Nanotherapy has recently emerged as an experimental treatment option for atherosclerosis. To fulfill its promise, robust noninvasive imaging approaches for subject selection and treatment evaluation are warranted. To that end, we present here a positron emission tomography (PET)-based method for quantification of liposomal nanoparticle uptake in the atherosclerotic vessel wall. We evaluated a modular procedure to label liposomal nanoparticles with the radioisotope zirconium-89 (89Zr). Their biodistribution and vessel wall targeting in a rabbit atherosclerosis model was evaluated up to 15 days after intravenous injection by PET/computed tomography (CT) and PET/magnetic resonance imaging (PET/MRI). Vascular permeability was assessed in vivo using three-dimensional dynamic contrast-enhanced MRI (3D DCE-MRI) and ex vivo using near-infrared fluorescence (NIRF) imaging. The 89Zr-radiolabeled liposomes displayed a biodistribution pattern typical of long-circulating nanoparticles. Importantly, they markedly accumulated in atherosclerotic lesions in the abdominal aorta, as evident on PET/MRI and confirmed by autoradiography, and this uptake moderately correlated with vascular permeability. The method presented herein facilitates the development of nanotherapy for atherosclerotic disease as it provides a tool to screen for nanoparticle targeting in individual subjects’ plaques.

OBJECTIVES:The goal of this study was to develop and validate a noninvasive imaging tool to visualize the in vivo behavior of high-density lipoprotein (HDL) by using positron emission tomography (PET), with an emphasis on its plaque-targeting abilities. BACKGROUND:HDL is a natural nanoparticle that interacts with atherosclerotic plaque macrophages to facilitate reverse cholesterol transport. HDL-cholesterol concentration in blood is inversely associated with risk of coronary heart disease and remains one of the strongest independent predictors of incident cardiovascular events. METHODS:Discoidal HDL nanoparticles were prepared by reconstitution of its components apolipoprotein A-I (apo A-I) and the phospholipid 1,2-dimyristoyl-sn-glycero-3-phosphocholine. For radiolabeling with zirconium-89 ((89)Zr), the chelator deferoxamine B was introduced by conjugation to apo A-I or as a phospholipid-chelator (1,2-distearoyl-sn-glycero-3-phosphoethanolamine-deferoxamine B). Biodistribution and plaque targeting of radiolabeled HDL were studied in established murine, rabbit, and porcine atherosclerosis models by using PET combined with computed tomography (PET/CT) imaging or PET combined with magnetic resonance imaging. Ex vivo validation was conducted by radioactivity counting, autoradiography, and near-infrared fluorescence imaging. Flow cytometric assessment of cellular specificity in different tissues was performed in the murine model. RESULTS:We observed distinct pharmacokinetic profiles for the two (89)Zr-HDL nanoparticles. Both apo A-I- and phospholipid-labeled HDL mainly accumulated in the kidneys, liver, and spleen, with some marked quantitative differences in radioactivity uptake values. Radioactivity concentrations in rabbit atherosclerotic aortas were 3- to 4-fold higher than in control animals at 5 days' post-injection for both (89)Zr-HDL nanoparticles. In the porcine model, increased accumulation of radioactivity was observed in lesions by using in vivo PET imaging. Irrespective of the radiolabel's location, HDL nanoparticles were able to preferentially target plaque macrophages and monocytes. CONCLUSIONS:(89)Zr labeling of HDL allows study of its in vivo behavior by using noninvasive PET imaging, including visualization of its accumulation in advanced atherosclerotic lesions. The different labeling strategies provide insight on the pharmacokinetics and biodistribution of HDL's main components (i.e., phospholipids, apo A-I).

each sequence. CNR is normalized per square root unit scan time. For SNR above, each scan technique is shown for T1=1000ms and T1=400ms side by side. CNR is the difference of these values. MERGE 6 MERGE 25 GRE 20 SPACE Figure 2: Pre(top), and post-contrast images (bottom). Arrow shows vessel wall. 7612 Optimal Sequence Weighting for 3D Dynamic Contrast Enhanced Imaging Philip M Robson, Claudia Calcagno, Sarayu Ramachandran, Mark E Lobatto, Julia Witjes, Venkatesh Mani, and Zahi A Fayad Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States