Assessing Therapeutic\nNanoparticle Accumulation in\nTumors Using Nanobubble-Based Contrast-Enhanced Ultrasound Imaging

This study explores the challenges associated with nanoparticle-based\ndrug delivery to the tumor parenchyma, focusing on the widely utilized\nenhanced permeability and retention effect (EPR). While EPR has been\na key strategy, its inconsistent clinical success lacks clear mechanistic\nunderstanding and is hindered by limited tools for studying relevant\nphenomena. This work introduces an approach that employs multiparametric\ndynamic contrast-enhanced ultrasound (CEUS) with a nanoscale contrast\nagent for noninvasive, real-time examination of tumor microenvironment\ncharacteristics. We demonstrate that CEUS imaging can: (1) evaluate\ntumor microenvironment features, (2) be used to help predict the distribution\nof doxorubicin-loaded liposomes in the tumor parenchyma, and (3) be\nused to predict nanotherapeutic efficacy. CEUS using nanobubbles (NBs)\nwas carried out in two tumor types of high (LS174T) and low (U87)\nvascular permeability. LS174T tumors consistently showed significantly\ndifferent time intensity curve (TIC) parameters, including area under\nthe rising curve (AUC<sub>R</sub>, 2.7×) and time to peak intensity\n(TTP, 1.9×) compared to U87 tumors. Crucially, a recently developed\ndecorrelation time (DT) parameter specific to NB CEUS dynamics successfully\npredicted the distribution of doxorubicin-loaded liposomes within\nthe tumor parenchyma (<i>r</i> = 0.86 ± 0.13). AUC<sub>R</sub>, TTP, and DT were used to correlate imaging findings to nanotherapeutic\nresponse with 100% accuracy in SKOV-3 tumors. These findings suggest\nthat NB-CEUS parameters can effectively discern tumor vascular permeability,\nserving as a biomarker for identifying tumor characteristics and predicting\nthe responsiveness to nanoparticle-based therapies. The observed differences\nbetween LS174T and U87 tumors and the accurate prediction of nanotherapeutic\nefficacy in SKOV-3 tumors indicate the potential utility of this method\nin predicting treatment efficacy and evaluating EPR in diseases characterized\nby pathologically permeable vasculature. Ultimately, this research\ncontributes valuable insights into refining drug delivery strategies\nand assessing the broader applicability of EPR-based approaches.