Preparation and optimization of acetaminophen nanosuspension through nanoprecipitation using microfluidic devices: an artificial neural networks study

Abstract

The purpose of this study was to find an artificial neural networks model for determining major factors impacting the stability of an acetaminophen nanosuspansion that was prepared using nanoprecipitation in microfluidic reactors. Four variables, namely concentration of surfactant, solvent and antisolvent flow rate and solvent temperature were used as input variables and time of sedimentation of nanoparticles was considered as output variable. The particle size of optimized formulation was measured by transmission electron microscope and dynamic light scattering. Comparing the 3D graphs from the model showed that antisolvent flow rate and temperature have direct relation with time of sedimentation, whereas solvent flow rate generally has reverse relation with the time of sedimentation. Concentration of surfactant was found to be the most important factor in determining the stability of nanosuspension.