Optimizing particle size for targeting diseased microvasculature: from experiments to artificial neural networks
Daniela P Boso1, Sei-Young Lee2, Mauro Ferrari3, Bernhard A Schrefler1, Paolo Decuzzi3
1Department of Structural and Transportation Engineering, University of Padova, Padova, Italy; 2Global Production Technology Center, Samsung Electronics Co Ltd, Republic of Korea; 3The Methodist Hospital Research Institute, Department of Nanomedicine and Biomedical Engineering, Houston, TX, USA
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 (dopt) exists for which the number (ns) 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 ns as a function of S and particle diameter (d), from which to eventually derive dopt. Artificial neural networks are trained using data from flow chamber experiments. Two networks are used, ie, ANN231 and ANN2321, exhibiting an accurate prediction for ns 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.
Keywords: nanoparticle, optimal configuration, vascular adhesion, laminar flow, wall shear rate, artificial neural networks
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