Methylclostebol Metabolism Discovery by Untargeted Analysis.

Area of Science:

• Analytical Chemistry
• Biochemistry
• Forensic Science

Background:

• High-resolution mass spectrometry (HRMS) coupled with multivariate statistical analysis offers an advanced method for metabolite detection and identification.
• This integrated approach maximizes data utilization, enhancing accuracy and minimizing false identifications in complex biological samples.

Purpose of the Study:

• To introduce and validate an alternative experimental strategy for metabolite identification.
• To improve the detection of biomarkers associated with the ingestion of prohibited substances, specifically methylclostebol (ClMT).

Main Methods:

• Urine samples from seven volunteers were collected pre- and post-administration of methylclostebol (ClMT) across three excretion intervals.
• Gas chromatography-high-resolution mass spectrometry (GC-HRMS) with low-energy electron ionization (< 18 eV) was used for metabolite analysis.
• Orthogonal partial least squares-discriminant analysis (OPLS-DA) and S-plot analysis were applied to processed data for biomarker identification.

Main Results:

• Clear separation of sample groups was achieved based on excretion intervals using OPLS-DA.
• Seven potential biomarkers related to methylclostebol ingestion were identified by comparing mass spectra with an in-house library.
• The OPLS-DA model effectively highlighted features contributing to sample classification.

Conclusions:

• The integrated HRMS and multivariate analysis approach effectively identifies novel biomarkers for prohibited substance intake, such as methylclostebol.
• This strategy provides a comprehensive information-gathering method for anti-doping applications.
• The method enhances the identification of new markers in the anti-doping field.