Streamlining Loss-in-Weight Feeding Process Development Using Predictive Tools

Continuous Direct Compression (CDC) has gained increased attention due to its advantages over batch processing. In CDC, each material is individually fed into the system at a predefined rate via loss-in-weight feeders, making it essential to understand their behavior during processing. This study aimed to predict the feeding performance and ideal setup for a new material, based on material properties. This was achieved by correlating material properties and feeder setup with feeding performance. A wide range of materials, including excipients, APIs, and spray-dried excipients, were physically and rheologically characterized, and their feeding performance evaluated. Results indicate that feeding performance is predominantly influenced by variables related to density, cohesion, and flowability, which can be obtained by performing three characterization analyses. In turn, these properties determine how different setup options influence feeding behavior. This workflow allows a data-driven approach, significantly reducing the time and material spent on process development.