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Rationale: This work demonstrates the high potential of combining high-resolution mass spectrometry with chemometric tools, using metabolomics as a guided tool for anti-doping analysis. The administration of 7-keto-DHEA was studied as a proofof-concept of the effectiveness of the combination of knowledge-based and machine-learning approaches to differentiate the changes due to the athletic activities from those due to the recourse to doping substances and methods. Methods: Urine samples were collected from five healthy volunteers before and after an oral administration by identifying three time intervals. Raw data were acquired by injecting less than 1 μL of derivatized samples into a model 8890 gas chromatograph coupled to a model 7250 accurate-mass quadrupole time-of-flight analyzer (both from Agilent Technologies), by using a low-energy electron ionization source; the samples were then preprocessed to align peak retention times with the same accurate mass. The resulting data table was subjected to multivariate analysis. Results: Multivariate analysis showed a high similarity between the samples belonging to the same collection interval and a clear separation between the different excretion intervals. The discrimination between blank and long excretion groups may suggest the presence of long excretion markers, which are particularly significant in anti-doping analysis. Furthermore, matching the most significant features with some of the metabolites reported in the literature data demonstrated the rationality of the proposed metabolomics-based approach. Conclusions: The application of metabolomics tools as an investigation strategy could reduce the time and resources required to identify and characterize intake markers maximizing the information that can be extracted from the data and extending the research field by avoiding a priori bias. Therefore, metabolic fingerprinting of prohibited substance intakes could be an appropriate analytical approach to reduce the risk of false-positive/negative results, aiding in the interpretation of "abnormal" profiles and discrimination of pseudo-endogenous steroid intake in the anti-doping field. [ABSTRACT FROM AUTHOR]

Evidence of the continuous rise of novel doping agents and novel doping strategies calls for the development of more accurate multi‐target screening methods. Direct multi‐target screening approaches are restricted to the targeted substances and their turnover. The development of effective "indirect" screening methods requires a priori a deep understanding of the metabolism of the substance. The biological passport has been demonstrated to be very effective, but it is limited to about 20 indirect parameters. The standard antidoping analytical methods are hence targeted and do not aim directly to identify unknown substances. Also, the detection of doping agents is limited by the excretion of the substance. This study considers metabolomics for the screening of performance enhancing hormone abuse by athletes, based on the following pieces of evidence: (1) hormones have a strong influence on human metabolism, changing several parameters in many tissues, organs, and bio‐fluids; (2) metabolomics has been demonstrated to be a very accurate tool to depict the metabolic status of several organisms, tissues, and for several human diseases, including hormone deficiencies; (3) metabolomics has been demonstrated to be able to distinguish hormone‐treated animals from controls in many species, without the need for a priori knowledge of the metabolism for the specific substance. The literature shows that metabolomics could be an appropriate tool to detect hormone abuse, keeping in mind the strength and the limitation of such an approach. [ABSTRACT FROM AUTHOR]