Correlation-based prediction of tissue-to-plasma partition coefficients using readily available input parameters

Abstract

1. Rationale: Tissue-to-plasma partition coefficients (Kp) that characterize the tissue distribution of a drug are important input parameters in physiologically based pharmacokinetic (PBPK) models. The aim of this study was to develop an empirically derived Kp prediction algorithm using input parameters that are available early in the investigation of a compound. 2. Methods: The algorithm development dataset (n = 97 compounds) was divided according to acidic/basic properties. Using multiple stepwise regression, the experimentally derived Kp values were correlated with the rat volume of distribution at steady state (Vss) and one or more physicochemical parameters (e.g. lipophilicity, degree of ionization and protein binding) to account for inter-organ variability of tissue distribution. 3. Results: Prediction equations for the value of Kp were developed for 11 tissues. Validation of this model using a test dataset (n = 20 compounds) demonstrated that 65% of the predicted Kp values were within a two-fold error deviation from the experimental values. The developed algorithms had greater prediction accuracy compared to an existing empirically derived and a mechanistic tissue-composition algorithm. 4. Conclusions: This innovative method uses readily available input parameters with reasonable prediction accuracy and will thus enhance both the usability and the confidence in the outputs of PBPK models.