Application of neural networks for the prediction of human pharmacokinetic parameters

Artificial neural network (ANN) is a method used in the prediction of response variables from a set of input and target parameters. The most commonly used network in the area of pattern recognition is the feed forward/back propagation (BPN) network. A method to predict human pharmacokinetic parameters has been proposed using BPN with a combination of physicochemical properties and animal pharmacokinetic parameters. The results were compared with in vitro estimation of the same pharmacokinetic parameters. Fourteen network models, using a variety of input variables, were developed. Protein binding, partition coefficients, dissociation constants, and the total clearance (Cltot) and volume of distribution (Vz) in rat and dog species of 41 drugs were evaluated for prediction of human total clearance and volume of distribution using the EDBD algorithm. The observations showed highest prediction for Cltot and Vz when rat and dog pharmacokinetics, combined with protein binding and partition coefficients of the drugs, were used as input parameters. Drugs with a partition coefficient (log P) < 1.17 showed predictability of 63.41% for Cltot and 48.78% for Vz. Drugs with low protein binding (approximately 20%) showed predictability of 19.51% for Cltot and 41.46% for Vz. Comparison with in vitro estimation showed no bias in the prediction of either clearance (p < 0.2) or volume of distribution (p < 0.5) by the two methods.