(364d) Digital Twin in Clinical Manufacturing: Flowsheet-Based Modeling for Process Characterization and Effective Scale-up

Batch pharmaceutical processes consist of discrete yet interconnected unit operations, where process dynamics, scheduling decisions, and operating variability influence overall process performance. A key challenge in process development and scale-up is understanding how upstream conditions impact downstream operations and how process fluctuations propagate across multiple steps. This work presents a flowsheet-based system modeling framework that not only unifies thermodynamics, reaction kinetics, and phase change behavior into a predictive digital representation of the manufacturing process, but also connect the unit operations through parameterized batch scheduling, enabling a systematic assessment of process dynamics.

What distinguishes this work is that the system model was not developed in isolation, but rather co-evolved with the process itself, serving as a digital twin engine that continuously incorporated learnings from clinical manufacturing to guide subsequent batches. Originating during NDA Certified batch and continuously refined during scale-up to GMP manufacturing, the model evolved and served as a living representation of the team’s process knowledge, updated as new data and unexpected scaleup outcomes emerged. This presentation unfolds the story of how the system model matured alongside the process.

We conclude by highlighting the value created by this framework, including its deployment across the process development lifecycle, from capturing evolving process knowledge to guiding late-stage development, scale-up, and tech transfer in a high-impact pipeline asset.