Abstract

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 datasets. Tissue binding, plasma protein binding, V_d, physiochemical, and physiological datasets were procured from literature and Genentech's internal database. 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 non-linear 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 datasets and found remarkably close statistical results. Brain and lung datasets also accurately predicted V_d, further validating the model. V_d prediction accuracy for compounds with LogD_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 cyno allometry.

Significance Statement We showed that tissue binding is comparable in three tissues across species and the fraction unbound in tissue can be interconverted with a dilution factor. In addition, we applied internal and external datasets to the volume of distribution model developed by Korzekwa and Nagar and found comparable V_d prediction accuracy between the V_d model and allometry. Our findings could potentially accelerate the drug R&D process by reducing the amount of resources associated with in vitro binding and animal experiments.