(42f) Model- and Data-Enabled Process Scale-up for High Shear Wet Granulation

Batch high shear wet granulation (HSWG) is often a critical step in manufacturing pharmaceutical tablet dosage forms. Like any typical batch process, HSWG is also developed at a bench-top scale (1-2 liter) or lab scale (10 liter) initially to save material and resources. Commercial scales are usually much larger (600/800 liters), hence scaling up of a batch HSWG is inevitable. There are several scale-up approaches for batch HSWG that are available in literature, e.g., - a) empirical/trial and error approaches (constant Froude number/tip speed), b) mechanistic approaches (granule growth/nucleation regime map). It has been previously mentioned in literature that scale-up strategies such as a constant Froude number or constant tip speed do not effectively emulate all the granulation mechanisms, hence can be unreliable[1]. Although these scale-up strategies are often inaccurate, industrial applications frequently default to them just because they are simple and easy to implement [2-5].

A mechanistic approach can provide a better understanding of the process and its scale-up needs. Some mechanistic modeling approaches for HSWG, e.g., population balance models[6] and discrete element models[7] can be unsuitable for scale-up due to their complexity and data-intensive nature. Therefore, a granule growth regime map approach[8] is a good compromise – it is built on fundamental physical principles but is easier to implement compared to other mechanistic approaches.

This work will first demonstrate the risks of using a constant Froude number scale-up approach and then introduce a scale-up model that combines the granule growth regime map equation with a PLS (projection to least squares) equation. The granule growth regime map is expressed in terms of two dimensionless numbers: the pore saturation number (Smax) and the Stokes deformation number (StDef). These two numbers define boundaries between various granulation regimes. In theory, if the granulation process is kept in the same regime, it will yield similar product irrespective of the scale. To scale-up the process, an acceptable regime map region that results in tablets with desired quality attributes is first identified from the bench-top and lab scale runs. Next, the scale-up model is used for designing further experiments within the acceptable regime, across different scales of interest.

In this scale-up model, the PLS equation was used to predict the granule porosity as a function of the granulation process inputs (material property and operating conditions). The predicted granule porosity is an input for calculating Smax and Stdef. The PyDEx[9] workflow was leveraged for model-based design of experiments (MBDoE). Following the systematic MBDoE approach, the scale-up model was refined further with meaningful experimental data. Finally, the HSWG process was successfully scaled from 1 liter (bench-top) to 600 liters (commercial).

References

[1] Sen, M. and S. García Muñoz, Development and implementation of a hybrid scale up model for a batch high shear wet granulation operation. AIChE Journal, 2021. 67(5): p. e17183.

[2] Pandey, P. and S.I.F. Badawy, Chapter 18 - A Quality By Design Approach to Scale-Up of High Shear Wet Granulation Process, in Handbook of Pharmaceutical Wet Granulation, A.S. Narang and S.I.F. Badawy, Editors. 2019, Academic Press. p. 615-650.

[3] Tao, J., et al., Evaluating Scale-Up Rules of a High-Shear Wet Granulation Process. Journal of Pharmaceutical Sciences, 2015. 104(7): p. 2323-2333.

[4] Singh, M., et al., Challenges and opportunities in modelling wet granulation in pharmaceutical industry – A critical review. Powder Technology, 2022. 403: p. 117380.

[5] Alves, A.R., et al., A review on the scale-up of high-shear wet granulation processes and the impact of process parameters. Particuology, 2024. 92: p. 180-195.

[6] Chaudhury, A., et al., Population Balance Model Development, Validation, and Prediction of CQAs of a High-Shear Wet Granulation Process: Towards QbD in Drug Product Pharmaceutical Manufacturing. Journal of Pharmaceutical Innovation, 2014. 9(1): p. 53-64.

[7] Nakamura, H., H. Fujii, and S. Watano, Scale-up of high shear mixer-granulator based on discrete element analysis. Powder Technology, 2013. 236: p. 149-156.

[8] Iveson, S.M. and J.D. Litster, Growth regime map for liquid-bound granules. AIChE Journal, 1998. 44(7): p. 1510-1518.

[9] Kusumo, K.P., et al., Risk mitigation in model-based experiment design: A continuous-effort approach to optimal campaigns. Computers & Chemical Engineering, 2022. 159: p. 107680