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

• Published in: International journal of nanomedicine Vol. 9; no. Issue 1; pp. 4953 - 4964
• Main Authors: Shalaby, Karim S, Soliman, Mahmoud E, Casettari, Luca, Bonacucina, Giulia, Cespi, Marco, Palmieri, Giovanni F, Sammour, Omaima A, El Shamy, Abdelhameed A
• Format: Journal Article
• Language: English
• Published: New Zealand Dove Medical Press Limited 01-01-2014; Taylor & Francis Ltd; Dove Press; Dove Medical Press
• Subjects: artificial neural networks (ANNs); biodegradable nanoparticles; Chemistry, Pharmaceutical - methods; Drug delivery systems; Drugs; Nanoparticles - chemistry; Nanotechnology - methods; Neural networks; Neural Networks (Computer); NMR; noscapine; Noscapine - chemistry; Nuclear magnetic resonance; Original Research; Particle Size; Physiological aspects; Polyethylene glycol; polyethylene glycol (PEG); Polyethylene Glycols - chemistry; polylactide (PLA); Properties; Spectrum analysis; Vehicles; United Kingdom; United States > US; Italy; polylactide (PLA); biodegradable nanoparticles; noscapine; artificial neural networks (ANNs); polyethylene glycol (PEG)
• ISSN: 1178-2013; 1176-9114; 1178-2013
• DOI: 10.2147/IJN.S68737

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.