(47d) A Multi-Dimensional Population Balance Model Validation Approach to High-Shear Wet Granulation (HSWG) Processes
AIChE Annual Meeting
Population Balance Modeling for Particle Formation Processes: Nucleation, Aggregation and Breakage Kernels
Monday, October 17, 2011 - 9:33am to 9:54am
Process understanding and control, by the implementation of Quality by Design (QbD) is critical to the pharmaceutical industry, to ensure high quality products. An important unit operation in the manufacture of final dosage forms is high shear wet granulation (HSWG). High shear granulation results in improved flow properties of the resulting granules for better weight control during tablet compression. HSWG can also provide effective control over granule porosity and density, thus maintaining content uniformity of the drug substance. These are some of the critical quality attributes that need to be tightly regulated due to their impact on dissolution, disintegration and overall bioavailability.
This study focuses on the development of a multi-dimensional population balance model that incorporates mechanistic descriptions of the main granulation rate processes. (e.g. nucleation, aggregation, breakage). The rate processes incorporate important material properties of solid and liquid as well as process and design parameters. The multi-dimensional model framework is able to track the evolutions and distributions of key granule properties such as size, liquid content and porosity. Lab-scale experimental data were used for model calibration. An objective function that minimizes the maximum likelihood function was formulated to estimate optimal parameters. Experimental data were further analyzed to improve model physics. The validated model was then used to predict the effect of key processing parameters (such as impeller speed, water amount etc) on granule characteristics. The model results were compared to experimentally obtained results.
Results show the potential of a model-based approach to HSWG processes by providing the industrial practitioner with more science-based approach that will then result in reducing the number of experiments needed during process optimization and scale-up studies.