Advertisement

Latent variable indirect response joint modeling of a continuous and a categorical clinical endpoint

  • Chuanpu HuEmail author
  • Philippe O. Szapary
  • Alan M. Mendelsohn
  • Honghui Zhou
Original Paper

Abstract

Informative exposure–response modeling of clinical endpoints is important in drug development. There has been much recent progress in latent variable modeling of ordered categorical endpoints, including the application of indirect response (IDR) models and accounting for residual correlations between multiple categorical endpoints. This manuscript describes a framework of latent-variable-based IDR models that facilitate easy simultaneous modeling of a continuous and a categorical clinical endpoint. The model was applied to data from two phase III clinical trials of subcutaneously administered ustekinumab for the treatment of psoriatic arthritis, where Psoriasis Area and Severity Index scores and 20, 50, and 70 % improvement in the American College of Rheumatology response criteria were used as efficacy endpoints. Visual predictive check and external validation showed reasonable parameter estimation precision and model performance.

Keywords

Discrete variable Multivariate analysis Population pharmacokinetic/pharmacodynamic modeling NONMEM Ustekinumab Psoriatic arthritis Well-controlled clinical trials 

Notes

Acknowledgments

The authors thank the Medical Affairs Publication Group of Janssen Scientific Affairs, LLC for their excellent editorial support.

References

  1. 1.
    Dayneka NL, Garg V, Jusko WJ (1993) Comparison of four basic models of indirect pharmacodynamic responses. J Pharmacokinet Biopharm 21(4):457–478PubMedCrossRefGoogle Scholar
  2. 2.
    Felson DT, Anderson JJ, Boers M et al (1995) American College of Rheumatology. Preliminary definition of improvement in rheumatoid arthritis. Arthritis Rheum 38(6):727–735PubMedCrossRefGoogle Scholar
  3. 3.
    Hutmacher MM, Krishnaswami S, Kowalski KG (2008) Exposure–response modeling using latent variables for the efficacy of a JAK3 inhibitor administered to rheumatoid arthritis patients. J Pharmacokinet Pharmacodyn 35:139–157PubMedCrossRefGoogle Scholar
  4. 4.
    Lacroix BD, Lovern MR, Stockis A, Sargentini-Maier ML, Karlsson MO, Friberg LE (2009) A pharmacodynamic Markov mixed-effects model for determining the effect of exposure to certolizumab pegol on the ACR20 score in patients with rheumatoid arthritis. Clin Pharm Therap 86(4):387–395.Google Scholar
  5. 5.
    Hu C, Xu Z, Rahman MU, Davis HM, Zhou H (2010) A latent variable approach for modeling categorical endpoints among patients with rheumatoid arthritis treated with golimumab plus methotrexate. J Pharmacokinet Pharmacodyn 37(4):309–321PubMedCrossRefGoogle Scholar
  6. 6.
    Hu C, Xu Z, Mendelsohn A, Zhou H (2013) Latent variable indirect response modeling of categorical endpoints representing change from baseline. J Pharmacokinet Pharmacodyn 40(1):81–91PubMedCrossRefGoogle Scholar
  7. 7.
    Hu C, Szapary PO, Yeilding N, Zhou H (2011) Informative dropout modeling of longitudinal ordered categorical data and model validation: application to exposure–response modeling of physician’s global assessment score for ustekinumab in patients with psoriasis. J Pharmacokinet Pharmacodyn 38(2):237–260PubMedCrossRefGoogle Scholar
  8. 8.
    Hu C, Yeilding N, Davis HM, Zhou H (2011) Bounded outcome score modeling: application to treating psoriasis with ustekinumab. J Pharmacokinet Pharmacodyn 38(4):497–517PubMedCrossRefGoogle Scholar
  9. 9.
    An X, Bentler PM (2011) Extended mixture factor analysis model with covariates for mixed binary and continuous responses. Stat Med 30:2634–2647PubMedGoogle Scholar
  10. 10.
    Laffont CM, Fink M, Gruet P, King JN, Seewald W, Concordet D (2012) Application of a new method for multivariate analysis of longitudinal ordinal data testing robenacoxib in canine osteoarthritis. PAGE 21 Abstr:2548Google Scholar
  11. 11.
    Beal SL, Sheiner LB, Boeckmann A, Bauer RJ (2009) NONMEM user’s guides (1989–2009). Icon Development Solutions, Ellicott CityGoogle Scholar
  12. 12.
    Hu C, Zhou H (2008) An improved approach for confirmatory phase III population pharmacokinetic analysis. J Clin Pharmacol 48:812–822PubMedCrossRefGoogle Scholar
  13. 13.
    Hu C, Zhang J, Zhou H (2011) Confirmatory analysis for phase III population pharmacokinetics. Pharm Stat 10(7):812–822Google Scholar
  14. 14.
    Zhu Y, Hu C, Lu M, Liao S, Marini JC, Yohrling J, Yeilding N, Davis HM, Zhou H (2009) Population pharmacokinetic modeling of ustekinumab, a human monoclonal antibody targeting IL-12/23p40, in patients with moderate to severe plaque psoriasis. J Clin Pharmacol 49(2):162–175PubMedCrossRefGoogle Scholar
  15. 15.
    Zhang L, Beal S, Sheiner L (2003) Simultaneous vs. sequential analysis for population PK/PD data I: best-case performance. J Pharmacokinet Pharmacodyn 30:387–404PubMedCrossRefGoogle Scholar
  16. 16.
    Karlsson MO, Holford NHG (2008) A tutorial on visual predictive checks. PAGE 17:2008Google Scholar
  17. 17.
    Hutmacher MM, French JL, Krishnaswamib S, Menonb S (2011) Estimating transformations for repeated measures modeling of continuous bounded outcome data. Stat Med 30:935–949PubMedCrossRefGoogle Scholar
  18. 18.
    Hu C, Sale M (2003) A joint model for nonlinear longitudinal data with informative dropout. J Pharmacokinet Pharmacodyn 30(1):83–103PubMedCrossRefGoogle Scholar
  19. 19.
    Woo S, Pawaskar D, Jusko WJ (2009) Methods of utilizing baseline values for indirect response models. J Pharmacokinet Pharmacodyn 36:381–405PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Chuanpu Hu
    • 1
    Email author
  • Philippe O. Szapary
    • 2
  • Alan M. Mendelsohn
    • 2
  • Honghui Zhou
    • 1
  1. 1.Pharmacokinetics and PharmacometricsBiologics Clinical Pharmacology, Janssen Research and Development, LLCSpring HouseUSA
  2. 2.Clinical ImmunologyJanssen Research and Development, LLCSpring HouseUSA

Personalised recommendations