Literature DB >> 29620124

Precise targeting of cancer metastasis using multi-ligand nanoparticles incorporating four different ligands.

P M Peiris1, F He, G Covarrubias, S Raghunathan, O Turan, M Lorkowski, B Gnanasambandam, C Wu, W P Schiemann, E Karathanasis.   

Abstract

Metastasis displays a highly heterogeneous cellular population with cancer cells continuously evolving. As a result, a single-ligand nanoparticle cannot account for the continuously changing expression of targetable biomarkers over time and space. To effectively direct nanoparticles to metastasis, we developed a multi-ligand nanoparticle by using four different types of ligands on the same nanoparticle that target biomarkers on the endothelium associated with metastatic disease. These vascular targets included αvβ3 integrin, P-selectin, EGFR and fibronectin. Using terminal and in vivo imaging studies, the targeting performance of the multi-ligand nanoparticles was compared to the single-ligand nanoparticle variants. All four single-ligand nanoparticle variants achieved significant targeting of lung metastasis in the 4T1 mouse model of breast cancer metastasis with about 2.5% of the injected dose being deposited into metastasis. A dual-ligand nanoparticle resulted in a nearly 2-fold higher deposition into lung metastases than its single-ligand counterparts. The multi-ligand nanoparticle significantly outperformed its targeting nanoparticle counterparts achieving a deposition of ∼7% of its injected nanoparticles into lung metastases. Using the high sensitivity of radionuclide imaging, PET imaging showed that a multi-ligand nanoparticle labeled with [18F]fluoride was able to precisely target metastatic disease at its very early stage of development in three different animal models of metastatic breast cancer.

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Year:  2018        PMID: 29620124      PMCID: PMC5908762          DOI: 10.1039/c8nr02513d

Source DB:  PubMed          Journal:  Nanoscale        ISSN: 2040-3364            Impact factor:   7.790


  69 in total

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Journal:  PLoS One       Date:  2018-10-18       Impact factor: 3.240

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