Abstract
Process understanding is emphasized in the process analytical technology initiative and the quality by design paradigm to be essential for manufacturing of biopharmaceutical products with consistent high quality. A typical approach to developing a process understanding is applying a combination of design of experiments with statistical data analysis. Hybrid semi-parametric modeling is investigated as an alternative method to pure statistical data analysis. The hybrid model framework provides flexibility to select model complexity based on available data and knowledge. Here, a parametric dynamic bioreactor model is integrated with a nonparametric artificial neural network that describes biomass and product formation rates as function of varied fed-batch fermentation conditions for high cell density heterologous protein production with E. coli. Our model can accurately describe biomass growth and product formation across variations in induction temperature, pH and feed rates. The model indicates that while product expression rate is a function of early induction phase conditions, it is negatively impacted as productivity increases. This could correspond with physiological changes due to cytoplasmic product accumulation. Due to the dynamic nature of the model, rational process timing decisions can be made and the impact of temporal variations in process parameters on product formation and process performance can be assessed, which is central for process understanding.
Highlights
Bioprocess development and optimization are essential elements of the biopharmaceutical business model and manufacturing economics
Process understanding is emphasized in the process analytical technology initiative and the quality by design paradigm to be essential for manufacturing of biopharmaceutical products with consistent high quality
A parametric dynamic bioreactor model is integrated with a nonparametric artificial neural network that describes biomass and product formation rates as function of varied fed-batch fermentation conditions for high cell density heterologous protein production with E. coli
Summary
Bioprocess development and optimization are essential elements of the biopharmaceutical business model and manufacturing economics. A related strategic approach to quality product development is quality by design (QbD) [3,4,5]. Bioprocesses are often affected by a large set of input and output parameters of which the critical process parameters (CPPs) and critical quality attributes (CQAs) are the parameters to identify and assess [7, 8]. Application of a DoE strategy provides understanding of the relationship between parameters and CQAs and leads to establishment of a design space and a Bioprocess Biosyst Eng (2016) 39:773–784 control space [3,4,5, 9, 13]. The control space defines the operational limits of the CPPs such that the quality of the CQAs can be ensured [10, 13]
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