Statistical comparison of dissolution profiles

Yifan Wang, Ronald D. Snee, Golshid Keyvan, Fernando J. Muzzio

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Statistical methods to assess similarity of dissolution profiles are introduced. Sixteen groups of dissolution profiles from a full factorial design were used to demonstrate implementation details. Variables in the design include drug strength, tablet stability time, and dissolution testing condition. The 16 groups were considered similar when compared using the similarity factor f2 (f2450). However, multivariate ANOVA (MANOVA) repeated measures suggested statistical differences. A modified principal component analysis (PCA) was used to describe the dissolution curves in terms of level and shape. The advantage of the modified PCA approach is that the calculated shape principal components will not be confounded by level effect. Effect size test using omega-squared was also used for dissolution comparisons. Effects indicated by omega-squared are independent of sample size and are a necessary supplement to p value reported from the MANOVA table. Methods to compare multiple groups show that product strength and dissolution testing condition had significant effects on both level and shape. For pairwise analysis, a post-hoc analysis using Tukey’s method categorized three similar groups, and was consistent with level-shape analysis. All these methods provide valuable information that is missed using f2 method alone to compare average profiles. The improved statistical analysis approach introduced here enables one to better ascertain both statistical significance and clinical relevance, supporting more objective regulatory decisions.

Original languageEnglish (US)
Pages (from-to)796-807
Number of pages12
JournalDrug Development and Industrial Pharmacy
Issue number5
StatePublished - 2016

All Science Journal Classification (ASJC) codes

  • Pharmacology
  • Pharmaceutical Science
  • Drug Discovery
  • Organic Chemistry


  • Dissolution profiles
  • Effect size
  • F factor
  • Multivariate data analysis
  • Principal component analysis
  • Statistical significance


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