Abstract
A multivariate study was performed aiming at the optimization of a recombinant rhamnose inducible E. coli induction system with alkaline phosphatase as target product. The effects of typical factors with impact on post- as well as pre-induction feeding rates were investigated with respect to the space–time yield of the target product. The goal was increased understanding as well as quantitative characterization of these factors with respect to their physiological impact on the model system. The optical density (OD) at which the culture was induced had a strong positive effect on the space–time yield. Pre-induction growth rate (k) had a second-order effect, while induction feed rate drop (J), a factor defining the linear post-induction feed rate, was interacting with (k). However, explanation of the observed effects to acquire more understanding regarding their effect on cell metabolism was not straight forward. Hence, the original process parameters were transformed into physiological more meaningful parameters and served as the basis for a multivariate data analysis. The observed variance with respect to observed volumetric activity was fully explained by the specific substrate uptake rate (qs) and induction OD, merging the process parameters pre-induction growth rate (k) and feed rate drop (J) into the physiological parameter specific substrate uptake rate (qs). After transformation of the response volumetric activity (U/ml) into the biomass specific activity (U/gbiomass), the observed variance was fully explained solely by the specific substrate uptake rate (qs). Due to physiological multivariate data analysis, the interpretation of the results was facilitated and factors were reduced. On the basis of the obtained results, it was concluded that the physiological parameter qs rather than process parameters (k, J, induction OD) should be used for process optimization with respect to the feeding profile.
Highlights
Following the recent quality by design (QbD) initiatives, pharmaceutical process development based on sound science to increase process understanding emerged as a key demand from the side of the regulatory bodies [1,2,3]
On the basis of this study, a generic science-based QbD methodology for the development of a feeding strategy for a process in red biotechnology is suggested, involving the use of physiological scaling parameters rather than empirically determined process parameters for the benefit of a faster, more cost-efficient process development according to QbD principles
The recombinant protein product was alkaline phosphatase. This is the same as native E. coli alkaline phosphatase, induced productivity has to be differentiated from native activity
Summary
Following the recent quality by design (QbD) initiatives, pharmaceutical process development based on sound science to increase process understanding emerged as a key demand from the side of the regulatory bodies [1,2,3]. The toolset for gaining process understanding includes design of experiments (DOE), multivariate data analysis (MVDA), quality risk management (QRM) and process analytical technology (PAT), as proposed from the regulatory authorities and exemplified in several excellent recent publications [4,5,6,7,8,9]. This contribution aims at providing a generic methodology on how to apply QbD in recombinant bioprocess development to gain and demonstrate process understanding. On the basis of this study, a generic science-based QbD methodology for the development of a feeding strategy for a process in red biotechnology is suggested, involving the use of physiological scaling parameters rather than empirically determined process parameters for the benefit of a faster, more cost-efficient process development according to QbD principles
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