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Nanobubbles for Precision Oncology: Preclinical Evaluation of Molecular Targeted Ultrasound Contrast Agents in a Rabbit Model

Berg, F. M.; Abenojar, E. C.; Nittayacharn, P.; Hoggard, N.; Tavri, S.; Matsunaga, F.; Cooley, M. B.; Wegierak, D.; Wang, X.; Rosol, T.; Basilion, J. P.; Exner, A. A.

2025-08-10 bioengineering
10.1101/2025.08.07.668901 bioRxiv
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PurposeTo evaluate the use of prostate-specific membrane antigen-targeted nanobubbles (PSMA-NBs) for contrast-enhanced ultrasound (CEUS) in a rabbit model, aiming to enhance prostate cancer imaging and guide clinical translation. Materials and MethodsPSMA-NBs were formulated using lipid encapsulation and PSMA-targeting ligands. Human PSMA-positive PC3pip-GFP cells were injected into the prostates of immunosuppressed rabbits to establish tumors. Tumor growth was monitored via B-mode ultrasound (US) and MRI. CEUS was conducted with PSMA-NBs and commercial microbubbles (MBs). Time-intensity curve (TIC) analysis, parametric mapping, and post-mortem histological correlation were performed. ResultsPSMA-NBs demonstrated 1.60-fold (p = 0.013) and 1.50-fold (p = 0.016) higher peak signal intensities in the tumor core and rim, respectively, compared to MBs, with significantly longer mean transit times (MTTs) in the core (4.20-fold; p = 0.001) and rim (4.50-fold; p < 0.001). At 10 minutes, PSMA-NBs retained detectable signals in tumor rim (7.0 {+/-} 3.0 a.u.), core (3.0 {+/-} 1.0 a.u.), and surrounding tissues (12.0 {+/-} 5.0 a.u.), unlike MBs. Larger tumors showed prolonged MTTs in the rim (3.70 {+/-} 0.50 min) and surrounding tissues (4.60 {+/-} 0.50 min) compared to the core (2.10 {+/-} 0.40 min, p < 0.001). TIC parameters (MTT, AUCwo) correlated with tumor viability, emphasizing PSMA-NBs ability to delineate viable regions. ConclusionPSMA-NBs significantly enhanced prostate cancer imaging, correlating with tumor viability and outperforming MBs. These findings support their potential to improve diagnostic precision and guide targeted therapy in prostate cancer.

Published in Bioactive Materials (predicted rank #18) · training set

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