TU BRAUNSCHWEIG

Pharmaceutical Process Systems Engineering

At InES, we apply process systems engineering and first principle modelling tools to design novel, cost-effective, environmentally benign, and intensified continuous pharmaceutical manufacturing (CPM) processes. We utilise process analysis principles to gain a better understanding of underlying pharmaceutical manufacturing mechanisms. Model-based process design concepts help identifying the best reaction routes and reactor configurations, respectively. Ideas of process monitoring ensure low life cycle cost and provide new insights in critical failure modes and drug quality control issues. We believe that the combination of these methods will enable us to explore the whole potential of CPM. In this way, InES as part of the Center of Pharmaceutical Engineering (PVZ) plays an active role in shaping the future of cost-efficient and personalised drugs.

Process Analysis

process analysisA detailed look at the underlying mechanisms provides a better process understanding. Here, first principle mathematical models are developed to gain some insight of relevant processing steps in the field of CPM. For instance, the derived models are used to figure out the most essential factors 1) of manufacturing processes, 2) of drug quality issues, and 3) upscaling problems, i.e. to transform lab processes into industrial processing units. Moreover, the models enable statements to be made about the system stability and controllability. As in any model-based framework model imperfections and parameter uncertainties should be addressed from the very first beginning to provide credible inferences. Thus, optimal experimental design principles are advanced to extract the most informative data and best possible models of pharmaceutical manufacturing processes, respectively. Basic mathematical tools recently used are: ordinary & partial differential equations, orthogonal collocation, global & moment free sensitivity measures, polynomial chaos expansion, and point estimate methods for advanced uncertainty analysis and propagation.



Process Design

process designUnlock the power of CPM by generating the most efficient process designs. Rigorous dynamic optimization problems are implemented and solved to identify the best reaction roots and processing unit configurations. Instead of costly trial and error procedures the computer-aided design helps to explore the design space efficiently and safely, i.e. without any hazards and risks. Moreover, innovative processing windows can be tested in this way for novel classes of drugs for which little to no best practice designs exist. To have the most practical impact, our model-based process design strategies take various target values into account, e.g. residence time, conversion, and selectivity. In addition, model imperfections are explicitly considered to ensure that identified best designs do not only exist in simulations but in real life examples of pharmaceutical manufacturing as well. Basic mathematical tools recently used are: elementary process functions, non-convex optimization, multi-objective design principles, and robustification concepts based on our uncertainty tools.



Process Monitoring

process monitoringAvoid undesired downtime to gain the maximum profit from pharmaceutical manufacturing processes. We at InES focus on continuous pharmaceutical manufacturing which are intended to run constantly. Here, our model-based concepts aim to monitor the most critical processing units reliably and to trigger, in case of need, appropriate intervention strategies in a timely manner. Moreover, our developed models as an integral part of soft-sensors help to make inaccessible pharmaceutical manufacturing quantities available. In this way, we contribute in process analytics and drug quality issues equally. Basic mathematical tools recently used are: Kalman filers, active and passive fault detection and isolation, information theory, and model selection/averaging.
 




  last changed 08.09.2017
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