This report highlights the drivers, challenges, and enablers of the hybrid modeling applications in biopharmaceutical industry. It is a summary of an expert panel discussion of European academics and industrialists with relevant scientific and engineering backgrounds. Hybrid modeling is viewed in its broader sense, namely as the integration of different knowledge sources in form of parametric and nonparametric models into a hybrid semi-parametric model, for instance the integration of fundamental and data-driven models. A brief description of the current state-of-the-art and industrial uptake of the methodology is provided. The report concludes with a number of recommendations to facilitate further developments and a wider industrial application of this modeling approach. These recommendations are limited to further exploiting the benefits of this methodology within process analytical technology (PAT) applications in biopharmaceutical industry.
The monitoring and control of biotech processes in different phases of the product lifecycle from early development to commercial production is key for accelerated development and stringent process controls. Effective methods of monitoring are required to develop, optimize, and maintain processes at a maximum efficiency and desired product quality. In the last decade more and more research has been devoted to developing specifically designed sensors, sampling strategies and integrated data management systems to allow better and more detailed process monitoring. Especially with the Process Analytical Technology (PAT) framework published by the Food and Drug Administration (FDA) in 2004 the measurement, monitoring, modeling and control of biotech processes has become more important. This article will describe general operational aspects of sensors, sampling technologies and methods of process monitoring, advanced applications like soft sensors and metabolic controls including integrated data management and analysis. Practical examples and case studies are used to illustrate the potential of soft sensors, models and advanced sensors.
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