(134e) Accelerating Biopharmaceutical Mixing Process Development with Hybrid Modeling and Computational Fluid Dynamics

Mixing operations in the biopharmaceutical manufacturing process, though straightforward in nature, still require extensive amounts of experimentation and validation; both aspects being resource and time intensive. Mixing is a highly dimensional problem as its key input parameters - impeller speed, mixing time, batch volume, product viscosity and vessel geometry - impact multiple aspects of product quality. A solid understanding of these relationships is critical to ensure patient safety and regulatory acceptance. Computational techniques have opened the door to significantly reducing the amount of experimentation and accelerating qualification efforts for new processes. The latter has become even more important in today’s world, with a greater need to respond rapidly to new public health threats such as COVID-19. We will present a case study of how Computational Fluid Dynamics (CFD) was recently used to accelerate the mixing process development timeline for Regeneron’s REGN-COV2 antibody cocktail by mapping mixer performance and mitigating risks to product quality. We will also detail a framework of how high-throughput experiments, online conductivity measurements, data-driven models and mechanistic models can all be combined to further enhance process development. Finally, we will share a verification and validation effort - aligned with the ASME’s VVUQ 40 standard and the FDA’s reporting guidance for computational modeling - that will serve to increase model credibility and expand the role of models in decision making. Putting each of these elements together, we envision the lean process development roadmap of the future: where computational methods supplant physical testing, product-specific development packages are reduced to a minimum, and rapid process development is a reality.