Determination of Best Nutritional Conditions for a Monoclonal Antibody-Producing Cell Line based on a Multivariate Data Analysis Approach
The design of mammalian cell culture processes as technological platform for monoclonal antibody (mAb) production is a complex task mainly due to partial knowledge of culture media composition impact on process outcomes. Faced with this problem, the present work aimed to characterize the metabolic profile during the early culture at lab-scale of a specific cell line transfected to obtain a monoclonal antibody (mAb) of therapeutic interest in the treatment of cancer, seeking most favorable nutritional conditions. The experimental design, based on the use of four different media in a two-liter scale culture, provided data on the content of 19 metabolites, cell concentration, and mAb concentration over the course of batches, where in the first case measurements were performed with liquid chromatography-mass spectrometry (LC-MS) as an advanced laboratory analytical support. The corresponding data-driven models, as a result of integrating Principal Component Analysis (PCA), Soft Independent Modeling of Class Analogies (SIMCA) and Partial Least Square Regression (PLSR) methods, revealed the actual difference among media regarding cell culture metabolic progression, and allowed to estimate cell growth behavior and mAb generation relative to biomass metabolites composition. Consequently, such an approach facilitated defining the metabolites that benefit the aforementioned cell culture process and those with a negative effect, as well as the choice of media that ensure the best nutritional conditions under technological and economic bases, thereby providing the essential elements for further media optimization.
