The monoclonal antibody (mAb) industry is witnessing unprecedented growth, with an increasing range of new molecules and biosimilars as well as disease targets approved than ever before. Competition necessitates pharmaceutical companies to reduce development/production costs and time-to-market. To this aim, mathematical modeling can aid traditional experiment-only-based process development by reducing the design space, integrating scales, and assisting in identifying optimal operating conditions in less time and with lower expense. Mathematical models have been employed by other industries for control and optimization purposes and are important decisional tools for testing scenarios, process configurations, operating conditions, etc. Herein, a predictive, experimentally validated mathematical model that captures cellular metabolism and growth with cell cycle, cell death (apoptosis), and mAb production in GS-NS0 cells is presented. The model utilizes cellular, metabolic, and gene expression data, highlighting how multiple data sources can be integrated in one tool with the aim of optimizing mammalian cell bioprocessing.
Keywords: bioprocessing; mammalian cells; mathematical modeling; monoclonal antibodies.
© 2019 The Authors. Biotechnology Journal Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.