPLICATION: Ginger's quality and nutrition can change when its skin is removed. This happens due to physical and biochemical reactions during scraping. The browning that occurs affects both the taste and health benefits of ginger, we can better understand how to prevent browning and maintain ginger's quality. This research sheds light on improving ginger processing techniques for better products.

Comparison of reversed-phase, hydrophilic interaction, and porous graphitic carbon chromatography columns for an untargeted toxicometabolomics study in pooled human liver microsomes, rat urine, and rat plasma

INTRODUCTION

Untargeted metabolomics studies are expected to cover a wide range of compound classes with high chemical diversity and complexity. Thus, optimizing (pre-)analytical parameters such as the analytical liquid chromatography (LC) column is crucial and the selection of the column depends primarily on the study purpose.

OBJECTIVES

The current investigation aimed to compare six different analytical columns. First, by comparing the chromatographic resolution of selected compounds. Second, on the outcome of an untargeted toxicometabolomics study using pooled human liver microsomes (pHLM), rat plasma, and rat urine as matrices.

METHODS

Separation and analysis were performed using three different reversed-phase (Phenyl-Hexyl, BEH C18, and Gold C18), two hydrophilic interaction chromatography (HILIC) (ammonium-sulfonic acid and sulfobetaine), and one porous graphitic carbon (PGC) columns coupled to high-resolution mass spectrometry (HRMS). Their impact was evaluated based on the column performance and the size of feature count, amongst others.

RESULTS

All three reversed-phase columns showed a similar performance, whereas the PGC column was superior to both HILIC columns at least for polar compounds. Comparing the size of feature count across all datasets, most features were detected using the Phenyl-Hexyl or sulfobetaine column. Considering the matrices, most significant features were detected in urine and pHLM after using the sulfobetaine and in plasma after using the ammonium-sulfonic acid column.

CONCLUSION

The results underline that the outcome of this untargeted toxicometabolomic study LC-HRMS metabolomic study was highly influenced by the analytical column, with the Phenyl-Hexyl or sulfobetaine column being the most suitable. However, column selection may also depend on the investigated compounds as well as on the investigated matrix.

Software-assisted automated detection and identification of "unknown" fentanyl analogues

Fentanyl and its non-pharmaceutical analogues (NPFs) are potent synthetic opioids, traditionally used for pain management, with ever-increasing illicit uses. Tightening the regulation for known fentanyls leads to new synthetic analogues in the opioid market. Furthermore, the Organization for the Prohibition of Chemical Weapons (OPCW) has recently issued a decision regarding aerosolized use of central nervous system (CNS)-acting agents, such as fentanyl and its analogues, under the concern that these materials could be misused for terror or war purposes. The ever-increasing development of new fentanyl analogues makes the task of detection and identification of these new, unknown analogues crucial. In this work, we introduce an automated tool for the detection and putative identification of "unknown" fentanyl analogues, using liquid chromatography-mass spectrometry (LC-MS) (high-resolution mass spectrometry [HRMS]) analysis, subsequently followed by data processing using the "Compound Discoverer" software. This software, in our modified use, enabled the automatic detection of various fentanyl analogues, by "digging" out components and comparing them to pre-calculated theoretical molecular ions of possible modifications or transformations on the fentanyl backbone structure (no library or database used). Subsequently, structural elucidation for the proposed component of interest is carried out by automated MS/MS data interpretation, as performed by the software. This method was explored on 12 fentanyl-based "unknown" analogues used as model examples, including chemical modifications such as fluorination and methylation. In all tested compounds, automatic detection and identification were achieved, even at concentrations as low as 1 ng/mL in an environmental soil matrix extract.

A Metabolomics-Based Study on the Discriminative Classification Models and Toxicological Mechanism of Estazolam Fatal Intoxication

