title: Cytotoxicity of chitosan/streptokinase nanoparticles as a function of size: An artificial neural networks study.
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.
authors.affiliation: Department of Medical Nanotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Hadi
authors.initials: H
authors.lastname: Baharifar
authors.affiliation: Department of Medical Nanotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran; Medical Biomaterials Research Center (MBRC), Tehran University of Medical Sciences, Tehran, Iran. Electronic address: aamani@sina.tums.ac.ir.
authors.forename: Amir
authors.initials: A
authors.lastname: Amani
mesh_terms: D000465:Algorithms; D002470:Cell Survival / Q000187:drug effects*; D048271:Chitosan / Q000633:toxicity*; D004305:Dose-Response Relationship, Drug; D004353:Drug Evaluation, Preclinical / Q000379:methods; D006863:Hydrogen-Ion Concentration; D053761:Nanocomposites / Q000633:toxicity* / Q000648:ultrastructure*; D016571:Neural Networks, Computer; D010316:Particle Size; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013300:Streptokinase / Q000633:toxicity*; D013329:Structure-Activity Relationship; D018675:Toxicity Tests / Q000379:methods
chemical_list: D048271:Chitosan; D013300:Streptokinase
keywords: Artificial neural networks; Chitosan; Size; Streptokinase; Toxicity