By Dogan Ornek, Nida Sayed, Matthew Sousa, Connor Crue, Ravi Chari, Stephanie Delzell, Adalia Cardoso, Roy Lin, and Philip J. Ramsey
Volume 24, Open Access (September 2025)
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…
Citation: Ornek, D; Sayed, N; Sousa, M; Crue, C; Chari, R; Delzell, S; Cardoso, A; Lin, R; Ramsey, PJ. Characterization and optimization of biologics manufacturing using space-filling designs and machine learning. BioProcess J, 2025; 24. http://dx.doi.org/10.12665/J24OA-Ornek
Posted online September 8, 2025