The therapeutic monoclonal antibody (mAb) market is witnessing an unprecedented growth having nearly duplicated since 2012. Faster regulatory approvals and an increasing number of biosimilars are among the most important mAb market expansion drivers. As the market grows, so does competition making timelines more aggressive. Additionally, political pressure from governments and regulatory authorities over prices of life-saving biological drugs is also increasing. As a consequence, biopharmaceutical companies need to reduce costs while being faster, particularly in having drug substance ready for clinical trials. To this aim, reducing process development times and costs is crucial.
In this thesis, mathematical models integrating several cellular activities important for biomanufacturing are developed. A previously published model development framework is followed to ensure the development of predictive models. An integrated framework combining multivariate data analysis and biomarker identification techniques for cell culture understanding supports the development of predictive mathematical models for two industrially relevant cell lines, GS-NS0 and GS-CHO, capturing cell cycle, metabolism, energy production, mAb production and apoptosis. As the models herein suggested can capture population heterogeneity by describing the cell cycle and gene expression, their applications to bioreactor optimization is envisaged. To this aim, the models developed in this work can be combined with the description of product quality attributes and/or computational fluid dynamics descriptions of bioreactors. These are deemed important applications to accomplish significant reduction in process development time and costs.