Experimentally integrated dynamic modelling for intuitive optimisation of cell based processes and manufacture

Modeling the selective growth advantage of genetically variant human pluripotent stem cells to identify opportunities for manufacturing process control

BACKGROUND AIMS

The appearance of genetically variant populations in human pluripotent stem cell (hPSC) cultures represents a concern for research and clinical applications. Genetic variations may alter hPSC differentiation potential or cause phenotype variation in differentiated cells. Further, variants may have properties such as proliferative rate, or response to the culture environment, that differ from wild-type cells. As such, understanding the behavior of these variants in culture, and any potential operational impact on manufacturing processes, will be necessary to control quality of putative hPSC-based products that include a proportion of variant threshold in their quality specification.

METHODS

Here we show a computational model that mathematically describes the growth dynamics between commonly occurring genetically variant hPSCs and their counterpart wild-type cells in culture.

RESULTS

We show that our model is capable of representing the growth behaviors of both wild-type and variant hPSCs in individual and co-culture systems.

CONCLUSIONS

This representation allows us to identify three critical process parameters that drive critical quality attributes when genetically variant cells are present within the system: total culture density, proportion of variant cells within the culture system and variant cell overgrowth. Lastly, we used our model to predict how the variability of these parameters affects the prevalence of both populations in culture.

Application of a simple unstructured kinetic and cost of goods models to support T-cell therapy manufacture

Manufacturing of cell therapy products requires sufficient understanding of the cell culture variables and associated mechanisms for adequate control and risk analysis. The aim of this study was to apply an unstructured ordinary differential equation-based model for prediction of T-cell bioprocess outcomes as a function of process input parameters. A series of models were developed to represent the growth of T-cells as a function of time, culture volumes, cell densities, and glucose concentration using data from the Ambr®15 stirred bioreactor system. The models were sufficiently representative of the process to predict the glucose and volume provision required to maintain cell growth rate and quantitatively defined the relationship between glucose concentration, cell growth rate, and glucose utilization rate. The models demonstrated that although glucose is a limiting factor in batch supplied medium, a delivery rate of glucose at significantly less than the maximal specific consumption rate (0.05 mg 1 × 10⁶  cell h^-1 ) will adequately sustain cell growth due to a lower glucose Monod constant determining glucose consumption rate relative to the glucose Monod constant determining cell growth rate. The resultant volume and exchange requirements were used as inputs to an operational BioSolve cost model to suggest a cost-effective T-cell manufacturing process with minimum cost of goods per million cells produced and optimal volumetric productivity in a manufacturing settings. These findings highlight the potential of a simple unstructured model of T-cell growth in a stirred tank system to provide a framework for control and optimization of bioprocesses for manufacture.

Regenerative medicine meets mathematical modelling: developing symbiotic relationships

Successful progression from bench to bedside for regenerative medicine products is challenging and requires a multidisciplinary approach. What has not yet been fully recognised is the potential for quantitative data analysis and mathematical modelling approaches to support this process. In this review, we highlight the wealth of opportunities for embedding mathematical and computational approaches within all stages of the regenerative medicine pipeline. We explore how exploiting quantitative mathematical and computational approaches, alongside state-of-the-art regenerative medicine research, can lead to therapies that potentially can be more rapidly translated into the clinic.

Digital Twins for Tissue Culture Techniques—Concepts, Expectations, and State of the Art

Techniques to provide in vitro tissue culture have undergone significant changes during the last decades, and current applications involve interactions of cells and organoids, three-dimensional cell co-cultures, and organ/body-on-chip tools. Efficient computer-aided and mathematical model-based methods are required for efficient and knowledge-driven characterization, optimization, and routine manufacturing of tissue culture systems. As an alternative to purely experimental-driven research, the usage of comprehensive mathematical models as a virtual in silico representation of the tissue culture, namely a digital twin, can be advantageous. Digital twins include the mechanistic of the biological system in the form of diverse mathematical models, which describe the interaction between tissue culture techniques and cell growth, metabolism, and the quality of the tissue. In this review, current concepts, expectations, and the state of the art of digital twins for tissue culture concepts will be highlighted. In general, DT’s can be applied along the full process chain and along the product life cycle. Due to the complexity, the focus of this review will be especially on the design, characterization, and operation of the tissue culture techniques.