Experimental Validation of a Cascade Control Strategy for Continuously Perfused Animal Cell Cultures

Abstract

This paper is dedicated to the experimental validation of a cascade control strategy for simultaneously regulating the glucose and biomass levels in continuously perfused HEK-293 cell cultures. The inner loop consists of a partial feedback linearization, which requires the estimation of the biomass specific growth rate and glucose uptake rate. This latter task is achieved by sliding mode observers, which do not require a priori process knowledge in the form of a process model. The linearized process is then regulated by the outer loop, including two classical PI controllers with autotuning. The four manipulated variables are a feed flow rate with low glucose concentration, another feed flow with a higher glucose content, a bleed flow, and a perfusion stream. The experimental results demonstrate the ability of the control strategy to reach and regulate the prescribed setpoints. The main advantage of the strategy is that it can be applied in a plug and play manner and shows satisfactory robustness. To the best of our knowledge, this is the first time that such a multivariable control strategy, together with sliding mode observers, is applied at the lab scale to an industrial process in the pharmaceutical sector.

Time Integrated Flux Analysis: Exploiting the Concentration Measurements Directly for Cost-Effective Metabolic Network Flux Analysis

“…Run 9 exhibits profiles of slightly lower concentrations for Asn, Thr, Pro and Lys, although these profiles seem almost parallel to the ones of run 10. The profiles of concentrations His, Thr, Met, Val, Ile, Leu, Phe and Trp diverge and exhibit an increasing difference between run 9 and 10 towards day 2 [23]. However, all of these differences seem to fall within typically encountered biological and analytical variation.…”

“…Comparing the triplicate runs 1 to 3, it strikes that run 3 shows significantly increased flux values for reactions 8 to 13 though slightly lower values for reaction 14. The third run exhibits a significantly lower concentrations profile for Asp [23], which might, in combination with the slight increases in the fluxes of reactions 23, 30 and 40, explain the difference. In case of the replicate runs 9 and 10, the trends between the fluxes of t1-t0 and t2-t1 are reversed.…”

The design basis for the integrated and continuous biomanufacturing framework

“…Essential to maintaining the balance between DO, pCO 2 accumulation, and foam generation is establishing bioreactor‐specific relationships for the parameters that govern this balance, including the gas transfer (i.e., k L a) for a given gas species and sparger design. Therefore, it is essential for perfusion bioreactor control architecture to permit the use of reasonably complex algorithms (Abbate et al, 2020 ) to manage these three control elements, as well as the ability to incorporate so‐called offline, at‐line, and online measurements such as cell density, temperature, lactic acid concentration, glucose concentration, and pH.…”

Model predictive control for steady-state performance in integrated continuous bioprocesses

“…For example, the metabolism of animal cells is complex and can be influenced by various mechanisms of metabolite activation. Predictive control strategies can be used to better emulate this biological complexity [23,35]. Besides the transient nature of animal cell culture bioprocesses, Zupke et al also mentions interactions between CQAs and their control levers and delays associated with analytics as strong drivers for MPC [27].…”

“…Use of the MPC control scheme, in which a mathematical model is used to predict its future trajectory, can minimize the generated error signal. This has been addressed, for example, by novel control schemes in perfusion and continuous systems [ 23 , 24 ] by supporting continuous bio production in downstream purifications [ 25 ] or by controlling product quality attributes in antibody manufacture [ 26 , 27 ]. A so-called single prediction controller (SPC) anticipates the future behavior based on only one prediction of the system output.…”