Abstract
Nanodrug-carrier delivery in the blood stream is strongly influenced by nanoparticle (NP) dispersion. This paper presents a numerical study on NP transport and dispersion in red blood cell (RBC) suspensions under shear and channel flow conditions, utilizing an immersed boundary fluid-structure interaction model with a lattice Boltzmann fluid solver, an elastic cell membrane model and a particle motion model driven by both hydrodynamic loading and Brownian dynamics. The model can capture the multiphase features of the blood flow. Simulations were performed to obtain an empirical formula to predict NP dispersion rate for a range of shear rates and cell concentrations. NP dispersion rate predictions from the formula were then compared to observations from previous experimental and numerical studies. The proposed formula is shown to accurately predict the NP dispersion rate. The simulation results also confirm previous findings that the NP dispersion rate is strongly influenced by local disturbances in the flow due to RBC motion and deformation. The proposed formula provides an efficient method for estimating the NP dispersion rate in modeling NP transport in large-scale vascular networks without explicit RBC and NP models.
Highlights
Predicting drug delivery is a critical task in drug development research and clinical trials [1,2]
In order to address the deficiencies in previously-developed models for predicting NP dispersion, this paper presents a numerical study on NP dispersion in red blood cell (RBC) suspensions that considers the effects of local flow field disturbances due to RBC motion
The NP dispersion rate was studied over a range of shear rates for a single layer of three cells
Summary
Predicting drug delivery is a critical task in drug development research and clinical trials [1,2]. In order to address the deficiencies in previously-developed models for predicting NP dispersion, this paper presents a numerical study on NP dispersion in RBC suspensions that considers the effects of local flow field disturbances due to RBC motion. This study provides insight into the underlying physics driving NP dispersion in these systems and develops simple, yet effective, formulae for predicting dispersion rate as a function of characteristic physiological parameters. These simple predictive formulae will provide an efficient approach for assessing NP dispersion under different flow conditions and hematocrit level, thereby facilitating practical modeling of NP transport and distribution in large-scale vascular systems [22].
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