Improvement of a 1D Population Balance Model for Twin-Screw Wet Granulation by Using Identifiability Analysis

Abstract

Recently, the pharmaceutical industry has undergone changes in the production of solid oral dosages from traditional inefficient and expensive batch production to continuous manufacturing. The latest advancements include increased use of continuous twin-screw wet granulation and application of advanced modeling tools such as Population Balance Models (PBMs). However, improved understanding of the physical process within the granulator and improvement of current population balance models are necessary for the continuous production process to be successful in practice. In this study, an existing compartmental one-dimensional PBM of a twin-screw granulation process was improved by altering the original aggregation kernel in the wetting zone as a result of an identifiability analysis. In addition, a strategy was successfully applied to reduce the number of model parameters to be calibrated in both the wetting zone and kneading zones. It was found that the new aggregation kernel in the wetting zone is capable of reproducing the particle size distribution that is experimentally observed at different process conditions as well as different types of formulations, varying in hydrophilicity and API concentration. Finally, it was observed that model parameters could be linked not only to the material properties but also to the liquid to solid ratio, paving the way to create a generic PBM to predict the particle size distribution of a new formulation.

Pharmaceutical Application of Process Understanding and Optimization Techniques: A Review on the Continuous Twin-Screw Wet Granulation

“…The L/S ratio has been extensively identified as the most predominant factor in preparing TSWG products with desired quality attributes [36,37]. It is reported that the low L/S ratio produces the granules with broad and bimodal size distribution, while the size distribution becomes narrow and monomodal at high L/S ratio that are too large to be directly used for tableting [18].…”

“…Based on the PBM framework, the model was also constantly improved. For instance, in improving a 1D PBM [37,44] for process simulation, a high-dimensional stochastic PBM was constructed to estimate the residence times for different screw element geometry [45]. A novel four-dimensional, stochastic PBM for twin-screw granulation was proposed to describe the mechanistic rates and track more complex particle properties and their transformations [46].…”

Linking material properties to 1D-PBM parameters towards a generic model for twin-screw wet granulation

“…Van Hauwermeiren et al [20] proposed a 1D PBM for TSWG, where aggregation and breakage are set as the main phenomena in the granulation. The number of model parameters in this PBM has been reduced by Barrera Jiménez et al [21] in view of identifiability, which increased the applicability to the industry. Ismail et al [22] analyzed the impact of process settings, i.e., liquid-to-solid (L/S) ratio, and screw speed, on 1D PBM parameters.…”

Advances in Twin-Screw Granulation

“…Moreover, the impact of critical process settings, including process temperature, screw design and barrel filling degree, were reviewed. Next to the research studies with formulation focus, equipment and process knowledge for TSG is also being developed through a combined experimental and theoretical (i.e., model-based) approach [6][7][8][9]. This is also quintessential because experiments are necessary to gather new knowledge about the system, whereas the theory is needed to put forth hypotheses to build further on this knowledge and gain fundamental understanding.…”

“…Since these models are semi-mechanistic and require adaptation to capture the physical processes within a TSG, an extensive experimental calibration using experimental datasets is done. Barrera Jiménez et al [9] proposed improvement in an existing compartmental one-dimensional PBM for a TSG process by altering the original aggregation kernel in the wetting zone by applying an identifiability analysis. This resulted in a reduction in the number of model parameters to be calibrated, and these model parameters could be linked to the material properties and the liquid to solid ratio, which allows the creation of a generic PBM to predict the particle size distribution.…”