Multi-Scale Modeling of Heterogeneities in Mammalian Cell Culture Processes

Efficient optimization of time-varying inputs in a fed-batch cell culture process using design of dynamic experiments

In cell culture process development, we rely largely on an iterative, one-factor-at-a-time procedure based on experiments that explore a limited process space. Design of experiments (DoE) addresses this issue by allowing us to analyze the effects of process inputs on process responses systematically and efficiently. However, DoE cannot be applied directly to study time-varying process inputs unless an impractically large number of bioreactors is used. Here, we adopt the methodology of design of dynamic experiments (DoDE) and incorporate dynamic feeding profiles efficiently in late-stage process development of the manufacture of therapeutic monoclonal antibodies. We found that, for the specific cell line used in this article, (1) not only can we estimate the effect of nutrient feed amount on various product attributes, but we can also estimate the effect, develop a statistical model, and use the model to optimize the slope of time-trended feed rates; (2) in addition to the slope, higher-order dynamic characteristics of time-trended feed rates can be incorporated in the design but do not have any significant effect on the responses we measured. Based on the DoDE data, we developed a statistical model and used the model to optimize several process conditions. Our effort resulted in a tangible improvement in productivity-compared with the baseline process without dynamic feeding, this optimized process in a 200-L batch achieved a 27% increase in titer and > 92% viability. We anticipate our application of DoDE to be a starting point for more efficient workflows to optimize dynamic process conditions in process development.

Population balance modelling captures host cell protein dynamics in CHO cell cultures

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.

Subspace Based Model Identification for an Industrial Bioreactor: Handling Infrequent Sampling Using Missing Data Algorithms

This manuscript addresses the problem of modeling an industrial (Sartorius) bioreactor using process data. In the context of the Sartorius Bioreactor, it is important to appropriately address the problem of dealing with a large number of variables, which are not always measured or are measured at different sampling rates, without taking recourse to simpler interpolation- or imputation-based approaches. To this end, a dynamic model for the Sartorius Bioreactor is developed via appropriately adapting a recently presented subspace model identification technique, which in turn uses nonlinear iterative partial least squares (NIPALS) algorithms to gracefully handle the missing data. The other key contribution is evaluating the ability of the identification approach to provide insight into the process by computing interpretable variables such as metabolite rates. The results demonstrate the ability of the proposed approach to model data from the Sartorius Bioreactor.

Systematic development of temperature shift strategies for Chinese hamster ovary cells based on short duration cultures and kinetic modeling

Temperature shift (TS) to a hypothermic condition has been widely used during protein production processes that use Chinese hamster ovary (CHO) cells. The effect of temperature on cell growth, metabolites, protein titer and quality depends on cell line, product, and other bioreactor conditions. Due to the large numbers of experiments, which typically last 2-3 weeks each, limited systematic TS studies have been reported with multiple shift temperatures and steps at different times. Here, we systematically studied the effect of temperature on cell culture performance for the production of two monoclonal antibodies by industrial GS and DG44 CHO cell lines. Three 2-8 day short-duration methods were developed and validated for researching the effect of many different temperatures on CHO cell culture and quality attributes. We found that minor temperature differences (1-1.5 °C) affected cell culture performance. The kinetic parameters extracted from the short duration data were subsequently used to compute and predict cell culture performance in extended duration of 10-14 days with multiple TS conditions for both CHO cell lines. These short-duration culture methods with kinetic modeling tools may be used for effective TS optimization to achieve the best profiles for cell growth, metabolites, titer and quality attributes. Although only three short-duration methods were developed with two CHO cell lines, similar short-duration methods with kinetic modeling may be applied for different hosts, including both microbial and other mammalian cells.

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.

