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.
Tag: <span>benchtop bioreactor mimic</span>
The price per patient for protein-based and monoclonal antibody (mAb) therapies runs into thousands of dollars per patient each year. These therapies cost considerably more to manufacture than small molecules. Hence, if mammalian or insect cell lines expressing high protein titres can be selected and optimized for protein expression using microscale bioreactor models early in development, then manufacturing costs can be reduced significantly…