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Multivariate data analysis of growth medium trends affecting antibody glycosylation
Author(s) -
Powers David N.,
Trunfio Nicholas,
VelugulaYellela Sai R.,
Angart Phillip,
Faustino Anneliese,
Agarabi Cyrus
Publication year - 2019
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1002/btpr.2903
Subject(s) - multivariate statistics , glycosylation , principal component analysis , bioprocess , multivariate analysis , univariate , glycan , computational biology , critical quality attributes , process analytical technology , computer science , biochemical engineering , microbiology and biotechnology , chemistry , biology , biochemistry , engineering , glycoprotein , artificial intelligence , machine learning , paleontology , particle size
Abstract Use of multivariate data analysis for the manufacturing of biologics has been increasing due to more widespread use of data‐generating process analytical technologies (PAT) promoted by the US FDA. To generate a large dataset on which to apply these principles, we used an in‐house model CHO DG44 cell line cultured in automated micro bioreactors alongside PAT with four commercial growth media focusing on antibody quality through N ‐glycosylation profiles. Using univariate analyses, we determined that different media resulted in diverse amounts of terminal galactosylation, high mannose glycoforms, and aglycosylation. Due to the amount of in‐process data generated by PAT instrumentation, multivariate data analysis was necessary to ascertain which variables best modeled our glycan profile findings. Our principal component analysis revealed components that represent the development of glycoforms into terminally galacotosylated forms (G1F and G2F), and another that encompasses maturation out of high mannose glycoforms. The partial least squares model additionally incorporated metabolic values to link these processes to glycan outcomes, especially involving the consumption of glutamine. Overall, these approaches indicated a tradeoff between cellular productivity and product quality in terms of the glycosylation. This work illustrates the use of multivariate analytical approaches that can be applied to complex bioprocessing problems for identifying potential solutions.