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Bootstrap Statistics and Its Application in Disintegration and Dissolution Data Analysis.

Santanu Kaity1, Sunil Kumar Sah1, Tukaram Karanwad2

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Molecular Pharmaceutics
|July 17, 2023
PubMed
Summary

Bootstrap statistics offer a reliable method for evaluating drug product similarity, especially for formulations with high unit-to-unit variability. This approach enhances the assessment of disintegration time and dissolution profiles for both conventional and 3D-printed dosage forms.

Keywords:
Additive formulationBootstrapDisintegrationDissolutionGeneric formulationSimilarityVariability

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Area of Science:

  • Pharmaceutical Sciences
  • Biostatistics
  • Drug Delivery

Background:

  • Disintegration time and drug dissolution rates are critical quality attributes for pharmaceutical products.
  • Comparing dissolution profiles between reference and test products is essential for demonstrating bioequivalence in generic drug development.
  • The similarity factor (f2) is a widely used statistical tool for dissolution profile comparison, but it has limitations with unit-to-unit variability.

Purpose of the Study:

  • To review and illustrate the application of bootstrap statistical analysis in evaluating drug product similarity.
  • To demonstrate the utility of bootstrap statistics for both conventional and additively manufactured solid dosage forms.
  • To highlight bootstrap's advantage over traditional methods like f2 when dealing with intra-batch variability.

Main Methods:

  • Review of case studies applying bootstrap statistical analysis to disintegration and dissolution data.
  • Comparison of bootstrap analysis with the traditional similarity factor (f2) method.
  • Application of bootstrap to dissolution data from both conventional and additively manufactured formulations.

Main Results:

  • Bootstrap statistical analysis effectively addresses the limitations of the f2 factor in predicting similarity for batches with unit-to-unit variability.
  • Bootstrap analysis provides statistically significant results and aids in understanding trends from diverse batch data.
  • The method is applicable and reliable for assessing in vitro product similarity in formulations exhibiting high intra-unit variability.

Conclusions:

  • Bootstrap statistics represent a promising and reliable tool for in vitro product similarity assessment.
  • This method is particularly valuable for evaluating formulations with significant intra-unit variability, including additively manufactured products.
  • Bootstrap analysis enhances the robustness of pharmaceutical equivalence evaluations.