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Partial Least Squares Regression-Based Robust Forward Control of the Tableting Process.

Yusuke Hattori1, Miki Naganuma1, Makoto Otsuka1

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

Pharmaceutics
|January 24, 2020
PubMed
Summary

This study developed a feed-forward control model using near-infrared (NIR) spectra and granule physical properties to optimize tablet thickness. The model accurately predicted process parameters for consistent tablet manufacturing.

Keywords:
feed-forward controlhigh-shear wet granulationnear-infrared spectroscopypartial least squares regressionprocess data analysisrobust controltableting

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Area of Science:

  • Pharmaceutical Manufacturing
  • Process Analytical Technology (PAT)
  • Chemometrics

Background:

  • Tablet quality is crucial in pharmaceutical manufacturing.
  • Controlling tablet thickness and weight requires precise process parameter management.
  • Near-infrared (NIR) spectroscopy offers rich information on granule properties.

Purpose of the Study:

  • To establish a robust feed-forward control model for tablet manufacturing.
  • To integrate NIR spectra and physical granule attributes for process control.
  • To improve automated and semi-automated pharmaceutical production.

Main Methods:

  • Partial Least Squares (PLS) regression was employed.
  • Near-infrared (NIR) spectra were used to assess chemical attributes (composition, moisture, polymorphism).
  • Physical attributes (particle size distribution, flowability, density) were incorporated.

Main Results:

  • The model effectively controlled tablet thickness by adjusting the distance between upper and lower punches.
  • NIR spectra and physical attributes were successfully correlated with process parameters.
  • Predicted process parameters showed good agreement with actual settings for desired tablet thickness.

Conclusions:

  • A robust feed-forward control model for tableting was successfully established.
  • The integration of NIR and physical attributes enhances process control accuracy.
  • This approach supports advancements in continuous pharmaceutical manufacturing.