Fatal intoxication with sedative-hypnotic drugs is increasing yearly. However, the plasma drug concentration data for fatal intoxication involving these substances are not systematic and even overlap with the intoxication group. Therefore, developing a more precise and trustworthy approach to determining the cause of death is necessary. This study analyzed mice plasma and brainstem samples using the liquid chromatography-high resolution tandem mass spectrometry (LC-HR MS/MS)-based metabolomics method to create discriminative classification models for estazolam fatal intoxication (EFI). The most perturbed metabolic pathway between the EFI and EIND (estazolam intoxication non-death) was examined, Both EIND and EFI groups were administered 500 mg of estazolam per 100 g of body weight. Mice that did not die beyond 8 hours were treated with cervical dislocation and were classified into the EIND groups; the lysine degradation pathway was verified by qPCR (Quantitative Polymerase Chain Reaction), metabolite quantitative and TEM (transmission electron microscopy) analysis. Non-targeted metabolomics analysis with EFI were the experimental group and four hypoxia-related non-drug-related deaths (NDRDs) were the control group. Mass spectrometry data were analyzed with Compound Discoverer (CD) 3.1 software and multivariate statistical analyses were performed using the online software MetaboAnalyst 5.0. After a series of analyses, the results showed the discriminative classification model in plasma was composed of three endogenous metabolites: phenylacetylglycine, creatine and indole-3-lactic acid, and in the brainstem was composed of palmitic acid, creatine, and indole-3-lactic acid. The specificity validation results showed that both classification models distinguished between the other four sedatives-hypnotics, with an area under ROC curve (AUC) of 0.991, and the classification models had an extremely high specificity. When comparing different doses of estazolam, the AUC value of each group was larger than 0.80, and the sensitivity was also high. Moreover, the stability results showed that the AUC value was equal to or very close to 1 in plasma samples stored at 4 °C for 0, 1, 5, 10 and 15 days; the predictive power of the classification model was stable within 15 days. The results of lysine degradation pathway validation revealed that the EFI group had the highest lysine and saccharopine concentrations (mean (ng/mg) = 1.089 and 1.2526, respectively) when compared to the EIND and control group, while the relative expression of SDH (saccharopine dehydrogenase) showed significantly lower in the EFI group (mean = 1.206). Both of these results were statistically significant. Furthermore, TEM analysis showed that the EFI group had the more severely damaged mitochondria. This work gives fresh insights into the toxicological processes of estazolam and a new method for identifying EFI-related causes of mortality.

Mechanistic Understanding of the Discrepancies between Common Peak Picking Algorithms in Liquid Chromatography–Mass Spectrometry-Based Metabolomics

Inconsistent peak picking outcomes are a critical concern in processing liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics data. This work systematically studied the mechanisms behind the discrepancies among five commonly used peak picking algorithms, including CentWave in XCMS, linear-weighted moving average in MS-DIAL, automated data analysis pipeline (ADAP) in MZmine 2, Savitzky-Golay in El-MAVEN, and FeatureFinderMetabo in OpenMS. We first collected 10 public metabolomics datasets representing various LC-MS analytical conditions. We then incorporated several novel strategies to (i) acquire the optimal peak picking parameters of each algorithm for a fair comparison, (ii) automatically recognize false metabolic features with poor chromatographic peak shapes, and (iii) evaluate the real metabolic features that are missed by the algorithms. By applying these strategies, we compared the true, false, and undetected metabolic features in each data processing outcome. Our results show that linear-weighted moving average consistently outperforms the other peak picking algorithms. To facilitate a mechanistic understanding of the differences, we proposed six peak attributes: ideal slope, sharpness, peak height, mass deviation, peak width, and scan number. We also developed an R program to automatically measure these attributes for detected and undetected true metabolic features. From the results of the 10 datasets, we concluded that four peak attributes, including ideal slope, scan number, peak width, and mass deviation, are critical for the detectability of a peak. For instance, the focus on ideal slope critically hinders the extraction of true metabolic features with low ideal slope scores in linear-weighted moving average, Savitzky-Golay, and ADAP. The relationships between peak picking algorithms and peak attributes were also visualized in a principal component analysis biplot. Overall, the clear comparison and explanation of the differences between peak picking algorithms can lead to the design of better peak picking strategies in the future.

In Vitro and In Vivo Toxicometabolomics of the Synthetic Cathinone PCYP Studied by Means of LC-HRMS/MS

Synthetic cathinones are one important group amongst new psychoactive substances (NPS) and limited information is available regarding their toxicokinetics and -dynamics. Over the past few years, nontargeted toxicometabolomics has been increasingly used to study compound-related effects of NPS to identify important exogenous and endogenous biomarkers. In this study, the effects of the synthetic cathinone PCYP (2-cyclohexyl-1-phenyl-2-(1-pyrrolidinyl)-ethanone) on in vitro and in vivo metabolomes were investigated. Pooled human-liver microsomes and blood and urine of male Wistar rats were used to generate in vitro and in vivo data, respectively. Samples were analyzed by liquid chromatography and high-resolution mass spectrometry using an untargeted metabolomics workflow. Statistical evaluation was performed using univariate and multivariate statistics. In total, sixteen phase I and one phase II metabolite of PCYP could be identified as exogenous biomarkers. Five endogenous biomarkers (e.g., adenosine and metabolites of tryptophan metabolism) related to PCYP intake could be identified in rat samples. The present data on the exogenous biomarker of PCYP are crucial for setting up analytical screening procedures. The data on the endogenous biomarker are important for further studies to better understand the physiological changes associated with cathinone abuse but may also serve in the future as additional markers for an intake.

