(499f) Mathematical Modeling and Optimization of the Upstream Monoclonal Antibody Production

Ou Yang, Marianthi Ierapetritou

Department of Chemical and Biochemical Engineering, Rutgers—The State University of New Jersey, 98 Brett Road, Piscataway, New Jersey 08854-8058, United States

Abstract

Mathematical modeling has been widely used in upstream biomanufacturing to simulate bioreactor operations and optimize and control the cell culture systems^1-2. A critical component of cell culture performance for monoclonal antibody (mAbs) production is glycosylation^3-4. The work done in the literature for cell culture simulation can be distinguished into kinetic-based model and stoichiometric method¹. Kinetic model such as cisternal maturation model is mainly used in Golgi and glycosylation simulation^5-8 which is a powerful way to understand dynamic change of glycosylation process and to predict product components under different operating conditions. However, the kinetic model involves a large number of parameters and requires extensive experimental data. In addition, many components concentration such as nucleotides and nucleotide sugars, glycoprotein distributions inside of CHO cells that are important inputs for model building, are hard to be accurately measured. Flux based method and Markov chain models on the other hand, apply stoichiometric method which do not require intricate parameter estimation but use flux to represent the possibility of each reaction happens in side of the cell^{4, 9}. This method is good for process understanding and product prediction with mutated cell lines but it assumes the quasi-steady state for intracellular metabolites and hard to predict product as operating conditions change.

In this study, a simplified single cell model is developed to dynamically simulate the CHO cell culture process in both fed-batch and perfusion bioreactors. The model contains unstructured cell culture model which is coupled to structured glycosylation model with reduced number of parameters. Cell culture model simulates cell growth and death, nutrients consumption and metabolites’ concentrations. The glycosylation model predicts major glycoprotein concentrations varying with time including intermediates glycosylated proteins and final product protein quality attributes. Experimental data are used for model parameter estimation and model validation. The operating conditions effect on cell culture and product compositions are investigated, including temperature, pH, ammonia concentration and other nutrients additions. Based on the correlations among operating conditions with product qualities, the process design space is defined. The overall process is optimized to achieve the required product specification. The purpose of this work is to apply mathematical modeling approaches to improve process understanding of mAbs production, achieve cell culture optimization and establish soft-sensors for variables that cannot be measured online experimentally.

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