Optimizing microfluidic preparation parameters of nanosuspension to evaluate stability in nanoprecipitation of stable-iodine (127I)

Abstract

The aim of the current study was the development of nanosuspension stability in nanoprecipitation using microfluidic devices. Also, it is desirable to understand how the microfluidic preparation parameters influenced the stability of the stable-iodine (127I) nanosuspension. In optimization process through artificial neural networks (ANNs), the relations between input and output variables were investigated for 37 samples obtained by microfluidic nanoprecipitation process. Solvent temperature, antisolvent flow rate, and solvent flow rate were used as input variables, and the sedimentation time and polydispersity index (PDI) were considered as output parameters. Sedimentation time as an indicator of physical stability of nanosuspension was evaluated by observation of a densely packed sediment. Also, size and PDI of different samples were determined by dynamic light scattering. The size of the optimized sample was confirmed by transmission electron microscopy. The result obtained from modeling showed that increasing solvent temperature and antisolvent flow rate led to a decrease in PDI and an increase in the sedimentation time. The antisolvent flow rate was determined as the most important factor that affected the sedimentation time and PDI. Increasing the solvent flow rate was identified as an adverse factor which increased PDI or decreased formulation’s sedimentation time. Optimization using ANN showed that microfluidic preparation parameters of nanosuspension as input variables had potential impacts on output parameters.