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Development of a tablet press feed frame lead lag determination model using in-line and off-line NIR measurements

By Van;Hauwermeiren, D; Peeters, MPeeters, E; Cogoni, G; Yang, LA; De;Beer, T

Published on

Abstract

For continuous pharmaceutical manufacturing of oral solid dosages, it is essential that product quality is measured inline. In this application, a continuous rotary tablet press is used. The goal is a model-based assessment of the quality of the blend in the feed frame to determine whether the concentration of the active pharmaceutical ingredient (API) will be within the prescribed limits. This is to achieve a better quality assurance than by offline testing of a small sample of tablets. In this way, product quality for real-time release (RTR) could be implemented. With a near-infrared (NIR) probe, the concentration of the API in the feed chute and the feed-frame were measured, as well as the API concentration of the tablets by an offline NIR measurement. These different data sets are connected and used for the residence time distribution characterization of the mixing dynamic of the tablet press. A residence time distribution model is fitted to the data, and is further used to compute the leadlag time. This yields information on how long it takes for a quantity of product to go from being measured in the feed frame until ending up in tablets. Further, it gives information on the occurrence of mixing in the feed-frame itself. These models allow making accurate predictions of whether tablets fall within specified concentration range in real-time. The real-time prediction can be used in combination with a control system both to maintain the quality of the blend as well as to know which tablets to discard. This real-time quality assurance will lead to less material waste and fewer declined batches of tablets.

Journal

International Journal of Pharmaceutics. Volume 612, 2022, 121284

DOI

10.1016/j.ijpharm.2021.121284

Type of publication

Peer-reviewed journal

Affiliations

  • Ghent University
  • Pfizer Inc

Article Classification

Research Article

Classification Areas

  • API
  • PAT
  • Modeling

Tags