Tablet Coating Scale-up Based on DEM Simulations: Going from Lab- to Industry-Scale

Introduction

Pan coating is a commonly used unit operation to apply a functional layer around tablet cores. The tablet cores are placed in a rotating drum, and a coating liquid is sprayed onto the moving tablets. The coating fulfils different functions; a special application is active coating, where the coating layer itself contains an active pharmaceutical ingredient (API). In this case, the variation of coating mass between tablets has to be consistently small.

To guarantee a consistently high uniformity, a large amount of process understanding is needed. To this end, a number of experimental investigations were done. In recent years, a strong trend emerged to complement this with numerical simulations^1,2.

Due to lengthy simulation times, these investigations often are restricted to small-scale cases. This limits the statements that can be done for the industry (full) scale process, and generally means that no or few large-scale data points are available for scale-up considerations.

In this work, the application of an active coating was investigated from lab scale to industry scale using Discrete Element Method (DEM) simulations (both commercial software and in-house code). The influence of process parameters on the quality attributes is studied for each scale. Further, it is studied how far scale-up rules known in literature³ are applicable, and new guidelines are considered.

  Material & Method

  Tablet coating process

The investigated tablet is a Gastrointestinal therapeutic systems (Adalat® GITS, Bayer Pharma AG, Germany) coated with an aqueous suspension containing candesartan cilexetil as API. Different sizes of pan coaters (BFC, L.B. Bohle, Germany) were used. The process parameters were set according to experimental runs and available formulation data. As the variation of this API between tablets has to be small, a high inter-tablet coating mass uniformity is vital. The central aim therefore was to improve the inter-tablet coating uniformity.

  Discrete Element Method (DEM) simulations

 

For the simulations, a commercial software (EDEM 2.5.1, DEM Solutions, UK) and an in-house developed code (XPS, Research Center Pharmaceutical Engineering GmbH, Austria) were combined to cover the scale range. The XPS software exploits the massively parallel structures of modern graphic cards to simulate number of particles that is sufficiently large to cover even the industrial scale with reasonable computation times. The material properties came from measurements⁴. Different methods for the modeling of a spray in DEM simulations were implemented⁵.

Figure 1: Snapshot of the Discrete Element Method simulation of the lab-scale coating drum.   Conclusion

In this work, two implementations of Discrete Element Method simulations were combined to describe a tablet coating process from lab scale to pilot scale. The parameters needed from simulation were taken from measurements where possible. For the pilot scale, comparison of the simulation result to available measurement data was done. On one hand, valuable insights into the process mechanics were gathered on each separate scale. On the other hand, covering the whole scale range allowed for a critical treatment of existing scale-up rules and the investigation towards new guidelines.

  References

1.            Ketterhagen, W. R., am Ende, M. T. & Hancock, B. C. Process modeling in the pharmaceutical industry using the discrete element method. J. Pharm. Sci. 98, 442–70 (2009).

2.            Toschkoff, G. & Khinast, J. G. Mathematical modeling of the coating process. Int J Pharm (2013).

3.            Mueller, R. & Kleinebudde, P. Prediction of tablet velocity in pan coaters for scale-up. Powder Technol. 173, 51–58 (2007).

4.            Just, S. et al. Experimental analysis of tablet properties for discrete element modeling of an active coating process. AAPS PharmSciTech 14, 402–11 (2013).

5.            Toschkoff, G. et al. Spray models for discrete element simulations of particle coating processes. Chem Eng Sci 101, 603–614 (2013).