Optimizing particle size for targeting diseased microvasculature: from experiments to artificial neural networks

Abstract

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.

Keywords:

• nanoparticle
• optimal configuration
• vascular adhesion
• laminar flow
• wall shear rate
• artificial neural networks

Supplementary material

Figure S1 Root mean squared error of the learning set of data for ANN231.

Figure S2 Root mean squared error of the learning set of data for ANN2321.

Table S1 Experimental data for the number of particles n_s adhering per unit area in a parallel plate flow chamber

Table S2 Values for mean and standard deviation of number of particles (n_s) adhering per unit area in parallel plate flow chamber experiments

Disclosure

The authors report no conflicts of interest in this work.