(233h) Towards a General Model for Twin-Screw Wet Granulation: Application of a Novel Validated PBM Model to a Hydrophilic Compound and Linking Model Parameters to Blend Properties

Authors: 
Van Hauwermeiren, D., Ghent University
Verstraeten, M., Ghent University
Lee, K., Pfizer Inc.
Turnbull, N., Pfizer Inc.
am Ende, M. T., Worldwide Research and Development, Pfizer Inc.
De Beer, T., Ghent University
Nopens, I., Ghent University
In a newly developed three compartmental PBM model with a novel kernel for aggregation, prediction of both unimodal and bimodal behaviour for different L/S ratios with one single aggregation kernel structure is achieved. The use of one single kernel to achieve this is a major step forward as it points out that similar mechanisms are occurring but at a different rate for different L/S ratios. The model structure and calibration is presented in another abstract. Moreover, the model was extended to include dependencies on operational conditions (screw speed, mass flow rate and L/S ratio) and validated using independent experimental data. This work is presented in a second abstract.

In this third abstract, the same novel PBM model is applied to a hydrophilic compound based on the assumption that the granulation behaviour is similar, i.e. the shape of the GSD is mainly dominated by L/S ratio.

From the calibration effort, it is observed that the GSD can be well predicted by the novel PBM structure, similar to the hydrophobic compound. This leads to the conclusion that the granulation mechanisms in TSWG are similar for different blends with different properties, but the process rates depend on the formulation.

Ultimately, the novel PBM forms the basis for a model that not only takes into account the operational settings screw speed, mass flow rate, and L/S ratio, but also the properties of the preblend. When linking the three parts (model parameters, blend properties, and experimental conditions), new blends can be tested in a more efficient manner by using the model to predict the expected behaviour. The model can help in predicting the GSD, and the operational conditions that lead to the optimal GSD. Moreover, the model can be used to indicate the locations in the operational settings space for which experiments would yield a high information content different mechanisms of the granulation. This is called optimal experimental design (OED) which can, opposed to traditional Design of Experiments (DOE), significantly reduce the amount of experiments required for new formulations.