New Insights Into Metandienone Metabolism by Untargeted Gas Chromatography-High-Resolution Mass Spectrometry

This study focused on the usefulness of an untargeted approach using high-resolution mass spectrometry (HRMS) data and multivariate statistical analysis as an exploratory tool to identify as many exposure markers for metandienone consumption as possible, without any a priori assumptions, reducing time and resources required compared with traditional approaches.

A total of 136 urine samples from six volunteers were collected before and after the administration of 10 mg of metandienone (17β-hydroxy-17α-methyl-androsta-1,4-dien-3-one, MD). The samples were analyzed using a GC-HRMS system (Agilent 8890 GC coupled to a 7250 GC/QTOF). Following feature extraction and data preprocessing, multivariate analysis was performed, dividing the dataset into three categories: blank, early, and late excretion.

Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to emphasize differences between specific sample conditions. The features that significantly influence classification in the OPLS-DA are considered key biomarkers. The S-plot identified nine features linked to metandienone consumption, with their identities verified by matching their mass spectra at a particular retention time.

This approach meets the analytical requirements for detecting the principal metabolites associated with metandienone administration. Its primary innovation lies in the simultaneous identification of key metabolites associated with this compound intake within a single analytical workflow, encompassing metabolites previously reported separately in the literature. Furthermore, the method demonstrates improved capability for identifying potential novel metabolic markers. Overall, its application represents an alternative analytical strategy that enables the acquisition of a more comprehensive metabolic profile while improving analytical efficiency in terms of time and resource use.