Application of multiple regression analysis in optimization of metronidazole-chitosan nanoparticles

The current work aims to developing MET-CSNPs nanocomposites as drug delivery system. The nanocomposites were prepared by ionic interactions method and optimized using multiple regression analysis. Independent variables included chitosan concentration (CS), tri poly phosphate concentration (TPP) and metronidazole concentration (MET); while dependent variables were percentage loading drug (LE), zeta potential and zeta size. Prepared nanocomposites were characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), thermal gravimetric analysis (TGA), scanning electron microscope (SEM) and in vitro drug release studies. TGA, FTIR and XRD studies indicated the presence of drug into final nanocomposites. In vitro drug release from nanocomposites was carried out and showed that the release rate of MET from the MET-CSNPs nanocomposites was very slow. These results indicate extended release of the drug from its respective nanocomposites, and therefore these nanocomposites have good potential to be used as extended-release formulation of the drugs.