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Statistical Analysis Practice in Dissolution Profile Comparison

f2 (similarity factor) rules

  • N=12 of (i) Reference (or prechange) and (ii) Test (or postchange) products
  • Use the Mean values only for calculation
  • Model Independent Method - most suitable for dissolution profile comparison when three to four or more dissolution time points are available
    • Same time points (minimally 3 times points)
    • Only one measurement should be considered after 85% dissolution of both the products
    • %RSD: NMT 20% at early points (e.g. 10 minutes); NMT 10% for all other points

What if f2 assumptions are not satisfied?
  • It is critical to identify a right tool/method in order to make meaningful assessment for product quality
  • Model independent statistical methods
    • f2 bootstrap (Shah, et al. 1998)
    • Tsong’s MSD method (Tsong, et al. 1996)
    • SK method (Saranadasa and Krishnamoorthy 2005)
    • Saranadasa’s Hotelling’s T2 based method (Saranadasa 2001)
    • Intersection union test (Berger and Hsu 1996)
  • Simulation studies were performed to evaluate the power and type I error of different approaches.
  • More than 250 cases were used for the establishment of decision tree and assessment.

Statistical Methods for Dissolution Profile Comparison

  • IUT is very conservative and has very low power to claim similarity.
  • SK method has good power to detect similarity and control of type I error when the two dissolution profiles are parallel. But when the underlying assumption of parallelism fails, SK method could be too liberal with high type I error (pass similarity when dissimilar).
  • Comparing to SK, f2 bootstrap and MSD method are relatively conservative for highly variable cases.
  • MSD is inconsistent in its result comparing to bootstrapped f2. MSD method is likely to be less discriminating and sensitive in some scenarios (e.g. Paixão, et al. 2017 and Mangas-Sanjuan, et al. 2016). But on the other hand, MSD method can also have higher power to detect similarity in some scenarios when the two profiles are similar.
  • f2 is a conservatively biased estimator. Although f2 and MSD are testing different hypotheses, comparisons may fail bootstrap and pass MSD in part because of the conservative bias of f2.

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