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.

This panel will move beyond the “which is better” debate to address how leading companies evaluate and select the right upstream strategy across their portfolios. Experts will share case studies and business studies on decision-making frameworks, considering factors such as molecule type, clinical phase, cost of goods, regulatory expectations, facility readiness, and long-term scalability. Discussions will also explore the role of modeling, digital twins, and AI-driven analytics in simulating productivity and risk trade-offs between fed-batch, intensified, and fully continuous processes.