Optimizing particle size for targeting diseased microvasculature: from experiments to artificial neural networks
Optimizing particle size for targeting diseased microvasculature: from experiments to artificial neural networks
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DOI：
10.2147/ijn.s20283
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发表时间：
2011-01-01
影响因子：
8
通讯作者：
Decuzzi, Paolo
中科院分区：
文献类型：
--
作者：
Boso, Daniela P.;Lee, Sei-Young;Decuzzi, Paolo
背景资料：具有不同尺寸、形状和表面性质的纳米颗粒正在被开发用于一系列疾病的早期诊断、成像和治疗。确定最佳配置，最大限度地提高纳米粒子在病变部位的积累是至关重要的。在这项工作中，使用平行板流动室装置，证明存在最佳粒径（d（opt）），对于该最佳粒径，粘附到血管壁的纳米颗粒的数量（n（s））最大化。这种直径取决于壁剪切速率（S）。人工神经网络被提出作为预测n（s）作为S和颗粒直径（d）的函数的工具，由此最终导出d（opt）。人工神经网络的训练使用的数据从流动室实验。使用两个网络，即ANN231和ANN2321，表现出对n（s）的准确预测及其对d和S的复杂函数依赖性。这表明，人工神经网络可以有效地用于最大限度地减少所需的实验数量，而不会影响研究的准确性。类似的程序可能同样有效地用于体内分析。
Background: Nanoparticles with different sizes, shapes, and surface properties are being developed for the early diagnosis, imaging, and treatment of a range of diseases. Identifying the optimal configuration that maximizes nanoparticle accumulation at the diseased site is of vital importance. In this work, using a parallel plate flow chamber apparatus, it is demonstrated that an optimal particle diameter (d(opt)) exists for which the number (n(s)) of nanoparticles adhering to the vessel walls is maximized. Such a diameter depends on the wall shear rate (S). Artificial neural networks are proposed as a tool to predict n(s) as a function of S and particle diameter (d), from which to eventually derive d(opt). Artificial neural networks are trained using data from flow chamber experiments. Two networks are used, ie, ANN231 and ANN2321, exhibiting an accurate prediction for n(s) and its complex functional dependence on d and S. This demonstrates that artificial neural networks can be used effectively to minimize the number of experiments needed without compromising the accuracy of the study. A similar procedure could potentially be used equally effectively for in vivo analysis.