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The research within the Laboratory of Process Analytical Technology is focused on the implementation of PAT systems in innovative pharmaceutical production processes of innovative drug dosage forms, and is performed in close collaboration with the Laboratory of Pharmaceutical Technology. The manufacturing processes currently considered are:

Implementing PAT systems in production processes includes several research areas:

  • The development implementation of (new) process analyzers (e.g., spectroscopic tools, imaging tools, …) in the process stream allowing real-time collection of critical process and (intermediate) product information.
  • Data-analysis methods (chemometrics) allowing to extract useful information from the sometimes huge datasets process analyzers supply. Process analyzers are only valuable if they provide the desired information with sufficient accuracy. Being able to build accurate and robust models to reliably translate the data (e.g., obtained spectra) into process or product knowledge is crucial.
  • Design of Experiments (DoE) to maximize the information content from experimental series meanwhile keeping the number of experiments low. As the process (step) endpoints and the intermediate or end product properties (e.g., product solid state, chemical properties, physical properties,…) are influenced by numerous process and formulation variables, appropriate experimental design approaches must be applied to find out which variables and variable interactions significantly influence processes and product properties. This approach is essential to increase process understanding and process knowledge. From previous research projects, it is evident that process and formulation parameters have significant interactions, indicating that formulation development and process development are not independent from each other. DoE can furthermore help to determine the optimum combination of process and formulation parameters, resulting in highly efficient processes which guarantee an end product with the required quality properties.
  • Statistical process control and visualization: a final aim of implementing PAT systems in pharmaceutical production processes is complete process control. Based on process knowledge and process models, the information obtained in real-time should be used for guiding the process to its desired state, possibly allowing real-time release. Early warnings should be given when a process is moving into an unwanted direction and the process models should allow to determine how process settings must be adapted by operators to lead the process to its desired state. This approach should help to avoid batch loss. Operators should be able to follow the progress of a process in a clear way, using visual easy-to-interpret tools about the process status to make appropriate conclusions and take (corrective) actions (detection of abnormal aberrations, decision to start the next process step, adjustments of process,…).
  • Mechanistic modeling: empirical modelling is based on historical data and as such they are of limited use in new applications outside the experimental space studied. Apart from cause-and-effect between variables, not much else is required in terms of process knowledge. Mechanistic modelling is based on the fundamental understanding of the underlying physics and chemistry governing the behaviour of the process. Hence, mechanistic modelling does not require much data for model development, and hence is not subject to the idiosyncrasies in data. Mechanistic modelling forces to fundamentally and completely understand processes. The different steps of mechanistic modelling are mentioned in appendix. This new approach of modelling of pharmaceutical processes should allow making useful process simulations and process predictions.
  • Process control and process engineering