Preparation of endostatin-loaded chitosan nanoparticles and evaluation of the antitumor effect of such nanoparticles on the Lewis lung cancer model.

Abstract

The purpose of this study was to prepare ES-loaded chitosan nanoparticles (ES-NPs) and evaluate the antitumor effect of these particles on the Lewis lung cancer model. ES-NPs were prepared by a simple ionic cross-linking method. The characterization of the ES-NPs, including size distribution, zeta potential, loading efficiency and encapsulation efficiency (EE), was performed. An in vitro release test was also used to determine the release behavior of the ES-NPs. Cell viability and cell migration were assayed to detect the in vitro antiangiogenic effect of ES-NPs. In order to clarify the antitumor effect of ES-NPs in vivo, the Lewis lung cancer model was used. ES-NPs were successfully synthesized and shown to have a suitable size distribution and high EE. The nanoparticles were spherical and homogeneous in shape and exhibited an ideal releasing profile in vitro. Moreover, ES-NPs significantly inhibited the proliferation and migration of human umbilical vascular endothelial cells (HUVECs). The in vivo antiangiogenic activity was evaluated by ELISA and immunohistochemistry analyses, which revealed that ES-NPs had a stronger antiangiogenic effect for reinforced anticancer activity. Indeed, even the treatment cycle in which ES-NPs were injected every seven days, showed stronger antitumor effect than the free ES injected for 14 consecutive days. Our study confirmed that the CS nanoparticle is a feasible carrier for endostatin to be used in the treatment of lung cancer.

DOI: 10.1080/10717544.2016.1247927

Cite this paper

@article{Ding2017PreparationOE, title={Preparation of endostatin-loaded chitosan nanoparticles and evaluation of the antitumor effect of such nanoparticles on the Lewis lung cancer model.}, author={Rui-Lin Ding and Fang Xie and Yue Hu and Shao-zhi Fu and Jingbo Wu and Juan Fan and Wen-feng He and Yu He and Ling-lin Yang and Sheng Lin and Q L Wen}, journal={Drug delivery}, year={2017}, volume={24 1}, pages={300-308} }