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Related Experiment Videos

Modeling of feed-forward control using the partial least squares regression method in the tablet compression process.

Yusuke Hattori1, Makoto Otsuka1

  • 1Research Institute of Pharmaceutical Sciences, Faculty of Pharmacy, Musashino University, 1-1-20 Shin-machi, Nishi-tokyo city, Tokyo 202-8585, Japan.

International Journal of Pharmaceutics
|April 9, 2017
PubMed
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This study introduces a new feed-forward control model for pharmaceutical tablet manufacturing, integrating near-infrared spectra and granule properties for automated process control in continuous manufacturing.

Area of Science:

  • Pharmaceutical Manufacturing
  • Process Analytical Technology
  • Chemical Engineering

Background:

  • Traditional batch manufacturing in pharmaceuticals relies on manual control.
  • Continuous manufacturing requires automated process parameter determination.
  • Feed-forward control is an innovative approach for process optimization.

Purpose of the Study:

  • To develop a feed-forward control model for tablet compression.
  • To integrate near-infrared (NIR) spectra and granule physical properties.
  • To enable automated process control in continuous pharmaceutical manufacturing.

Main Methods:

  • Development of a partial least squares regression (PLSR) model.
  • Integration of NIR spectral data and granule physical properties.
Keywords:
Continuous manufacturingFeed-forward control compression processGranuleNear-infrared spectroscopyPartial least squares regression

Related Experiment Videos

  • Utilizing product properties (tablet weight, thickness) as independent variables.

    • Main Results:

    • Successful development of a feed-forward control model for tablet compression.
    • Demonstrated the effectiveness of integrating NIR spectra and physical granule properties.
    • Identified key product properties for accurate prediction of process parameters.

    Conclusions:

    • The developed PLSR model enables automated control in continuous tablet manufacturing.
    • Integrating diverse data sources (NIR, physical properties, product attributes) is crucial.
    • This approach enhances efficiency and consistency in pharmaceutical production.