Volume 37, Issue 20
RESEARCH ARTICLE

Regulatory assessment of drug dissolution profiles comparability via maximum deviation

Kathrin Moellenhoff

Department of Mathematics, Ruhr‐Universität Bochum, Bochum, 44801 Germany

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Holger Dette

Corresponding Author

E-mail address: holger.dette@rub.de

Department of Mathematics, Ruhr‐Universität Bochum, Bochum, 44801 Germany

Correspondence

Holger Dette, Department of Mathematics, Ruhr‐Universität Bochum, Bochum 44801, Germany.

Email: holger.dette@rub.de

Olivier Collignon, European Medicines Agency, 30 Churchill Place, Canary Wharf London E14 5EU, UK.

Email: olivier.collignon@lih.lu; olivier.collignon@ema.europa.eu

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Evangelos Kotzagiorgis

European Medicines Agency, 30 Churchill Place, Canary Wharf, London, E14 5EU UK

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Stanislas Volgushev

Department of Statistical Sciences, University of Toronto, Toronto, ON, M5S Canada

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Olivier Collignon

E-mail address: olivier.collignon@lih.lu

E-mail address: olivier.collignon@ema.europa.eu

European Medicines Agency, 30 Churchill Place, Canary Wharf, London, E14 5EU UK

Competences Center in Methodology and Statistics, Luxembourg Institute of Health, Strassen, 1445 Luxembourg

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First published: 03 June 2018
Citations: 3

Abstract

In drug development, comparability of dissolution profiles of 2 different formulations is usually assessed using the similarity factor f2. In practice, the drug dissolution profiles are deemed similar if the f2 exceeds 50, which occurs when a 10% maximum difference in the mean percentage of the dissolved drug at each time point between test and reference formulation is obtained. According to the Guideline on the Investigation of Bioequivalence (CPMP/EWP/QWP/1401/98 Rev. 1/ Corr **) use of the f2 is however restricted by a set of validity conditions. If some of these conditions are not satisfied, the f2 is not considered suitable, and alternative statistical methods are needed. In this article, we propose an inferential framework based on the maximum deviation between curves to test the comparability of drug dissolution profiles. The new methodology is applicable regardless whether the validity criteria of the f2 are met or not. Contrary to the f2, this approach also integrates the variability of the measurements over time and not only their average. To benchmark our method, we performed simulations informed by 3 real case studies provided by the European Medicines Agency and extracted from dossiers submitted to the Centralised Procedure for Marketing Authorisation Application. In the scenarios of the simulation study, the new method controlled its type I error rate when the maximum deviation was greater than the similarity acceptance limit of 10%. The power exceeded 80% for small values of the maximum deviation, while the test was more conservative for intermediate ones. Our results were also very robust to sampling variations. Based on these positive findings, we encourage applicants to consider the new maximum deviation–based method as a valid alternative to the f2, especially when the validity criteria of the latter are not met.

Number of times cited according to CrossRef: 3

  • Equivalence of regression curves sharing common parameters, Biometrics, 10.1111/biom.13149, 76, 2, (518-529), (2019).
  • Author response to the Letter to the Editor “Equivalence analyses of dissolution profiles with the Mahalanobis distance: A regulatory perspective and a comparison with a parametric maximum deviation‐based approach”, Biometrical Journal, 10.1002/bimj.201900047, 61, 5, (1138-1140), (2019).
  • Equivalence analyses of dissolution profiles with the Mahalanobis distance: a regulatory perspective and a comparison with a parametric maximum deviation‐based approach, Biometrical Journal, 10.1002/bimj.201800325, 61, 3, (779-782), (2018).

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