On the model‐based optimization of secreting mammalian cell (GS‐NS0) cultures

The global bio‐manufacturing industry requires improved process efficiency to satisfy the increasing demands for biochemicals, biofuels, and biologics. The use of model‐based techniques can facilitate the reduction of unnecessary experimentation and reduce labor and operating costs by identifying the most informative experiments and providing strategies to optimize the bioprocess at hand. Herein, we investigate the potential of a research methodology that combines model development, parameter estimation, global sensitivity analysis, and selection of optimal feeding policies via dynamic optimization methods to improve the efficiency of an industrially relevant bioprocess. Data from a set of batch experiments was used to estimate values for the parameters of an unstructured model describing monoclonal antibody (mAb) production in GS‐NS0 cell cultures. Global Sensitivity Analysis (GSA) highlighted parameters with a strong effect on the model output and data from a fed‐batch experiment were used to refine their estimated values. Model‐based optimization was used to identify a feeding regime that maximized final mAb titer. An independent fed‐batch experiment was conducted to validate both the results of the optimization and the predictive capabilities of the developed model. The successful integration of wet‐lab experimentation and mathematical model development, analysis, and optimization represents a unique, novel, and interdisciplinary approach that addresses the complicated research and industrial problem of model‐based optimization of cell based processes. Biotechnol. Bioeng. 2015;112: 536–548. © 2014 Wiley Periodicals, Inc.

[1]  Fang Zhang,et al.  Metabolic characteristics of recombinant Chinese hamster ovary cells expressing glutamine synthetase in presence and absence of glutamine , 2006, Cytotechnology.

[2]  David C James,et al.  Engineering mammalian cell factories for improved recombinant monoclonal antibody production: lessons from nature? , 2005, Biotechnology and bioengineering.

[3]  Constantinos C. Pantelides,et al.  Monte Carlo evaluation of derivative-based global sensitivity measures , 2009, Reliab. Eng. Syst. Saf..

[4]  F. Breedveld,et al.  Therapeutic monoclonal antibodies , 2000, The Lancet.

[5]  A. Ben-Zvi,et al.  Optimization of microalgal productivity using an adaptive, non-linear model based strategy. , 2012, Bioresource technology.

[6]  F. Gòdia,et al.  Modification of glucose and glutamine metabolism in hybridoma cells through metabolic engineering , 1999, Cytotechnology.

[7]  J E Bailey,et al.  Mathematical Modeling and Analysis in Biochemical Engineering: Past Accomplishments and Future Opportunities , 1998, Biotechnology progress.

[8]  L. Häggström,et al.  Defined protein and animal component‐free NS0 fed‐batch culture , 2007, Biotechnology and bioengineering.

[9]  S. Dhir,et al.  Dynamic optimization of hybridoma growth in a fed-batch bioreactor. , 2000, Biotechnology and bioengineering.

[10]  K. Davey.  Applicability of the Davey (linear Arrhenius) predictive model to the lag phase of microbial growth , 1991 .

[11]  R L Buchanan,et al.  Effect of temperature history on the growth of Listeria monocytogenes Scott A at refrigeration temperatures. , 1991, International journal of food microbiology.

[12]  Tom Ross,et al.  Comparison of Arrhenius‐type and Bêlehrádek‐type models for prediction of bacterial growth in foods , 1991 .

[13]  Farlan S Veraitch,et al.  Enhanced growth in NS0 cells expressing aminoglycoside phosphotransferase is associated with changes in metabolism, productivity, and apoptosis , 2005, Biotechnology and bioengineering.

[14]  P. F. Greenfield,et al.  Hybridoma growth limitations: The roles of energy metabolism and ammonia production , 2004, Cytotechnology.

[15]  Michel Perrier,et al.  Optimization of fed-batch culture of hybridoma cells using dynamic programming: single and multi feed cases , 1992 .

