Abstract
Hybridoma cells are commonly grown for the production of monoclonal antibodies (MAb). For monitoring and control purposes of the bioreactors, dynamic models of the cultures are required. However these models are difficult to infer from the usually limited amount of available experimental data and do not focus on target protein production optimization. This paper explores an experimental case study where hybridoma cells are grown in a sequential batch reactor. The simplest macroscopic reaction scheme translating the data is first derived using a maximum likelihood principal component analysis. Subsequently, nonlinear least-squares estimation is used to determine the kinetic laws. The resulting dynamic model reproduces quite satisfactorily the experimental data, as evidenced in direct and cross-validation tests. Furthermore, model predictions can also be used to predict optimal medium renewal time and composition.
Highlights
Therapeutic products are subject to exponential demands and cost-lowering process improvements, leading to the intensification of growth conditions in the bio-pharmaceutical industry and the sharp increase of the related market economy
Previous optimization studies of hybridoma cell cultures for monoclonal antibody (MAb) production were usually conducted using simple mathematical models based on macroscopic reaction schemes such as in [1,2]
A macroscopic model with kinetics accounting for overflow metabolism, where glucose and glutamine are the main substrates, was proposed in [3]
Summary
Therapeutic products (vaccines, antibodies, etc.) are subject to exponential demands and cost-lowering process improvements, leading to the intensification of growth conditions in the bio-pharmaceutical industry and the sharp increase of the related market economy. Previous optimization studies of hybridoma cell cultures for MAb production were usually conducted using simple mathematical models based on macroscopic reaction schemes such as in [1,2]. This methodology was further extended in [6], where an insightful geometric interpretation is provided, and maximum likelihood principal component analysis (MLPCA) is used to estimate the reaction number and stoichiometric matrix In this study, the latter approach is applied to the culture of hybridoma cells in sequential batch reactors (SBR). The latter approach is applied to the culture of hybridoma cells in sequential batch reactors (SBR) This mode of operation is common in industrial practice, and poses the question of the information content of data sets collected during the several batches.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
