Artificial intelligence (AI) is accelerating the journey from bench to bedside for therapeutic proteins, vaccines, cell and gene-based therapies, and other well characterized biologics. This plenary session will showcase how in the later stages of product development and in commercial manufacturing the state-of-the-art digital technologies can transform every stage of biotherapeutic manufacturing and control.

AI powered process development platforms combine mechanistic and machine learning models to shrink experimental design spaces, improve predictions, shorten tech transfer timelines, and derisk commercial scaleup.

Digital twin high fidelity, data driven replicas of upstream and downstream unit operations are now enabling predictive engines for critical quality attribute (CQA) monitoring and forecasting, providing early-warning soft-sensors, real-time what if scenario testing and closed loop control with an outlook to delivering adaptive processes that learn and self-optimize across scales.

On the analytical front, AI augmented methods development and validation leverage advanced chemometrics, pattern recognition, and generative algorithms to automate workflows, harmonize transfers between global sites, and enable rapid methods deployment.

Machine-learning enhanced visual inspection methods continue to progress. Conventional automated visual inspection of parenteral products can be significantly improved by image analysis powered with machine learning/deep learning, reducing the misidentification of defects and accelerating the production process by minimizing additional manual inspections.

For all these aspects integrated data pipelines are enablers that stream continuous process and product data into predictive models.

This session will present recent breakthroughs and applications of AI/ML, followed by a panel discussion on current challenges and future directions. Attendees will gain actionable insights into deploying AI responsibly to improve efficiency, robustness and agility in the manufacture of biologics.

Session Speakers:

Designing the AI-Enabled CMC Ecosystem for Drug Development and Manufacturing

Pablo Rolandi, Amgen Inc.

Machine Learning-Supported (Process) Models: Promises and Practical Regulatory Challenges

Christina Heinlein, F. Hoffmann-La Roche Ltd

Integrating Artificial Intelligence into Pharmaceutical Manufacturing

Tina Kiang, CDER, FDA

Additional Panelist:

Jayda Siggers, Health Canada

Gert Thurau

Appearances