A Novel, Risk-Based Approach for Predicting the Optimum Set of Process and Cell Culture Parameters for Scaling Upstream Bioprocessing

The ability to scale a cell culture effectively and efficiently, from lab to manufacturing, is critical to maximizing productivity whilst minimizing the risk of run failures and delays that can cost millions of dollars per month. The task of scaling well, however, is still considered to be a challenge by many upstream scientists, and this can be an exercise in trial and error. Traditionally, scaling has most often been performed using arithmetic in a spreadsheet and/or simple “back of an envelope” calculations. For some, it may even come in the form of support from a team of data scientists using advanced analytical software. This dependency on what some consider to be complex mathematics or statistics has resulted in the common consideration of using just one scaling parameter at a time, one scale at a time. However, it is difficult to determine easily or optimally, from the start, whether a process successfully transfers across scales based on only one process parameter, at one scale. In this article, we describe the benefits of using a risk-based approach to scaling, and the development of a software scaling tool known as BioPAT® Process Insights for predictive scale conversion across different bioreactor scales. BioPAT Process Insights can be used to consider multiple parameters and across multiple scales simultaneously, from the start of a scaling workflow. We briefly describe how it was used in a proof-of-concept scale-up study to allow a faster, more cost-effective process transfer from 250 mL to 2000 L. In summary, using BioPAT Process Insights, in conjunction with a bioreactor range that has comparable geometry and physical similarities across scales, has the potential to help biopharma manufacturing facilities reach 2000 L production-scale volumes with fewer process transfer steps, saving both time and money during scale-up of biologics and vaccines. INTRODUCTION With speed-to-clinic and speed-to-market becoming increasingly critical to the delivery of biologics and vaccines, developing rapid methods to scale a cell culture process between bioreactors has come into sharp focus. The ability to select an optimum set of process parameters that scales up from laboratory through larger pilot-scale and finally, production-scale bioreactors is imperative to reduce the risk of process performance differences upon scale‐up, and similarly vice versa for scale-down.[1] Scaling effectively and efficiently can also help maximize production yield and product quality. Sub-optimal selection of process parameters when transferring to the next scale can have a negative impact on key process indicators (KPIs) such as viable cell concentration (VCC), cell viability, cell diameter, and product titer. Similarly, critical quality attributes (CQAs) of a biologic, such as its glycan profile[2], can also be detrimentally affected. To address significant process discrepancies, scientists typically need to undertake up to three engineering runs and one lock-down run to optimize process performance, which can take several months. The cost of re-runs during process transfer can be high, especially at manufacturing scales where the costs of media alone can run into thousands of US dollars. In addition, if process transfer causes production delays, one study estimates that for a biologic with $1 billion sales annually, it can cost up to $80 million for each month[3] of delay and emphasizes why scaling well is so important. To help minimize costs and bioprocessing scientists’ time, scale-up studies to select the best performing clone and then optimize process development are typically run in small, lower cost-per-run model systems. These include shake flasks and automated miniature bioreactors.