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Related Experiment Videos

Didanosine extended-release matrix tablets: optimization of formulation variables using statistical experimental

Carla Sánchez-Lafuente1, Sandra Furlanetto, Mercedes Fernández-Arévalo

  • 1Departamento de Farmacia y Tecnología Farmacéutica, c/Prof. García González s/n, 41012 Sevilla, Spain.

International Journal of Pharmaceutics
|April 17, 2002
PubMed
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Optimizing didanosine release from matrix tablets involved statistical design. Key factors influencing drug release were drug content and polymer ratio, with larger polymer particle size favoring release.

Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Chemical Engineering

Background:

  • Directly-compressed matrix tablets are a common dosage form for controlled drug release.
  • Didanosine release from insoluble polymer matrices requires careful optimization of formulation and process variables.
  • Understanding the interplay between formulation components and processing parameters is crucial for predictable drug delivery.

Purpose of the Study:

  • To optimize didanosine release from matrix tablets using statistical experimental design.
  • To identify key process and formulation variables affecting drug release kinetics.
  • To develop a reliable model for predicting and controlling drug release profiles.

Main Methods:

  • Applied statistical experimental design, including D-optimal and Doehlert designs, for screening and response-surface studies.

Related Experiment Videos

  • Investigated the influence of tablet compression force, polymer ratio, polymer particle size, and drug content.
  • Utilized the desirability function for simultaneous optimization of multiple drug release responses.

    • Main Results:

    • Drug content and polymer ratio significantly impacted didanosine release.
    • Increased polymer particle size favored drug release, while compression force showed no significant effect.
    • The optimized formulation achieved predicted drug release profiles, demonstrating model reliability.

    Conclusions:

    • Statistical experimental design effectively identified critical variables for optimizing didanosine release.
    • The developed model reliably predicts drug release from extended-release matrix tablets.
    • This approach facilitates the preparation of matrix tablets with tailored and predictable drug release characteristics.