Jan. 1, 2020, midnight
Hannah L. Keizer
Remove from favorites
Add to favorites
Roll compaction/dry granulation stands as a widely employed method in the pharmaceutical sector for granulation. Particularly in the initial stages of solid dosage form formulation development, simulating this process is highly valuable. The hybrid modeling approach offers the capability to forecast roll compaction process parameters, ensuring the creation of ribbons with the targeted solid fraction. Historically, prediction accuracy for the solid fraction of ribbons has fallen short. This deficiency has been attributed to the oversight of elastic recovery within the model. In this investigation, the swift in-die and gradual out-of-die elastic recovery of diverse excipients featuring distinct compaction properties were explored.
A methodology was devised to rectify the impact of compacts' elastic recovery in roll compaction simulation, significantly enhancing the prediction accuracy of the solid fraction. The outcomes were effectively integrated into the model via an additional learning step. Furthermore, these insights were applied to emulate a formulation containing an Active Pharmaceutical Ingredient (API). Through modeling, precise prediction of the process settings required for obtaining ribbons with the desired solid fraction was achieved, employing only a modest amount of material.