Use of artificial neural networks for analysis of the factors affecting particle size in mebudipine nanoemulsion. Issue 12 (13th August 2019)

• Record Type: Journal Article
• Authors: Khani, Samira; Abbasi, Shayan; Keyhanfar, Fariborz; Amani, Amir
• Is Part Of: Journal of biomolecular structure & dynamics. Volume 37:Issue 12(2019)
• Page Start: 3162
• Page End: 3167
• Publication Date: 2019-08-13
• Subjects: artificial neural networks -- Mebudipine -- nanoemulsion -- particle size; Biomolecules -- Periodicals; Molecular structure -- Periodicals; Molecular Biology -- Periodicals; Biomechanics -- Periodicals; 572
• DOI: 10.1080/07391102.2018.1510341
• Languages: English
• ISSNs: 0739-1102

Abstract

Abstract: In this study, a nanoemulsion containing mebudipine [composed of ethyl oleate (oil phase), Tween 80 (T80), Span 80 (S80) (surfactants), polyethylene glycol 400, ethanol (cosurfactants), and deionized water] was prepared with the aim of improving its bioavailability for an effective antihypertensive therapy. Particle size of the formulation was measured by dynamic light scattering. Then, artificial neural networks were used in identifying factors that influence the particle size of the nanoemulsion. Three variables, namely, amount of surfactant system (T80 + S80), amount of polyethylene glycol, and amount of ethanol as cosurfactants, were considered as input values and the particle size was used as output. The developed model showed that all the three inputs had some degrees of effect on particles size: increasing the value of each input decreased the size. Furthermore, amount of surfactant was found to be the dominant factor in controlling the final particle size of nanoemulsion. Communicated by Ramaswamy H. Sarma