Macroscopic Dynamic Modeling of Sequential Batch Cultures of Hybridoma Cells: An Experimental Validation

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.

Variable selection and parameter estimation of viral amplification in vero cell cultures dedicated to the production of a dengue vaccine

“…On the one hand, attempts to describe lactate as an overflow product of glucose metabolism do not succeed in improving the predictive capability of the model (not shown here). Nevertheless, lactate is well known to present behaviours highly dependent on the internal cell metabolic requirements/conditions and is therefore difficult to represent …”

In-silico Optimization of a Batch Bioreactor for mAbs Production in Relationship to the Net Evolution of the Hybridoma Cell Culture

“…To partly avoid optimal policy implementation problems, various alternatives are suggested in the literature. For instance, an observer-based robust control strategy for controlling overflow metabolism cultures operated in fedbatch mode in order to maximize the biomass productivity [31,46], or on-line optimal operation based on the time-varying characteristics of the biomass culture during the batch [18,28,30,46], or during a batch-to-batch operation mode [29], or by adjusting the biomass load in the bioreactor according to the time-varying characteristics of the biomass culture, in a semi-continuous operating mode [30].…”

“…This in-silico analysis opens the possibility to optimize the bioreactor performances by placing the setpoint (SP) in the most favourable location, by adjusting the substrate and biomass initial load according to the characteristics of the modified / improved biomass with an enhanced evolution (with the below k net ≠ 0). This in-silico analysis is also a valuable tool to determine an optimal operation of the bioreactor based on the timevarying characteristics of the biomass culture during the batch [18,28], or during a batch-to-batch operation mode (not approached here [29] ), or by adjusting the biomass load in the bioreactor according to the time-varying characteristics of the biomass culture, in a semi-continuous operating mode (not approached here [30] ).…”

“…However, when significant inconsistecies vs. experimental evidence are reported, such checks will lead to the process lumped model updating/completions, and re-estimation of some of their parameters for correcting its adequacy in order to perform futures bioreactor analyses, and to eventually correct its optimal operating policy. To partly avoid such problems various alternatives are suggested in the literature, such as an observer-based robust control strategy for controlling overflow metabolism cultures operated in fed-batch mode in order to maximize the biomass productivity [18,[28][29]31], even if their implementation is difficult and costly.…”

Model-Based Optimization of Mannitol Production by Using a Sequence of Batch Reactors for a Coupled Bi-Enzymatic Process—A Dynamic Approach

“…Sequential Batch-to-batch Reactors (SeqBR) (Figure 2) consist of a certain number of (usually identical) BR operated in series. The BR content is transferred from every BR to the next one, with adjusting the reactants and/or biocatalyst(s) amounts (concentrations) at the beginning of each BR, to reach optimal levels (off-line determined in this paper) [13,14]; iii.…”