Issue 13, 2017

Albumin-based nanoparticles loaded with hydrophobic gadolinium chelates as T1T2 dual-mode contrast agents for accurate liver tumor imaging

Abstract

Magnetic resonance contrast agents with T1T2 dual mode contrast capability have attracted considerable interest because they offer complementary and synergistic diagnostic information, leading to high imaging sensitivity and accurate diagnosis. Here, we reported a facile strategy to construct albumin based nanoparticles loaded with hydrophobic gadolinium chelates by hydrophobic interaction for magnetic resonance imaging (MRI). We synthesized a glycyrrhetinic acid-containing Gd-DOTA derivative (GGD) and loaded GGD molecules into BSA nanoparticles to form GGD–BSA nanoparticles (GGD–BSA NPs). The large size and porous structure endow GGD–BSA NPs with geometrical confinement, which restricts the tumbling of GGD and the diffusion of surrounding water molecules. As a result, GGD–BSA NPs exhibit ultrahigh T1 and T2 relaxivities, which are approximately 8-fold higher than those of gadolinium-based clinical contrast agents at 0.5 T. Besides, due to the intrinsic properties of their components, GGD–BSA NPs show good biocompatibility in vitro and in vivo, which warrants their great potential in clinical translation. Furthermore, GGD–BSA NPs show remarkable sensitivity in noninvasive detection of liver tumors by self-confirmed T1T2 dual-mode contrast-enhanced MRI. All of these merits make GGD–BSA NPs a potential candidate for fruitful biomedical and preclinical applications.

Graphical abstract: Albumin-based nanoparticles loaded with hydrophobic gadolinium chelates as T1–T2 dual-mode contrast agents for accurate liver tumor imaging

Supplementary files

Article information

Article type
Paper
Submitted
15 Feb 2017
Accepted
04 Mar 2017
First published
09 Mar 2017

Nanoscale, 2017,9, 4516-4523

Albumin-based nanoparticles loaded with hydrophobic gadolinium chelates as T1T2 dual-mode contrast agents for accurate liver tumor imaging

L. Wang, H. Lin, L. Ma, J. Jin, T. Shen, R. Wei, X. Wang, H. Ai, Z. Chen and J. Gao, Nanoscale, 2017, 9, 4516 DOI: 10.1039/C7NR01134B

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