Mechanistic Models of Compaction Property Prediction for APIs and Formulations

The proposed talk will address the suitability of mechanistic models for compaction property prediction. Few researchers reported compaction property prediction models which are empirical to semi-mechanistic in nature having lack of rigorous validation using practical APIs. Thus, identifying a suitable model relies on the applicability of inherent compaction mechanism delineated in the model. The talk will focus a specific mechanistic model after further modification and validation with practical APIs. The model workflow will be applicable to all three compaction property metrics (tabletability, compressibility and compactibility) for multicomponent mixtures. In addition, such properties for a challenging API (poorly compressible) can also be predicted by an inverse approach from the known properties of the API containing binary mixture and the corresponding excipients. Finally, the importance of these applicable workflows will be discussed along with other critical metrics of a formulation to optimize careful selection of excipients to enable robust formulation design.