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Monoclonal antibodies (mAbs) have been extensively studied for their wide therapeutic and research applications. Increases in mAb titre has been achieved mainly by cell culture media/feed improvement and cell line engineering to increase cell density and specific mAb productivity. However, this improvement has shifted the bottleneck to downstream purification steps. The higher accumulation of the main cell-derived impurities, host cell proteins (HCPs), in the supernatant can negatively affect product integrity and immunogenicity in addition to increasing the cost of capture and polishing steps. Mathematical modelling of bioprocess dynamics is a valuable tool to improve industrial production at fast rate and low cost. Herein, a single stage volume-based population balance model (PBM) has been built to capture Chinese hamster ovary (CHO) cell behaviour in fed-batch bioreactors. Using cell volume as the internal variable, the model captures the dynamics of mAb and HCP accumulation extracellularly under physiological and mild hypothermic culture conditions. Model-based analysis and orthogonal measurements of lactate dehydrogenase activity and double-stranded DNA concentration in the supernatant show that a significant proportion of HCPs found in the extracellular matrix is secreted by viable cells. The PBM then served as a platform for generating operating strategies that optimise antibody titre and increase cost-efficiency while minimising impurity levels. [ABSTRACT FROM AUTHOR]

The therapeutic efficacy of monoclonal antibodies (mAbs) is dependent on their glycosylation patterns. As the largest group of currently approved biopharmaceuticals, the microheterogeneity in mAb oligosaccharide profiles deriving from mammalian cell production is a challenge to the biopharmaceutical industry. Disengaging the glycosylation process from the cell may offer significant enhancement of product quality and allow better control and reproducibility in line with the Quality-by-Design paradigm. Three potential designs of an Artificial Golgi reactor implementing targeted sequential glycosylation of mAbs are proposed including a (1) microcapillary film reactor, (2) packed bed reactor with nonporous pellets, and (3) packed bed reactor with porous pellets. Detailed mathematical models are developed to predict their performance for a range of design and operational parameters. While all three reactor designs can achieve desired conversion levels, the choice of a particular one depends on the required throughput and the associated cost of enzymes and co-substrates. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2959-2973, 2016 [ABSTRACT FROM AUTHOR]

Monoclonal antibodies (mAbs) are one of the most important products of the biopharmaceutical industry. Their therapeutic efficacy depends on the post-translational process of glycosylation, which is influenced by manufacturing process conditions. Herein, we present a dynamic mathematical model for mAb glycosylation that considers cisternal maturation by approximating the Golgi apparatus to a plug flow reactor and by including recycling of Golgi-resident proteins (glycosylation enzymes and transport proteins [TPs]). The glycosylation reaction rate expressions were derived based on the reported kinetic mechanisms for each enzyme, and transport of nucleotide sugar donors [NSDs] from the cytosol to the Golgi lumen was modeled to serve as a link between glycosylation and cellular metabolism. Optimization-based methodologies were developed for estimating unknown enzyme and TP concentration profile parameters. The resulting model is capable of reproducing glycosylation profiles of commercial mAbs. It can further reproduce the effect gene silencing of the FucT glycosylation enzyme and cytosolic NSD depletion have on the mAb oligosaccharide profile. All novel elements of our model are based on biological evidence and generate more accurate results than previous reports. We therefore believe that the improvements contribute to a more detailed representation of the N-linked glycosylation process. The overall results show the potential of our model toward evaluating cell engineering strategies that yield desired glycosylation profiles. Additionally, when coupled to cellular metabolism, this model could be used to assess the effect of process conditions on glycosylation and aid in the design, control, and optimization of biopharmaceutical manufacturing processes. © 2011 American Institute of Chemical Engineers Biotechnol. Prog., 2011 [ABSTRACT FROM AUTHOR]

