Streamlined development of continuous blending through integrated process understanding and predictive modeling

Within the framework of continuous manufacturing of drug products, continuous blending plays a central role in the quality of the final product. However, the interplay between material properties and process parameters remains complex and system-dependent. This work presents an integrated experimental and predictive framework to characterize and optimize blending performance in a 70-mm GEA linear continuous blender. A full factorial design study was conducted using different raw materials and blend mixtures covering a wide range of powder properties such as flowability, cohesion, and density. The main goal was to quantify how blender speed, throughput, and blender configuration influence hold-up mass (HUM), mean residence time (MRT), number of blade passes (NBP), and time to reach steady state. Distinct blending regimes were identified, with materials exhibiting shear- or transport-dominated behavior depending on their properties and process conditions. A partial least squares (PLS) model was then created to predict HUM from material attributes and process conditions, enabling the estimation of MRT and NBP profiles for new formulations, with only two material characterization tests: bulk/tapped density and shear cell yield locus. Finally, tablet content uniformity studies and residence time distribution studies showed that higher mixing element configurations and adequate hold-up mass are crucial for effective macromixing and reducing throughput variability from the feeders, especially for low-dose formulations.