Study of the Copolymer Structure Effect on Physicochemical Characteristics and In Vitro Stability of PLGA–PEG Nanoparticles Loaded 9-Nitrocamptothecin

Katayoun Derakhashandeh; Maryam Haghkhah; Mahmood Amiri

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Study of the Copolymer Structure Effect on Physicochemical Characteristics and In Vitro Stability of PLGA–PEG Nanoparticles Loaded 9-Nitrocamptothecin

Author(s):

Katayoun Derakhashandeh^{1, 2,*},

Maryam Haghkhah^{2, 3},

Mahmood Amiri⁴

1Novel Drug Delivery Research Center, Faculty of Pharmacy, Kermanshah University of Medical Sciences, Iran

2Department of pharmaceutics, Faculty of Pharmacy, Kermanshah University of Medical Sciences, Iran

3Medical Biology Research Centre, Kermanshah University of Medical Sciences, Kermanshah, Iran

4Student research committee, Kermanshah University of Medical Sciences, Kermanshah, Iran

Journal of Reports in Pharmaceutical Sciences:Vol. 1, issue 2; 107-117

Published online:Dec 28, 2012

Article type:Research Article

Received:Dec 09, 2012

Accepted:Dec 22, 2012

How to Cite:Katayoun DerakhashandehMaryam HaghkhahMahmood AmiriStudy of the Copolymer Structure Effect on Physicochemical Characteristics and In Vitro Stability of PLGA–PEG Nanoparticles Loaded 9-Nitrocamptothecin.J Rep Pharm Sci.1(2):e147771.

Abstract

9-nitrocamptothecin (9-NC) is a semisynthetic and a low soluble analogue of camptothecin alkaloids that target nuclear enzyme topoisomerase I. The unstable lactone form of 9-NC in biological fluids is required for its cytotoxic activity. To improve aqueous solubility and stability in biological media, 9-NC was loaded in polymeric nanoparticles. In this paper, we studied the effect of PEG percent (0, 5, 10, 15) in PLGAPEG copolymer on physicochemical properties of nanocarriers. To acquire an optimum formulation, a Generalized Regression Neural Networks (GRNN) and a Multi-Layer Perceptron (MLP) as potent statistical methods were employed. The drug loaded parameters were the input vectors of the GRNN and included the amount of polymer and emulsifier, volume of external and internal phases. The nanoparticles drug loading constitutes the output vector of each network. In this way, GRNN and MLP are trained to investigate the functional influence of input variables on the output response. PLGAPEG nanoparticles were prepared by nanopercipitation method. Zeta Sizer, DSC, SEM and Franz diffusion cell were used to measure physicochemical properties of optimized formulations. The PEG percent in PLGA-PEG copolymer has an effective role in drug loading which can be attributed to the hydrophobic nature of drug and amphiphilic nature of copolymer. The size of nanoparticle decreased by increasing the PEG percent in copolymer which can be attributed to emulsifying nature of PEG. Release rate decreased by increasing the percent of PEG in PLGA-PEG nanoparticles but in vitro stability increased. DSC thermograms and FTIR results showed that 9-NC was encapsulated in PLGA-PEG nanoparticles in its amorphous form. According to artificial neural network (ANN) data, we found that PLGA-PEG (15%) had best physicochemical characteristic compare to the other copolymers. The optimum formulation showed that nanoparticles could be potential carriers for delivery of unstable and low soluble drugs especially for anticancer agents.

Keywords

PLGA-PEG nanoparticles

9-Nitrocamptothecin

Physicochemical properties

In vitro stability

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