Assessment of Prediction Models for Punch Sticking in Tablet Formulations

Abstract

Punch sticking is a common tablet compression manufacturing issue experienced during late-stage large-scale manufacturing. Prediction of punch sticking propensity and identification of the sticking component is important for early-stage formulation development. Application of novel predictive capabilities offers early-stage sticking propensity assessment. 16 API compounds were utilised to assess punch sticking prediction using removable punch tip tooling. API descriptors were tested for sticking correlation using multivariate analysis. NIR imaging, SEM-EDX and Raman microscopy were used to examine the material adhered to the punch tips. Predictive modelling using linear and non-linear equations proved inaccurate in punch sticking mass prediction. PCA analysis identified sticking correlated physical descriptors and provided a dataset and method for further descriptor studies. Raman microscopy was identified as a suitable technique for chemical identification of punch sticking material, which offers insight towards a mechanistic understanding.

Keywords

• punch sticking
• predictive modelling
• formulation development
• tabletting
• raman microscopy