Tan Jifu, Wang Shunqiang, Yang Jie, Liu Yaling
Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, 18015.
Comput Struct. 2013 Jun 1;122:128-134. doi: 10.1016/j.compstruc.2012.12.019.
Prediction of nanoparticle (NP) distribution in a vasculature involves transport phenomena at various scales and is crucial for the evaluation of NP delivery efficiency. A combined particulate and continuum model is developed to model NP transport and delivery processes. In the particulate model ligand-receptor binding kinetics is coupled with Brownian dynamics to study NP binding on a microscale. An analytical formula is derived to link molecular level binding parameters to particulate level adhesion and detachment rates. The obtained NP adhesion rates are then coupled with a convection-diffusion-reaction model to study NP transport and delivery at macroscale. The binding results of the continuum model agree well with those from the particulate model. The effects of shear rate, particle size and vascular geometry on NP adhesion are investigated. Attachment rates predicted by the analytical formula also agree reasonably well with the experimental data reported in literature. The developed coupled model that links ligand-receptor binding dynamics to NP adhesion rate along with macroscale transport and delivery processes may serve as a faster evaluation and prediction tool to determine NP distribution in complex vascular networks.
预测纳米颗粒（NP）在脉管系统中的分布涉及不同尺度的传输现象，对于评估NP递送效率至关重要。开发了一种颗粒与连续介质相结合的模型来模拟NP的传输和递送过程。在颗粒模型中，配体-受体结合动力学与布朗动力学相结合，以研究微观尺度上的NP结合。推导了一个解析公式，将分子水平的结合参数与颗粒水平的粘附和脱离速率联系起来。然后将获得的NP粘附速率与对流-扩散-反应模型相结合，以研究宏观尺度上的NP传输和递送。连续介质模型的结合结果与颗粒模型的结果吻合良好。研究了剪切速率、颗粒大小和血管几何形状对NP粘附的影响。解析公式预测的附着速率也与文献报道的实验数据相当吻合。所开发的将配体-受体结合动力学与NP粘附速率以及宏观传输和递送过程联系起来的耦合模型，可作为一种更快的评估和预测工具，用于确定复杂血管网络中的NP分布。
