Capturing mixing effects on aggregation kinetics of monoclonal antibodies during viral inactivation

Mathematical models play a central role in biopharmaceutical manufacturing, especially within the Quality by Design framework. For these models to be effectively used in optimization tasks, they must be both reliable and capable of delivering results in an affordable computational time. This work proposes a strategy to model aggregate formation during viral inactivation in the context of monoclonal antibody downstream processing. These units often display mixing-sensitive behavior because aggregation kinetics is controlled by local pH, whose spatial heterogeneities arise from titrant addition at a defined feed point. To address this challenge, compartment models (CMs) are employed. This modeling approach captures spatial inhomogeneities within the unit by leveraging flow-exchange information derived from a single steady-state Computational Fluid Dynamics (CFD) simulation involving only the solution of mass, momentum and turbulence equations. Results obtained by comparing compartment models with both perfectly mixed models and full CFD simulations including aggregation kinetics demonstrate that CMs can reproduce the CFD results with good approximation, while reducing computational time by orders of magnitude.