The AAPS Journal

, 21:47 | Cite as

Trial Design and Statistical Considerations on the Assessment of Pharmacodynamic Similarity

  • Peijuan ZhuEmail author
  • Chyi-Hung Hsu
  • Jason Liao
  • Steven Xu
  • Liping Zhang
  • Honghui Zhou
Research Article


Pharmacodynamics (PD) similarity is an important component to support the claim of similarity between two drugs or devices. This article investigates the trial design and statistical considerations in the equivalence test of PD endpoints. Using bone resorption marker CTX as a case study, the relationship between the PD readouts and drug potency was explored to evaluate the sensitivity of the PD endpoint and guide equivalence margin selection. For PD data that have high baseline variability, one conventional similarity assessment method was to apply baseline-normalization followed by the standard bioequivalence (BE) test (Lancet Haematol. 4:e350–61, 2017, Ann Rheum Dis. 2017). This study showcased the drawbacks of the conventional method for PD data that were close to inhibition saturation, as the baseline-normalization significantly skewed the distribution of the PD data toward non-log-normal. In such cases, the standard BE test can produce an inflated type I error. Alternatively, ANCOVA, when applied to the un-normalized PD data with the baseline as a covariate, produced a satisfactory type I error with sufficient power. Therefore, ANCOVA was recommended for equivalence test of PD markers that has a saturated inhibition profile and high variability at baseline. Moreover, the relationship between PD readouts and drug potency was used to explore the sensitivity of the PD endpoint and it could help justify the equivalence margins, since the standard 80% to 125% BE margin often does not apply to PD. Finally, a decision tree was proposed to help guide the design of the PD equivalence study in the choice of PD endpoints and statistical methods.


Analysis of covariance (ANCOVA) Equivalence Baseline normalization Pharmacodynamics (PD) Statistical test Trial simulation 


Supplementary material

12248_2019_321_MOESM1_ESM.docx (149 kb)
ESM 1 (DOCX 148 kb)


