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Process simulation in the pharmaceutical industry: a review of some basic physical models.

D M Kremer1, B C Hancock

  • 1Pharmaceutical Research and Development, Pfizer, Inc., Global Research and Development, Groton/New London Laboratories, Connecticut 06340, USA. douglas.m.kremer@pfizer.com

Journal of Pharmaceutical Sciences
|February 1, 2006
PubMed
Summary

This review examines process modeling in pharmaceutical manufacturing. It covers continuum and discrete models, discussing challenges and future research for drug product development.

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Area of Science:

  • Pharmaceutical Science
  • Chemical Engineering
  • Process Systems Engineering

Background:

  • Process modeling is crucial for understanding and optimizing pharmaceutical manufacturing.
  • Diverse physical processes in drug production require varied modeling approaches.
  • Existing models operate across a wide range of length and time scales.

Purpose of the Study:

  • To review process modeling techniques applied to pharmaceutical manufacturing and delivery.
  • To discuss the application of both continuum and discrete models.
  • To identify challenges and future research directions in pharmaceutical process modeling.

Main Methods:

  • Literature review of process modeling studies in the pharmaceutical industry.
  • Categorization of models based on scale: continuum (large-scale) and discrete (particle-scale).

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  • Analysis of challenges and opportunities for practical implementation and future research.

    • Main Results:

    • Process models are widely applied across various pharmaceutical manufacturing stages.
    • Both continuum and discrete modeling approaches are essential for different phenomena.
    • Significant challenges exist in the practical application of these models.

    Conclusions:

    • Process modeling is a vital tool for pharmaceutical development and manufacturing.
    • Further research is needed to overcome implementation challenges and advance modeling capabilities.
    • Future work should focus on integrating different scales and improving model validation.