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DISSOLUTION PROFILE SIMILARITY ANALYSES—STATISTICAL PRINCIPLES, METHODS AND CONSIDERATIONS

  • Meeting Report
  • Theme: Current and Novel Approaches towards Dissolution Profile Comparison Harmonization
  • Published:
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Abstract

The pharmaceutical industry and regulatory agencies rely on dissolution similarity testing to make critical product decisions as part of drug product life cycle management. Accordingly, the application of mathematical approaches to evaluate dissolution profile similarity is described in regulatory guidance with the emphasis given to the similarity factor f2 with little discussion of alternative methods. In an effort to highlight current practices to assess dissolution profile similarity and to strive toward global harmonization, a workshop entitled “In Vitro Dissolution Similarity Assessment in Support of Drug Product Quality: What, How, When” was held on May 21–22, 2019 at the University of Maryland, Baltimore. This manuscript provides in-depth discussion of the mathematical principles of the model-independent statistical methods for dissolution profile similarity analyses presented in the workshop. Deeper understanding of the testing objective and statistical properties of the available statistical methods is essential to identify methods which are appropriate for application in practice. A decision tree is provided to aid in the selection of an appropriate statistical method based on the underlying characteristics of the drug product. Finally, the design of dissolution profile studies is addressed regarding analytical and statistical recommendations to sufficiently power the study. This includes a detailed discussion on evaluation of dissolution profile data for which several batches per reference and/or test product are available.

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Change history

  • 01 August 2022

    The original article was updated to include missing supplemental files.

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ACKNOWLEDGEMENTS

The meeting organizers are very grateful to Dr. James Polli and Ms. Ann Anonsen for their tremendous efforts in organizing this workshop and to all speakers, facilitators and scribes whose participation was key in having a very informative workshop.

Funding

This work was supported in part by a Center of Excellence in Regulatory Science and Innovation (CERSI) grant to the University of Maryland from the U.S. Food and Drug Administration (Grant: U01FD005946).

Author information

Author notes

  1. Sandra Suarez-Sharp

    Present address: Regulatory Affairs, Simulations Plus Inc, 42505 10th Street West, Lancaster, CA, 93534, United States of America

  2. Zachary Bergeron

    Present address: Sage Therapeutics, 215 First St, Cambridge, MA, 02142, United States of America

Authors and Affiliations

  1. Global Biostatistics and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Binger Straße 173, 55216, Ingelheim Am Rhein, Germany

    Thomas Hoffelder

  2. Consultant in CMC Statistical Studies, 3091 Midlane Drive, Wadsworth, IL, 60083, United States of America

    David Leblond

  3. Pfizer Inc, Eastern Point Road, Groton, Connecticut, 06340, United States of America

    Leslie Van Alstine & Dorys Argelia Diaz

  4. Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States of America

    Sandra Suarez-Sharp

  5. Center for Mathematical Sciences, Merck Manufacturing Division, Merck & Co., Inc, Kenilworth, NJ, 07033, United States of America

    Krista Witkowski

  6. Statistics and Decision Sciences, Manufacturing and Applied Statistics, Janssen Pharmaceutical R&D LLD, Raritan, NJ, 08869, United States of America

    Stan Altan

  7. Data and Statistical Sciences, AbbVie Inc, North Chicago, IL, 60064, United States of America

    James Reynolds & Yanbing Zheng

  8. Agios Pharmaceuticals, 88 Sidney St., Cambridge, MA, 02143, United States of America

    Zachary Bergeron

  9. CMC Statistics, Biostatistics, GlaxoSmithKline, David Jack Centre for R&D, Ware, SG12 0DP, Hertfordshire, UK

    Kevin Lief

  10. Pharmaceutical Sciences, Merck & Co., Inc., 770 Sumneytown Pike, West Point, PA, 19486, United States of America

    Andreas Abend

Authors
  1. Thomas Hoffelder
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  3. Leslie Van Alstine
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  5. Sandra Suarez-Sharp
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  7. Stan Altan
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  11. Yanbing Zheng
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  12. Andreas Abend
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All authors contributed to the manuscript, commented on previous versions, read and approved the final manuscript.

Corresponding author

Correspondence to Thomas Hoffelder.

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Hoffelder, T., Leblond, D., Van Alstine, L. et al. DISSOLUTION PROFILE SIMILARITY ANALYSES—STATISTICAL PRINCIPLES, METHODS AND CONSIDERATIONS. AAPS J 24, 54 (2022). https://doi.org/10.1208/s12248-022-00697-y

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