Development of LC-HRMS untargeted analysis methods for nasal epithelial lining fluid exposomics

BACKGROUND

The nasal mucosa, as a primary site of entry for inhaled substances, contains both inhaled xenobiotic and endogenous biomarkers. Nasal mucosa can be non-invasively sampled (nasal epithelial lining fluid "NELF") and analyzed for biological mediators. However, methods for untargeted analysis of compounds inhaled and/or retained in the nasal mucosa are needed.

OBJECTIVES

This study aimed to develop a high resolution LC-MS untargeted method to analyze collected NELF. Profiling of compounds in NELF samples will also provide baseline data for future comparative studies to reference.

METHODS

Extracted NELF analytes were injected to LC-ESI-MS. After spectrum processing, an in-house library provided annotations with high confidence, while more tentative annotation proposals were obtained via ChemSpider database matching.

RESULTS

The established method successfully detected unique molecular signatures within NELF. Baseline profiling of 27 samples detected 2002 unknown molecules, with 77 and 463 proposed structures by our in-house library and Chemspider matching. High confidence annotations revealed common metabolites and tentative annotations implied various environmental exposure biomarkers are also present in NELF.

SIGNIFICANCE

The experimental pipeline for analyzing NELF samples serves as simple and robust method applicable for future studies to characterize identities/effects of inhaled substances and metabolites retained in the nasal mucosa.

IMPACT STATEMENT

The nasal mucosa contains exogenous and endogenous compounds. The development of an untargeted analysis is necessary to characterize the nasal exposome by deciphering the identity and influence of inhaled compounds on nasal mucosal biology. This study established a high resolution LC-MS based untargeted analysis of non-invasively collected nasal epithelial lining fluid. Baseline profiling of the nasal mucosa (n = 27) suggests the presence of environmental pollutants, along with detection of endogenous metabolites. Our results show high potential for the analytical pipeline to facilitate future respiratory health studies involving inhaled pollutants or pharmaceutical compounds and their effects on respiratory biology.

Pathway-Based Metabolomics Analysis Reveals Biosynthesis of Key Flavor Compounds in Mango

Mango is a tropical fruit with global demand as a result of its high sensory quality and nutritional attributes. Improving fruit quality at the consumer level could increase demand, but fruit quality is a complex trait requiring a deep understanding of flavor development to uncover key pathways that could become targets for improving sensory quality. Here, a pathway-based metabolomics (untargeted and targeted) approach was used to explore biosynthetic mechanisms of key flavor compounds with five core metabolic pathways (butanoate metabolism, phenylalanine biosynthesis and metabolism, terpenoid backbone biosynthesis, linoleic and linolenic acid pathway, and carbon fixation and sucrose metabolism) in three mango cultivars. The relationships between flavor precursors and flavor compounds were identified using correlation analysis. With these novel strategies, differentially regulated metabolic flux through the pathways was first elucidated, demonstrating possible mechanisms of key flavor formation and regulation in mango fruits.

Software-Assisted Automated Detection and Identification of "Unknown" Analogues: Implementation on V-Type Nerve Agents

V-type nerve agents are among the most toxic organophosphorus chemical warfare agents, and they are under strict regulation and supervision by the OPCW (Organization for the Prohibition of Chemical Weapons). The V-type class of materials refers to a potentially large number of analogues and isomers. In order to expose instances of unfulfillment of the OPCW treaty, it is essential to have the ability to detect and identify "unknown" analogues of this family, even in the absence of an analytical standard. This work demonstrates a new automated tool for the detection and identification of V-type analogues, using high-resolution-accurate-mass LC-MS analysis, followed by "Compound Discoverer" software data processing. This software, originally developed for metabolism and metabolomics screening, is used here to automatically detect various V-type analogues by picking peaks and comparing them to "in-silico" calculated modifications made on a predefined basic V-backbone structure (according to the OPCW definitions for V-type agents). Subsequently, a complete structural elucidation for the proposed molecular formula is obtained by MS/MS data analysis of the suspected component, for both the V-type analogue (using ESI(+) analysis) as well as its hydrolysis product (using ESI(-) analysis) for a better elucidation of the phosphonate "head" structure. This method was found to be useful for the detection and identification of several "unknown" analogues, at low ng/mL levels in soil extracts.

