Effects of a subacute treatment in rats by a fresh cola extract on EEG and pharmacokinetics

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

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.

Prediction of Drug Tissue to Plasma Concentration Ratios Using a Measured Volume of Distribution in Combination With Lipophilicity

One of the drug specific parameters needed in physiologically based pharmacokinetic (PBPK) models is the tissue to plasma drug concentration ratios (K(p) values). The aim of this study was to develop an empirical method for predicting K(p) values using a preclinically determined in vivo volume of distribution, in combination with descriptors for drug lipophilicity. Pharmacokinetic data in laboratory animals for a wide range of drug compounds were collected. Obtained correlations between K(p) values for muscle and other tissues, in a training set of 49 compounds, were used to predict K(p) values for a test set of 22 compounds, based on their volume of distribution and lipophilicity. Predicted K(p) values agreed well with experimentally determined values (n = 118), especially for noneliminating tissues (r(2) = 0.81) with 72% and 87% being within a factor +/-2 and +/-3, respectively. In conclusion, we present an empirical method based on a measured volume of distribution and a drug lipophilicity descriptor, which can be used to predict tissue K(p) values with reasonable accuracy.