Nanoparticle holds significant promise as the next generation of drug carrier that can realize targeted therapy with minimal toxicity. To improve the delivery efficiency of nanoparticles, it is important to study their transport and deposition in blood flow. Many factors, like particle size, vessel geometry and blood flow rate, have significant influence on the particle transport, thus on the deposition fraction and distribution. In this thesis, computational fluid dynamics (CFD) simulations of blood flow and drug particle deposition were conducted in four models representing the human lung vasculature: artificial artery geometry, artificial vein geometry, original geometry and over-smoothed original geometry. Flow conditions used included both steady-state inlet flow and pulsatile inlet flow. Parabolic flow pattern and lumped mathematic model were used for inlet and outlet boundary conditions respectively. Blood flow was treated as laminar and Newtonian. Particle trajectories were calculated in each of these models by solving the integrated force balance on the particle, and adding a stochastic Brownian term at each step. A receptor-ligand model was integrated to simulate the particle binding probability. The results indicate the following: (i) Pulsatile flow can accelerate the particle binding activity and improve the particle deposition fraction on bifurcation areas; (ii) Unlike drug delivery in lung respiratory system, particle diffusion is very weak in blood flow, no clear relationship between the particle size and deposition area was found in our four-generation lung vascular model; and (iii) Surface imperfections have the dominant effect on particle deposition fraction over a wide range of particle sizes. Ideal artificial geometry is not sufficient to predict drug deposition, and an accurate image based geometry is required.
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