(556e) Prediction of Extrusion Design Space from Rheology Data

Hot-melt extrusion (HME) is a widely used technique to produce amorphous solid dispersions, enabling enhancement of the solubility and bioavailability of poorly water-soluble drugs. However, the development of HME processes traditionally requires significant amounts of material, time, and iterative experimental procedure to identify suitable operating parameters such as drug loading, temperature profile, and screw speed. To address these challenges, we have developed a predictive approach that integrates rheological characterization of drug–polymer systems to define critical processing conditions prior to extrusion. Rheological measurements, including temperature sweep, oscillation amplitude sweep, and powder rheology, were performed on drug–polymer blends to evaluate their thermal softening behavior, viscoelastic properties, and melt processability. These data were used to calculate key parameters such as powder flowability, complex viscosity and the relaxation time of polymer, enabling the identification of the minimum processable temperature, the optimal range of rotation speeds, and the threshold of residence time. The study introduces a framework for constructing dimensionless extrusion maps based on these rheological inputs. These maps indicate the boundaries of the possible extrusion design space, considering thermal and mechanical limitation of the drug-polymer blends. Experimental extrusion trials across multiple drug–polymer systems validated the predictive power of the model, showing good agreement between predicted and observed process windows. This approach significantly reduces the reliance on trial-and-error experimentation, supporting more efficient and systematic process development. Beyond pharmaceuticals application, the methodology holds potential applicability in the food and polymer industries, where hot-melt extrusion is also used to process complex, multi-component systems. By leveraging fundamental rheological properties, this work establishes a robust, generalizable foundation for rational HME process design.