(695e) End to End Cell Culture Modeling: Improving Process Development Agility. Starting and Ending With Data

Cell culture processes are widely used in biotechnology and pharmaceutical industries to produce recombinant proteins, monoclonal antibodies, vaccines, and other biologics. These processes are complex and nonlinear, involving multiple interacting factors that affect cell growth, metabolism, and product formation. Designing and optimizing these processes to achieve high productivity and meet quality criteria is time and resource intensive. In silico modeling can be used in the process development workflow to speed up development time and reduce the need for experimentation. Kinetic modeling is one approach that has been successfully used to simulate these complex processes.

In this work, a kinetic model capable of simulating and optimizing fed batch and perfusion-based cell culture processes is presented. This model is derived from the work by Kontoravdi et al. [1] and focuses on the metabolic changes and dynamic character of cell culture processes, especially with complex feeding strategies. Accurately representing lactate metabolism is a central step in model design due to lactate’s important role in overall cell metabolism as an energy source and a growth inhibitor. To properly account for lactate as a viable energy source, its consumption must be accounted for in the model's kinetic and mass balance layers. The consumption of lactate is dependent on many factors, such as glucose concentration, which can make it difficult to accurately model using conventional kinetic methods. Here, we represent lactate’s consumption and its central role in cell growth by using Michaelis Menten and more complex rate laws which is crucial in processes with complex feeding strategies due to resulting fluctuations in lactate production and consumption. The model performance will be compared versus Kontoravdi et al.’s model using a variety of analyses to understand model accuracy, robustness, and capability. This lactate implementation provides additional value to the in silico process development workflow by enabling high simulation accuracy and adding further mechanistic principles.

References:

[1] Kontoravdi C, Pistikopoulos EN, Mantalaris A. Systematic development of predictive mathematical models for animal cell cultures. Computers & Chemical Engineering. 2010;34(8):1192–1198. doi:https://doi.org/10.1016/j.compchemeng.2010.03.012.