Erythropoietin, testosterone and growth hormone are drugs of choice for those athletes wishing to gain a competitive advantage as they are most difficult to detect using the current anti-doping methods. The illicit use of such substances violates the spirit of clean sport and fair competition. Since its formation in 1999, the World Anti-Doping Agency (WADA) has been responsible for drug testing. Direct detection of substances complemented by the indirect Athlete Biological Passport (ABP) approach has been developed and implemented by WADA with some success. The similarity between exogenous substances and endogenous products, the limited detection window and the often sophisticated doping strategies challenge the existing anti-doping methods. A paradigm shift is needed that focuses on the identification of biomarkers that are triggered by a doping substance. Recent progress of high-throughput technologies aimed at characterising genetic variation, gene regulation, protein and metabolite, collectively known as an omics profile, is enabling advances of unprecedented speed and efficiency that should encourage a new era not only in medicine but also in the field of anti-doping. New mathematical approaches that are capable of recognising and extracting patterns from the large and complex omics data are in high demand. Notably, deep learning algorithms have great potential to transform a wide range of features (input) to actionable knowledge (output) in data-intensive disciplines albeit challenges remain in interpreting these models and generating testable hypotheses in biology and medicine. In summary, concerted international research efforts across disciplines are required to systematically analyse the multilayered omics data obtained from the high-throughput state-of-art technologies for identifying robust biomolecules indicative of doping, which may be incorporated into the ABP or serve as a stand alone test.
18 - 21 May 2019
European Society of Endocrinology