Rationale: The proposed metabolomic workflow, based on coupling high-resolution mass spectrometry with computational tools, can be an alternative strategy for metabolite detection and identification. This approach allows the extension of the investigation field to chemically different compounds, maximizing the information obtainable from the data and minimizing the time and resources required.
Methods: Urine samples were collected from five healthy volunteers before and after oral administration of 3β-hydroxyandrost-5-ene-7,17-dione as a model compound and defining three excretion time intervals. Raw data were acquired in both positive and negative ionization modes using an Agilent Technologies 1290 Infinity II series HPLC coupled to a 6545 Accurate-Mass Quadrupole Time-of-Flight. They were then processed to align peak retention times with the same accurate mass, and the resulting data matrix was subjected to multivariate analysis.
Results: Multivariate analysis (PCA and PLS-DA models) demonstrated high similarity between samples belonging to the same collection time interval and clear discrimination between different excretion intervals. The blank and long excretion groups were distinguished, suggesting the presence of long excretion markers, which are of remarkable interest in anti-doping analyses. The correspondence of some significant features with metabolites reported in the literature confirmed the rationale and usefulness of the proposed metabolomic approach.
Conclusions: The presented study proposes a metabolomics workflow for the early detection and characterization of drug metabolites by untargeted urinary analysis to reduce the range of substances still excluded from routine screening. Its application has detected minor steroid metabolites, as well as unexpected endogenous alterations, proving to be an alternative strategy that can allow gathering a more complete range of information in the antidoping field.
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