Dataset Information

Investigation of Bemethyl Biotransformation Pathways by Combination of LC-MS/HRMS and In Silico Methods.

ABSTRACT: Bemethyl is an actoprotector, an antihypoxant, and a moderate psychostimulant. Even though the therapeutic effectiveness of bemethyl is well documented, there is a gap in knowledge regarding its metabolic products and their quantitative and qualitative characteristics. Since 2018, bemethyl is included to the Monitoring Program of the World Anti-Doping Agency, which highlights the challenge of identifying its urinary metabolites. The objective of the study was to investigate the biotransformation pathways of bemethyl using a combination of liquid chromatography-high-resolution mass spectrometry and in silico studies. Metabolites were analyzed in a 24 h rat urine collected after oral administration of bemethyl at a single dose of 330 mg/kg. The urine samples were prepared for analysis by a procedure developed in the present work and analyzed by high performance liquid chromatography-tandem mass spectrometry. For the first time, nine metabolites of bemethyl with six molecular formulas were identified in rat urine. The most abundant metabolite was a benzimidazole-acetylcysteine conjugate; this biotransformation pathway is associated with the detoxification of xenobiotics. The BioTransformer and GLORY computational tools were used to predict bemethyl metabolites in silico. The molecular docking of bemethyl and its derivatives to the binding site of glutathione S-transferase has revealed the mechanism of bemethyl conjugation with glutathione. The findings will help to understand the pharmacokinetics and pharmacodynamics of actoprotectors and to improve antihypoxant and adaptogenic therapy.

SUBMITTER: Belinskaia DA

PROVIDER: S-EPMC8396642 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundThe 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.ObjectivesThis 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.MethodsExtracted 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.ResultsThe 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.SignificanceThe 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 statementThe 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.

Dataset Information

Investigation of Bemethyl Biotransformation Pathways by Combination of LC-MS/HRMS and In Silico Methods.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets