Comparison of in-vivo and in-silico methods used for prediction of tissue: plasma partition coefficients in rat.

OBJECTIVES: To use methods from the literature to predict rat tissue:plasma partition coefficients (Kps) and volume of distribution values. Determine which model provides the most accurate predictions to increase confidence in the use of predicted pharmacokinetic parameters in physiologically based pharmacokinetic modelling.
METHODS: Six models were used to predict Kps and four to predict V(ss) for a dataset of 81 compounds in 11 rat tissues, and the predictions were compared with experimentally derived values. KEY
FINDINGS: Kp predictions made by the Rodgers et al. model were the most accurate, with 77% within threefold of experimental values. The Poulin & Theil model was the most accurate for the prediction of V(ss) , with 87% of predictions within threefold.
CONCLUSIONS: This study has shown that in-silico models available in the literature can be used to accurately predict Kp and V(ss) in rat. The Rodgers et al. model has been shown to provide the most accurate Kp predictions, with consistent accuracy across all drug classes and tissues. It was also the most accurate V(ss) predictor when no in-vivo data were used as input. However, transporter systems and other mechanisms that are not yet fully understood need to be incorporated into these types of models in the future to further increase their applicability.