Comparative Analyses of Bioequivalence Assessment Methods for In Vitro Permeation Test Data.

Pharm Stat

Office of Biostatistics, Office of Translational Science, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.

Published: August 2024

AI Article Synopsis

  • The text discusses the use of in vitro permeation tests (IVPT) to determine the bioequivalence (BE) of dermatological drug products, focusing on data analysis methods established by the FDA.
  • It compares the FDA's draft IVPT guidance with alternative methodologies, highlighting the effectiveness of empirical formulas over model-based approaches, particularly in dealing with variability in test subjects.
  • The research underscores the critical importance of accurate model selection in bioequivalence assessments and emphasizes the reliability of empirical methods for IVPT data analysis.

Article Abstract

For topical, dermatological drug products, an in vitro option to determine bioequivalence (BE) between test and reference products is recommended. In particular, in vitro permeation test (IVPT) data analysis uses a reference-scaled approach for two primary endpoints, cumulative penetration amount (AMT) and maximum flux (J), which takes the within donor variability into consideration. In 2022, the Food and Drug Administration (FDA) published a draft IVPT guidance that includes statistical analysis methods for both balanced and unbalanced cases of IVPT study data. This work presents a comprehensive evaluation of various methodologies used to estimate critical parameters essential in assessing BE. Specifically, we investigate the performance of the FDA draft IVPT guidance approach alongside alternative empirical and model-based methods utilizing mixed-effects models. Our analyses include both simulated scenarios and real-world studies. In simulated scenarios, empirical formulas consistently demonstrate robustness in approximating the true model, particularly in effectively addressing treatment-donor interactions. Conversely, the effectiveness of model-based approaches heavily relies on precise model selection, which significantly influences their results. The research emphasizes the importance of accurate model selection in model-based BE assessment methodologies. It sheds light on the advantages of empirical formulas, highlighting their reliability compared to model-based approaches and offers valuable implications for BE assessments. Our findings underscore the significance of robust methodologies and provide essential insights to advance their understanding and application in the assessment of BE, employed in IVPT data analysis.

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Source
http://dx.doi.org/10.1002/pst.2434DOI Listing

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