(555b) Advanced Control Strategies Towards the Intensification of Monoclonal Antibody Production

Authors: 
Papathanasiou, M. M., Imperial College London
Campano, A. Q., Imperial College London
Steinebach, F., ETH Zurich
Mueller-Spaeth, T., ChromaCon AG
Morbidelli, M., ETH Zurich
Mantalaris, A., Imperial College London
Pistikopoulos, E. N., Texas A&M Energy Institute, Texas A&M University

Advanced control strategies towards the intensification of monoclonal antibody production

Maria M. Papathanasioua,d, Ana Quiroga Campanoa, Fabian Steinebachb, Thomas Mueller-Spaethc, Massimo Morbidellib, Athanasios Mantalarisa, Efstratios N. Pistikopoulosd*

aDept. of Chemical Engineering, Centre for Process Systems Engineering (CPSE), Imperial College London SW7 2AZ, London, U.K

bInstitute for Chemical and Bioengineering, ETH Zurich, Wolfgang-Pauli-Str. 10/HCI F 129, CH-8093 Zurich, Switzerland

cChromaCon AG, Technoparkstr. 1, CH-8005 Zurich, Switzerland

dArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station TX 77843

*stratos@tamu.edu

 

Keywords: process intensification, continuous processing, monoclonal antibodies, multi-parametric control

Process intensification plays a dominant role in the improvement of sustainability and productivity of biopharmaceutical processes. In particular, during the past few years there has been increasing demand on product quality and higher titers in the production of monoclonal antibodies (mAbs) [1]. The latter consists of two main parts: (1) the upstream processing (USP), where the cells are cultured and the therapeutic agent is produced and (2) the downstream processing (DSP) that involves the isolation/purification of the targeted product. Both the upstream and the downstream processing, however, are affected by various parameters related to the design of the bioreactor, the methods chosen for separation/purification as well as the operation mode (e.g. batch or continuous).

In order to develop environmental friendly, cost effective processes, current industrial trends suggest gradual shift from batch to continuous operation. Along with process intensification efforts, this can be facilitated by the use of advanced computational tools that will derive continuous operating conditions under minimum cost. In this work we present a complete computational study on the development of advanced control strategies for the production and purification of monoclonal antibodies. We follow the PAROC framework and software platform [2, 3] for the development of advanced multi-parametric control strategies that comprises four main steps: (i) development of a high-fidelity process model, (ii) approximation of the complex, process model into a linear state space representation, (iii) design of the multi-parametric controller, (iv) ‘closed-loop’, in-silico validation of the controller against the process model. The controller development is based on the derivation of multi-parametric Model Predictive Control (mp-MPC) policies that allow the solution of the optimization problem offline, improving the controller performance during online operation.

The examined system is described by (1) the upstream and (2) the downstream processing. The upstream process considers fed-batch culturing of GS-NS0 cells producing a chimeric IgG4 antibody. The mathematical model describing the system is based on multiple energy metabolic pathways and considers standard Monod kinetics. The model monitors the population of viable, early apoptotic and death cells, as well as the consumption/production profile of glucose and lactate, amino acids and the ATP balance. It comprises 16 differential and 23 algebraic equations, 41 variables and 45 parameters. The model is simulated in gPROMS® ModelBuilder v4.0.0 and the results are tested against experimental data. Due to its high complexity, the model cannot be used directly for the design of advanced controllers. It has to be therefore reduced to a linear state space model used for the formulation and solution of the multi-parametric programming problem (MATLAB®).

The purification of the upstream mixture (downstream processing) is performed using the Multicolumn Countercurrent Solvent Gradient Purification (MCSGP) process [4]. Similarly, using the model described by Krättli, et al. [5] we develop an advanced control strategy to ensure maximum recovery yield, while satisfying purity constraints [3, 6]. The ‘in-silico’ connection of the two systems is realized using the output of the upstream processing as a disturbance for the downstream system.

The results of this study form a solid basis for advanced “in-silico” experiments to determine the optimal operation conditions and evaluate the efficiency of the designed controllers. Additionally, such experiments will highlight the process bottlenecks and will provide the user with the necessary insight on the system dynamics, thus facilitating the realization of continuous monoclonal antibody production.

Acknowledgements

Financial support from the European Commission (OPTICO/G.A. No.280813) is gratefully acknowledged.

References

[1]          P. Gronemeyer, R. Ditz, and J. Strube, "Trends in Upstream and Downstream Process Development for Antibody Manufacturing," Bioengineering, vol. 1, pp. 188-212, 2014.

[2]          E. N. Pistikopoulos, "Perspectives in multiparametric programming and explicit model predictive control," AIChE Journal, vol. 55, pp. 1918-1925, 2009.

[3]          E. N. Pistikopoulos, N. A. Diangelakis, R. Oberdieck, M. M. Papathanasiou, I. Nascu, and M. Sun, "PAROC—An integrated framework and software platform for the optimisation and advanced model-based control of process systems," Chemical Engineering Science, p. In press.

[4]          L. Aumann and M. Morbidelli, "A continuous multicolumn countercurrent solvent gradient purification (MCSGP) process," Biotechnology and Bioengineering, vol. 98, pp. 1043-1055, 2007.

[5]          M. Krättli, F. Steinebach, and M. Morbidelli, "Online control of the twin-column countercurrent solvent gradient process for biochromatography," Journal of Chromatography A, vol. 1293, pp. 51-59, 6/7/ 2013.

[6]          M. M. Papathanasiou, F. Steinebach, G. Stroehlein, T. Müller-Späth, I. Nascu, R. Oberdieck, et al., "A control strategy for periodic systems - application to the twin-column MCSGP," in Computer Aided Chemical Engineering, ed, 2015, p. accepted for publication.