Fermentanomics: Relating quality attributes of a monoclonal antibody to cell culture process variables and raw materials using multivariate data analysis

• Published in: Biotechnology progress 2015-11, Vol.31 (6), p.1586-1599
• Main Authors: Rathore, Anurag S., Kumar Singh, Sumit, Pathak, Mili, Read, Erik K., Brorson, Kurt A., Agarabi, Cyrus D., Khan, Mansoor
• Format: Article
• Language: English
• Subjects: amino acid supplementation; Amino acids; Amino Acids - analysis; Amino Acids - chemistry; Amino Acids - metabolism; Animals; Antibodies, Monoclonal - analysis; Antibodies, Monoclonal - metabolism; batch modeling; Bioreactors; cell culture media; Cell Culture Techniques - methods; chemometrics; Computational Biology - methods; Culture media; Culture Media - analysis; Culture Media - chemistry; Culture Media - metabolism; Fermentation - genetics; Fermentation - physiology; Glycosylation; Hybridomas - metabolism; Metabolites; Mice; monoclonal antibody; Multivariate Analysis; multiway-PLS; Process control; quality attributes; Raw materials; Research Design
• ISSN: 8756-7938; 1520-6033
• DOI: 10.1002/btpr.2155

Fermentanomics is an emerging field of research and involves understanding the underlying controlled process variables and their effect on process yield and product quality. Although major advancements have occurred in process analytics over the past two decades, accurate real‐time measurement of significant quality attributes for a biotech product during production culture is still not feasible. Researchers have used an amalgam of process models and analytical measurements for monitoring and process control during production. This article focuses on using multivariate data analysis as a tool for monitoring the internal bioreactor dynamics, the metabolic state of the cell, and interactions among them during culture. Quality attributes of the monoclonal antibody product that were monitored include glycosylation profile of the final product along with process attributes, such as viable cell density and level of antibody expression. These were related to process variables, raw materials components of the chemically defined hybridoma media, concentration of metabolites formed during the course of the culture, aeration‐related parameters, and supplemented raw materials such as glucose, methionine, threonine, tryptophan, and tyrosine. This article demonstrates the utility of multivariate data analysis for correlating the product quality attributes (especially glycosylation) to process variables and raw materials (especially amino acid supplements in cell culture media). The proposed approach can be applied for process optimization to increase product expression, improve consistency of product quality, and target the desired quality attribute profile. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1586–1599, 2015