Quantification of the impact of partition coefficient prediction methods on physiologically based pharmacokinetic model output using a standardized tissue composition

Abstract

Tissue:plasma partition coefficients are key parameters in physiologically based pharmacokinetic (PBPK) models, yet the coefficients are challenging to measure in vivo. Several mechanistic-based equations have been developed to predict partition coefficients using tissue composition information and the compound's physicochemical properties, but it is not clear which, if any, of the methods is most appropriate under given circumstances. Complicating the evaluation, each prediction method was developed, and is typically employed, using a different set of tissue composition information, thereby making a controlled comparison impossible. This study proposed a standardized tissue composition for humans that can be used as a common input for each of the five frequently used prediction methods. These methods were implemented in R and were used to predict partition coefficients for 11 drugs, classified as strong bases, weak bases, acids, neutrals, and zwitterions.PBPKmodels developed in R (mrgsolve) for each drug and each set of partition coefficient predictions were compared with respective observed plasma concentration data. Percent root mean square error and half-life percent error were used to evaluate the accuracy of the PBPK model predictions using each partition coefficient method as summarized by strong bases, weak bases, acids, neutrals, and zwitterions characterization. The analysis indicated that no partition coefficient method consistently yielded the most accurate PBPK model predictions. As such, PBPK model predictions using all partition coefficient methods should be considered during drug development.