Two-pore physiologically based pharmacokinetic model with de novo derived parameters for predicting plasma PK of different size protein therapeutics.

Abstract

Two-pore PBPK models have been used for characterizing the PK of protein therapeutics since 1990s. However, widespread utilization of these models is hampered by the lack of a priori parameter values, which are typically estimated using the observed data. To overcome this hurdle, here we have presented the development of a two-pore PBPK model using de novo derived parameters. The PBPK model was validated using plasma PK data for different size proteins in mice. Using the "two pore theory" we were able to establish the relationship between protein size and key model parameters, such as: permeability-surface area product (PS), vascular reflection coefficient (σ), peclet number (Pe), and glomerular sieving coefficient (θ). The model accounted for size dependent changes in tissue extravasation and glomerular filtration. The model was able to a priori predict the PK of 8 different proteins: IgG (150kDa), scFv-Fc (105kDa), F(ab)2 (100kDa, minibody (80kDa), scFv2 (55kDa), Fab (50kDa), diabody (50kDa), scFv (27kDa), and nanobody (13kDa). In addition, the model was able to provide unprecedented quantitative insight into the relative contribution of convective and diffusive pathway towards trans-capillary mass transportation of different size proteins. The two-pore PBPK model was also able to predict systemic clearance (CL) versus Molecular Weight relationship for different size proteins reasonably well. As such, the PBPK model proposed here represents a bottom-up systems PK model for protein therapeutics, which can serve as a generalized platform for the development of truly translational PBPK model for protein therapeutics.