Identifying Drivers of Downstream Yield Variability Using Integrated Process Models: An Application to API Manufacturing

We introduce a novel two-level method to address systematic yield variability in biopharmaceutical batch processes. At the first level (inter-step), we utilize process-wide connectivity data to identify the specific process step where performance variability occurs. A sequential and orthogonalized partial least squares (SO-PLS) model is then developed to trace the origin of these variabilities, linking data blocks across the flowsheet and filtering correlated information. Once a critical step is identified, the second level (intra-step) employs unit-specific PLS models to capture the internal dynamics of that step, using entire batch trajectories for modeling. In collaboration with process experts, this level isolates variable trajectories that drive the systematic variability. Applied to a commercial batch process producing an active pharmaceutical ingredient (API), this method reveals that downstream yield is impacted by variability during cell culture production. Furthermore, a detailed analysis of bioreactor data identifies key manipulated variable trajectories, specifically the dosage of glucose and NH 3 , impacting cell culture production. Validation of process improvement hypotheses is conducted in collaboration with process experts, enhancing transparency and yielding valuable insights.