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Related Experiment Videos

Optimization study on the formulation of roxithromycin dispersible tablet using experimental design.

K Y Weon1, K T Lee, S H Seo

  • 1Galenical Research Team/R&D Center, Handok Pharmaceuticals Co, Ltd, Taeso-myon, Chungbuk Province, Korea.

Archives of Pharmacal Research
|November 4, 2000
PubMed
Summary

This study optimized roxithromycin dispersible tablets using a systematic formulation process. Response surface methodology effectively improved tablet hardness, friability, and disintegration time.

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Area of Science:

  • Pharmaceutical Sciences
  • Formulation Development
  • Drug Delivery Systems

Background:

  • Current roxithromycin formulations require enhancement of physical and pharmaceutical characteristics.
  • Dispersible tablets offer an alternative dosage form for improved patient compliance and drug release.

Purpose of the Study:

  • To develop and optimize a roxithromycin dispersible tablet formulation.
  • To investigate the impact of key excipients on tablet properties using experimental design.

Main Methods:

  • Employed a 2(3) factorial design for screening and Central Composite Design with response surface methodology for optimization.
  • Investigated the effects of binder (X1), disintegrant (X2), and lubricant (X3) on tablet hardness (Y1), friability (Y2), and disintegration time (Y3).

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  • Utilized three replicates at center levels to assess experimental error and surface curvature.

    • Main Results:

    • Generated model equations correlating excipient levels with tablet responses.
    • Determined the order of influence for each variable on tablet properties: X3 > X2 > X1.
    • Optimized formulation compositions were computed and validated through a demonstration study.

    Conclusions:

    • A systematic formulation optimization process, utilizing response surface methodology, is effective for developing roxithromycin dispersible tablets.
    • The study successfully identified optimal excipient levels to achieve desired tablet characteristics.
    • This approach enables objective selection of the best formulations with a limited number of experiments.