Systematic evaluation of five classical mechanistic methods for tissue-plasma partition coefficients across diverse tissues and compound classes.

Tissue-plasma partition coefficient (Kp) is a key parameter governing drug distribution and the predictive accuracy of physiologically based pharmacokinetic models. Although several classical mechanistic methods have been developed for Kp prediction, systematic comparisons across multiple tissues and compound classes remain limited. In this study, we systematically compared 5 classical mechanistic methods using a standardized rat dataset to assess their predictive performance and applicability. A PubMed literature search was performed to compile a dataset of 781 Kp values from 119 compounds across 11 rat tissues, covering 6 compound classes. Physicochemical properties were collected from literatxure and public databases. Five mechanistic methods-Poulin and Theil, Berezhkovskiy, Rodgers and Rowland, Willmann, and Schmitt-were implemented in R software (R Foundation for Statistical Computing) according to their published algorithms. Predictive performance was evaluated using 3-fold error proportion, average fold error, absolute average fold error, and root mean square error. Overall, the Rodgers and Rowland method performed the best (68.63% within 3-fold error; absolute average fold error = 2.62, average fold error= 0.82). Across compound classes, the Rodgers and Rowland method was superior for most classes, particularly strong bases (74.24% within 3-fold error), whereas the Poulin and Theil method performed better for neutral compounds (71.15% within 3-fold error). Across tissues, predictions were more accurate for skin and heart, whereas kidney, adipose, and brain remained challenging. These findings indicate that Kp prediction performance varies substantially across different in silico methods, compound classes, and tissue types. SIGNIFICANCE STATEMENT: This study systematically evaluates 5 classical mechanistic methods for predicting tissue-plasma partition coefficients across 11 rat tissues and 6 compound classes using a standardized dataset of 781 values. Our findings identify the most accurate and robust method, reveal compound-specific and tissue-specific limitations, and provide practical guidance for physiologically based pharmacokinetic modeling, supporting improved drug distribution prediction in preclinical research and drug development.