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Design of experiments applications in bioprocessing: Chromatography process development using split design of experiments
Author(s) -
Shekhawat Lalita K.,
Godara Avinash,
Kumar Vijesh,
Rathore Anurag S.
Publication year - 2018
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.2730
Subject(s) - design of experiments , factorial experiment , bioprocess , process (computing) , bottleneck , fractional factorial design , parametric statistics , interaction , central composite design , process design , computer science , set (abstract data type) , main effect , process engineering , biological system , mathematics , response surface methodology , machine learning , statistics , engineering , process integration , chemical engineering , programming language , biology , embedded system , operating system
Development of a chromatographic step in a time and resource efficient manner remains a serious bottleneck in protein purification. Chromatographic performance typically depends on raw material attributes, feed material attributes, process factors, and their interactions. Design of experiments (DOE) based process development is often chosen for this purpose. A challenge is, however, in performing a DOE with such a large number of process factors. A split DOE approach based on process knowledge in order to reduce the number of experiments is proposed. The first DOE targets optimizing factors that are likely to significantly impact the process and their effect on process performance is unknown. The second DOE aims to fine‐tune another set of interacting process factors, impact of whom on process performance is known from process understanding. Furthermore, modeling of a large set of output response variables has been achieved by fitting the output responses to an empirical equation and then using the parametric constants of the equation as output response variables for regression modeling. Two case studies involving hydrophobic interaction chromatography for removal of aggregates and cation exchange chromatography for separation of charge variants and aggregates have been utilized to illustrate the proposed approach. Proposed methodology reduced total number of experiments by 25% and 72% compared to a single DOE based on central composite design and full factorial design, respectively. The proposed approach is likely to result in a significant reduction in resources required as well as time taken during process development. © 2018 American Institute of Chemical Engineers Biotechnol. Prog ., 35: e2730, 2019