In silico vascular modeling for personalized nanoparticle delivery.
In silico vascular modeling for personalized nanoparticle delivery.
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DOI：
10.2217/nnm.12.124
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发表时间：
2013-03
期刊：
影响因子：
--
通讯作者：
Decuzzi P
中科院分区：
文献类型：
--
作者：
Hossain SS;Zhang Y;Liang X;Hussain F;Ferrari M;Hughes TJ;Decuzzi P
预测纳米颗粒在患者特定动脉树中的沉积，作为血管结构、流动条件、受体表面密度和纳米颗粒特性的函数。根据CT血管造影图像重建患者特定的血管几何结构。等几何分析框架集成了一个特殊的边界条件的纳米粒子的坚定的壁粘附。采用平行板流动室系统对计算模型进行了体外验证。颗粒粘附受到患者特异性属性（如分支角度和受体密度）变化的显著影响。粘附模式与壁剪切率的空间和时间分布密切相关。对于所考虑的情况，较大的颗粒（2.0 μm）粘附在动脉树的下部分支中的数量是其1.2倍，而较小的颗粒（0.5 μm）存款在上部分支中的数量更多。我们的计算框架结合患者特定属性可用于合理选择纳米颗粒特性，以个性化，从而优化治疗干预。
To predict the deposition of nanoparticles in a patient-specific arterial tree as a function of the vascular architecture, flow conditions, receptor surface density, and nanoparticle properties. The patient-specific vascular geometry is reconstructed from CT Angiography images. The Isogeometric Analysis framework integrated with a special boundary condition for the firm wall adhesion of nanoparticles is implemented. A parallel plate flow chamber system is used to validate the computational model in vitro. Particle adhesion is dramatically affected by changes in patient-specific attributes, such as branching angle and receptor density. The adhesion pattern correlates well with the spatial and temporal distribution of the wall shear rates. For the case considered, the larger (2.0 μm) particles adhere ≈ 2 times more in the lower branches of the arterial tree, whereas the smaller (0.5 μm) particles deposit more in the upper branches. Our computational framework in conjunction with patient specific attributes can be used to rationally select nanoparticle properties to personalize, thus optimize, therapeutic interventions.