Systematic development of predictive mathematical models for animal cell cultures

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 modelbased control and optimisation.3

PAROC—An integrated framework and software platform for the optimisation and advanced model-based control of process systems

“…The sensitivity analysis indices are usually computed through Monte-Carlo numerical integration Kontoravdi et al (2005), Kontoravdi et al (2010), Yue et al (2008), and Kiparissides et al (2009) Use of sensitivity analysis in the context of biomedical engineering in order to asses the robustness of complex biological and biomedical models and quantify uncertainty Homma and Saltelli (1996), Saltelli et al (2000), and Saltelli et al (2010) Development of global sensitivity analysis as a tool to detect parameter interactions, in particular variance based methods Narciso and Pistikopoulos (2008) Combination of linear model reduction and linear multi-parametric model-predictive control (mp-MPC) Rivotti et al (2012) A model order reduction via empirical Grammians (Hahn and Edgar, 2002) is combined with a mp-MPC algorithm Lambert et al (2013a) Using Monte-Carlo integrations, N step ahead affine representations are created policies. The calculation of such policies, e.g.…”

Dynamic Optimization of Bioprocesses

“…Model development followed the framework outlined in Figure 1 and described in detail in [4]. Specifically, once the initial mechanistic model was formulated based on knowledge about the underlying biological phenomena, the model structure was verified by comparison to literature or preliminary experimental data.…”

“…In a previous study [4], we presented the development and validation of a model of antibody-producing mammalian cell cultures. The model and the underlying assumptions are presented Appendix A.…”

Revisiting Verhulst and Monod models: analysis of batch and fed-batch cultures

“…Portner and Schafer (1996) found that irrespective of the cultivation mode, i.e., batch or fed-batch, the growth of the cell lines follows the same kinetics and, therefore, it is possible that data gained from batch experiments can be extended to continuous fed-batch cultures (Kontoravdi et al 2010;Hu 2004). Therefore, in the present work, a numerical approach was developed to estimate the Monod model constants, using transformed batch Data-2, -3 and -4, and then the batch models, thus arrived at, were applied to their respective bolus and continuous fed-batch cultures.…”

“…(18) is unstructured, unsegregated and deterministic, requiring no knowledge of underlying mechanism of biological phenomena. They are very useful for model-based application for monitoring and control of bioprocesses (Kontoravdi et al 2010). …”