Process optimization is a key development step that precedes scale-up and tech-transfer in a manufacturing environment. New England Biolabs (NEB) and GE Healthcare (GEHC) set the objective of improving filtration clarification and concentration process steps with the target of improving overall efficiency, and reducing the labor time and expense associated with these unit operations…
Tag: <span>doe</span>
Efficient bioprocess characterization is essential for both regulatory compliance and commercial viability of biologics. Traditional approaches using resolution III/IV screening designs followed by response surface methodology are time-consuming, costly, and not always effective in identifying the important experimental effects. Definitive screening designs (DSDs) represent a novel class of three-level screening designs that can simultaneously evaluate main effects and quadratic relationships. While DSDs are increasingly used in bioprocess development, practical implementation guidelines remain limited. This case study bridges this gap by introducing a model-based framework to identify critical process parameters (CPPs) and optimize operating ranges for robust biologics production using plasmid DNA (pDNA). Minimal 14-run DSDs evaluated six input parameters and successfully identified CPPs and optimal operating ranges. This approach reduces experimental requirement by >50% compared to traditional designs, providing an efficient and economical strategy for bioprocess characterization and optimization.
This study assessed a novel statistical approach using space-filling designs (SFDs) and self-validating ensemble modeling (SVEM) machine learning to efficiently identify key process factors using recombinant adeno-associated virus type 9 (rAAV9) gene therapy manufacturing as a case study. Based on risk assessment of parameters that may impact rAAV9 production, we have evaluated six process parameters using 24-run SFDs generated by the JMP statistical software. SFDs are a new class of design of experiment (DoE) created with the objective of covering the entire design space as completely as possible; this in turn allows more accurate modeling of complex response surface behavior typically found in bioprocesses.
Cation exchange chromatography is typically utilized in bind-and-elute mode for monoclonal antibody purification. However, during purification process development for a novel monoclonal antibody (MAb) intended for clinical use, it was determined that bind-and-elute conditions were not sufficient for removing significant levels of antibody aggregate. Based on preliminary purification data, an alternative purification method, operation of the cation exchange process in flow-through mode, was investigated.
Traditionally, the Six Sigma framework has underpinned quality improvement and assurance in biopharmaceutical manufacturing process management. This paper proposes a neural network (NN) approach to vaccine yield classification and compares it to an existing multiple linear regression approach. As part of the Six Sigma process, this paper shows how a data mining framework can be used to extract further value and insight from the data gathered during the manufacturing process, and how insights into yield classification can be used in the quality improvement process.