Accelerating pharmaceutical tablet development by transfer of powder compaction equipment across types and scales.

Roller compaction is a key unit operation in a dry granulation line for pharmaceutical tablet manufacturing. During product development, one would like to find the roller compactor (RC) settings that are required to achieve a desired ribbon solid fraction. These settings can be determined from the compression profile of the powder mixture being compacted and a mathematical model that interprets it. However, establishing compression profiles in an RC requires relatively large amounts of powder, which are expensive and may not be available during drug development. As a cost-effective alternative to an RC, a compactor simulator (CS) can be used, which is a small-scale equipment that uses minimal amounts of powder to build the compression profile. However, since the working principles of a CS and an RC are different, the compression profiles obtained from the two devices for a given powder are also different. In this study, we propose a transfer learning approach that allows the RC compression profile of a given powder to be easily predicted from the compression profile obtained in a CS for the same powder. Based on the well-known Johanson model and on the mass correction factor theory, we examine the compaction behavior of six formulations, two of which including active ingredients, and we find that the mass correction factor does not depend significantly on the powder being compacted. We develop a simple, generalized correlation (transfer model) that allows the mass correction factor to be predicted solely as a function of the pressure at which the compaction is carried out. By using the proposed transfer model, the prediction of the RC compression profiles for the validation powders is significantly improved over the case where a constant value of the mass correction factor is used.