MetIDfyR: An Open-Source R Package to Decipher Small-Molecule Drug Metabolism through High-Resolution Mass Spectrometry

Metabolites identification of anabolic steroid bolasterone in vitro and in rats by high resolution liquid chromatography mass spectrometry

Bolasterone (7α,17α-dimethyltestosterone) and anabolic androgenic steroids are included in the World Anti-Doping Agency's Prohibited list of substances. This study aimed to evaluate the metabolism of bolasterone through in vitro experiments using rat liver microsomes and in vivo experiments using rat urine after oral administration. Urine samples were collected over a 168-h period. Bolasterone and its metabolites were detected by liquid chromatography coupled with a Q-Exactive Obitrap mass spectrometry (LC-HRMS). Ultimately 16 hydroxylated metabolites (M1-M16), one metabolite from the reduction of the 3-keto function and 4-ene (M17), and one glucuronic acid conjugated metabolite (M18) were detected. Metabolites M17 and M18 were confirmed by comparison with available reference or authentic standards. Metabolic modifications in the structure of the parent bolasterone result in different fragmentation patterns. Based on the sensitivity of the HRMS data, characteristic ions such as m/z 121.064 (C8 H9 O) generated from ring A of the mono-hydroxylated metabolites and 121.101 (C9 H13 ) generated from ring D of the di-hydroxylated metabolites were observed that helped differentiate between the obtained metabolites. The structures of fragment ions were tentatively proposed based on their fragmentation pathways, where the significant ions were correlated to the possible structural fragments. In conclusion, new metabolites of bolasterone were detected and characterized by the use of the full-scan and dd-MS/MS using LC-HRMS, and this data can be useful for providing metabolite information for the interpretation of mass spectra of anabolic bolasterone analogues for doping screening tests.

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.

HRPIF data mining based on data-dependent/independent acquisition for Rhei Radix et Rhizoma metabolite screening in rats

In traditional Chinese medicine (TCM), components with identical nuclei often share structural similarity, indicating the possibility of similar second-level mass spectrometry (MS/MS) fragments. High-resolution product-ion filter (HRPIF) technique can be utilized to identify metabolites, with similar fragments, in vivo. In principle, this technique applies to TCM; however, its application has been restricted due to the limitations of traditional MS/MS data acquisition. Therefore, a novel analysis strategy, based on data-dependent acquisition (DDA) and data-independent acquisition (DIA) datasets, has been developed for the determination of template product ions and efficient non-targeted identification of TCM-related components in vivo by HRPIF and background subtraction (BS). This DDA-DIA combination strategy, taking Rhei Radix et Rhizoma as a test case, identified 71 anthraquinone prototype components in vitro (36 of which were discovered for the first time), and 45 related components in vivo, confirming glucuronidation and sulfation as the main reactions. The developed strategy could rapidly identify TCM-related components in vivo with high sensitivity, indicating the immense importance of this novel HRPIF data mining technology in TCM analysis.

Development of a Standardized Microflow LC Gradient to Enable Sensitive and Long-Term Detection of Synthetic Anabolic-Androgenic Steroids for High-Throughput Doping Controls

Synthetic androgenic anabolic steroids (AAS) are banned compounds and considered as major threats by both racing and sports international authorities. Hence, doping control laboratories are continually looking into analytical improvements to increase their detection capabilities, notably by means of emerging technologies. To enhance analytical performances for the detection of synthetic AAS such as stanozolol, specific chromatographic procedures have been developed using recent quaternary liquid chromatography technology originally designed for high-throughput standardized proteomics connected to mass spectrometry. Applying the newly designed elution procedures described in this paper to the analyses of stanozolol and its metabolites in complex matrixes revealed improved sensitivity compared to previously described high-throughput methods. Indeed, we report the consistent and reliable detection of 16β-hydroxy-stanozolol down to 10 pg/mL in equine urine and being detectable up-to 3 months after a microdosing administration. Furthermore, a five months long elimination of stanozolol and its metabolites could be monitored on horse mane sections after a single dose administration. Our work highlights novel solutions to detect AAS with improved sensitivity. The application of such developments constitutes new landmarks for doping control laboratories and could be extended to other targeted compounds in residue analysis, toxicology, and metabolomics. Based on this work, the developed chromatographic method is now freely available within the Evosep Plus program.

Systematic, Modifying Group-Assisted Strategy Expanding Coverage of Metabolite Annotation in Liquid Chromatography-Mass Spectrometry-Based Nontargeted Metabolomics Studies

From microbes to human beings, nontargeted metabolic profiling by liquid chromatography (LC)-mass spectrometry (MS) has been commonly used to investigate metabolic alterations. Still, a major challenge is the annotation of metabolites from thousands of detected features. The aim of our research was to go beyond coverage of metabolite annotation in common nontargeted metabolomics studies by an integrated multistep strategy applying data-dependent acquisition (DDA)-based ultrahigh-performance liquid chromatography (UHPLC)-high-resolution mass spectrometry (HRMS) analysis followed by comprehensive neutral loss matches for characteristic metabolite modifications and database searches in a successive manner. Using pooled human urine as a model sample for method establishment, we found 22% of the detected compounds having modifying structures. Major types of metabolite modifications in urine were glucuronidation (33%), sulfation (20%), and acetylation (6%). Among the 383 annotated metabolites, 100 were confirmed by standard compounds and 50 modified metabolites not present in common databases such as human metabolite database (HMDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were structurally elucidated. Practicability was tested by the investigation of urines from pregnant women diagnosed with gestational diabetes mellitus vs healthy controls. Overall, 83 differential metabolites were annotated and 67% of them were modified metabolites including five previously unreported compounds. To conclude, the systematic modifying group-assisted strategy can be taken as a useful tool to extend the number of annotated metabolites in biological and biomedical nontargeted studies.

New software tools, databases, and resources in metabolomics: updates from 2020

BACKGROUND

Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success.

AIM OF THE REVIEW

In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community.

KEY SCIENTIFIC CONCEPTS OF REVIEW

In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.

Comprehensive characterization of the peroxisome proliferator activated receptor-δ agonist GW501516 for horse doping control analysis

According to international sport institutions, the use of peroxisome proliferator activated receptor (PPAR)-δ agonists is forbidden at any time in athlete career due to their capabilities to increase physical and endurance performances. The (PPAR)-δ agonist GW501516 is prohibited for sale but is easily available on internet and can be used by cheaters. In the context of doping control, urine is the preferred matrix because of the non-invasive nature of sampling and providing broader exposure detection times to forbidden molecules but often not detected under its native form due to the organism's metabolism. Even if urinary metabolism of G501516 has been extensively studied in human subjects, knowledge on GW501516 metabolism in horses remains limited. To fight against doping practices in horses' races, GW501516 metabolism has to be studied in horse urine to identify and characterize the most relevant target metabolites to ensure an efficient doping control. In this article, in vitro and in vivo experiments have been conducted using horse S9 liver microsome fractions and horse oral administration route, respectively. These investigations determined that the detection of GW501516 must be performed in urine on its metabolites because the parent molecule was extremely metabolized. To maximize analytical method sensitivity, the extraction conditions have been optimized. In accordance with these results, a qualitative analytical method was validated to detect the abuse of GW501516 based on its most relevant metabolites in urine. This work enabled the Laboratoire des Courses Hippiques (LCH) to highlight two cases of illicit administration of this forbidden molecule in post-race samples.