Prediction of pharmacokinetic parameters and the assessment of their variability in bioequivalence studies by artificial neural networks.
PURPOSE: The methodology of predicting the pharmacokinetic parameters (AUC, cmax, tmax) and the assessment of their variability in bioequivalence studies has been developed with the use of artificial neural networks. METHODS: The data sets included results of 3 distinct bioequivalence studies of oral verapamil products, involving a total of 98 subjects and 312 drug applications. The modeling process involved building feedforward/backpropagation neural networks. Models for pharmacokinetic parameter prediction were also used for the assessment of their variability and for detecting the most influential variables for selected pharmacokinetic parameters. Variables of input neurons based on logistic parameters of the bioequivalence study, clinical-biochemical parameters, and the physical examination of individuals. RESULTS: The average absolute prediction errors of the neural networks for AUC, cmax, and tmax prediction were: 30.54%, 39.56% and 30.74%, respectively. A sensitivity analysis demonstrated that for verapamil the three most influential variables assigned to input neurons were: total protein concentration, aspartate aminotransferase (AST) levels, and heart-rate for AUC, AST levels, total proteins and alanine aminotransferase (ALT) levels, for cmax, and the presence of food, blood pressure, and body-frame for tmax. CONCLUSIONS: The developed methodology could supply inclusion or exclusion criteria for subjects to be included in bioequivalence studies.