Tuesday March 10Tuesday, 10 March 2026 - PT (Pacific Time, GMT-08:00)

Mammalian cell culture processes have been used for decades to produce therapeutic antibodies. These bioprocesses consist of costly reagents using cell lines with long doubling times and require aseptic conditions that often necessitate expensive single use equipment. The development of an Upstream process for commercial production also involves generation of numerous data sets to demonstrate process understanding and control. The result of these numerous challenges and requirements is that the development of a process takes 10-15 years with a cost of at least $100M. The reduction of timeline and cost has the potential to be achieved through use of computational tools that have matured greatly over the last several years. Machine learning based models of the cell culture production bioreactor have been developed with the goal of not only reducing the development load but also aiding in process prediction. Examples that highlight the potential of these tools are presented.

• Develop risk-based and science-based analytical control strategy
• Considerations for assay development, qualification and validation
• Considerations for stability and shelf-life studies
• Common pitfalls & how to avoid them