Evaluation of Tissue Binding in Three Tissues across Five Species and Prediction of Volume of Distribution from Plasma Protein and Tissue Binding with an Existing Model

Volume of distribution (V_d) is a primary pharmacokinetic parameter used to calculate the half-life and plasma concentration-time profile of drugs. Numerous models have been relatively successful in predicting V_d, but the model developed by Korzekwa and Nagar is of particular interest because it utilizes plasma protein binding and microsomal binding data, both of which are readily available in vitro parameters. Here, Korzekwa and Nagar's model was validated and expanded upon using external and internal data sets. Tissue binding, plasma protein binding, V_d, physiochemical, and physiologic data sets were procured from literature and Genentech's internal data base. First, we investigated the hypothesis that tissue binding is primarily governed by passive processes that depend on the lipid composition of the tissue type. The fraction unbound in tissues (fu_tissue) was very similar across human, rat, and mouse. In addition, we showed that dilution factors could be generated from nonlinear regression so that one fu_tissue value could be used to estimate another one regardless of species. More importantly, results suggested that microsomes could serve as a surrogate for tissue binding. We applied the parameters from Korzekwa and Nagar's V_d model to two distinct liver microsomal data sets and found remarkably close statistical results. Brain and lung data sets also accurately predicted V_d, further validating the model. V_d prediction accuracy for compounds with log D_7.4 > 1 significantly outperformed that of more hydrophilic compounds. Finally, human V_d predictions from Korzekwa and Nagar's model appear to be as accurate as rat allometry and slightly less accurate than dog and cynomolgus allometry.

SIGNIFICANCE STATEMENT

This study shows that tissue binding is comparable across five species and can be interconverted with a dilution factor. In addition, we applied internal and external data sets to the volume of distribution model developed by Korzekwa and Nagar and found comparable V_d prediction accuracy between the V_d model and single-species allometry. These findings could potentially accelerate the drug research and development process by reducing the amount of resources associated with in vitro binding and animal experiments.