Celikovic Selma, Rehrl Jakob, Fraga Rúben Martins, Steinberger Martin, Khinast Johannes, Horn Martin, Sacher Stephan
Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 13/2, 8010 Graz, Austria; Institute of Automation and Control, Graz University of Technology, Inffeldgasse 21b, 8010 Graz, Austria.
Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 13/2, 8010 Graz, Austria.
Eur J Pharm Biopharm. 2025 Jun;211:114700. doi: 10.1016/j.ejpb.2025.114700. Epub 2025 Mar 24.
The pharmaceutical industry is currently moving away from traditional approaches to quality control with off-line quality tests, limited in-line process monitoring and minimal control strategies towards more sophisticated methods. This transition addresses several critical aspects, including the reduction of ecological and economic footprints and ensuring the safety for patients and personnel. In that context, the initial step is the application of process monitoring tools, such as process analytical technology (PAT) and soft sensors, for real-time product quality assessment. This will enable real-time release testing (RTRT), which redefines conventional approaches by relying solely on the process data reported by equipment or collected from sensors to predict the product quality. However, the implementation of RTRT requires reliable material tracking algorithms, which align the process data with the product's characteristics. This study proposes a modern digital RTRT strategy that aligns process data collected from a state-of-the-art manufacturing line with a sophisticated process monitoring strategy for specific product quantities, i.e., single dosage units (tablets). To trace the material through the production line and align it to the collected process data, residence time distribution (RTD) models and material tracking algorithms were developed. The digital RTRT strategy was designed and demonstrated using the industrial manufacturing line ConsiGma-25. The developed strategy makes full product quality information digitally available, including critical quality attributes (CQAs) and processing conditions experienced during the production. The obtained results were validated using traditionally established off-line methods.