Zakaria Amribt

Hybridoma cell culture optimization using nonlinear model predictive control

Abstract This work addresses the application of control systems to the optimization of a monoclonal antibodies (MAb) production chain. The attention is focused on the maximization of hybridoma fed-batch culture productivity. The proposed model presents kinetics showing strong nonlinearities through min-max functions expressing overflow metabolism. A nonlinear model predictive control (NMPC) algorithm, choosing the best trajectory over a moving finite horizon among different sequences of inputs, is suggested in order to optimize productivity. Sensitivities of selected objective functions are considered in a minimax robust version of the NMPC in order to choose the best configuration with respect to practical operating conditions.

Parameter Identification for State Estimation: Design of an Extended Kalman Filter for Hybridoma Cell Fed-Batch Cultures

The monitoring and optimization of hybridoma cell fed-batch cultures depend on the availability of appropriate on-line sensors for the main culture components. A simple and efficient approach to maintain hybridoma cultures in the optimal operating conditions is to regulate the substrate concentrations at the critical values (G=Gcrit and/or Gn=Gncrit) such as to control the hybridoma cells at the critical metabolism state. However, reliable glucose and glutamine probes are currently rare and/or very expensive on the market and it is necessary to design software sensors which are at same time cheap and reliable and that can be used for online measurement. In this study, the overflow metabolism model is used to develop an extended Kalman filter for online estimation of glucose and glutamine in hybridoma cell fed-batch cultures based on the considered available measurements (biomasses (on-line), lactate and ammonia (on-line or off-line)). The observability conditions are examined, and the performances are analysed with simulations of hybridoma cell fed-batch cultures. Glutamine estimation sensitivity is enforced by minimizing a cost function combining a usual least-squares criterion with a state estimation sensitivity criterion.

Optimal Operation of Hybridoma Cell Fed-Batch Cultures Using the Overflow Metabolism Model: Numerical and Analytical Approach

Abstract The maximization of biomass productivity in fed-batch cultures of hybridoma cells is analyzed based on the overflow metabolism model. Due to overflow metabolism, often attributed to limited oxygen capacity, lactate and ammonia are formed when the substrate concentrations (glucose and glutamine) are above a critical value, which results in a decrease in biomass productivity. Optimal feeding rate, on the one hand, for a single feed stream containing both glucose and glutamine and, on the other hand, for two separate feed streams of glucose and glutamine are determined using a Nelder-Mead simplex optimization algorithm. The optimal multi exponential feed rate trajectory improves the biomass productivity by 10% as compared to the optimal single exponential feed rate. Moreover, this result is validated by the one obtained with the analytical approach in which glucose and glutamine are fed to the culture such as to control the hybridoma cells at the critical metabolism state, which allows maximizing the biomass productivity.

Macroscopic modelling of overflow metabolism in fed-batch cultures of hybridoma cells

Abstract A macroscopic model that takes into account phenomena of overflow metabolism within glycolysis and glutaminolysis is proposed to simulate hybridoma HB-58 cell cultures. The model of central carbon metabolism is reduced to a set of macroscopic reactions. The macroscopic model describes three metabolism states: respiratory metabolism, overflow metabolism and critical metabolism. It is validated with experimental data of fed-batch hybridoma cultures and successfully predicts the dynamics of cell growth and death, substrate consumption (glutamine and glucose) and metabolites production (lactate and ammonia). Model parameters and confidence intervals are obtained via a non linear least squares identification. This model, on the one hand, allows quantitatively describing overflow metabolism in mammalian cell cultures and, one the other hand, will be valuable for monitoring and control of fed-batch cultures in order to optimize the process.