Artículos de revistas
A physiologically based pharmacokinetic model to predict the superparamagnetic iron oxide nanoparticles (SPIONs) accumulation in vivo
Fecha
2017-04Registro en:
Henrique Silva, Adny; Lima, Enio Junior; Vasquez Mansilla, Marcelo; Zysler, Roberto Daniel; Mojica Pisciotti, Mary Luz; et al.; A physiologically based pharmacokinetic model to predict the superparamagnetic iron oxide nanoparticles (SPIONs) accumulation in vivo; Walter de Gruyter GmbH; European Journal of Nanomedicine; 9; 2; 4-2017; 79-90
1662-596X
CONICET Digital
CONICET
Autor
Henrique Silva, Adny
Lima, Enio Junior
Vasquez Mansilla, Marcelo
Zysler, Roberto Daniel
Mojica Pisciotti, Mary Luz
Locatelli, Claudriana
Kumar Reddy Rajoli, Rajith
Owen, Andrew
Creczynski Pasa, Tânia Beatriz
Siccardi, Marco
Resumen
Superparamagnetic iron oxide nanoparticles (SPIONs) have been identified as a promising material for biomedical applications. These include as contrast agents for medical imaging, drug delivery and/or cancer cell treatment. The nanotoxicological profile of SPIONs has been investigated in different studies and the distribution of SPIONs in the human body has not been fully characterized. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model to predict the pharmacokinetics of SPIONs. The distribution and accumulation of SPIONs in organs were simulated taking into consideration their penetration through capillary walls and their active uptake by specialized macrophages in the liver, spleen and lungs. To estimate the kinetics of SPION uptake, a novel experimental approach using primary macrophages was developed. The murine PBPK model was validated against in vivo pharmacokinetic data, and accurately described accumulation in liver, spleen and lungs. After validation of the murine model, a similar PBPK approach was developed to simulate the distribution of SPIONs in humans. These data demonstrate the utility of PBPK modeling for estimating biodistribution of inorganic nanoparticles and represents an initial platform to provide computational prediction of nanoparticle pharmacokinetics.