journal article·2013-01

Engineering of magnetic DNA nanoparticles for tumor-targeted therapy

Hossein Hosseinkhani, Yi-Ru Chen, Wenjie He, Po-Da Hong, Dah-Shyong Yu, Abraham J. Domb

Journal of Nanoparticle Research· Volume 15· Issue 1
Publisher page

Abstract

This study aims to engineer novel targeted delivery system composed of magnetic DNA nanoparticles to be effective as an efficient targeted gene therapy vehicle for tumor therapy. A polysaccharide, dextran, was chosen as the vector of plasmid DNA-encoded NK4 that acts as an HGF-antagonist and anti-angiogenic regulator for inhibitions of tumor growth, invasion, and metastasis. Spermine (Sm) was chemically introduced to the hydroxyl groups of dextran to obtain dextran-Sm. When Fe2+ solution was added to the mixture of dextran-Sm and a plasmid DNA, homogenous DNA nanoparticles were formed via chemical metal coordination bonding with average size of 230 nm. Characterization of DNA nanoparticles was performed via dynamic light scattering measurement, electrophoretic light scattering measurement, as well as transmission electron microscope. DNA nanoparticles effectively condensed plasmid DNA into nanoparticles and enhanced the stability of DNA, while significantly improved transfection efficiency in vitro and tumor accumulation in vivo. In addition, magnetic DNA nanoparticles exhibited high efficiency in antitumor therapy with regards to tumor growth as well as survival of animals evaluated in the presence of external magnetic field. We conclude that the magnetic properties of these DNA nanoparticles would enhance the tracking of non-viral gene delivery systems when administrated in vivo in a test model. These findings suggest that DNA nanoparticles effectively deliver DNA to tumor and thereby inhibiting tumor growth.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: RNA Interference and Gene Delivery · Advanced biosensing and bioanalysis techniques · Bacteriophages and microbial interactions

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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