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Purpose <p><i>In vitro</i> dissolution testing is a critical quality attribute for solid dosage forms. Apart from similarity factor (f2), other alternatives namely model independent and dependent methods are suggested by regulatory agencies. Current manuscript attempts to compare various model independent approaches on dissolution similarity.</p> Methods <p>Dissolution data with various degrees of variability (10–20%, 40–50%, 70–80%) are compared using similarity factor f2 (estimated, expected, bias corrected with percentile &amp; BCa intervals) and novel approaches such as EDNE, SE, T2EQ, and MSD. Further, a flow chart is proposed to assist selection of suitable methodology.</p> Results <p>The expected f2 was stringent as compared to other f2 types&#xa0;and the Bca confidence intervals approach increased chance of acceptance&#xa0;as compared to conventional f2 bootstrap. Further, EDNE results synchronized with f2 analysis. Outcome from SE, T2EQ approaches depends on value of equivalence margin. MSD approach was most stringent as compared to others. Finally, a decision tree has been proposed to facilitate the selection of appropriate methodology for similarity analysis with consideration of regulatory perspectives.</p> Conclusions <p>Overall, various model independent approaches are compared for dissolution similarity analysis. This comprehensive guidance will assist formulation and biopharmaceutics scientists to enhance the success rate of similarity while ensuring regulatory compliance and thus helps to achieve drug product with consistent performance.</p> Graphical Abstract <p></p>

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Dissolution Profiles Comparison Using Various Model Independent Statistical Approaches: Can We Increase Chance of Similarity?

  • 期刊论文

  • 发表日期：

    2025年7月18日

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  • Rajkumar Boddu,
  • Karthik Parsa,
  • Priyansh Pandya,
  • Sivacharan Kollipara

摘要

Purpose

In vitro dissolution testing is a critical quality attribute for solid dosage forms. Apart from similarity factor (f2), other alternatives namely model independent and dependent methods are suggested by regulatory agencies. Current manuscript attempts to compare various model independent approaches on dissolution similarity.

Methods

Dissolution data with various degrees of variability (10–20%, 40–50%, 70–80%) are compared using similarity factor f2 (estimated, expected, bias corrected with percentile & BCa intervals) and novel approaches such as EDNE, SE, T2EQ, and MSD. Further, a flow chart is proposed to assist selection of suitable methodology.

Results

The expected f2 was stringent as compared to other f2 types and the Bca confidence intervals approach increased chance of acceptance as compared to conventional f2 bootstrap. Further, EDNE results synchronized with f2 analysis. Outcome from SE, T2EQ approaches depends on value of equivalence margin. MSD approach was most stringent as compared to others. Finally, a decision tree has been proposed to facilitate the selection of appropriate methodology for similarity analysis with consideration of regulatory perspectives.

Conclusions

Overall, various model independent approaches are compared for dissolution similarity analysis. This comprehensive guidance will assist formulation and biopharmaceutics scientists to enhance the success rate of similarity while ensuring regulatory compliance and thus helps to achieve drug product with consistent performance.

Graphical Abstract