Quality by design (QbD) is a scheme for the development, manufacture, and approval of pharmaceutical products. The end goal of QbD is to ensure product quality by building it into the manufacturing process. The main regulatory bodies are encouraging its implementation to the manufacture of all new pharmaceuticals including biological products. Monoclonal antibodies (mAbs) are currently the leading products of the biopharmaceutical industry. It has been widely reported that glycosylation directly influences the therapeutic mechanisms by which mAbs function in vivo. In addition, glycosylation has been identified as one of the main sources of monoclonal antibody heterogeneity, and thus, a critical parameter to follow during mAb manufacture. This article reviews the research on glycosylation of mAbs over the past 2 decades under the QbD scope. The categories presented under this scope are: (a) definition of the desired clinical effects of mAbs, (b) definition of the glycosylation-associated critical quality attributes (glycCQAs) of mAbs, (c) assessment of process parameters that pose a risk for mAb glycCQAs, and (d) methods for accurately quantifying glycCQAs of mAbs. The information available in all four areas leads us to conclude that implementation of QbD to the manufacture of mAbs with specific glycosylation patterns will be a reality in the near future. We also foresee that the implementation of QbD will lead to the development of more robust and efficient manufacturing processes and to a new generation of mAbs with increased clinical efficacy. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010 [ABSTRACT FROM AUTHOR]

Abstract: Fed-batch cultures are used in producing monoclonal antibodies industrially. Existing protocols are developed empirically. Model-based tools aiming to improve productivity are useful with model reliability and computational demand being important. Herein, a systematic framework for developing predictive models is presented comprising of model development, global sensitivity analysis, optimal experimental design for parameter estimation, and predictive capability checking. Its efficacy and validity are demonstrated using a fed-batch structured/unstructured model of antibody-secreting hybridoma cultures. Global sensitivity analysis is first used to identify sensitive model parameters (initial values estimated from batch cultures). Information-rich data from an optimally designed fed-batch experiment are then used to estimate these parameters, resulting in good agreement between simulation and experimental results. Finally, the model''s predictive capability is confirmed by comparison with an independent set of fed-batch cultures. This approach systematises the process of developing predictive cell culture models at a minimum experimental cost, enabling model-based control and optimisation. [Copyright &y& Elsevier]

A comprehensive dynamic model to describe monoclonal antibody (MAb)-producing mammalian cell cultures is presented featuring: (i) cell growth kinetics, (ii) cell metabolism, (iii) MAb synthesis and secretion and (iv) MAb glycosylation. Its simulation results are in good agreement with experimental data for macroscopic culture variables and qualitative observations for glycosylation. The combined model allows us to examine in silico and a priori the impact of feeding schedules on the extent of MAb glycosylation and productivity. Optimisation results for different feeding strategies and their effect on glycosylation patterns are also presented, based on which product quality monitoring strategies can be developed. [Copyright &y& Elsevier]

The addition of nutrients and accumulation of metabolites in a fed-batch culture of Chinese hamster ovary (CHO) cells leads to an increase in extracellular osmolality in late stage culture. Herein, we explore the effect of osmolality on CHO cell growth, specific monoclonal antibody (mAb) productivity and glycosylation achieved with the addition of NaCl or the supplementation of a commercial feed. Although both methods lead to an increase in specific antibody productivity, they have different effects on cell growth and antibody production. Osmolality modulation using NaCl up to 470 mOsm kg−1 had a consistently positive effect on specific antibody productivity and titre. The addition of the commercial feed achieved variable results: specific mAb productivity was increased, yet cell growth rate was significantly compromised at high osmolality values. As a result, Feed C addition to 410 mOsm kg−1 was the only condition that achieved a significantly higher mAb titre compared to the control. Additionally, Feed C supplementation resulted in a significant reduction in galactosylated antibody structures. Cell volume was found to be positively correlated to osmolality; however, osmolality alone could not account for observed changes in average cell diameter without considering cell cycle variations. These results help delineate the overall effect of osmolality on titre and highlight the potentially negative effect of overfeeding on cell growth. [ABSTRACT FROM AUTHOR]

The article presents a study which discusses development of quantitative and mechanistic model for monoclonal antibody glycosylation as a function of nutrient availability during cell culture. Topics discussed include role of monoclonal antibodies (mAbs) in drug manufacture, need of quality by design (QbD) principles in process development, and mathematical model consisting of modular elements such as cell culture dynamics and nucleotide sugar (NS) metabolism.

