| Pharmaceutical processing is much more stringent with regulatory requirements for the processing and handling stages and the product quality specifications must be met at every step during the manufacturing operation. In the pharmaceutical manufacturing, the unit operation that is one among the most widely used, is the powder blending operation. The scope of this work is to characterize and document the complex powder blending process by means of a robust predictive model and use it to enhance operational efficiency and improve on the established monitoring and control strategies.;The implementation of QbD (Quality by Design) strategies [1] to continuous processing stages allows for improved process control, higher cost-efficiency without compromising on the quality or efficacy of the final product. It also would alleviate the need for further scale up studies. In this work, a population balance model (PBM) has been formulated and validated to model the complex dynamics within a continuous powder mixing process, with the focus on the blending operation taking place within pharmaceutical tablet manufacturing. PBM modeling was selected to model the blending unit operation as it not only serves as a dynamic and highly effective tool, but also due to its relative computational simplicity.;The model was designed to determine the critical quality attributes (such as RTD (residence time distribution), API composition and RSD (relative standard deviation) of the product by incorporating the key process parameters such as the impeller RPM, dimensions of the blender and design parameters such as the number of compartments (both axial and radial), etc. The model obtained has been subsequently validated to check the fit between the predicted values of these CQAs (Critical Quality Attributes) against experimentally obtained data during the same time intervals. The model has the potential use for process improvement by implementation in a PAT (Process Analytical Technique) system for designing improved monitoring, control and optimization techniques. [2]. |