Identifying congeners and transformation products of organic contaminants within complex chemical mixtures in impacted surface waters with a top-down non-targeted screening workflow

Over 350,000 compounds are registered for production and use including a high number of congeners found in complex chemical mixtures (CCMs). With such a high number of chemicals being released in the environment and degraded into transformation products (TPs), the challenge of identifying contaminants by non-targeted screening (NTS) is massive. "Bottom-up" studies, where compounds are subjected to conditions simulating environmental degradation to identify new TPs, are time consuming and cannot be relied upon to study the TPs of hundreds of thousands of compounds. Therefore, the development of "top-down" workflows, where the structural elucidation of unknown compounds is carried directly on the sample, is of interest. In this study, a top-down NTS workflow was developed using molecular networking and clustering (MNC). A total of 438 compounds were identified including 176 congeners of consumer product additives and 106 TPs. Reference standards were used to confirm the identification of 53 contaminants among them lesser-known pharmaceuticals (aliskiren, sitagliptin) and consumer product additives (lauramidopropyl betaine, 2,2,4-trimethyl-1,2-dihydroquinoline). The MNC tools allowed to group similar TPs and congeners together. As such, several previously unknown TPs of pesticides (metolachlor) and pharmaceuticals (gliclazide, irbesartan) were identified as tentative candidates or probable structures. Moreover, some congeners that had no entry on global repositories (PubChem, ChemSpider) were identified as probable structures. The workflow worked efficiently with oligomers containing ethylene oxide moieties, and with TPs structurally related to their parent compounds. The top-down approach shown in this study addresses several issues with the identification of congeners of industrial compounds from CCMs. Furthermore, it allows elucidating the structure of TPs directly from samples without relying on bottom-up studies under conditions discussed herein. The top-down workflow and the MNC tools show great potential for data mining and retrospective analysis of previous NTS studies.

Chemometric-Guided Approaches for Profiling and Authenticating Botanical Materials

Botanical supplements with broad traditional and medicinal uses represent an area of growing importance for American health management; 25% of U.S. adults use dietary supplements daily and collectively spent over $9. 5 billion in 2019 in herbal and botanical supplements alone. To understand how natural products benefit human health and determine potential safety concerns, careful in vitro, in vivo, and clinical studies are required. However, botanicals are innately complex systems, with complicated compositions that defy many standard analytical approaches and fluctuate based upon a plethora of factors, including genetics, growth conditions, and harvesting/processing procedures. Robust studies rely upon accurate identification of the plant material, and botanicals' increasing economic and health importance demand reproducible sourcing, as well as assessment of contamination or adulteration. These quality control needs for botanical products remain a significant problem plaguing researchers in academia as well as the supplement industry, thus posing a risk to consumers and possibly rendering clinical data irreproducible and/or irrelevant. Chemometric approaches that analyze the small molecule composition of materials provide a reliable and high-throughput avenue for botanical authentication. This review emphasizes the need for consistent material and provides insight into the roles of various modern chemometric analyses in evaluating and authenticating botanicals, focusing on advanced methodologies, including targeted and untargeted metabolite analysis, as well as the role of multivariate statistical modeling and machine learning in phytochemical characterization. Furthermore, we will discuss how chemometric approaches can be integrated with orthogonal techniques to provide a more robust approach to authentication, and provide directions for future research.

Altered metabolic pathways elucidated via untargeted in vivo toxicometabolomics in rat urine and plasma samples collected after controlled application of a human equivalent amphetamine dose

Amphetamine is widely consumed as drug of abuse due to its stimulating and cognitive enhancing effects. Since amphetamine has been on the market for quite a long time and it is one of the most commonly used stimulants worldwide, to date there is still limited information on its effects on the metabolome. In recent years, untargeted toxicometabolomics have been increasingly used to study toxicity-related pathways of such drugs of abuse to find and identify important endogenous and exogenous biomarkers. In this study, the acute effects of amphetamine intake on plasma and urinary metabolome in rats were investigated. For this purpose, samples of male Wistar rats after a single dose of amphetamine (5 mg/kg) were compared to a control group using an untargeted metabolomics approach. Analysis was performed using normal and reversed phase liquid chromatography coupled to high-resolution mass spectrometry using positive and negative ionization mode. Statistical evaluation was performed using Welch's two-sample t test, hierarchical clustering, as well as principal component analysis. The results of this study demonstrate a downregulation of amino acids in plasma samples after amphetamine exposure. Furthermore, four new potential biomarkers N-acetylamphetamine, N-acetyl-4-hydroxyamphetamine, N-acetyl-4-hydroxyamphetamine glucuronide, and amphetamine succinate were identified in urine. The present study complements previous data and shows that several studies are necessary to elucidate altered metabolic pathways associated with acute amphetamine exposure.

