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 āback of an envelopeā 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 BioPATĀ® 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.