Rethinking Upstream Process Development with AI-Driven Hybrid Modelling

Sabine Arnold 
Boehringer Ingelheim

Hybrid modeling, integrating mechanistic understanding with data-driven approaches, is emerging as a powerful paradigm in upstream process development. In the era of artificial intelligence, AI-driven hybrid models enable a more holistic representation of complex cell culture systems by bridging biological knowledge and experimental data. This talk explores how such approaches can transform process understanding, reduce experimental effort, and shorten development timelines at lower costs. A forward-looking perspective is presented on how hybrid modeling may redefine the future of upstream bioprocessing.

Sabine

Sabine Arnold

Global Development CMC Biologicals

Sabine is a biochemical engineer by training and earned her PhD in Biochemical Engineering from the University of Stuttgart, Germany. She has more than 20 years of experience in the mathematical modelling of microbial and mammalian bioprocesses. Currently, she is a Senior Scientist in upstream process development at Boehringer Ingelheim. Her work focuses on the application of advanced data analytics and mathematical modelling, combining mechanistic and AI-based approaches to support and optimize bioprocess development.

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