(561e) Towards a Generic Model for Twin-Screw Wet Granulation: Calibration and Validation of an Improved PBM for Multiple APIs.

Recently the pharmaceutical industry has undergone changes in the way of producing oral solid dosages (tablets) from traditional inefficient and expensive batch production to continuous. Recent advances in the pharmaceutical industry include increased use of twin-screw wet granulation (TSWG) in the manufacturing of solid dosage and application of advanced modeling tools such as Population Balance Models (PBM). The twin-screw wet granulation unit is a unit operation of the ConsiGmaTM-25 continuous powder-to-tablet process line from GEA Pharma Systems. However, improved understanding of the physical process properties within the TSWG, expanding knowledge of the effect of the active pharmaceutical ingredient (API)/formulation on the granulation mechanisms, and the improvement of current PBM models are necessary to successfully operate this continuous production process.

As an initial effort to look into process mechanisms present in the twin-screw wet granulator, a unique dataset was collected at different locations inside the granulator and reported in (Verstraeten et al., 2017). Here, the main factor that affects the granule size distribution (GSD) is the liquid-to-solid ratio (L/S). At low L/S ratio, the GSD exhibits bimodality, whereas at high L/S ratio the GSD shows unimodal behavior. From this work, a 1D-PBM compartmental model was developed for predicting the GSD at the outlet of the granulator starting from the pre-blend using aggregation and breakage as the main phenomena that take place in the granulation process. The full model is composed of three PBM models in series (Van Hauwermeiren et al., 2018). One for each zone for which data was gathered: the wetting zone (pre-blend to after the addition of the liquid binder), kneading zone 1 (from after the wetting zone to after the first kneading elements), kneading zone 2 (just before the second kneading elements up until the end of the granulator barrel).

Since none of the traditional kernels from the literature could model the bimodal behavior observed from the measurements in the wetting zone, a new aggregation kernel was proposed that was capable to capture different types of behavior at different L/S ratio conditions with one single kernel. The breakage kernel (only used in the kneading zones) is a combination of attrition and uniform breakage (Van Hauwermeiren et al., 2018). This work was conducted for a single formulation, therefore, for general applicability, this needs to be extended by adding the effect of formulation properties. In that way, a true generic PBM can be constructed.

In the present work the 1D-PBM compartmental model was calibrated with a new dataset collected for two hydrophobic and two hydrophilic formulations, with different process conditions, at 4 different locations: the wetting zone, kneading zone 1, kneading zone 2, and at the end of the granulator.

To attain the desired behavior in the wetting zone, some mathematical modifications were made to the aggregation kernel to accurately predict the location and the size of the two peaks for the bimodal behavior and simultaneously incorporate the influence of the L/S ratio. Further, the model parameters that affect the bimodal behavior were linked to specific formulation properties.

The aforementioned changes in the aggregation kernel, constitute improvements to the model reducing the number of parameters to be calibrated from five to two. This parameter reduction makes the model more clear and interpretable. Further, it removes any parameter identifiability issues so that a clear link between model parameters and both process settings as well as formulation properties can be determined.

The PBM was calibrated and validated for multiple formulations (with different APIs content) and process settings. These developments allow us to assess the effect of the formulation properties on granulation behavior. For a new formulation, its properties can now be used to make a simulation of the possible GSDs so that with only a limited number of experiments, the optimal range can be determined. This general PBM for TSWG will greatly reduce the amount of material needed for experimentation and it can reduce the time-to-market for a new product.