in silico Vascular Modeling for Personalized Nanoparticle Delivery
Abstract
Drug biodistribution, bioavailability, efficacy and toxicity are unavoidably patient-specific. Here, computational models are utilized 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 Isogeometric Analysis framework, with an experimentally validated special boundary condition for the firm wall adhesion of nanoparticles, is used. The adhesion pattern correlates well with the spatial and temporal distribution of the wall shear rates. For the case considered, the larger (2.0 micron) particles adhere approximately 2 times more in the lower branches of the arterial tree whereas the smaller (0.5 microns) particles deposit more in the upper branches. A change in patient-specific attributes, such as the branching angle and receptor density, dramatically affect particle adhesion. Our computational framework can be used to rationally select nanoparticle properties in conjunction with patient specific attributes to personalize, thus optimize, therapeutic interventions.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 2012
- Accession Number
- ADA558915
Entities
People
- Fazle Hussain
- Mauro Ferrari
- Paolo Decuzzi
- Shaolie S. Hossain
- Thomas J.R. Hughes
- Xinghua Liang
- Yongjie Zhang
Organizations
- University of Texas at Austin