Prediction of Chromatographic Retention Time in High-Resolution Anti-Doping Screening Data Using Artificial Neural Networks

The computational generation of gradient retention time data for retrospective detection of suspected sports doping species in postanalysis human urine sample data is presented herein. Retention data for a selection of 86 compounds included in the London 2012 Olympic and Paralympic Games drug testing schedule were used to train, verify, and test a range of computational models for this purpose. Spiked urine samples were analyzed using solid phase extraction followed by ultrahigh-pressure gradient liquid chromatography coupled to electrospray ionization high-resolution mass spectrometry. Most analyte retention times varied <= 0.2 mm over the relatively short runtime of 10 mm. Predicted retention times were within 0.5 mm of experimental values for 12 out of 15 blind test compounds (largest error: 0.97 mm). Minimizing the variance in predictive ability across replicate networks of identical architecture is presented for the first time along with a quantitative discussion of the contribution of each selected molecular descriptor toward the overall predicted value. The performance of neural computing predictions for isobaric compound retention time is also discussed. This work presents the application of neural networks to the prediction of gradient retention time in archived high-resolution urine analysis sample data for the first time in the field of anti-doping.