(155d) Development and Modelling of Process Control for Scale up and Optimisation of Pharmaceutical High Shear Wet Granulation

High shear wet granulation (HSWG) is a widely used method in pharmaceutical tablet manufacturing, offering improved powder flowability, content uniformity, and processing efficiency. HSWG is a complex, multivariate process. Equipment, formulation and process parameters all impact on granulation and the resultant granule properties which in turn influence downstream unit operations and properties of resultant tablets. This presents challenges for process development and scale-up, which typically rely on traditional empirical approaches and trial-and-error methods [1].

In-line and at-line process analytical technology (PAT) provides real-time information of the granulation process, and offer opportunities for process development, control and scale-up. Previous work has demonstrated the applicability of drag force flow (DFF) sensors as an in-line PAT tool to provide real-time monitoring of granulation dynamics. In-line DFF measurements along with machine learning and evolutionary equation discovery (M2E3D) [2] has been used to develop predictive models for tablet tensile strength, demonstrating strong agreement with experimental data across tablet compression platforms [3,4].

In addition, work has begun using in-line DFF sensors for optimal granulation end-point determination for implementation in pilot and manufacturing scales. Lab scale experiments and computational modelling facilitate the creation of models to determine optimal granulation end point, using in-line DFF, across formulations. Hydrodynamic characterisation of granulating equipment across scales will ultimately facilitate model transfer into manufacturing scales, minimising empirical scale-up work required. By leveraging in-line monitoring and computational modelling, this work advances HSWG process control, offering a scalable, data-driven approach for minimising waste, reducing process variability, and optimising pharmaceutical manufacturing.

[1] B. Liu, J. Wang, J. Zeng, L. Zhao, Y. Wang, Y. Feng, R. Du, A review of high shear wet granulation for better process understanding, control and product development, Powder Technology, 381 (2021) 204-223

[2] A.L. Nicusan, C. Windows-Yule, PyMED: Multiphase Materials Exploration via Evolutionary Equation Discovery, https://github.com/uob-positron-imaging-centre/MED (2022)

[3] I. Munu, A.L. Nicusan, J. Crooks, K. Pitt, C. Windows-Yule, A. Ingram, Predicting tablet properties using In-Line measurements and evolutionary equation Discovery: A high shear wet granulation study, International Journal of Pharmaceutics, 661 (2024).

[4] I. Munu, A.L. Nicusan, J. Crooks, K. Pitt, C. Windows-Yule, A. Ingram, Using in-line measurement and statistical analyses to predict tablet properties compressed using a Styl'One compaction simulator: A high shear wet granulation study, International Journal of Pharmaceutics, 669 (2025).