SCALE-UP OF PHARMACEUTICAL TWIN-SCREW WET GRANULATION BASED ON THE PROCESS SIMULATION USING GENETIC PROGRAMMING

Abstract

Wet granulation is a crucial process in the manufacturing of pharmaceutical tablets. For several years, twin-screw wet granulation has been noticeably studied as a continuous method for granulation in the pharmaceutical industry because of its ability for producing uniform granules with greater flowability and consistency. Some big challenges still exist in controlling the desired granule properties when different-sized extruders are used in product development. Introducing new technology to the Quality-by-Design approach and scaling up a wet granulation system on the twin-screw extruder demands a robust process understanding and improvement of knowledge by studying the dominance of critical process parameters on the properties of the granules. Therefore, it was vital to study the possibility of scaling up between different-sized twin-screw extruders and observe the effect of variations in the process concerning granule properties. The current study intended to understand the behavior of the wet granulation process in the two popular sizes of twin-screw extruders used in pharmaceutical manufacturing, 18 mm, and 27 mm. Two key dimensionless groups, the Reynolds number, and the Péclet number were studied to evaluate their impact on the flow behavior and mixing performance during wet granulation of a sustained released formulation. It was realized that the two dimensionless groups exhibited inconsistent effects on granule properties, including the upper moment (d90) and span of the particle size distribution and granule fracture strength. In this study, the influence of material flow, residence time distribution, degree of channel fill, and mixing intensity on the wet granulation process were explored in detail for both extruders. The higher influence of the Péclet number on wet granulation was greatly dependent on the degree of fill, whereas the Reynolds number had the least effect on the residence time distribution and mixing performance. The significance of the fill level for scale-up on the twin-screw extruder was huge to ensure steady mixing during granulation and reduce variation in the product. The interaction between the screw speed and the material feed rate was therefore examined with particular focus on the resultant fill level and a method was suggested for quantifying the degree of fill for the two extruders. It was noticed that variation in the free channel space greatly influenced the properties of the granules when produced from the two different-sized extruders. Hence, the degree of fill and scale of the twin-screw extruder had a very significant effect on the granule properties. Genetic programming was employed as an Artificial intelligence tool for modeling the granule properties which developed valuable mathematical equations. These equations were very useful for establishing important scaling laws for the wet granulation system on the twin-screw extruder.