Prediction of Drug Metabolism with In Silico Models: A Case Study of Doping Detection

Doping, the use of prohibited substances or methods to enhance athletic performance, poses significant challenges to the integrity of professional sports. This practice, driven by immense pressure on athletes, undermines fair play and athlete health, while also raising ethical and legal concerns for sports organizations. Doping detection involves sophisticated scientific techniques, particularly mass spectrometry, to identify and quantify banned substances in biological samples. Anabolic androgenic steroids are commonly detected, but newer substances like selective androgen receptor modulators (SARMs) are also prevalent due to their performance-enhancing effects and reduced detectability. A recent case in Brazil highlighted the complexity of doping detection when a soccer player tested positive for andarine, a SARM, likely due to inadvertent exposure to flutamide in a shampoo. Detailed analysis of the metabolism of both substances demonstrated that similar metabolites could form, leading to false positive results. This case underscores the importance of ongoing research, robust scientific investigation and the refinement of anti-doping protocols to ensure accurate and fair outcomes in sports. Computational prediction of drug metabolism emerged as a valuable tool in this context, aiding in the anticipation of potential metabolites and preventing unjust accusations.