effect of processing/formulation parameters on particle size of nanoemulsions containing ibuprofen - an artificial neural networks study

Background: nanoemulsions are colloidal transparent systems for the delivery of hydrophobic drugs. this study aimed to determine the effect of parameters affecting particle size of a nanoemulsion containing ibuprofen using artificial neural networks (anns). methods: nanoemulsion samples with different values of independent variables, namely, concentration of ethanol, ibuprofen and tween 80 as well as exposure (homogenization) time were prepared and their particle size was measured using dynamic light scattering (dls). the data were then modelled by anns. results: from the results, increasing the exposure time had a positive effect on reducing droplet size. the effect of concentration of ethanol and tween 80 on droplet size depended on the amount of ibuprofen. our results demonstrate that ibuprofen concentration also had a reverse relation with the size of the nanoemulsions. conclusion: it was concluded that to obtain minimum particle size, exposure (homogenization)time should be maximized.