Towards the development of automated fed-batch cell culture processes at microscale

The cell density effect in animal cell-based bioprocessing: Questions, insights and perspectives

One of the main challenges in the development of bioprocesses based on cell transient expression is the commonly reported reduction of cell specific productivity at increasing cell densities. This is generally known as the cell density effect (CDE). Many efforts have been devoted to understanding the cell metabolic implications to this phenomenon in an attempt to design operational strategies to overcome it. A comprehensive analysis of the main studies regarding the CDE is provided in this work to better define the elements comprising its cause and impact. Then, examples of methodologies and approaches employed to achieve successful transient expression at high cell densities (HCD) are thoroughly reviewed. A critical assessment of the limitations of the reported studies in the understanding of the CDE is presented, covering the leading hypothesis of the molecular implications. The overall analysis of previous work on CDE may offer useful insights for further research into manufacturing of biologics.

Modern Sensor Tools and Techniques for Monitoring, Controlling, and Improving Cell Culture Processes

The growing biopharmaceutical industry has reached a level of maturity that allows for the monitoring of numerous key variables for both process characterization and outcome predictions. Sensors were historically used in order to maintain an optimal environment within the reactor to optimize process performance. However, technological innovation has pushed towards on-line in situ continuous monitoring of quality attributes that could previously only be estimated off-line. These new sensing technologies when coupled with software models have shown promise for unique fingerprinting, smart process control, outcome improvement, and prediction. All this can be done without requiring invasive sampling or intervention on the system. In this paper, the state-of-the-art sensing technologies and their applications in the context of cell culture monitoring are reviewed with emphasis on the coming push towards industry 4.0 and smart manufacturing within the biopharmaceutical sector. Additionally, perspectives as to how this can be leveraged to improve both understanding and outcomes of cell culture processes are discussed.

Recent advances in fed-batch microscale bioreactor design

Advanced fed-batch microbioreactors mitigate scale up risks and more closely mimic industrial cultivation practices. Recently, high throughput microscale feeding strategies have been developed which improve the accessibility of microscale fed-batch cultivation irrespective of experimental budget. This review explores such technologies and their role in accelerating bioprocess development. Diffusion- and enzyme-controlled feeding achieve a continuous supply of substrate while being simple and affordable. More complex feed profiles and greater process control require additional hardware. Automated liquid handling robots may be programmed to predefined feed profiles and have the sensitivity to respond to deviations in process parameters. Microfluidic technologies have been shown to facilitate both continuous and precise feeding. Holistic approaches, which integrate automated high-throughput fed-batch cultivation with strategic design of experiments and model-based optimisation, dramatically enhance process understanding whilst minimising experimental burden. The incorporation of real-time data for online optimisation of feed conditions can further refine screening. Although the technologies discussed in this review hold promise for efficient, low-risk bioprocess development, the expense and complexity of automated cultivation platforms limit their widespread application. Future attention should be directed towards the development of open-source software and reducing the exclusivity of hardware.

A genome-scale nutrient minimization forecast algorithm for controlling essential amino acid levels in CHO cell cultures

Mammalian cell culture processes rely heavily on empirical knowledge in which process control remains a challenge due to the limited characterization/understanding of cell metabolism and inability to predict the cell behaviors. This study facilitates control of Chinese hamster ovary (CHO) processes through a forecast-based feeding approach that predicts multiple essential amino acids levels in the culture from easily acquired viable cell density data. Multiple cell growth behavior forecast extrapolation approaches are considered with logistic curve fitting found to be the most effective. Next, the nutrient-minimized CHO genome-scale model is combined with the growth forecast model to generate essential amino acid forecast profiles of multiple CHO batch cultures. Comparison of the forecast with the measurements suggests that this algorithm can accurately predict the concentration of most essential amino acids from cell density measurement with error mitigated by incorporating off-line amino acids concentration measurements. Finally, the forecast algorithm is applied to CHO fed-batch cultures to support amino acid feeding control to control the concentration of essential amino acids below 1-2 mM for lysine, leucine, and valine as a model over a 9-day fed batch culture while maintaining comparable growth behavior to an empirical-based culture. In turn, glycine production was elevated, alanine reduced and lactate production slightly lower in control cultures due to metabolic shifts in branched-chain amino acid degradation. With the advantage of requiring minimal measurement inputs while providing valuable and in-advance information of the system based on growth measurements, this genome model-based amino acid forecast algorithm represent a powerful and cost-effective tool to facilitate enhanced control over CHO and other mammalian cell-based bioprocesses.

Model-based workflow for scale-up of process strategies developed in miniaturized bioreactor systems

Miniaturized bioreactor (MBR) systems are routinely used in the development of mammalian cell culture processes. However, scale-up of process strategies obtained in MBR- to larger scale is challenging due to mainly non-holistic scale-up approaches. In this study, a model-based workflow is introduced to quantify differences in the process dynamics between bioreactor scales and thus enable a more knowledge-driven scale-up. The workflow is applied to two case studies with antibody-producing Chinese hamster ovary cell lines. With the workflow, model parameter distributions are estimated first under consideration of experimental variability for different scales. Second, the obtained individual model parameter distributions are tested for statistical differences. In case of significant differences, model parametric distributions are transferred between the scales. In case study I, a fed-batch process in a microtiter plate (4 ml working volume) and lab-scale bioreactor (3750 ml working volume) was mathematically modeled and evaluated. No significant differences were identified for model parameter distributions reflecting process dynamics. Therefore, the microtiter plate can be applied as scale-down tool for the lab-scale bioreactor. In case study II, a fed-batch process in a 24-Deep-Well-Plate (2 ml working volume) and shake flask (40 ml working volume) with two feed media was investigated. Model parameter distributions showed significant differences. Thus, process strategies were mathematically transferred, and model predictions were simulated for a new shake flask culture setup and confirmed in validation experiments. Overall, the workflow enables a knowledge-driven evaluation of scale-up for a more efficient bioprocess design and optimization.