Mechanistic modelling of fluid bed granulation, Part II: Eased process development via degree of wetness

Steady-state modeling of a new continuous API dryer: Reduced-order model and investigation of dryer performance

In the present study, a reduced-order model is proposed to analyze a novel continuous dryer with an application in the pharmaceutical industry. The model was validated using process data from ibuprofen drying test runs, and the results were in good agreement with the experimental data. The test substance was an ibuprofen paste with an initial LOD of up to 30 w%. The simulations showed that the contact heat transfer coefficient can be correlated with the degree of wetness. Furthermore, a set of simulations was performed to analyze the influence of input parameters on the dryer's performance: i) the inlet air flow rate and ii) the inlet air temperature. The simulation results demonstrated that a variation in the inlet air temperature significantly affects the air temperature profile, while the inlet air flow rate has a minor effect. Besides, it was also established that the inlet solid LoD has the most considerable effect on the product quality (e.g., final solid moisture content). The results showed a deviation of less than 10% for the product LoD and the product temperature in most cases.

A linear scale-up approach to fluid bed granulation

Fluid bed granulation (FBG) is used extensively in the pharmaceutical industry and it is known to be a complex process, because the final product quality of the FBG process is determined by a complex interplay between the process parameters, fluid dynamics, and material properties. Due to this complexity, the FBG process is inherently nonlinear and as such difficult to scale-up. The field of chemical engineering has shown that complex nonlinear processes can be assumed to be linear under limiting conditions. We leverage this idea and present a linear scale-up approach (LiSA) to the FBG process. We derive the key LiSA equation from first principles, and then use it in combination with the similarity principle for scale-up purposes. Furthermore, we present a novel regression-based LiSA. The regression-based LiSA is founded on the hypothesis that there is a linear relationship between the moisture content and a scaling parameter called the Maus factor. This hypothesis is based on our experience and it is shown to be plausible due to high R² values ranging from 0.86 to 0.98. Moreover, we successfully demonstrate that LiSA is effective under typical industrial process settings by applying it to two different formulations during pharmaceutical drug product development.