Statistical Analysis of a Method to Predict Drug-Polymer Miscibility

Matthias Manne Knopp, Niels Erik Olesen, Yanbin Huang, René Holm, Thomas Rades

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    In this study, a method proposed to predict drugepolymer miscibility from differential scanning calorimetry measurements was subjected to statistical analysis. The method is relatively fast and inexpensive and has gained popularity as a result of the increasing interest in the formulation of drugs as amorphous solid dispersions. However, it does not include a standard statistical assessment of the experimental uncertainty by means of a confidence interval. In addition, it applies a routine mathematical operation known as “transformation to linearity,” which previously has been shown to be subject to a substantial bias. The statistical analysis performed in this present study revealed that the mathematical procedure associated with the method is not only biased, but also too uncertain to predict drugepolymer miscibility at room temperature. Consequently, the statistical inference based on the mathematical procedure is problematic and may foster uncritical and misguiding interpretations. From a statistical perspective, the drugepolymer miscibility prediction should instead be examined by deriving an objective function, which results in the unbiased, minimum variance properties of the least-square estimator as provided in this study.
    Original languageEnglish
    JournalJournal of Pharmaceutical Sciences
    Volume105
    Issue number1
    Pages (from-to)362-367
    ISSN0022-3549
    DOIs
    Publication statusPublished - 2016

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