Abstract
Diagnosis of cardiovascular disease is currently limited by the testing modality. Serum tests for biomarkers can provide quantification of severity but lack the ability to localize the source of the cardiovascular disease, while imaging technology such as angiography and ultrasound can only determine areas of reduced flow but not the severity of tissue ischemia. Targeted imaging with ultrasound contrast agents offers the ability to locally image as well as determine the degree of ischemia by utilizing agents that will cause the contrast agent to home to the affected tissue. Ultrasound molecular imaging via targeted microbubbles (MB) is currently limited by its sensitivity to molecular markers of disease relative to other techniques (e.g., radiolabeling). We hypothesize that computational modeling may provide a useful first approach to maximize microbubble binding by defining key parameters governing adhesion. Adhesive dynamics (AD) was used to simulate the fluid dynamic and stochastic molecular binding of microbubbles to inflamed endothelial cells. Sialyl Lewis(X) (sLe(x)), P-selectin aptamer (PSA), and ICAM-1 antibody (abICAM) were modeled as the targeting receptors on the microbubble surface in both single- and dual-targeted arrangements. Microbubble properties (radius [R(c)], kinetics [k(f), k(r)], and densities of targeting receptors) and the physical environment (shear rate and target ligand densities) were modeled. The kinetics for sLe(x) and PSA were measured with surface plasmon resonance. R(c), shear rate, and densities of sLe(x), PSA, or abICAM were varied independently to assess model sensitivity. Firm adhesion was defined as MB velocity <2% of the free stream velocity. AD simulations revealed an optimal microbubble radius of 1-2 µm and thresholds for kf(in) ( >10(2) s(-1)) and kr(o) (<10(-3) s(-1)) for firm adhesion in a multi-targeted system. State diagrams for multi-targeted microbubbles suggest sLe(x) and abICAM microbubbles may require 10-fold more ligand to achieve firm adhesion at higher shear rates than sLe(x) and PSA microbubbles. The AD model gives useful insight into the key parameters for stable microbubble binding, and may allow flexible, prospective design, and optimization of microbubbles to enhance clinical translation of ultrasound molecular imaging.