Modular Self-Assembling Dendrimer Nanosystems for Magnetic Resonance and Multimodality Imaging of Tumors

Bioimaging is a powerful tool for diagnosing tumors but remains limited in terms of sensitivity and specificity. Nanotechnology-based imaging probes able to accommodate abundant imaging units with different imaging modalities are particularly promising for overcoming these limitations. In addition, the nanosized imaging agents can specifically increase the contrast of tumors by exploiting the enhanced permeability and retention effect. A proof-of-concept study is performed on pancreatic cancer to demonstrate the use of modular amphiphilic dendrimer-based nanoprobes for magnetic resonance (MR) imaging (MRI) or MR/near-infrared fluorescence (NIRF) multimodality imaging. Specifically, the self-assembly of an amphiphilic dendrimer bearing multiple Gd3+ units at its terminals, generates a nanomicellar agent exhibiting favorable relaxivity for MRI with a good safety profile. MRI reveals an up to two-fold higher contrast enhancement in tumors than in normal muscle. Encapsulating the NIRF dye within the core of the nanoprobe yields an MR/NIRF bimodal imaging agent for tumor detection that is efficient both for MRI, at Gd3+ concentrations 1/10 the standard clinical dose, and for NIRF imaging, allowing over two-fold stronger fluorescence intensities. These self-assembling dendrimer nanosystems thus constitute effective probes for MRI and MR/NIRF multimodality imaging, offering a promising nanotechnology platform for elaborating multimodality imaging probes in biomedical applications. Modular self-assembling dendrimer probes are developed for magnetic resonance (MR) imaging and MR/near-infrared fluorescence (NIRF) bimodality imaging of tumors. These probes show high relaxivity at high magnetic field strength, giving excellent MR imaging contrast at low Gd-doses down to 1/10th of the standard clinical dose alongside effective NIRF imaging. This study highlights the promise of self-assembling dendrimers for multimodality imaging.image