Cell culture media development for antibody production is beginning to incorporate artificial intelligence as an alternative to traditional trial-and-error workflows.
This presentation will outline a framework based on predictivity, proactivity, and process, and discuss how data modeling, raw material awareness, and analytical practices can be brought together to support media design and scale-up planning. We will describe how machine learning methods can help examine complex formulation
space and identify media components associated with titer and product quality outcomes. The session will also consider how integrating process data with modeling
approaches may shape future strategies in antibody manufacturing.