"Organized stress" for robust scale-up of intensified production process with fed-batch seed bioreactor.

Process intensification has been widely used for many years in the mammalian biomanufacturing industry to increase productivity, agility and flexibility while reducing production costs. The most commonly used intensified processes are operated using a perfusion or fed-batch seed bioreactor enabling a higher than usual seeding density in the fed-batch production bioreactor. Hence, as part of the growth phase is shifted to the seed bioreactor, there is a lower split ratio, which increases the criticality of the seed bioreactor and could impact production performance. Therefore, such intensified processes should be designed and characterized for robust process scale-up. This research work is focused on intensified processes with high seeding density inoculated from seed bioreactor in fed-batch mode. The impact of the feeding strategy and specific power input (P/V) in the seed bioreactor and on the production step with two different cell lines (CL1 and CL2) producing two different monoclonal antibodies was investigated. Cell culture performance in the production bioreactor has been improved due to more stressful conditions for the cells in the seed bioreactor and the impact of the production bioreactor P/V on the production performance was limited. This is the first reported study highlighting a positive impact of cellular stress in seed bioreactors on intensified production bioreactor with the introduction of the "organized stress" concept.

Predictive macroscopic modeling of cell growth, metabolism and monoclonal antibody production: Case study of a CHO fed-batch production.

We describe a systematic approach to establish predictive models of CHO cell growth, cell metabolism and monoclonal antibody (mAb) formation during biopharmaceutical production. The prediction is based on a combination of an empirical metabolic model connecting extracellular metabolic fluxes with cellular growth and product formation with mixed Monod-inhibition type kinetics that we generalized to every possible external metabolite. We describe the maximum specific growth rate as a function of the integral viable cell density (IVCD). Moreover, we also take into account the accumulation of metabolites in intracellular pools that can influence cell growth. This is possible even without identification and quantification of these metabolites as illustrated with fed-batch cultures of Chinese Hamster Ovary (CHO) cells producing a mAb. The impact of cysteine and tryptophan on cell growth and cell productivity was assessed, and the resulting macroscopic model was successfully used to predict the impact of new, untested feeding strategies on cell growth and mAb production. This model combining piecewise linear relationships between metabolic rates, growth rate and production rate together with Monod-inhibition type models for cell growth did well in predicting cell culture performance in fed-batch cultures even outside the range of experimental data used for establishing the model. It could therefore also successfully be applied for in silico prediction of optimal operating conditions.

Application of a genome-scale model in tandem with enzyme assays for identification of metabolic signatures of high and low CHO cell producers.

Biopharmaceutical industrial processes are based on high yielding stable recombinant Chinese Hamster Ovary (CHO) cells that express monoclonal antibodies. However, the process and feeding regimes need to be adapted for each new cell line, as they all have a slightly different metabolism and product performance. A main limitation for accelerating process development is that the metabolic pathways underlying this physiological variability are not yet fully understood. This study describes the evolution of intracellular fluxes during the process for 4 industrial cell lines, 2 high producers and 2 low producers (n = 3), all of them producing a different antibody. In order to understand from a metabolic point of view the phenotypic differences observed, and to find potential targets for improving specific productivity of low producers, the analysis was supported by a tailored genome-scale model and was validated with enzymatic assays performed at different days of the process. A total of 59 reactions were examined from different key pathways, namely glycolysis, pentose phosphate pathway, TCA cycle, lipid metabolism, and oxidative phosphorylation. The intracellular fluxes did not show a metabolic correlation between high producers, but the degree of similitude observed between cell lines could be confirmed with additional experimental observations. The whole analysis led to a better understanding of the metabolic requirements for all the cell lines, allowed to the identification of metabolic bottlenecks and suggested targets for further cell line engineering. This study is a successful application of a curated genome-scale model to multiple industrial cell lines, which makes the metabolic model suitable for process platform.

Segmented linear modeling of CHO fed-batch culture and its application to large scale production.

We describe a systematic approach to model CHO metabolism during biopharmaceutical production across a wide range of cell culture conditions. To this end, we applied the metabolic steady state concept. We analyzed and modeled the production rates of metabolites as a function of the specific growth rate. First, the total number of metabolic steady state phases and the location of the breakpoints were determined by recursive partitioning. For this, the smoothed derivative of the metabolic rates with respect to the growth rate were used followed by hierarchical clustering of the obtained partition. We then applied a piecewise regression to the metabolic rates with the previously determined number of phases. This allowed identifying the growth rates at which the cells underwent a metabolic shift. The resulting model with piecewise linear relationships between metabolic rates and the growth rate did well describe cellular metabolism in the fed-batch cultures. Using the model structure and parameter values from a small-scale cell culture (2 L) training dataset, it was possible to predict metabolic rates of new fed-batch cultures just using the experimental specific growth rates. Such prediction was successful both at the laboratory scale with 2 L bioreactors but also at the production scale of 2000 L. This type of modeling provides a flexible framework to set a solid foundation for metabolic flux analysis and mechanistic type of modeling. Biotechnol. Bioeng. 2017;114: 785-797. © 2016 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.

Macroscopic modeling of mammalian cell growth and metabolism.

We review major modeling strategies and methods to understand and simulate the macroscopic behavior of mammalian cells. These strategies comprise two important steps: the first step is to identify stoichiometric relationships for the cultured cells connecting the extracellular inputs and outputs. In a second step, macroscopic kinetic models are introduced. These relationships together with bioreactor and metabolite balances provide a complete description of a system in the form of a set of differential equations. These can be used for the simulation of cell culture performance and further for optimization of production.