Model-Based Methods in the Biopharmaceutical Process Lifecycle

Model-based methods are increasingly used in all areas of biopharmaceutical process technology. They can be applied in the field of experimental design, process characterization, process design, monitoring and control. Benefits of these methods are lower experimental effort, process transparency, clear rationality behind decisions and increased process robustness. The possibility of applying methods adopted from different scientific domains accelerates this trend further. In addition, model-based methods can help to implement regulatory requirements as suggested by recent Quality by Design and validation initiatives. The aim of this review is to give an overview of the state of the art of model-based methods, their applications, further challenges and possible solutions in the biopharmaceutical process life cycle. Today, despite these advantages, the potential of model-based methods is still not fully exhausted in bioprocess technology. This is due to a lack of (i) acceptance of the users, (ii) user-friendly tools provided by existing methods, (iii) implementation in existing process control systems and (iv) clear workflows to set up specific process models. We propose that model-based methods be applied throughout the lifecycle of a biopharmaceutical process, starting with the set-up of a process model, which is used for monitoring and control of process parameters, and ending with continuous and iterative process improvement via data mining techniques.

Automatic identification of structured process models based on biological phenomena detected in (fed-)batch experiments

In this paper, we present a set of methods to automatically propose structured process models from an automated analysis of (fed-)batch experiments. Therefore, the measurements are numerically compensated for the influence of feeding and sampling, and the qualitative behavior of the measurements is revealed. As measurements from fermentations are inherently noisy, we introduce a method that divides the compensated curves into several episodes in a probabilistic framework to better handle these shortcomings. The probability of biological phenomena that reveal crucial information about the underlying reaction network is calculated. Since the phenomena detection is measurement-driven, its reliability depends on the measurement situation, e.g., the number of samples taken and experiments considered, measurement noise, etc. We show a possible approach to test the uncertainty of the phenomena detection against these influences. Finally, model structures are proposed automatically based on the detected biological phenomena. An experimental validation of the approach is shown, using real fermentation data from fed-batch cultivations of Streptomyces tendae.