title: LC-MS-Based Metabolomics Discovers Purine Endogenous Associations with Low-Dose Salbutamol in Urine Collected for Antidoping Tests.
abstract: Current antidoping analytical methods are tailored mainly to the targeting of known drugs and endogenous molecules. This causes difficulties in rapidly reacting to emerging threats, such as designer drugs, biological therapeutic agents, and technologies. Biomarkers are considered as a promising approach for the fight against these threats to sport. The main purpose of this study was to find surrogate biomarkers induced by the intake of small amounts of the model compound salbutamol and explore a sensitive approach to help screen for possible drug misuse. Urine samples (91) from athletes with detectable salbutamol (30) and negative samples (61) were analyzed using a UHPLC-MS. A third group (30) was created by spiking salbutamol into negative samples to eliminate confounding effects. Data were then analyzed in XCMS to extract metabolic features. Orthogonal partial least squares-discriminant analysis was performed to select features correlated with detectable salbutamol (p(corr) > 0.5) and ROC analysis was performed to measure the predictive potential of the markers. Univariate analysis including Mann-Whitney U test and Spearman's correlation was conducted on selected markers. A total of 7000 metabolic features were parsed, one feature identified as hypoxanthine increased with salbutamol (p < 0.001). The ROC curve of hypoxanthine returned an AUC of 0.79 (p < 0.001). Correlation with salbutamol (r = 0.415, p < 0.01, Spearman's correlation) showed hypoxanthine and purine metabolism have association with salbutamol administration. This surrogate discovery approach needs further PK studies but in the meantime can be used as an intelligence-based complementary approach for targeting of athletes to be further tested.
authors.affiliation: Institute of Pharmaceutical Science, Faculty of Life Sciences and Medicine, King's College London , London, United Kingdom.
authors.forename: Yaoyao
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Drug Control Centre, King's College London , London, United Kingdom.
authors.forename: Richard
authors.initials: R
authors.lastname: Caldwell
authors.affiliation: Drug Control Centre, King's College London , London, United Kingdom.
authors.forename: David A
authors.initials: DA
authors.lastname: Cowan
authors.affiliation: Institute of Pharmaceutical Science, Faculty of Life Sciences and Medicine, King's College London , London, United Kingdom.
authors.forename: Cristina
authors.initials: C
authors.lastname: Legido-Quigley
mesh_terms: D000420:Albuterol / Q000008:administration & dosage* / Q000378:metabolism / Q000652:urine*; D015415:Biomarkers / Q000378:metabolism / Q000652:urine; D002851:Chromatography, High Pressure Liquid; D004300:Doping in Sports / Q000517:prevention & control*; D005260:Female; D006801:Humans; D008297:Male; D013058:Mass Spectrometry; D055432:Metabolomics; D058573:Performance-Enhancing Substances / Q000008:administration & dosage* / Q000378:metabolism / Q000652:urine*; D011687:Purines / Q000378:metabolism*; D012372:ROC Curve
chemical_list: D015415:Biomarkers; D058573:Performance-Enhancing Substances; D011687:Purines; D000420:Albuterol; C030985:purine