[16]  Wei-Shou Hu,et al.  Alteration of mammalian cell metabolism by dynamic nutrient feeding , 2004, Cytotechnology.

[17]  Athanasios Mantalaris,et al.  Computational approach for understanding and improving GS-NS0 antibody production under hyperosmotic conditions. , 2012, Journal of bioscience and bioengineering.

[18]  Daniel I. C. Wang,et al.  Energy metabolism and ATP balance in animal cell cultivation using a stoichiometrically based reaction network , 2000, Biotechnology and bioengineering.

[19]  W M Miller,et al.  Phosphate feeding improves high-cell-concentration NS0 myeloma culture performance for monoclonal antibody production. , 2000, Biotechnology and bioengineering.

[20]  Axel Munack,et al.  Adaptive, Model‐Based Control by the Open‐Loop‐Feedback‐Optimal (OLFO) Controller for the Effective Fed‐Batch Cultivation of Hybridoma Cells , 2002, Biotechnology progress.

[21]  Ming-Chi Lam.  In silico dynamic optimisation studies for batch/fed-batch mammalian cell suspension cultures producing biopharmaceuticals , 2008 .

[22]  N. Zaritzky,et al.  Mathematical modelling of microbial growth in packaged refrigerated beef stored at different temperatures. , 1998, International journal of food microbiology.

[23]  G Stephanopoulos,et al.  Optimization of fed‐batch penicillin fermentation: A case of singular optimal control with state constraints , 1989, Biotechnology and bioengineering.

[24]  S. Lee,et al.  Fed‐batch culture of Escherichia coli for L‐valine production based on in silico flux response analysis , 2011, Biotechnology and bioengineering.

[25]  B. Buckland,et al.  Fed-batch culture of recombinant NS0 myeloma cells with high monoclonal antibody production. , 1997, Biotechnology and bioengineering.

[26]  J. Prosser,et al.  Cell density-regulated recovery of starved biofilm populations of ammonia-oxidizing bacteria , 1997, Applied and environmental microbiology.

[27]  John P. Barford,et al.  An unstructured kinetic model of macromolecular metabolism in batch and fed-batch cultures of hybridoma cells producing monoclonal antibody , 2000 .

[28]  Efstratios N. Pistikopoulos,et al.  Global Sensitivity Analysis Challenges in Biological Systems Modeling , 2009 .

[29]  Konstantinos P. Koutsoumanis,et al.  Applicability of an Arrhenius Model for the Combined Effect of Temperature and CO2 Packaging on the Spoilage Microflora of Fish , 2000, Applied and Environmental Microbiology.

[30]  K Bernaerts,et al.  Predictive modelling of the microbial lag phase: a review. , 2004, International journal of food microbiology.

[31]  R. R. Rhinehart,et al.  Dynamic reoptimization of a fed-batch fermentor. , 1999, Biotechnology and bioengineering.

[32]  P. Lazo,et al.  Amino acids and glucose utilization by different metabolic pathways in ascites-tumour cells. , 2005, European journal of biochemistry.

[33]  Dirk Lütkemeyer,et al.  Effects of dissolved oxygen levels and the role of extra- and intracellular amino acid concentrations upon the metabolism of mammalian cell lines during batch and continuous cultures , 1998, Cytotechnology.

[34]  H. Lim,et al.  Computational algorithms for optimal feed rates for a class of fed‐batch fermentation: Numerical results for penicillin and cell mass production , 1986, Biotechnology and bioengineering.

[35]  P Lane,et al.  Improvement of a mammalian cell culture process by adaptive, model-based dialysis fed-batch cultivation and suppression of apoptosis , 2003, Bioprocess and biosystems engineering.

[36]  G. T. Tsao,et al.  Cybernetic modeling of microbial growth on multiple substrates , 1984, Biotechnology and bioengineering.

[37]  Efstratios N. Pistikopoulos,et al.  'Closing the loop' in biological systems modeling - From the in silico to the in vitro , 2011, Autom..