A Predictive Mathematical Model of Cell Cycle, Metabolism, and Apoptosis of Monoclonal Antibody‐Producing GS–NS0 Cells. Issue 11 (19th August 2019)

• Record Type: Journal Article
• Authors: Grilo, António L.; Mantalaris, Athanasios
• Is Part Of: Biotechnology journal. Volume 14:Issue 11(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-08-19
• Subjects: bioprocessing -- mammalian cells -- mathematical modeling -- monoclonal antibodies; Biotechnology -- Periodicals; 660.605
• DOI: 10.1002/biot.201800573
• Languages: English
• ISSNs: 1860-6768

Abstract

Abstract : 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. Abstract : In spite of the wide use of DoE, biologics process development remains experimentation‐based. Mathematical models, as that herein suggested, provide a more rational alternative. An integrated cell cycle, metabolism and apoptosis model of mAb‐producing GS‐NS0 cells able to drive and optimize process development in shorter times, reducing time‐to‐clinic is presented and experimentally validated.