Abstract
The goal of this study is to develop a macroscopic mechanistic model describing growth and production within fed-batch cultivations of CHO cells. The model should be used for process characterization as well as for process monitoring including real-time parameter adaptations. The model proved to be able to describe a data-set of 40 processes differing in clones, scales, and process conditions with a normalized root mean square error of approximately 10%. However, due to limited parameter identifiability and limited knowledge about physiologically meaningful parameter values, a broad range of parameters could describe the data with similar quality. This hampered comparison of the model parameters as well as their real-time estimation. Therefore an iterative workflow combining techniques like sensitivity and identifiability analysis, analysis of the specific rates as well as structural adaptations of the parameter space is developed. By applying it the parameter variability could be reduced by 80% with similar predictive power as the original parameters. Summing up, based on a mechanistic CHO model, a generic and transferrable workflow is created for target-oriented parameter estimation in case of limited parameter identifiability. Finally, we suggest a methodology, which fits ideally into the frame of Process Analytical Technology aiming to increase process understanding.
Published Version
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