Model development and prediction of particle size distribution, density and friability of a comilling operation in a continuous pharmaceutical manufacturing process

Nirupaplava Metta, Maxim Verstraeten, Michael Ghijs, Ashish Kumar, Elisabeth Schafer, Ravendra Singh, Thomas De Beer, Ingmar Nopens, Philippe Cappuyns, Ivo Van Assche, Marianthi Ierapetritou, Rohit Ramachandran

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

The comilling process plays an important role in solid oral dosage manufacturing. In this process, the granulated products are comminuted to the required size distribution through collisions created from a rotating impeller. In addition to predicting particle size distribution, there is a need to predict other critical quality attributes (CQAs) such as bulk density and tapped density, as these impact tablet compaction behavior. A comprehensive modeling approach to predict the CQAs is needed to aid continuous process modeling in order to simulate interaction with the tablet press operation. In the current work, a full factorial experiment design is implemented to understand the influence of granule strength, impeller speed and residual moisture content on the CQAs. A population balance modeling approach is applied to predict milled particle size distribution and a partial least squares modeling approach is used to predict bulk and tapped density of the milled granule product. Good agreement between predicted and experimental CQAs is achieved. An R2 value of 0.9787 and 0.7633 is obtained when fitting the mean particle diameters of the milled product and the time required to mill the granulated material respectively.

Original languageEnglish (US)
Pages (from-to)271-282
Number of pages12
JournalInternational Journal of Pharmaceutics
Volume549
Issue number1-2
DOIs
StatePublished - Oct 5 2018

All Science Journal Classification (ASJC) codes

  • Pharmaceutical Science

Keywords

  • Breakage
  • Conical screen mill
  • Partial least squares model
  • Population balance model

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