Inflation of the Type I Error: Investigations on Regulatory Recommendations for Bioequivalence of Highly Variable Drugs
We investigated different evaluation strategies for bioequivalence trials with highly variable drugs on their resulting empirical type I error and empirical power. The classical ‘unscaled’ crossover design with average bioequivalence evaluation, the Add-on concept of the Japanese guideline, and the current ‘scaling’ approach of EMA were compared.
Simulation studies were performed based on the assumption of a single dose drug administration while changing the underlying intra-individual variability.
Inclusion of Add-on subjects following the Japanese concept led to slight increases of the empirical α-error (≈7.5%). For the approach of EMA we noted an unexpected tremendous increase of the rejection rate at a geometric mean ratio of 1.25. Moreover, we detected error rates slightly above the pre-set limit of 5% even at the proposed ‘scaled’ bioequivalence limits.
With the classical ‘unscaled’ approach and the Japanese guideline concept the goal of reduced subject numbers in bioequivalence trials of HVDs cannot be achieved. On the other hand, widening the acceptance range comes at the price that quite a number of products will be accepted bioequivalent that had not been accepted in the past. A two-stage design with control of the global α therefore seems the better alternative.
KEY WORDSbioequivalence highly variable drug pharmacokinetic replicate design scaling
Analysis of variances
Area under the curve
Maximum drug concentration
Coefficient of variance
Coefficient of variance calculated from the residual error in an ANOVA
European Medicines Agency
Food and Drug Administration
Geometric mean ratio
Highly variable drug
Regulatory constant of 0.760
Lower acceptance limit for bioequivalence
Residual error of an ANOVA calculation
Residual error of an ANOVA calculation of two Reference administrations in a partial replicate study design
Upper acceptance limit for bioequivalence
- 1.Guideline on the Investigation of Bioequivalence (CPMP/EWP/QWP/1401/98 Rev. 1/Corr, January 2010.Google Scholar
- 4.Note for Guidance on the Investigation of Bioavailability and Bioequivalence (CPMP/EWP/QWP/1401/98). January 2002.Google Scholar
- 9.Howe WG. Approximate confidence limits on the mean of X + Y where X and Y are two tabled independent variables. J Am Stat Assoc. 1974;69:789–94.Google Scholar
- 18.Endrenyi L, Tothfalusi L. Regulatory conditions for the determination of bioequivalence of highly variable drugs. J Pharm Sci. 2009;12(1):138–49.Google Scholar
- 20.Guideline for Bioequivalence Studies of Generic Products, 医薬品の生物学的同等性試 験ガイドライン,発医薬品の生物学的同 等性試験ガイドライン, 発医薬品の生物学的 同等性試験ガイドライン, Ministry of Health, Labour and Welfare (MHLW). Japan; 2012.Google Scholar
- 21.Chow SC, Liu JP. Design and analysis of clinical trials. 2nd ed. Hoboken: Wiley-Interscience; 2004. p. 483. ISBN 0-471-24985-8.Google Scholar
- 23.SAS Institute Inc. SAS/STAT 9.2 SAS User’s Guide. Cary, North Carolina, USA: SAS; 2002–2008.Google Scholar
- 24.GraphPad Prism version 5.04 for Windows. La Jolla California USA: GraphPad Software Inc.Google Scholar
- 25.Wonnemann M. Different approaches for the assessment of bioequivalence of highly variable drug products—comparison of rules given in the current European, Canadian and Japanese guidelines. Master thesis, Ruprecht-Karls University of Heidelberg; 2012.Google Scholar
- 26.Guidance notes for applicants for consent to distribute new and changed medicines and related products, New Zealand Medicines and medical devices safety authority (MedSafe) New Zealand Regulatory Guidelines for Medicines. New Zealand; 2001.Google Scholar