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Cohesive Powder Feeding Modelling
Continuous manufacturing is emerging as a reliable, robust, and even preferred means to produce oral solid dosage forms. As dosage unit potency is determined by the relative flowrates of the solid components, precise control of powder feed rates and levels of variability is essential to adequate control of product quality. Material is typically introduced into a continuous process by loss-in-weight (LIW) feeders. LIW feeders consist of a reservoir hopper filled with powder sitting above horizontal screws that rotate at a controlled speed. The entire assembly sits atop a scale and change in mass is recorded over time as the screws rotate and pull material from the hopper and dispense at the end of the screw. Screw design and speed can be altered to achieve a wide range of flow rates for a given material. However, consistent feed rates can be difficult to achieve due to the cohesive nature of materials in pharmaceutical formulations, most notably the active pharmaceutical ingredient (API). The API is typically in the micron particle size range where interparticle forces are similar to the gravitational force per particle. High feed rate variability might result in high unit dosage variability, which may pose challenges to continuously manufacture some oral formulations. In addition, as multiple kilograms of material are necessary to fill the reservoir hopper to relevant fill levels, proper evaluation for feasibility of continuous manufacturing may have to be delayed until later in process development. At this point switching to continuous processing from typical batch processing (or the reverse if the evaluation is unsuccessful) can be costly and delay development and filing timelines.
The objective of this proposal is to seek a partner to develop a model that scientists, operators, and engineers can use to predict feeding performance (flow rate and associated variability) of a granular material based on a short list of material properties, which can be measured on a small sample of material. Such a model can provide appropriate estimates of probabilities of success for continuous manufacturing early on during development.