Physiologically based pharmacokinetic modeling of intravenously administered nanoformulated substances

The use of nanobiomaterials (NBMs) is becoming increasingly popular in the field of medicine. To improve the understanding on the biodistribution of NBMs, the present study aimed to implement and parametrize a physiologically based pharmacokinetic (PBPK) model. This model was used to describe the biodistribution of two NBMs after intravenous administration in rats, namely, poly(alkyl cyanoacrylate) (PACA) loaded with cabazitaxel (PACA-Cbz), and Liplmage (TM) 815. A Bayesian parameter estimation approach was applied to parametrize the PBPK model using the biodistribution data. Parametrization was performed for two distinct dose groups of PACA-Cbz. Furthermore, parametrizations were performed three distinct dose groups of Liplmage (TM) 815, resulting in a total of five different parametrizations. The results of this study indicate that the PBPK model can be adequately parametrized using biodistribution data. The PBPK parameters estimated for PACA-Cbz, specifically the vascular permeability, the partition coefficient, and the renal clearance rate, substantially differed from those of Liplmage (TM) 815. This emphasizes the presence of kinetic differences between the different formulations and substances and the need of tailoring the parametrization of PBPK models to the NBMs of interest. The kinetic parameters estimated in this study may help to establish a foundation for a more comprehensive database on NBM-specific kinetic information, which is a first, necessary step towards predictive biodistribution modeling. This effort should be supported by the development of robust in vitro methods to quantify kinetic parameters.

Automated summary

The use of nanobiomaterials in medicine is gaining popularity. This study implemented and parametrized a physiologically based pharmacokinetic (PBPK) model to better understand the biodistribution of two nanobiomaterials in rats. The results showed significant kinetic differences between the two materials, highlighting the need for tailored parametrization of PBPK models. These findings contribute to the establishment of a comprehensive database for predictive biodistribution modeling.