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.
Tag: <span>boston institute technology</span>
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.