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Size-Dependent Polymeric Nanoparticle Distribution in a Static versus Dynamic Microfluidic Blood Vessel Model: Implications for Nanoparticle-Based Drug Delivery

[Image: see text] Nanoparticles (NPs) have been widely investigated in the nanomedicine field. One of the main challenges is to accurately predict the NP distribution and fate after administration. Microfluidic platforms acquired huge importance as tools to model the in vivo environment. In this stu...

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Detalles Bibliográficos
Autores principales: Gimondi, Sara, Ferreira, Helena, Reis, Rui L., Neves, Nuno M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10189782/
https://www.ncbi.nlm.nih.gov/pubmed/37207132
http://dx.doi.org/10.1021/acsanm.3c00481
Descripción
Sumario:[Image: see text] Nanoparticles (NPs) have been widely investigated in the nanomedicine field. One of the main challenges is to accurately predict the NP distribution and fate after administration. Microfluidic platforms acquired huge importance as tools to model the in vivo environment. In this study, we leveraged a microfluidic platform to produce FITC-labeled poly(lactide-co-glycolide)-block-poly(ethylene glycol) (PLGA-PEG) NPs with defined sizes of 30, 50, and 70 nm. The study aimed to compare the ability of NPs with differences of 20 nm in size to cross an endothelial barrier using static (Transwell inserts) and dynamic (microfluidic perfusion device) in vitro models. Our results evidence a size-dependent NP crossing in both models (30 > 50 > 70 nm) and highlight the bias deriving from the static model, which does not involve shear stresses. The permeation of each NP size was significantly higher in the static system than in the dynamic model at the earliest stages. However, it gradually decreased to levels comparable with those of the dynamic model. Overall, this work highlights clear differences in NP distribution over time in static versus dynamic conditions and distinct size-dependent patterns. These findings reinforce the need for accurate in vitro screening models that allow for more accurate predictions of in vivo performance.