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

Daniela P. Boso, Sei Young Lee, Mauro Ferrari, Bernhard A. Schrefler, Paolo Decuzzi

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1517-1526
Number of pages10
JournalInternational Journal of Nanomedicine
Issue number1
StatePublished - Jul 18 2011


  • Artificial neural networks
  • Laminar flow
  • Nanoparticle
  • Optimal configuration
  • Vascular adhesion
  • Wall shear rate

ASJC Scopus subject areas

  • Biophysics
  • Bioengineering
  • Biomaterials
  • Organic Chemistry
  • Drug Discovery


Dive into the research topics of 'Optimizing particle size for targeting diseased microvasculature: From experiments to artificial neural networks'. Together they form a unique fingerprint.

Cite this