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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,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 concentration-time (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,dPL )], which differentiates between acidic and neutral phospholipids. The K  p,mem method was parameterized using published rat K  pu 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,mem versus 11 of 19 for K p,dPL With one outlier removed for K  p,mem and two for K  p,dPL , the V  ss predictions for R  2 were 0.80 and 0.79 for the K  p,mem and K  p,dPL methods, respectively. The C-t profiles were also predicted and compared. Overall, the K  p,mem method predicted the V  ss and C-t profiles equally or better than the K  p,dPL method. An advantage of using f  um to parameterize membrane partitioning is that f  um 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.

drug metabolism and disposition/dmd online

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