(533d) Modeling Two-Dimensional Moisture Content and Size Evolution of Pharmaceutical Granules in a Semicontinuous Fluid Bed Dryer

In the transition to continuous manufacturing, the pharmaceutical industry is embedding mathematical modeling in its strategy for future process development and optimization. It hereby utilizes the benefits of modelling with respect to saving cost and materials of in-silico studies, and gaining and fixing process knowledge in the model structure and its calibrated parameters. As such, mechanistic modeling studies on the ConsiGma‑25™ continuous wet granulation line have contributed to understanding of the process phenomena in function of the critical process parameters (CPP). The semicontinuous fluid bed dryer, configured to handle a continuous inflow of wet granules coming from a twin-screw wet granulator, has proven to be a complex part of the line. In a novel data collection in which granule moisture content was collected in conjunction with granule size, it was found that both properties are important with respect to the drying rate of the material. The 2D distribution dynamics are also influenced by the dryer’s CPPs, i.e. filling time of the dryer cell and dryer inlet air temperature and flow (De Leersnyder et al., 2018).

A modeling study of this process was performed in (Ghijs et al., 2019). It comprised a model relating single granule drying behavior to the evolution in moisture content seen in the semicontinuous process, including the effect of granule size. The model of (Mortier et al., 2012) represented the drying phenomena on single granule scale, and was thus applied in a sequential addition mode in order to emulate the semicontinuous drying behavior. The model performed well when applied to the data from (De Leersnyder et al., 2018) collected at a dryer cell fill level of 1 kg of granules in the cell. However, lower levels could not yet be validated mechanistically. Therefore, in order to enhance model’s predictive power for wider application, the work indicates that a more accurate inclusion of fluidization, air conditions and filling effects would benefit the model. This work presents these augmentations.

While the model of (Ghijs et al., 2019) assumed an ideal and constant fluidization behavior, the reality in the ConsiGma‑25™ dryer is presumably different as air flow, particle size distribution and moisture content influence fluidization quality. The approach from (Peglow et al., 2007) is therefore incorporated into the model. It relates single particle drying kinetics to batch behavior by taking into account fluidization behavior conceptually, predicting the fraction of the drying air that brings the granules in suspension and the fraction that bypasses the granules. The single particle drying kinetics in this case still stem from the mechanistic model of (Mortier et al., 2012).
Other features of the model in (Peglow et al., 2007) are the mass and heat balances for exchange of water vapor and heat between granules and drying air. This updates the average relative humidity of the drying air in the fluid bed accordingly. That in turn influences the drying rate of the granules present in the bed. The heat and mass balances, which also include the heat transfer from the dryer to the environment, are moreover tailored to the ConsiGma‑25™ dryer itself according to the work of (Mortier, Gernaey, De Beer, & Nopens, 2014).
A final model augmentation is the tracking of granules and the drying regimes they are subjected to throughout the drying process. Indeed, granules in the fluid bed chamber entered the system at different times due to the continuous filling of the cell. Moreover, those entering at a later time are moreover starting their drying curve in different conditions than these entering first in an empty cell. The different air conditions are captured with the heat and mass balances. The tracking of the granules per size fraction and moisture content occurs with a 2D population balance model (PBM). The latter models the moisture content distribution of the granules per size fraction. The range of moisture content of the granules is discretized into a number of levels, which serves as the grid for the PBM. The number of granules in each moisture content level is then tracked, and evolves due to the drying. The decrease in moisture content is modeled by a negative growth rate, as in the work of (Mortier, Gernaey, De Beer, & Nopens, 2013). Also the continuous inflow of wet granules into the dryer cell is introduced into the PBM as a source term in the moisture content level of the wet granules. This way, the entire population can be tracked for the semicontinuous operation.

It is concluded that a more accurate representation of semicontinuous drying of pharmaceutical granules has been achieved, capturing the influence of the CPPs of the drying process mechanistically. The model concept, implementation, simulation and calibration methodology will be presented.

References

De Leersnyder, F., Vanhoorne, V., Bekaert, H., Vercruysse, J., Ghijs, M., Bostijn, N., ... De Beer, T. (2018). Breakage and drying behaviour of granules in a continuous fluid bed dryer: Influence of process parameters and wet granule transfer. European Journal of Pharmaceutical Sciences, 115, 223–232. http://doi.org/10.1016/j.ejps.2018.01.037

Ghijs, M., Schäfer, E., Kumar, A., Cappuyns, P., Van Assche, I., De Leersnyder, F., ... Nopens, I. (2019). Modeling of Semicontinuous Fluid Bed Drying of Pharmaceutical Granules With Respect to Granule Size. Journal of Pharmaceutical Sciences, 1–8. http://doi.org/10.1016/j.xphs.2019.01.013

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