On the factors influencing the extrusion strain, particle size and dissolution behavior of multiparticulate systems obtained by extrusion/spheronization

Abstract

Abstract The increased need for predicting the properties of granular systems has become a growing challenge for the pharmaceutical industry. Experimental design has hastened developments in the field of pharmaceuticals and helped scientists to better understand the behavior of particles in the wet granulation process. In this research, the production of pellets containing binary mixtures of high-dose paracetamol (40, 60 and 80%) and microcrystalline cellulose (Microcel® MC 101) was evaluated. Three commercial grades of the drug at three concentrations each were used. Pellets were prepared by axial and radial extrusion systems and then spheronized. The formulation and process, as input variables, were analyzed using a response surface methodology (RSM). The responses considered as output variables were energy spent on the extrusion, particle size distribution of the granules and the parameters of the Weibull function, time ( τ d ) and shape ( β ), in terms of dissolution behavior. The results indicated that less energy is spent during the radial extrusion process for commercial-grade paracetamol with 80% of drug and fine particle size. Also, these conditions resulted in a higher percentage of the pellet being in the main size fraction (0.800–1.250 mm) and a larger time required to dissolve 63.2% of the drug ( τ d). It was observed that the τ d value decreased with increasing particle size of the powder formulation. The β parameter was strongly influenced by the drug load and paracetamol grade for both extrusion procedures. The higher the content (percentage) and the smaller the particle size of the drug the higher the β parameter, indicating a parabolic shape changing to sigmoidal shape for the behavior of this parameter. These findings concerning formulations containing paracetamol can be extended to other drugs of equivalent particle size and water-solubility. The RSM tool was able to identify the manner and amplitude of the effects of the input variables on the output variables studied.