Predictive macroscopic models of cell growth, metabolism and monoclonal antibody production of fed-batch processes at various scales
July 16-20, 2017
Recently, the pharmaceutical industry is increasingly focusing on early drug development which comes with increasing constraints to accelerate process development, reduce costs and demonstrate a deep understanding cell culture processes. However, cellular metabolism is very complex and by far not fully understood. Cells can be cultivated in various types of bioreactors applying sophisticated feeding strategies mostly based on experience and series of experiments. Modern systems biology promises modeling of such processes on the basis of a system-wide understanding of cellular processes but is still unable to deliver predictive models in due time at reasonable cost. Practically applicable, predictive models are highly demanded in industry for the purpose of process optimization and control. To this end, we developed a systematic methodology for metabolic and cell growth modeling that is directly applicable in an industrial environment. We demonstrate that the models developed are able to predict a wide range of new experimental cell culture conditions.
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Bassem Ben Yahia, Laetitia Malphettes, Boris Gourevitch, and Elmar Heinzle, "Predictive macroscopic models of cell growth, metabolism and monoclonal antibody production of fed-batch processes at various scales" in "Biochemical and Molecular Engineering XX", Wilfred Chen, University of Delaware, USA Nicole Borth, Universität für Bodenkultur, Vienna, Austria Stefanos Grammatikos, UCB Pharma, Belgium Eds, ECI Symposium Series, (2017). http://dc.engconfintl.org/biochem_xx/69
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