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Research Article

Application of multiple regression analysis in optimization of anastrozole-loaded PLGA nanoparticles

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Pages 105-114 | Received 22 Nov 2012, Accepted 13 May 2013, Published online: 24 Jul 2013
 

Abstract

The present investigation deals with development of anastrozole-loaded PLGA nanoparticles (NPs) as an alternate to conventional cancer therapy. The NPs were prepared by nanoprecipitation method and optimized using multiple regression analysis. Independent variables included drug:polymer ratio (X1), polymer concentration in organic phase (X2) and surfactant concentration in aqueous phase (X3) while dependent variables were percentage drug entrapment (PDE) and particle size (PS). Results of desirability criteria, check point analysis and normalized error were considered for selecting the formulation with highest PDE and lowest PS. Prepared NPs were characterized for zeta potential, transmission electron microscopy (TEM), differential scanning calorimetry (DSC) and in vitro drug release studies. DSC and TEM studies indicated absence of any drug–polymer interaction and spherical nature of NPs, respectively. In vitro drug release showed biphasic pattern exhibiting Fickian diffusion-based release mechanism. This delivery system of anastrozole is expected to reduce the side effects associated with the conventional cancer therapy by reducing dosing frequency.

Acknowledgements

The authors are grateful to All India Council of Technical Education, New Delhi, India, for providing National Doctoral Fellowship to Abhinesh Kumar. Authors also acknowledge Sun Pharma Advanced Research Centre, Vadodara, India, for gift sample of anastrozole, PURAC Biomaterials, The Netherlands, for gift sample of PLGA (PURASORB PDLG 5002A) and BASF, Ludwigshafen, Germany, for gift sample of Poloxamer 188.

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