Prediction of drug distribution within blood

Drug distribution in blood, defined as drug blood-to-plasma concentration ratio (R-b), is a fundamental pharmacokinetic parameter. it relates the plasma clearance to the blood clearance, enabling the physiological interpretation of this parameter. Although easily experimentally deter-mined, Rb values are lacking for the vast majority of drugs. We present a systematic approach using mechanistic, partial least squares (PLS) regression and artificial neural network (ANN) models to relate various in vitro and in silico molecular descriptors to a dataset of 93 drug Rb values collected in the literature. The ANN model resulted in the best overall approach, with r(2) = 0.927 and r(2) = 0.871 for the train and the test sets, respectively. PLS regression presented r(2) = 0.557 for the train and r(2) = 0.656 for the test set. The mechanistic model provided the worst results, with r(2) = 0.342 and, additionally, is limited to drugs with a basic ionised group with pKa < 7. The ANN model for drug distribution in blood can be a valuable tool in clinical pharmacokinetics as well as in new drug design, providing predictions of Rb with a percentage of correct values within a 1.25-fold error of 86%, 84% and 87% in the train, test and validation set of data. (c) 2008 Elsevier B.V. All rights reserved.