Cyclin and DNA distributed cell cycle model for GS-NS0 cells

Mammalian cell cultures are intrinsically heterogeneous at different scales (molecular to bioreactor). The cell cycle is at the centre of capturing heterogeneity since it plays a critical role in the growth, death, and productivity of mammalian cell cultures. Current cell cycle models use biological variables (mass/volume/age) that are non-mechanistic, and difficult to experimentally determine, to describe cell cycle transition and capture culture heterogeneity. To address this problem, cyclins-key molecules that regulate cell cycle transition-have been utilized. Herein, a novel integrated experimental-modelling platform is presented whereby experimental quantification of key cell cycle metrics (cell cycle timings, cell cycle fractions, and cyclin expression determined by flow cytometry) is used to develop a cyclin and DNA distributed model for the industrially relevant cell line, GS-NS0. Cyclins/DNA synthesis rates were linked to stimulatory/inhibitory factors in the culture medium, which ultimately affect cell growth. Cell antibody productivity was characterized using cell cycle-specific production rates. The solution method delivered fast computational time that renders the model's use suitable for model-based applications. Model structure was studied by global sensitivity analysis (GSA), which identified parameters with a significant effect on the model output, followed by re-estimation of its significant parameters from a control set of batch experiments. A good model fit to the experimental data, both at the cell cycle and viable cell density levels, was observed. The cell population heterogeneity of disturbed (after cell arrest) and undisturbed cell growth was captured proving the versatility of the modelling approach. Cell cycle models able to capture population heterogeneity facilitate in depth understanding of these complex systems and enable systematic formulation of culture strategies to improve growth and productivity. It is envisaged that this modelling approach will pave the model-based development of industrial cell lines and clinical studies.

A novel quantitative model of cell cycle progression based on cyclin-dependent kinases activity and population balances

Cell cycle regulates proliferative cell capacity under normal or pathologic conditions, and in general it governs all in vivo/in vitro cell growth and proliferation processes. Mathematical simulation by means of reliable and predictive models represents an important tool to interpret experiment results, to facilitate the definition of the optimal operating conditions for in vitro cultivation, or to predict the effect of a specific drug in normal/pathologic mammalian cells. Along these lines, a novel model of cell cycle progression is proposed in this work. Specifically, it is based on a population balance (PB) approach that allows one to quantitatively describe cell cycle progression through the different phases experienced by each cell of the entire population during its own life. The transition between two consecutive cell cycle phases is simulated by taking advantage of the biochemical kinetic model developed by Gérard and Goldbeter (2009) which involves cyclin-dependent kinases (CDKs) whose regulation is achieved through a variety of mechanisms that include association with cyclins and protein inhibitors, phosphorylation-dephosphorylation, and cyclin synthesis or degradation. This biochemical model properly describes the entire cell cycle of mammalian cells by maintaining a sufficient level of detail useful to identify check point for transition and to estimate phase duration required by PB. Specific examples are discussed to illustrate the ability of the proposed model to simulate the effect of drugs for in vitro trials of interest in oncology, regenerative medicine and tissue engineering.

Flow-cytometry and cell sorting: an efficient approach to investigate productivity and cell physiology in mammalian cell factories

The performance of cell lines used for the production of biotherapeutic proteins typically depends on the number of cells in culture, their specific growth rate, their viability and the cell specific productivity (qP). Therefore both cell line development and process development are trying to (a) improve cell proliferation to reduce lag-phase and achieve high number of cells; (b) delay cell death to prolong the production phase and improve culture longevity; (c) and finally, increase qP. All of these factors, when combined in an optimised process, concur to increase the final titre and yield of the recombinant protein. As cellular performance is at the centre of any improvement, analysis methods that enable the characterisation of individual cells in their entirety can help in identifying cell types and culture conditions that perform exceptionally well. This observation of cells and their complexity is reflected by the term "cytomics" and flow cytometry is one of the methods used for this purpose. With its ability to analyse the distribution of physiological properties within a population and to isolate rare outliers with exceptional properties, flow cytometry ideally complements other methods used for optimisation, including media design and cell engineering. In the present review we describe approaches that could be used, directly or indirectly, to analyse and sort cellular phenotypes characterised by improved growth behaviour, reduced cell death or high qP and outline their potential use for cell line and process optimisation.