Statistical optimization of chitosan nanoparticles as protein vehicles, using response surface methodology

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Abstract

Background: There has been increased attention given to polymeric nanoparticles as protein carriers. In this regard, chitosan/tripolyphosphate (TPP) nanoparticles are considered to be a simple and efficient carrier. However, to have an ideal protein release profile, we need to optimize the properties of the carrier. Methods: This study examined the influence of 4 critical process parameters on the physicochemical characteristics of final nanoparticles. Chitosan-based nanoparticles were produced by ionic gelation, and then the size, polydispersity and zeta potential of those resulting nanoparticles were evaluated. Subsequently, the encapsulation efficiency of bovine serum albumin as model protein was investigated. Results: The morphologies of nanoparticles were characterized using field emission scanning electron microscopy (FE-SEM). Linear mathematical models were presented for each response through 3 levels using Central Composite Design with the help of design of experiments software, and formulation optimization was performed. Conclusions: Such research will serve as a basic study in protein loading into TPP cross-linked chitosan nanoparticles.

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Kiaie, N., Aghdam, R. M., Tafti, S. H. A., & Emami, S. H. (2016). Statistical optimization of chitosan nanoparticles as protein vehicles, using response surface methodology. Journal of Applied Biomaterials and Functional Materials, 14(4), e413–e422. https://doi.org/10.5301/jabfm.5000278

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