Concurrent study of stability and cytotoxicity of a novel nanoemulsion system – an artificial neural networks approach. (3rd April 2017)

• Record Type: Journal Article
• Authors: Seyedhassantehrani, Negar; Karimi, Roya; Tavoosidana, Gholamreza; Amani, Amir
• Is Part Of: Pharmaceutical development and technology. Volume 22:Number 3(2017)
• Page Start: 383
• Page End: 389
• Publication Date: 2017-04-03
• Subjects: Nanoemulsion -- artificial neural networks -- optimization -- stability -- cell viability; Drug delivery systems -- Periodicals; Pharmaceutical technology -- Periodicals; Drugs -- Administration -- Research -- Periodicals; Drug Delivery Systems -- Periodicals; Pharmaceutical Preparations -- Periodicals; Technology, Pharmaceutical -- Periodicals; 615
• DOI: 10.1080/10837450.2016.1212878
• Languages: English
• ISSNs: 1083-7450

Abstract

Abstract: Problems commonly associated with using nanoemulsions are their cytotoxic effects and low stability profiles. Here, for the first time, concentrations of ingredients of a nanoemulsion system were investigated to obtain the most stable nanoemulsion system with the least cytotoxic effect on MCF7 cell line. Artificial neural networks (ANNs) were used to model the experimentally obtained data. Surfactant concentration was found to be the dominant factor in determining the stability – surfactant concentration above a critical point made the preparation unstable, while it appeared not to be influencing the cytotoxicity. Concentration of oil showed a direct relationship to the cytotoxicity with a minimum value required to provide an acceptable safety profile for the preparation. Co-surfactant appeared not to be considerably effective on neither stability nor cytotoxicity. To obtain the optimum preparation with maximum stability and minimum cytotoxicity, surfactant and oil values need to be kept at their maximum and minimum possible, respectively.