A multi-step machine learning approach for accelerating QbD-based process development of protein spray drying.

This study proposes a new material-efficient multi-step machine learning (ML) approach for the development of a design space (DS) for spray drying proteins. Typically, a DS is developed by performing a design of experiments (DoE) with the spray dryer and the protein of interest, followed by deriving the DoE models via multi-variate regression. This approach was followed as a benchmark to the ML approach.

Steady-state modeling of a new continuous API dryer: Reduced-order model and investigation of dryer performance.

In the present study, a reduced-order model is proposed to analyze a novel continuous dryer with an application in the pharmaceutical industry. The model was validated using process data from ibuprofen drying test runs, and the results were in good agreement with the experimental data. The test substance was an ibuprofen paste with an initial LOD of up to 30 w%.

RTD modeling of a continuous dry granulation process for process control and materials diversion.

Disturbance propagation during continuous manufacturing processes can be predicted by evaluating the residence time distribution (RTD) of the specific unit operations. In this work, a dry granulation process was modelled and four scenarios of feeding events were simulated. We performed characterization of the feeders and developed RTD models for the blender and the roller compactor based on impulse-response measurements via color tracers.