(82e) In silico Modeling of Roller Compaction Processes for Scale-up and Tech Transfer: One Step Closer to Digital Twins | AIChE

(82e) In silico Modeling of Roller Compaction Processes for Scale-up and Tech Transfer: One Step Closer to Digital Twins

Authors 

Rolandi, P. A., Amgen Inc.
Schlegel, F., Amgen Inc
Chung, J., Amgen
Gour, S., Amgen
Daftardar, S., Amgen
Nguyen, D., Amgen
Shen, D., Amgen
Chowdhury, A., Process Systems Enterprise
Sengupta, R., University of California Santa Barbara
Ryan, K., Amgen Inc.
Dry granulation via roller compaction is a key unit operation in the manufacture of immediate release tablets. One of the challenges for late-stage development is identifying optimal roller compaction processing parameters for scale up through numerous Design of Experiment (DoE) studies, which require manufacturing time, drug substance and analysis. In this work, a first-principles in silico model available in gPROMS was extended to guide selection of key DoE studies and reduce the number of large-scale runs needed to identify optimal processing parameters. The prediction accuracy of this model was demonstrated for the scale up of the roller-compaction process at a commercial manufacturing site, with prediction errors within a few percent. This case study demonstrates the possibility of using this “VirtualPactor” model as a tool to reduce the number of experiments required to configure commercial roller-compaction process parameters. Instead of carrying out trial-and-error experiments to define the inputs for a large-scale roller compactor, product teams can carry out a DoE study on a small-scale roller compactor, use the data from the study to calibrate the VirtualPactor model, and use the calibrated model to find the inputs for the large-scale compactor that satisfy the output specifications. This model is developed into a web application for use by domain experts in an effort to democratize access to advanced modeling techniques and enable Quality by Design during process scale-up and tech transfer.