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Inflammation, a precursor to many diseases including cancer and atherosclerosis, induces differential surface expression of specific vascular molecules. Blood-borne nanoparticles (NPs), loaded with therapeutic and imaging agents, can recognize and use these molecules as vascular docking sites. Here, a computational model is developed within the isogeometric analysis framework to understand and predict the vascular deposition of NPs within an inflamed arterial tree. The NPs have a diameter ranging from 0.1 to 2.0 μm and are decorated with antibodies directed toward three endothelial adhesion molecules, namely intravascular cell adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1), and E-selectin, whose surface density depends on the local wall shear stress. Results indicate VCAM-1 targeted NPs adhere more, with ICAM-1 directed NPs adhering least efficiently, resulting in approximately an order-of-magnitude lower average particle surface density. ICAM-1 and E-selectin directed 0.5 μm NPs are distributed more uniformly (heterogeneity index ≈ 0.9 and 1.0, respectively) over the bifurcating vascular branches compared to their VCAM-1 counterparts (heterogeneity index ≈ 1.4). When the NPs are coated with antibodies for VCAM-1 and E-selectin in equal proportions, a more uniform vascular distribution is achieved compared with VCAM-1-only targeted particles, thus demonstrating the advantage of NP multivalency in vascular targeting. Furthermore, the larger NPs (2 μm) adhere more (≈ 200%) in the lower branches compared to the upper branch. This computational framework provides insights into how size, ligand type, density, and multivalency can be manipulated to enhance NP vascular adhesion in an individual patient.

Vascular deposition patterns for nanoparticles in an inflamed patient-specific arterial tree