Pharmaceutical Research

, Volume 32, Issue 1, pp 135–143 | Cite as

Inflation of the Type I Error: Investigations on Regulatory Recommendations for Bioequivalence of Highly Variable Drugs

  • Meinolf WonnemannEmail author
  • Cornelia Frömke
  • Armin Koch
Research Paper



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.


bioequivalence 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. 1.
    Guideline on the Investigation of Bioequivalence (CPMP/EWP/QWP/1401/98 Rev. 1/Corr, January 2010.Google Scholar
  2. 2.
    Hauck WW, Hauschke D, Diletti E, Bois FY, Steinijans VW, Anderson S. Choice of student's t- or Wilcoxon-based confidence intervals for assessment of average bioequivalence. J Biopharm Stat. 1997;7(1):179–89.PubMedCrossRefGoogle Scholar
  3. 3.
    Schuirmann DJ. A comparison of the two one sided tests procedure and the power approach for assessing the equivalence of average bioavailability. J Pharmacokinet Biopharm. 1987;15:657–80.PubMedCrossRefGoogle Scholar
  4. 4.
    Note for Guidance on the Investigation of Bioavailability and Bioequivalence (CPMP/EWP/QWP/1401/98). January 2002.Google Scholar
  5. 5.
    Tothfalusi L, Endrenyi L, Arieta AG. Evaluation of bioequivalence for highly variable drugs with scaled average bioequivalence. Clin Pharmacokinet. 2009;48(11):725–43.PubMedCrossRefGoogle Scholar
  6. 6.
    Karalis V, Symilides M, Macheras P. On the leveling-off properties of the new bioequivalence limits for highly variable drugs of the EMA guideline. Eur J Pharm Sci. 2011;44(4):497–505.PubMedCrossRefGoogle Scholar
  7. 7.
    Patterson SD, Zariffa NMD, Montague TH, Howland K. Non-traditional study designs to demonstrate average bioequivalence for highly variable drug products. Eur J Clin Pharmacol. 2001;57:663–70.PubMedCrossRefGoogle Scholar
  8. 8.
    Karalis V, Sylmillides M, Macheras P. Bioequivalence of highly variable drugs: a comparison of the newly proposed regulatory approaches by FDA and EMA. Pharm Res. 2012;29(4):1066–77.PubMedCrossRefGoogle Scholar
  9. 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
  10. 10.
    Patnaik RN, Lesko LJ, Chen ML, Williams RL. Individual bioequivalence: new concepts in the statistical assessment of bioequivalence metrics. Clin Pharmacokinet. 1997;33:1–6.PubMedCrossRefGoogle Scholar
  11. 11.
    Chen ML, Lesko LJ. Individual bioequivalence revisited. Clin Pharmacokinet. 2001;40(10):701–6.PubMedCrossRefGoogle Scholar
  12. 12.
    Phillips KF. Power of the two one-sided tests procedure in bioequivalence. J Pharmacokinet Biopharm. 1990;18:137–44.PubMedCrossRefGoogle Scholar
  13. 13.
    Tothfalusi L, Endrenyi L, Midha KK. Scaling or wider bioequivalence limits for highly variable drugs and for the special case of C(max). Int J Clin Pharmacol Ther. 2003;41(5):217–25.PubMedCrossRefGoogle Scholar
  14. 14.
    Tothfalusi L, Endrenyi L. Limits for the scaled average bioequivalence of highly variable drugs and drug products. Pharm Res. 2003;20(3):382–9.PubMedCrossRefGoogle Scholar
  15. 15.
    Tothfalusi L, Endrenyi L, Midha KK, Rawson MJ, Hubbard JW. Evaluation of the bioequivalence of highly-variable drugs and drug products. Pharm Res. 2002;18(6):728–33. Comment in Pharm Res 19(3):227–8.CrossRefGoogle Scholar
  16. 16.
    Haider SH, Davit B, Chen ML, Connor D, Lee LM, Li QH, et al. Bioequivalence approaches for highly variable drugs and drug products. Pharm Res. 2008;25(1):237–41.CrossRefGoogle Scholar
  17. 17.
    Haider SH, Makhlouf F, Schuirmann DJ, Hyslop T, Davit B, Conner D, et al. Evaluation of a scaling approach for the bioequivalence of highly variable drugs. AAPS. 2008;10(3):450–4.CrossRefGoogle Scholar
  18. 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
  19. 19.
    Davit BM, Chen ML, Conner DP, Haidar SH, Kim S, Lee CH, et al. Implementation of a reference-scaled average bioequivalence approach for highly variable generic drug products by the US food and drug administration. AAPS J. 2012;14(4):915–24.PubMedCentralPubMedCrossRefGoogle Scholar
  20. 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. 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
  22. 22.
    Tothfalusi L, Endrenyi L. Sample sizes for designing bioequivalence studies for highly variable drugs. J Pharm Pharm Sci. 2012;15(1):73–84. Scholar
  23. 23.
    SAS Institute Inc. SAS/STAT 9.2 SAS User’s Guide. Cary, North Carolina, USA: SAS; 2002–2008.Google Scholar
  24. 24.
    GraphPad Prism version 5.04 for Windows. La Jolla California USA: GraphPad Software Inc.Google Scholar
  25. 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. 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

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Meinolf Wonnemann
    • 1
    • 2
    Email author
  • Cornelia Frömke
    • 1
    • 3
  • Armin Koch
    • 1
  1. 1.Institut für BiometrieMedizinische Hochschule HannoverHannoverGermany
  2. 2.Frankfurt a.MGermany
  3. 3.Institut für Biometrie, Epidemiologie und InformationsverarbeitungStiftung Tierärztliche Hochschule HannoverHannoverGermany

Personalised recommendations