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Process characterization for metal‐affinity chromatography of an Fc fusion protein: a design‐of‐experiments approach
Author(s) -
Shukla Abhinav A.,
Sorge Laura,
Boldman Joshua,
Waugh Steve
Publication year - 2001
Publication title -
biotechnology and applied biochemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.468
H-Index - 70
eISSN - 1470-8744
pISSN - 0885-4513
DOI - 10.1042/ba20010019
Subject(s) - robustness (evolution) , fractional factorial design , fusion , computer science , design of experiments , process design , factorial experiment , process (computing) , biological system , elution , biochemical engineering , process engineering , chromatography , chemistry , mathematics , process integration , machine learning , engineering , biology , statistics , biochemistry , linguistics , philosophy , gene , operating system
The utility of a design‐of‐experiments approach was investigated for process characterization of a metal‐affinity chromatographic purification process for an Fc fusion protein. This approach gave a better understanding of some of the key process variables as well as robustness for this step in the purification process. Single‐variable experiments were employed to screen some of the potentially important variables in this step. Ranges for these variables were set based on prior experience in clinical manufacturing with similar processes. Following these experiments, a fractional factorial study was employed to further investigate the most important variables and their interactions. Key operational variables that had an impact on step yield and eluate purity were identified. In addition, the study helped identify a worst‐case scenario for the step purity and helped assure that the rest of the process would successfully purify the product. This paper demonstrates the utility of a design‐of‐experiments approach for the characterization and validation of process chromatography steps in downstream processing. In addition, this study emphasizes the utility of robustness studies early in process development and establishes a strategy for future robustness studies.