Process Modeling and Mechanistic Models

Overview

Computational representations of bioprocessing unit operations using first-principles chemical engineering or empirical fitting to predict process behavior, reduce experimental burden, support regulatory filings, and enable model-based lifecycle management Mechanistic and machine learning process models are being pursued as a basis for regulatory filings that rely on modeling data rather than exhaustive experimental campaigns, which would accelerate lifecycle management and reduce development costs for approved products. Bayesian optimization combined with multi-project FAIR data allows companies to create global process models that localize to new molecules using as few as 5 targeted experiments, demonstrating that model-driven development already delivers measurable competitive advantage. (Segment 5 (20:00-25:00))