(587b) Facile Size Isolation and Peptide Conjugation to Lipid-Coated Microbubbles: Application to Molecular Imaging of Renal Tumor Models

Ultrasound is rapidly emerging as a practical modality for molecular imaging this is capable of real-time, deep-tissue viewing and simultaneous intervention. Two important design criteria for probe development in ultrasound molecular imaging are ligand conjugation and contrast agent size. The former dictates to what degree the microbubble will selectively adhere to target tissue, while the latter determines the echo signature. More control of both will provide better quantification of biomarker expression. Lipid-coated microbubbles are well recognized for their biocompatibility and echogenicity. They can be fabricated by simple emulsification procedures that result in high yields. For example, over one billion microbubbles per milliliter of solution can be made within just a few seconds. Fortuitously, the resulting suspension is polydisperse in size with separate peaks centered at 1-2 micron and 4-5 micron diameter. Using chemical engineering principles, such as population balance modeling of migration in a gravitation field, these peaks can be rapidly and reproducibly isolated and characterized. Computational modeling and experimental validation of the technique will be presented. Following size isolation, peptide ligands can be efficiently conjugated using maleimide-thiol chemistry. This conjugation procedure will be presented as well.

Size-isolated, targeted microbubbles were used in a preliminary evaluation of ultrasound molecular imaging for angiogenesis in mouse model of a solid tumor in the kidney. Imaging with a high-frequency, small-animal scanner showed selective accumulation of RGD-peptide bearing microbubbles in the tumor vessels compared to no-ligand microbubbles. Tumor vessel architecture and perfusion also were evaluated. These early results show promise for ultrasound molecular imaging as a research tool for studying angiogenesis and the response to therapy in genetically driven renal tumor models.