Original Article
Cytotoxicity of chitosan/streptokinase nanoparticles as a function of size: An artificial neural networks study

https://doi.org/10.1016/j.nano.2015.09.002Get rights and content

Highlights

  • For the first time, a comprehensive model was generated to study the relationship between size and toxicity in a polymeric nanoparticle.

  • Factors such as polymer concentration, pH of solution and stirring time were found to be affecting the size. However, they did not show a direct effect on cytotoxicity profiles of nanoparticles.

  • Size of nanoparticles was found to be the major factor in determining the cytotoxicity.

Abstract

Predicting the size and toxicity of chitosan/streptokinase nanoparticles at various values of processing parameters was the aim of this study. For the first time, a comprehensive model could be developed to determine the cytotoxicity of the nanoparticles as a function of their size. Then, artificial neural networks were used for identifying main factors influencing self-assembly prepared nanoparticles size and cytotoxicity. Three variables included polymer concentration; pH and stirring time were used for a modeling study. A second modeling was performed to evaluate the influence of particles' size on toxicity. Experimentally data modeled using ANNs was validated against unseen data. The response surfaces generated from the software demonstrated that chitosan concentration is the dominant factor with a direct effect on size. Results also showed that the most important factor in determining the particles' toxicity is size—smaller particles showed more toxic effects, regardless of the effect of other input parameters.

From the Clinical Editor

The understanding of toxicity of nanoparticles is of prime importance. In this article, the authors generated a model to visualize the relationship between nanoparticle size and its cellular toxicity, using chitosan/streptokinase nanoparticles. The data generated here would help the design of future nanoparticles of appropriate sizes for the application in the clinical setting.

Section snippets

Methods

Recombinant SK (Streptococcus equisimilis H46A gene in E. coli) and Mrc-5 cell line were purchased from Pasteur Institute (Iran), Cs (Mw = 100 kDa, DD = 93%) was purchased from Easter Holding Group (China), DMEM and fetal bovine serum were purchased from Gibco (USA), penicillin/streptomycin was purchased from Sigma-Aldrich (USA), 3-(4,5-dimethyl- 2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT), DMSO and acetic acid were purchased from Merck Chemicals (Germany).

Size

To determine the effect of input variables on size, in each figure, one input variable was fixed in low, mid-level or high value, then, the impacts of other two inputs on the output were studied. The trained model showed R2 values of 0.94, 0.91 and 0.93 for the unseen, test and train data, respectively, which is an indicative of a quality predictive model. Figure 1 details the effect of Cs concentration and pH on size of particles when stirring time is set at low, medium or high level. From the

Discussion

In this study, we used self-assembly method for preparing Cs/SK nanoparticles. Self-assembly can be defined as spontaneous organization of molecular units into structures by non-covalent interactions. Driving force of self-assembly is tendency of the system to approach equilibrium by reducing its free energy. So, factors affecting non-covalent interactions such as pH, molecular motion and concentration are expected to affect self-assembly process too. In our work, considering the previous

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    This research has been supported by Tehran University of Medical Science & Health Services grant no. 92-01-87-20704.

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