Prediction of Tissue-Plasma Partition Coefficients Using Microsomal Partitioning: Incorporation into Physiologically based Pharmacokinetic Models and Steady-State Volume of Distribution Predictions.

Drug distribution is a necessary component of models to predict human pharmacokinetics. A new membrane-based tissue-plasma partition coefficient ( ) method ( ) to predict unbound tissue to plasma partition coefficients ( ) was developed using in vitro membrane partitioning [fraction unbound in microsomes ( )], plasma protein binding, and log The resulting values were used in a physiologically based pharmacokinetic (PBPK) model to predict the steady-state volume of distribution ( ) and concentration-time (C-t) profiles for 19 drugs. These results were compared with predictions using a standard method [the differential phospholipid prediction method ( )], which differentiates between acidic and neutral phospholipids. The method was parameterized using published rat data and tissue lipid composition. The values were well predicted with = 0.8. When used in a PBPK model, the predictions were within 2-fold error for 12 of 19 drugs for versus 11 of 19 for K With one outlier removed for and two for , the predictions for were 0.80 and 0.79 for the and methods, respectively. The C-t profiles were also predicted and compared. Overall, the method predicted the and C-t profiles equally or better than the method. An advantage of using to parameterize membrane partitioning is that data are used for clearance prediction and are, therefore, generated early in the discovery/development process. Also, the method provides a mechanistically sound basis for membrane partitioning and permeability for further improving PBPK models. SIGNIFICANCE STATEMENT: A new method to predict tissue-plasma partition coefficients was developed. The method provides a more mechanistic basis to model membrane partitioning.