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In the past, our lab proposed a two-pore PBPK model for different-size protein therapeutics using de novo derived parameters and the model was validated using plasma PK data of different-size antibody fragments digitized from the literature (Li Z, Shah DK, J Pharmacokinet Pharmacodynam 46(3):305–318, 2009). To further validate the model using tissue distribution data, whole-body biodistribution study of 6 different-size proteins in mice were conducted. Studied molecules covered a wide MW range (13–150 kDa). Plasma PK and tissue distribution profiles is 9 tissues were measured, including heart, lung, liver, spleen, kidney, skin, muscle, small intestine, large intestine. Tumor exposure of different-size proteins were also evaluated. The PBPK model was validated by comparing percentage predictive errors (%PE) between observed and model predicted results for each type of molecule in each tissue. Model validation showed that the two-pore PBPK model was able to predict plasma, tissues and tumor PK of all studied molecules relatively well. This model could serve as a platform for developing a generic PBPK model for protein therapeutics in the future. [ABSTRACT FROM AUTHOR]

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 (150 kDa), scFv-Fc (105 kDa), F(ab)2 (100 kDa, minibody (80 kDa), scFv2 (55 kDa), Fab (50 kDa), diabody (50 kDa), scFv (27 kDa), and nanobody (13 kDa). 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. [ABSTRACT FROM AUTHOR]

Purpose: To establish a continuous relationship between the size of various antibody fragments and their systemic clearance (CL) in mice. Methods: Two different orthogonal approaches have been used to establish the relationship. First approach uses CL values estimated by non-compartmental analysis (NCA) to establish a correlation with protein size. The second approach simultaneously characterizes the PK data for all the proteins using a 2-compartment model to establish a relationship between protein size and pharmacokinetic (PK) parameters. Results: Simple mathematical functions (e.g. sigmoidal, power law) were able to characterize the CL vs. protein size relationship generated using the investigated proteins. The relationship established in mouse was used to predict rat, rabbit, monkey, and human relationships using allometric scaling. The predicted relationships were found to capture the available spares data from each species reasonably well. Conclusions: The CL vs. protein size relationship is important for establishing a robust quantitative structure-PK relationship (QSPKR) for protein therapeutics. The relationship presented here can help in a priori predicting plasma exposure of therapeutic proteins, and together with our previously established relationship between plasma and tissue concentrations of proteins, it can predict the tissue exposure of non-binding proteins simply based on molecular weight/radius and dose. [ABSTRACT FROM AUTHOR]