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Modeling the Effect of Amino Acids and Copper on Monoclonal Antibody Productivity and Glycosylation: A Modular Approach
Author(s) -
Luo Yu,
Lovelett Robert J.,
Price J. Vincent,
Radhakrishnan Devesh,
Barnthouse Kristopher,
Hu Ping,
Schaefer Eugene,
Cunningham John,
Lee Kelvin H.,
Shivappa Raghunath B.,
Ogunnaike Babatunde A.
Publication year - 2021
Publication title -
biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.144
H-Index - 84
eISSN - 1860-7314
pISSN - 1860-6768
DOI - 10.1002/biot.202000261
Subject(s) - chinese hamster ovary cell , glycosylation , modular design , monoclonal antibody , computer science , process (computing) , critical quality attributes , quality by design , productivity , biochemical engineering , computational biology , biological system , chemistry , biology , biochemistry , engineering , antibody , receptor , macroeconomics , particle size , economics , immunology , operating system
In manufacturing monoclonal antibodies (mAbs), it is crucial to be able to predict how process conditions and supplements affect productivity and quality attributes, especially glycosylation. Supplemental inputs, such as amino acids and trace metals in the media, are reported to affect cell metabolism and glycosylation; quantifying their effects is essential for effective process development. We aim to present and validate, through a commercially relevant cell culture process, a technique for modeling such effects efficiently. While existing models can predict mAb production or glycosylation dynamics under specific process configurations, adapting them to new processes remains challenging, because it involves modifying the model structure and often requires some mechanistic understanding. Here, a modular modeling technique for adapting an existing model for a fed‐batch Chinese hamster ovary (CHO) cell culture process without structural modifications or mechanistic insight is presented. Instead, data is used, obtained from designed experimental perturbations in media supplementation, to train and validate a supplemental input effect model, which is used to “patch” the existing model. The combined model can be used for model‐based process development to improve productivity and to meet product quality targets more efficiently. The methodology and analysis are generally applicable to other CHO cell lines and cell types.