Investigating the Effects of Mixing Dynamics on Twin-Screw Granule Quality Attributes via the Development of a Physics-Based Process Map.

Twin-screw granulation (TSG) is an emerging continuous wet granulation technique that has not been widely applied in the industry due to a poor mechanistic understanding of the process. This study focuses on improving this mechanistic understanding by analyzing the effects of the mixing dynamics on the granule quality attributes (PSD, content uniformity, and microstructure). Mixing is an important dynamic process that simultaneously occurs along with the granulation rate mechanisms during the wet granulation process. An improved mechanistic understanding was achieved by identifying and quantifying the physically relevant intermediate parameters that affect the mixing dynamics in TSG, and then their effects on the granule attributes were analyzed by investigating their effects on the granulation rate mechanisms. The fill level, granule liquid saturation, extent of nucleation, and powder wettability were found to be the key physically relevant intermediate parameters that affect the mixing inside the twin-screw granulator. An improved geometrical model for the fill level was developed and validated against existing experimental data. Finally, a process map was developed to depict the effects of mixing on the temporal and spatial evolution of the materials inside the twin-screw granulator. This process map illustrates the mechanism of nucleation and the growth of the granules based on the fundamental material properties of the primary powders (solubility and wettability), liquid binders (viscosity), and mixing dynamics present in the system. Furthermore, it was shown that the process map can be used to predict the granule product quality based on the granule growth mechanism.

Multi-dimensional population balance model development using a breakage mode probability kernel for prediction of multiple granule attributes.

Milling affects not only particle size distributions but also other important granule quality attributes, such as API content and porosity, which can have a significant impact on the quality of the final drug form. The ability to understand and predict the effects of milling conditions on these attributes is crucial. A hybrid population balance model (PBM) was developed to model the Comil, which was validated using experimental results with an R2 of above 0.9. This presented model is dependent on the process conditions, material properties and equipment geometry, such as the classification screen size. In order to incorporate the effects of different quality attributes in the model physics, the dimensionality of the PBM was increased to account for changes in API content and porosity, which also produced predictions for these attributes in the results. Additionally, a breakage mode probability kernel was used to introduce dynamic breakage modes by predicting the probability of attrition and impact mode, which are dependent on the process conditions and feed properties at each timestep.

End-point determination of heterogeneous formulations using inline torque measurements for a high-shear wet granulation process.

In this study, the torque profiles of heterogeneous granulation formulations with varying powder properties in terms of particle size, solubility, deformability, and wettability, were studied, and the feasibility of identifying the end-point of the granulation process for each formulation based on the torque profiles was evaluated. Dynamic median particle size (d50) and porosity were correlated to the torque measurements to understand the relationship between torque and granule properties, and to validate distinction between different granulation stages based on the torque profiles made in previous studies. Generally, the torque curves obtained from the different granulation runs in this experimental design could be categorized into two different types of torque profiles. The primary factor influencing the likelihood of producing each profile was the binder type used in the formulation. A lower viscosity, higher solubility binder resulted in a type 1 profile. Other contributing factors that affected the torque profiles include API type and impeller speed. Material properties such as the deformability and solubility of the blend formulation and the binder were identified as important factors affecting both granule growth and the type of torque profiles observed. By correlating dynamic granule properties with torque values, it was possible to determine the granulation end-point based on a pre-determined target median particle size (d50) range which corresponded to specific markers identified in the torque profiles. In type 1 torque profiles, the end-point markers corresponded to the plateau phase, whereas in type 2 torque profiles the markers were indicated by the inflection point where the slope gradient changes. Additionally, we proposed an alternative method of identification by using the first derivative of the torque values, which facilitates an easier identification of the system approaching the end-point. Overall, this study identified the effects of different variations in formulation parameters on torque profiles and granule properties and implemented an improved method of identification of granulation end-point that is not dependent on the different types of torque profiles observed.

Optimization of key energy and performance metrics for drug product manufacturing.

During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors (Sampat et al., 2022), this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. The results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.