  1. 1.
    Jurczak W, Moreira I, Kanakasetty GB, Munhoz E, Echeveste MA, Giri P, et al. Rituximab biosimilar and reference rituximab in patients with previously untreated advanced follicular lymphoma (ASSIST-FL): primary results from a confirmatory phase 3, double-blind, randomised, controlled study. Lancet Haematol. 2017;4:e350–61.CrossRefGoogle Scholar
  2. 2.
    Smolen JS, Cohen SB, Tony H-P, Scheinberg M, Kivitz A, Balanescu A, et al. A randomised, double-blind trial to demonstrate bioequivalence of GP2013 and reference rituximab combined with methotrexate in patients with active rheumatoid arthritis. Ann Rheum Dis. 2017;76:1598–1602.Google Scholar
  3. 3.
    Zhu P, Ji P, Wang Y. Using clinical PK/PD studies to support no clinically meaningful differences between a proposed biosimilar and the reference product. AAPS J. 2018;20:89.CrossRefGoogle Scholar
  4. 4.
    Putnam WS, Prabhu S, Zheng Y, Subramanyam M, Y-MC W. Pharmacokinetic, pharmacodynamic and immunogenicity comparability assessment strategies for monoclonal antibodies. Trends Biotechnol. 2010;28:509–16.CrossRefGoogle Scholar
  5. 5.
    Evans C, Cipolla D, Chesworth T, Agurell E, Ahrens R, Conner D, et al. Equivalence considerations for orally inhaled products for local action—ISAM/IPAC-RS European workshop report. J Aerosol Med Pulm Drug Deliv. 2012;25:117–39.CrossRefGoogle Scholar
  6. 6.
    Zhang M, Yang J, Tao L, Li L, Ma P, Fawcett JP. Acarbose bioequivalence: exploration of new Pharmacodynamic parameters. AAPS J. 2012;14:345–51.CrossRefGoogle Scholar
  7. 7.
    Sutjandra L, Rodriguez RD, Doshi S, Ma M, Peterson MC, Jang GR, et al. Population pharmacokinetic meta-analysis of Denosumab in healthy subjects and postmenopausal women with osteopenia or osteoporosis. Clin Pharmacokinet. 2011;50:793–807.CrossRefGoogle Scholar
  8. 8.
    Wu AHB. Biological and analytical variation of clinical biomarker testing: implications for biomarker-guided therapy. Curr Heart Fail Rep. 2013;10:434–40.CrossRefGoogle Scholar
  9. 9.
    Trouvin A-P, Jacquot S, Grigioni S, Curis E, Dedreux I, Roucheux A, et al. Usefulness of monitoring of B cell depletion in rituximab-treated rheumatoid arthritis patients in order to predict clinical relapse: a prospective observational study. Clin Exp Immunol. 2015;180:11–8.CrossRefGoogle Scholar
  10. 10.
    Melani L. Efficacy and safety of ezetimibe coadministered with pravastatin in patients with primary hypercholesterolemia: a prospective, randomized, double-blind trial. Eur Heart J. 2003;24:717–28.CrossRefGoogle Scholar
  11. 11.
    Chung Chow S, Endrenyi L. Statistical issues in bioavailability/bioequivalence studies. J Bioequivalence Bioavailabil [Internet]. 2011 [cited 2018 Sep 4];01. Available from: Accessed 10 Oct 2018.
  12. 12.
    Varki R, Pequignot E, Leavitt MC, Ferber A, Kraft WK. A glycosylated recombinant human granulocyte colony stimulating factor produced in a novel protein production system (AVI-014) in healthy subjects: a first-in human, single dose, controlled study. BMC Clin Pharmacol. 2009;9(2).Google Scholar
  13. 13.
    Waller CF, Bronchud M, Mair S, Challand R. Comparison of the pharmacodynamic profiles of a biosimilar filgrastim and Amgen filgrastim: results from a randomized, phase I trial. Ann Hematol. 2010;89:971–8.CrossRefGoogle Scholar
  14. 14.
    Liao JJ, Li Y, Jiang X. Comparability of pharmacodynamics profiles with an application to a biosimilar study. J Biometrics Biostatist [Internet]. 2017 [cited 2018 Oct 2];08. Available from: 10 Oct 2018.
  15. 15.
    Zhu P, Sy SKB, Skerjanec A. Application of Pharmacometric analysis in the Design of Clinical Pharmacology Studies for biosimilar development. AAPS J. 2018;20:40.CrossRefGoogle Scholar
  16. 16.
    Eastell R, Szulc P. Use of bone turnover markers in postmenopausal osteoporosis. Lancet Diab Endocrinol. 2017;5:908–23.CrossRefGoogle Scholar
  17. 17.
    van Schaick E, Zheng J, Ruixo JJP, Gieschke R, Jacqmin P. A semi-mechanistic model of bone mineral density and bone turnover based on a circular model of bone remodeling. J Pharmacokinet Pharmacodyn. 2015;42:315–32.CrossRefGoogle Scholar
  18. 18.
    Marathe A, Peterson MC, Mager DE. Integrated cellular bone homeostasis model for Denosumab pharmacodynamics in multiple myeloma patients. J Pharmacol Exp Ther June. 5(326):555–62.Google Scholar
  19. 19.
    Smolen JS, Cohen SB, Tony H-P, Scheinberg M, Kivitz A, Balanescu A, et al. A randomised, double-blind trial to demonstrate bioequivalence of GP2013 and reference rituximab combined with methotrexate in patients with active rheumatoid arthritis. Ann Rheum Dis. 2017;76:1598–602.CrossRefGoogle Scholar
  20. 20.
    Mahmoud HK, El Nahas Y, Abdel Moaty M, Abdel Fattah R, El Emary M, El Metnawy W. Kinetics of BCR-ABL transcripts in Imatinib Mesylate treated chronic phase CML (CPCML), a predictor of response and progression free survival. Int J Biomed Sci. 2009;5:223–8.PubMedPubMedCentralGoogle Scholar
  21. 21.
    Zheng J, van SE, Wu LS, Jacqmin P, Ruixo JJP. Using early biomarker data to predict long-term bone mineral density: application of semi-mechanistic bone cycle model on denosumab data. J Pharmacokinet Pharmacodyn. 2015;42:333–47.CrossRefGoogle Scholar
  22. 22.
    FDA. Prolia (denosumab) label [Internet]. [cited 2018 Sep 4]. Available from: Accessed 10 Oct 2018.
  23. 23.
    Walker E, Nowacki AS. Understanding equivalence and noninferiority testing. J Gen Intern Med. 2011;26:192–6.CrossRefGoogle Scholar
  24. 24.
    Julious SA. Sample sizes for clinical trials with Normal data. Stat Med. 2004;23:1921–86.CrossRefGoogle Scholar
  25. 25.
    FDA. Guidance for industry: clinical pharmacology data to support a demonstration of biosimilarity to a reference product [Internet]. 2017. Available from: Accessed 10 Oct 2018.
  26. 26.
    Schrock R. Cell-based potency assays: expectations and realities. Bioprocess J. 2012;11:4–12.CrossRefGoogle Scholar
  27. 27.
    Vickers AJ. The use of percentage change from baseline as an outcome in a controlled trial is statistically inefficient: a simulation study. BMC Med Res Methodol. 2001;1:6.CrossRefGoogle Scholar
  28. 28.
    Ling Z, Kun H. How to analyze change from baseline: absolute or percentage [internet]. [cited 2018 Sep 12]. Available from: Accessed 10 Oct 2018.
  29. 29.
    Harbeck N, Lipatov O, Frolova M, Udovitsa D, Topuzov E, Ganea-Motan DE, et al. Randomized, double-blind study comparing proposed biosimilar LA-EP2006 with reference pegfilgrastim in breast cancer. Future Oncol. 2016;12:1359–67.CrossRefGoogle Scholar
  30. 30.
    Puri A, Niewiarowski A, Arai Y, Nomura H, Baird M, Dalrymple I, et al. Pharmacokinetics, safety, tolerability and immunogenicity of FKB327, a new biosimilar medicine of adalimumab/Humira, in healthy subjects. Br J Clin Pharmacol. 2017;83:1405–15.CrossRefGoogle Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2019

Authors and Affiliations

  1. 1.Janssen Research and Development IncRaritanUSA
  2. 2.Merck & Co., IncNorth WalesUSA
  3. 3.Janssen Research & Development IncSpring HouseUSA

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