In silico vascular modeling for personalized nanoparticle delivery.

Aims: 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.

Materials & Methods: The patient-specific vascular geometry is reconstructed from computed tomography angiography images. The isogeometric analysis framework integrated with a special boundary condition for the firm wall adhesion of nanoparticles is implemented. A parallel plate flow chamber system is used to validate the computational model in vitro.

Results: Particle adhesion is dramatically affected by changes in patient-specific attributes, such as branching angle and receptor density. The adhesion pattern correlates well with the spatial and temporal distribution of the wall shear rates. For the case considered, the larger (2.0 µm) particles adhere two-times more in the lower branches of the arterial tree, whereas the smaller (0.5 µm) particles deposit more in the upper branches.

Conclusion: Our computational framework in conjunction with patient-specific attributes can be used to rationally select nanoparticle properties to personalize, and thus optimize, therapeutic interventions.