(422c) A Combined Experimental and Computational Approach Using Discrete Element Method for the Development of a Mechanistically Motivated Breakage Kernel

Authors: 
Metta, N., Rutgers, The State University of New Jersey
Ierapetritou, M., Rutgers, The State University of New Jersey
Ramachandran, R., Rutgers University
Pharmaceutical manufacturing is gradually shifting from a batch to continuous process. Continuous manufacturing is known for its potential advantages in cost, efficiency and reduced â??time to marketâ?? that increases the life span of the product. However, this emphasizes a greater need to understand the impact of material and process variability on product quality as disturbances can propagate downstream [1]. In addition, the Quality by design (QbD) approach introduced by the US food and drug administration (FDA) requires that companies demonstrate the ability to predict the effect of material and process variability on product quality. Process modeling thus has a significant role to play in the transition to continuous manufacturing and the successful implementation of QbD.

Direct compaction, dry granulation (DG) and wet granulation (WG) routes for continuous manufacturing include numerous unit operations to process powder feed and make tablets as final product. The conical screen mill (Co-mill) is a widely used equipment in DG and WG routes for the purpose of breaking granules to a required size distribution. It is important for granules to adhere to a consistent and pre-determined size distribution as it impacts tablet compaction and drug bioavailability. The granule breakage mechanism in co-mill is a complex phenomenon where fracture and eventual breakage occur due to a rotating impellor and the smaller sized granules are discharged through a screen.

The population balance model (PBM) approach that has been used in the literature to simulate a co-mill process requires the formulation of a breakage kernel that represents the mill breakage phenomenon. Past work done by us has used a breakage kernel that is a function of impellor speed and granule size [2]. The model was calibrated and validated using experimental data. However, material properties and milling conditions were not exclusively identified in the breakage function which limits its applicability. In addition, PBM does not capture particle-scale interactions that would render more accuracy to the model. Discrete element modeling (DEM) has potential to bridge this gap. Development of discrete element models (DEM) for breakage processes so far have been focused on ball mills, grinding mills, crushers etc. Despite its prevalence in pharmaceutical industry, work on DEM analysis of co-mill is very limited. This is possibly due to additional challenges posed such as, potential for continuous feed, discharge through screen, introduction of additional complexity in the particle-wall interactions from a rotating impellor, dearth of published experimental data etc.

Current work proposes a methodology to develop a breakage kernel that captures particle level dynamics through DEM and material properties through co-milling experiments. The DEM model takes into account threshold impact energy above which granules break. The impact energy distribution data for various size classes and impellor speeds is obtained from DEM. Co-mill experiments at various impellor speeds yield corresponding observed size distributions. The data from DEM and lab experiments are used to calibrate the breakage kernel through which material specific kernel parameters are determined. A multi-scale modeling framework utilizing capabilities from DEM, PBM as well as experimental data is thus established. This methodology to develop breakage kernel that identifies particle scale information, material and process specific parameters, and equipment geometry is implemented and qualitatively validated against experimental sensitivities.

References

[1] A. Rogers, A. Hashemi, M. Ierapetritou, Modeling of Particulate Processes for the Continuous Manufacture of Solid-Based Pharmaceutical Dosage Forms, Processes, 1 (2013) 67.

[2] D. Barrasso, S. Oka, A. Muliadi, J.D. Litster, C. Wassgren, R. Ramachandran, Population Balance Model Validation and Predictionof CQAs for Continuous Milling Processes: toward QbDin Pharmaceutical Drug Product Manufacturing, Journal of Pharmaceutical Innovation, 8 (2013) 147-162.