Abstract

Monoclonal antibodies (mAb) are complex molecules that exhibit high specificity and affinity making them suitable for novel diagnostic and therapeutic applications. Model-based techniques could be used to develop optimization strategies to design feeding regimes that maximize mAb titer in mammalian cell cultures. Existing feeding strategies depend mainly on glucose and glutamate supply, neglecting the exhaustion of other essential amino acids and the energy requirements for the proliferation and maintenance of cells. In this work, cell composition and energy requirements have been considered in the development of a novel dynamic predictive model for GS-NS0 cells producing cB72.3 mAb. The model describes the production and consumption of ATP based on glucose and amino acids energy metabolic networks. The successful coupling of growth kinetics equations and stoichiometric balances and the in vitro/in silico approach has enabled us to develop the first dynamic model that predicts the ATP content in mammalian cell cultures.