Prediction of mycophenolic acid exposure in renal transplantation recipients by artificial neural network

The paper is aimed to establish an artificial neural network (ANN) for predicting mycophenolic acid (MPA) area under the plasma concentration-time curve (AUC) in renal transplantation recipients.        64 Chinese renal transplantation recipients receiving mycophenolate mofetil (MMF) were investigated.  10 timed samples were drawn at different days after transplantation.  Plasma MPA concentration was determined by HPLC method and area under curve over the period of 0 to 12 h (AUC_{0−12 h}) was calculated using the linear trapezoidal rule.  ANN was established after network parameters were optimized using momentum method   in combination with genetic algorithm.  Furthermore, the predictive performance of ANN was compared   with that of multiple linear regression (MLR).  When using plasma MPA concentration of 0, 0.5, 2 h after MMF administration to predict MPA AUC_{0−12 h}, mean prediction error and mean absolute prediction error were −1.53% and 9.12%, respectively.  Accuracy and precision of prediction by ANN were superior to that of MLR prediction, and similar results could be found when using plasma MPA concentration of 0, 0.5 h to predict MPA AUC_{0−12 h}.  The accuracy and precision of ANN prediction were superior to that of MLR prediction, and ANN can be used to predict MPA AUC_{0−12 h}.