journal article·2014-02

Optimization and evaluation of chelerythrine nanoparticles composed of magnetic multiwalled carbon nanotubes by response surface methodology

Yong Huang, Yulin Yuan, Zhide Zhou, Jintao Liang, Zhencheng Chen, Guiyin Li

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Abstract

Abstract In this study, a new chelerythrine nanomaterial targeted drug delivery system (Fe3O4/MWNTs-CHE) was designed with chelerythrine (CHE) as model of antitumor drug and magnetic multiwalled carbon nanotubes (Fe3O4/MWNTs) nanocomposites as drug carrier. The process and formulation variables of Fe3O4/MWNTs-CHE were optimized using response surface methodology (RSM) with a three-level, three-factor Box–Behnken design (BBD). Mathematical equations and response surface plots were used to relate the dependent and independent variables. The experimental results were fitted into second-order response surface model. When Fe3O4/MWNTs:CHE ratio was 20.6:1, CHE concentration was 172.0 μg/mL, temperature was 34.5 °C, the drug loading content and entrapment efficiency were 3.04 ± 0.17% and 63.68 ± 2.36%, respectively. The optimized Fe3O4/MWNTs-CHE nanoparticles were characterized by scanning electron microscopy (SEM), Zeta potential, in vitro drug release and MTT assays. The in vitro CHE drug release behavior from Fe3O4/MWNTs-CHE displayed a biphasic drug release pattern and followed Korsmeyer–Peppas model with Fickian diffusion mechanism for drug release. The results from MTT assays suggested that the Fe3O4/MWNTs-CHE could effectively inhibit the proliferation of human hepatoma cells (HepG2), which displayed time or concentration-dependent manner. All these preliminary studies were expected to provide a theoretical basis and offer new methods for preparation efficient magnetic targeted drug delivery systems.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Berberine and alkaloids research · Nanoparticle-Based Drug Delivery · Nanomaterials for catalytic reactions

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

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