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Research Article

The influence of API concentration on the roller compaction process: modeling and prediction of the post compacted ribbon, granule and tablet properties using multivariate data analysis

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Pages 1470-1478 | Received 23 May 2014, Accepted 21 Aug 2014, Published online: 12 Sep 2014
 

Abstract

Objective: While previous research has demonstrated roller compaction operating parameters strongly influence the properties of the final product, a greater emphasis might be placed on the raw material attributes of the formulation. There were two main objectives to this study. First, to assess the effects of different process variables on the properties of the obtained ribbons and downstream granules produced from the rolled compacted ribbons. Second, was to establish if models obtained with formulations of one active pharmaceutical ingredient (API) could predict the properties of similar formulations in terms of the excipients used, but with a different API.

Materials and methods: Tolmetin and acetaminophen, chosen for their different compaction properties, were roller compacted on Fitzpatrick roller compactor using the same formulation. Models created using tolmetin and tested using acetaminophen. The physical properties of the blends, ribbon, granule and tablet were characterized. Multivariate analysis using partial least squares was used to analyze all data.

Results: Multivariate models showed that the operating parameters and raw material attributes were essential in the prediction of ribbon porosity and post-milled particle size. The post compacted ribbon and granule attributes also significantly contributed to the prediction of the tablet tensile strength.

Conclusions: Models derived using tolmetin could reasonably predict the ribbon porosity of a second API. After further processing, the post-milled ribbon and granules properties, rather than the physical attributes of the formulation were needed to predict downstream tablet properties. An understanding of the percolation threshold of the formulation significantly improved the predictive ability of the models.

Acknowledgements

The authors thank The Fitzpatrick Corporation and FMC Biopolymer for donating the roller compactor and MCC used in these experiments.

Declaration of interest

The authors report no declarations of interest. The authors thank the Consortium for the Advancement of Manufacturing of Pharmaceuticals (CAMP) for funding this research

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