Determination of factors controlling the particle size and entrapment efficiency of noscapine in PEG/PLA nanoparticles using artificial neural networks

• Main Authors: Shalaby KS, Soliman ME, Casettari L, Bonacucina G, Cespi M, Palmieri GF, Sammour OA, El Shamy AA
• Format: Article
• Language: English
• Published: Dove Medical Press 2014-10-01
• Series: International Journal of Nanomedicine
• Online Access: http://www.dovepress.com/determination-of-factors-controlling-the-particle-size-and-entrapment--peer-reviewed-article-IJN
• ISSN: 1178-2013

Karim S Shalaby,1 Mahmoud E Soliman,1 Luca Casettari,2 Giulia Bonacucina,3 Marco Cespi,3 Giovanni F Palmieri,3 Omaima A Sammour,1 Abdelhameed A El Shamy1,† 1Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt; 2Department of Biomolecular Sciences, School of Pharmacy, University of Urbino, Urbino, Italy; 3School of Pharmacy, University of Camerino, Camerino, Italy †Abdel Hameed El-Shamy passed away on August 25, 2013 Abstract: In this study, di- and triblock copolymers based on polyethylene glycol and polylactide were synthesized by ring-opening polymerization and characterized by proton nuclear magnetic resonance and gel permeation chromatography. Nanoparticles containing noscapine were prepared from these biodegradable and biocompatible copolymers using the nanoprecipitation method. The prepared nanoparticles were characterized for size and drug entrapment efficiency, and their morphology and size were checked by transmission electron microscopy imaging. Artificial neural networks were constructed and tested for their ability to predict particle size and entrapment efficiency of noscapine within the formed nanoparticles using different factors utilized in the preparation step, namely polymer molecular weight, ratio of polymer to drug, and number of blocks that make up the polymer. Using these networks, it was found that the polymer molecular weight has the greatest effect on particle size. On the other hand, polymer to drug ratio was found to be the most influential factor on drug entrapment efficiency. This study demonstrated the ability of artificial neural networks to predict not only the particle size of the formed nanoparticles but also the drug entrapment efficiency. This may have a great impact on the design of polyethylene glycol and polylactide-based copolymers, and can be used to customize the required target formulations. Keywords: noscapine, polyethylene glycol (PEG), polylactide (PLA), biodegradable nanoparticles, artificial neural networks (ANNs)