(W0930-09-49) Biopharma Mixing Scale-Up: Small-Scale and Computational Studies

Purpose: Mixing is a critical step in biopharmaceutical manufacturing, influencing drug substance and product development across various phases. This work addresses the traditional question of how to systematically scale-up the mixing process using representative small-scale experiments and computational tools. We also explore novel mixing challenges posed by new, demanding holding conditions and advanced biologic modalities, such as virus-like particle mixing

Methods: A combination of engineering scale-up principles, computational analysis, and historical data is leveraged to define the recommended mixing design space. This hybrid approach ensures reliable extrapolation from small-scale to large-scale operations, maintaining process fidelity and product quality. By addressing the nuances of new biologic modalities, we provide a framework for optimizing mixing processes in the evolving landscape of biopharmaceutical manufacturing. We used small-scale glass beaker mixing studies to assess product quality. Computational fluid dynamics (CFD) simulations were performed using M-Star and Ansys Fluent, with simulation runs validated against homogeneity results. To ensure both homogeneity and product quality, we retained the power/volume factor and non-dimensional shear rate across different scales of mixing vessels. Historical data from mixing tank vendor and existing engineering scale-up rules were integrated to establish a robust design space, addressing specific challenges associated with virus-like particles and other sensitive biologic hold-time conditions.

Results: The small-scale glass beaker studies demonstrated that product quality is maintained even at very high RPMs, provided the mixing time remains short. By integrating volume, mixing RPM, and mixing time with CFD and scale-up rules, we established a design space that ensures both product quality and homogeneity. Additionally, the CFD results were validated to predict cooling and heating times for jacketed vessels, providing valuable data for budgeting hold times during manufacturing.

Conclusion: By integrating scale-up rules, historical data, and small-scale studies and CFD, we established a reliable design space that ensures both product quality and homogeneity. Additionally, validated CFD results can accurately predict cooling and heating times for jacketed vessels, aiding in the effective budgeting of hold times during manufacturing. This approach enhances the reliability and efficiency of the mixing scale-up process, providing a robust framework for optimizing biopharmaceutical manufacturing.

Biopharma mixing scale-up tools

Mixing for 50L mixing vessel