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Related Experiment Videos

Modeling weight variability in a pan coating process using Monte Carlo simulations.

Preetanshu Pandey1, Manoj Katakdaunde, Richard Turton

  • 1West Virginia University, Department of Chemical Engineering, PO Box 6102, Morgantown, WV 26506, USA.

AAPS Pharmscitech
|January 20, 2007
PubMed
Summary
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This study optimized tablet coating processes by analyzing weight gain mass coating variability (CV(m)) using video imaging and simulations. Coating time significantly impacts CV(m), with longer times reducing variability for better pharmaceutical manufacturing.

Area of Science:

  • Pharmaceutical Technology
  • Process Engineering
  • Computational Modeling

Background:

  • Pan coating is crucial for pharmaceutical dosage forms, but achieving consistent weight gain mass coating variability (CV(m)) remains challenging.
  • Understanding process variables is key to optimizing coating uniformity and product quality in pharmaceutical manufacturing.

Purpose of the Study:

  • To investigate process variables influencing weight gain mass coating variability (CV(m)) in pan coating devices.
  • To utilize novel video-imaging techniques and Monte Carlo simulations for analyzing coating dynamics.

Main Methods:

  • Employed novel video-imaging methods to capture tablet movement dynamics (location, velocity, circulation time).
  • Utilized Monte Carlo simulations, incorporating experimental data, to model coating variability.

Related Experiment Videos

  • Investigated effects of pan speed, loading, tablet size, coating time, spray flux, area, and shape.

    • Main Results:

    • Weight gain mass coating variability (CV(m)) showed an inverse relationship with the square root of coating time.
    • Increased spray area reduced CV(m), while spray shape had minimal impact.
    • Monte Carlo simulations provided good agreement with experimental trends but generally underpredicted CV(m).

    Conclusions:

    • The developed model offers a basis for process parameter adjustments during scale-up.
    • The simulation approach can predict necessary changes to maintain consistent CV(m) when altering variables.
    • Optimizing coating time and spray area are key strategies for reducing variability in pan coating processes.