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

How to Cite

Rhodes, E. P., Everett, J., Whiteside, P., Kraus, D., Cram, M. & Dawson, N., (2022) “Assessment of Prediction Models for Punch Sticking in Tablet Formulations”, British Journal of Pharmacy 7(2). doi: https://doi.org/10.5920/bjpharm.1118

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Authors

Edward Paul Rhodes (Pfizer)
Jeremy Everett (University of Greenwich)
Paul Whiteside (Pfizer)
Debbie Kraus (Pfizer)
Michael Cram (Pfizer)
Neil Dawson (Pfizer)

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Creative Commons Attribution 4.0

Competing Interests

The authors declare no conflicts of interest or competing financial interests.

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This article has been peer reviewed.

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