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

Abstract

1. Rationale: Tissue-to-plasma partition coefficients (K_p) 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 K_p 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 K_p values were correlated with the rat volume of distribution at steady state (V_ss) 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 K_p were developed for 11 tissues. Validation of this model using a test dataset (n = 20 compounds) demonstrated that 65% of the predicted K_p 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.

• Disposition
• mathematical models
• pharmacokinetics
• physiological model
• tissue partition

Appendix

Table A1. Development set A of moderate to strong bases to construct a predictive regression equation.

Table A2. Development set B of acids, neutrals and weak bases to construct a predictive regression equation.

Table A3. Test set A for moderate to strong bases to evaluate prediction accuracy.

Table A4. Test set B for acids, neutrals and weak bases to evaluate prediction accuracy.