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 (K-p) method (K-p,K-mem) to predict unbound tissue to plasma partition coefficients (K-pu) was developed using in vitro membrane partitioning [fraction unbound in microsomes (f(um))], plasma protein binding, and log P. The resulting K-p values were used in a physiologically based pharmacokinetic (PBPK) model to predict the steady-state volume of distribution (V-ss) and concentrationtime (C-t) profiles for 19 drugs. These results were compared with K-p predictions using a standard method [the differential phospholipid K-p prediction method (K-p,K-dPL)], which differentiates between acidic and neutral phospholipids. The K-p,K-mem method was parameterized using published rat K. data and tissue lipid composition. The K-pu values were well predicted with R-2 = 0.8. When used in a PBPK model, the V-ss predictions were within 2-fold error for 12 of 19 drugs for K-p,K-mem versus 11 of 19 for K-p,K-dPL. With one outlier removed for K-p,K-mem and two for K-p,K-dPL, the V-ss predictions for R-2 were 0.80 and 0.79 for the K(p,mem )and K-p,K-dPL methods, respectively. The C-t profiles were also predicted and compared. Overall, the K-p,K-mem method predicted the V-ss and C-t profiles equally or better than the K-p,K-dPL method. An advantage of using f(um) to parameterize membrane partitioning is that f em 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.