Bernd Van Snick

Laboratory of Pharmaceutical Technology
Ottergemsesteenweg 460
B-9000 Gent(Belgium)
Tel. : +32-9-264.80.39
E-mail : Bernd.vansnick@ugent.be
Education : Industrial Pharmacist and Master in Drug Development

Innovation in pharmaceutical manufacturing of oral solid dosage forms via continuous processing

General aim
Continuous processing is gaining momentum for pharmaceutical manufacturing. Recently, GEA Pharma Systems launched a modular ‘3-in-1’ manufacturing platform with the following manufacturing routes: continuous wet granulation (WG) via twin screw granulation, continuous dry granulation (DG) via roller compaction and continuous direct compression (DC). Process knowledge about this innovative manufacturing platform is limited within the pharmaceutical industry. Therefore, it is the goal to build a scientific knowledge base for the continuous manufacturing platform which is crucial to bridge the gap between the existing technology and its industrial implementation.

Specific aims and work packages
With this project, process understanding and knowledge will be build up specifically for the feeding and blending stage of the continuous manufacturing system. Specific aspects need to be investigated:
•   Characterization of raw material properties for conventionally used excipients and model active pharmaceutical ingredients.
•   Evaluation of loss in weight feeding (GEA compact feeder, K-tron KT-20 and Brabender FW40) and continuous blending performance (GEA continuous dry powder blender) in terms of process parameters and material properties.
•   Implementation of process analytical technology (PAT) tools to enable process monitoring.
•   Derive and model the residence time distributions (RTDs) at each unit operation of the manufacturing system as a function of process parameters and material properties.
•   These RTDs allows to characterize transport and mixing, to trace materials and predict the API concentration at each unit operations and the integrated manufacturing system.
•   Demonstrate real-time release strategy combining PAT and predictive RTD models.