Combined data mining strategy for the systematic identification of sport drug metabolites in urine by liquid chromatography time-of-flight mass spectrometry

The development of comprehensive methods able to tackle with the systematic identification of drug metabolites in an automated fashion is of great interest. In this article, a strategy based on the combined use of two complementary data mining tools is proposed for the screening and systematic detection and identification of urinary drug metabolites by liquid chromatography full-scan high resolution mass spectrometry. The proposed methodology is based on the use of accurate mass extraction of diagnostic ions (compound-dependent information) from in-source CID fragmentation without precursor ion isolation along with the use of automated mass extraction of accurate-mass shifts corresponding to typical biotransformations (non compound-dependent information) that xenobiotics usually undergo when metabolized. The combined strategy was evaluated using LC–TOFMS with a suite of nine sport drugs representative from different classes (propranolol, bumetanide, clenbuterol, ephedrine, finasteride, methoxyphenamine, methylephedrine, salbutamol and terbutaline), after single doses administered to rats. The metabolite identification coverage rate obtained with the systematic method (compared to existing literature) was satisfactory, and provided the identification of several non-previously reported metabolites. In addition, the combined information obtained helps to minimize the number of false positives. As an example, the systematic identification of urinary metabolites of propranolol enabled the identification of up to 24 metabolites, 15 of them non previously described in literature, which is a valuable indicator of the usefulness of the proposed systematic procedure.