(542a) Continuous Mixing Technology: Development of Digital Twin

Authors: 
Toson, P. - Presenter, Research Center Pharmaceutical Engineering
Doshi, P., Worldwide Research and Development, Pfizer Inc.
Siegmann, E., Research Center Pharmaceutical Engineering
Blackwood, D. O., Pfizer Worldwide Research and Development
Lee, K., Pfizer Inc.
Kimber, J., Pfizer Worldwide Research and Development
Brandon, J., Pfizer Inc.
Wilsdon, D., Pfizer Ltd. Discovery Park House IPC009
Jain, A., Worldwide Research and Development, Pfizer Inc.
Khinast, J. G., Research Center Pharmaceutical Engineering
Jajcevic, D., Research Center Pharmaceutical Engineering
Continuous manufacturing has many advantages over batch processing, including smaller footprints, faster turnaround times, and better control of process parameters and product quality. Direct compression is a minimal setup for a continuous tableting line and consists only of three unit operations: feeding of the individual components, mixing and tableting. The main goal of the mixing step is to achieve content uniformity in the final product, which is a critical quality attribute (CQAs).

This work focuses on a vertical continuous mixing device termed Continuous Mixing Technology (CMT). The main of goal CMT goal is to mix incoming powder feed so that the content uniformity of the exiting blend stays in specification even if there are variations in the mass flows of the individual components from the feeding process. To enhance the process understanding of this mixing process and expedite the development time with minimal material consumption, we have been developing computational model of CMT device using Discrete Element Method. Our previous work focused on (DEM) simulations to gain a very detailed process insight using the commercial software package XPS (Extended Particle System) at low mass throughput of 10kg/h [1].

Recently, DEM model has been extended to simulate production-scale processes with a hold-up mass of up to 1000g and mass throughput of 30kg/h with 5.7million particles [2]. These simulations yield particle-level residence times, making it possible to create RTDs as function of process parameters (e.g. hold-up mass, throughput, impeller speed) and material parameters (e.g. different material lots with different flowability). Extensive validation of these simulations is carried out by comparing against RTD data collected through tracer experiments over a wide operating space. This validation study show that simulation results are in good agreement with the experimental data establishing DEM model as a Digital Twin of the physical process.

References:

[1] Toson et al., Int. J. Pharm. 552, 1–2 (2018), 288–300. doi:10.1016 IJP pharm 2018.09.032.

[2] Toson et al., AICHE meeting 2019.