Philip Ramsey, Consultant and Professor, North Haven Group and University of New Hampshire
Patricia D. McNeill, PhD, Associate Director of Cell Culture Development, Lundbeck Seattle BioPharmaceuticals, Inc.
Scientists in biopharma working along the CMC development pathway are challenged by the increasing complexity of biologic-based therapeutics and processes that produce them. Many critical responses exist (often 10-20) that are themselves functions of numerous, highly interactive input process factors.
We use a large case study to show that current experimental design strategies combined with current response surface methods are generally inadequate to deal with the complex kinetic behaviors observed to be ever-changing across the design space. The case study consists of a 7-factor, hybrid experimental design used to develop a bioprocess with 13 critical responses. Employing a combination of SVEM, neural network models, and the hybrid experimental design, we show that accurate predictive models can be estimated for all responses that interpolate satisfactorily throughout the design space.
Furthermore, we show that the powerful tools in JMP and the Prediction Profiler, specifically the Design Space Profiler, are essential to visualizing and understanding the experimental design space and optimizing the bioprocess. We also discuss the need for new optimal design strategies. JMP Pro 17 is used throughout the talk.