Cell-free protein synthesis (CFPS) is an emerging tool for the rapid production of difficult-to-express proteins as well as for identifying protein synthesis bottlenecks. In CFPS, the biotic phase is substituted by extracts of living cells devoid of any of their own genetic material. The main advantage is that these systems delineate cell growth from recombinant protein production, enabling the expression of targets that would otherwise place too big a burden on living cells. We have conducted a techno-economic analysis of a CFPS system to produce monoclonal antibodies (mAbs) using extracts of Chinese hamster ovary (CHO) cells. We compare the performance of the CFPS system with two alternative production strategies: stable and transient gene expression in CHO cells. Our assessment shows that the viability of CFPS for mAb production requires a significant increase in the product yield and the recycling of high-cost components such as DNA. Nevertheless, CFPS shows significant promise for personalized medicine applications, providing a platform for on-demand production and simplified supply chains. [ABSTRACT FROM AUTHOR]

Downstream processing of protein products of mammalian cell culture currently accounts for the largest fraction of the total production cost. A major challenge is the removal of host cell proteins, which are cell-derived impurities. Host cell proteins are potentially immunogenic and can compromise product integrity during processing and hold-up steps. There is an increasing body of evidence that the type of host cell proteins present in recombinant protein preparations is a function of cell culture conditions and handling of the harvest cell culture fluid. This, in turn, can affect the performance of downstream purification steps as certain species are difficult to remove and may require bespoke process solutions. Herein, we review recent research on the interplay between upstream process conditions, host cell protein composition and their downstream removal in antibody production processes, identifying opportunities for increasing process understanding and control. We further highlight advances in analytical and computational techniques that can enable the application of quality by design. [ABSTRACT FROM AUTHOR]

Image, graphical abstract • Constrained global sensitivity analysis proposed for design space identification. • cGSA explores range of process inputs in silico subject to output constraints. • Applied to model of antibody-producing Chinese hamster ovary (CHO) cell culture system. • Findings verified experimentally for antibody titre and glycosylation as outputs. The manufacture of protein-based therapeutics presents unique challenges due to limited control over the biotic phase. This typically gives rise to a wide range of protein structures of varying safety and in vivo efficacy. Herein we propose a computational methodology, enabled by the application of constrained Global Sensitivity Analysis, for efficiently exploring the operating range of process inputs in silico and identifying a design space that meets output constraints. The methodology was applied to an antibody-producing Chinese hamster ovary (CHO) cell culture system: we explored >8000 feeding strategies to identify a subset of manufacturing conditions that meet constraints on antibody titre and glycan distribution as an attribute of product quality. Our computational findings were then verified experimentally, confirming the applicability of this approach to a challenging production system. We envisage that this methodology can significantly expedite bioprocess development and increase operational flexibility. [ABSTRACT FROM AUTHOR]

• Stoichiometric model accurately describes CHO cell metabolism. • Artificial neural network used to model secreted antibody glycosylation. • Hybrid framework links extracellular culture conditions to recombinant protein quality. • Framework can be updated in-process with commonly monitored process variables. • Sets basis for bioprocess control. In-process quality control of biotherapeutics, such as monoclonal antibodies, requires computationally efficient process models that use readily measured process variables to compute product quality. Existing kinetic cell culture models can effectively describe the underlying mechanisms but require considerable development and parameterisation effort. Stoichiometric models, on the other hand, provide a generic, parameter-free means for describing metabolic behaviour but do not extend to product quality prediction. We have overcome this limitation by integrating a stoichiometric model of Chinese hamster ovary (CHO) cell metabolism with an artificial neural network that uses the fluxes of nucleotide sugar donor synthesis to compute the profile of Fc N-glycosylation, a critical quality attribute of antibody therapeutics. We demonstrate that this hybrid framework accurately computes glycan distribution profiles using a set of easy-to-obtain experimental data, thus providing a platform for process control applications. [ABSTRACT FROM AUTHOR]

• Development of a hybrid model for CHO cell culture using in silico training datasets • Model development focuses on antibody product synthesis and glycosylation • Glycosylation is one of main quality indicators for therapeutic antibodies • Hybrid model used as basis for the design of dynamic optimisation studies • Hybrid model used for reactive scheduling studies that assure product quality Chinese hamster ovary (CHO) cell culture systems are the most widely used platform for the industrial production of monoclonal antibodies (mAbs). The optimisation of manufacturing conditions for these high-value products is largely conducted off-line with little or no monitoring of mAb quality in-process. Here, we propose DigiGlyc, a hybrid model of these systems that predicts the critical quality attribute of mAb galactosylation. Having shown that DigiGlyc describes a wide range of experimental data well, we demonstrate that it can be used for the design of reactive optimisation studies. This hybrid formulation offers considerable gains in computational speed compared to mechanistic models with no loss in fidelity and can therefore underpin future online control and optimisation studies. [ABSTRACT FROM AUTHOR]