Metabolomic Studies for the Evaluation of Toxicity Induced by Environmental Toxicants on Model Organisms

Environmental pollution causes significant toxicity to ecosystems. Thus, acquiring a deeper understanding of the concentration of environmental pollutants in ecosystems and, clarifying their potential toxicities is of great significance. Environmental metabolomics is a powerful technique in investigating the effects of pollutants on living organisms in the environment. In this review, we cover the different aspects of the environmental metabolomics approach, which allows the acquisition of reliable data. A step-by-step procedure from sample preparation to data interpretation is also discussed. Additionally, other factors, including model organisms and various types of emerging environmental toxicants are discussed. Moreover, we cover the considerations for successful environmental metabolomics as well as the identification of toxic effects based on data interpretation in combination with phenotype assays. Finally, the effects induced by various types of environmental toxicants in model organisms based on the application of environmental metabolomics are also discussed.

Phase I In Vitro Metabolic Profiling of the Synthetic Cannabinoid Receptor Agonists CUMYL-THPINACA and ADAMANTYL-THPINACA

Synthetic cannabinoid receptor agonists (SCRAs) remain popular drugs of abuse. As many SCRAs are known to be mostly metabolized, in vitro phase I metabolic profiling was conducted of the two indazole-3-carboxamide SCRAs: CUMYL-THPINACA and ADAMANTYL-THPINACA. Both compounds were incubated using pooled human liver microsomes. The sample clean-up consisted of solid phase extraction, followed by analysis using liquid chromatography coupled to a high resolution mass spectrometer. In silico-assisted metabolite identification and structure elucidation with the data-mining software Compound Discoverer was applied. Overall, 28 metabolites were detected for CUMYL-THPINACA and 13 metabolites for ADAMATYL-THPINACA. Various mono-, di-, and tri-hydroxylated metabolites were detected. For each SCRA, an abundant and characteristic di-hydroxylated metabolite was identified as a possible in vivo biomarker for screening methods. Metabolizing cytochrome P450 isoenzymes were investigated via incubation of relevant recombinant liver enzymes. The involvement of mainly CYP3A4 and CYP3A5 in the metabolism of both substances were noted, and for CUMYL-THPINACA the additional involvement (to a lesser extent) of CYP2C8, CYP2C9, and CYP2C19 was observed. The results suggest that ADAMANTYL-THPINACA might be more prone to metabolic drug-drug interactions than CUMYL-THPINACA, when co-administrated with strong CYP3A4 inhibitors.

Untargeted Metabolomics Analysis Using FTIR and UHPLC-Q-Orbitrap HRMS of Two Curculigo Species and Evaluation of their Antioxidant and α-Glucosidase Inhibitory Activities

Curculigo orchioides and C. latifolia have been used as traditional medicines such as antidiabetic and anticancer. This study measured the total phenolics and flavonoid contents as well as analyzed the functional groups and chemical compounds using Fourier-transform infrared (FTIR) spectra and UHPLC-Q-Orbitrap-HRMS profiling for the discrimination of plant parts, geographical origin, and compounds that presumably have a significant contribution as antioxidant and α-glucosidase inhibitors on both plants. The total phenolics and flavonoids contents in Curculigo species varied from 142.09 to 452.47 mg gallic acid equivalent (GAE/g) and from 0.82 to 5.44 mg quercetin equivalent (QE/g), respectively. The lowest IC₅₀ for antioxidant and α-glucosidase inhibitory activities is presented by C. latifolia from a higher altitude region. Principal component analysis (PCA) from FTIR and UHPLC-Q-Orbitrap-HRMS data could discriminate the plant parts and geographical origin. Partial least squares (PLS) analysis has identified several functional groups, such as O–H, C–H, C=O, C–C, C–O, and chemical compounds, unknown-185 and unknown-85, that are most likely to contribute to the antioxidant and α-glucosidase inhibitory activities.