Predicting the probability of successful efficacy of a dissociated agonist of the glucocorticoid receptor from dose–response analysis

  • Daniela J. Conrado
  • Sriram Krishnaswami
  • Satoshi Shoji
  • Sheela Kolluri
  • Judith Hey-Hadavi
  • Dorothy McCabe
  • Ricardo Rojo
  • Brinda K. Tammara
Original Paper


PF-04171327 is a dissociated agonist of the glucocorticoid receptor (DAGR) being developed to retain anti-inflammatory efficacy while reducing unwanted effects. Our aim was to conduct a longitudinal dose–response analysis to identify the DAGR doses with efficacy similar to or greater than prednisone 10 mg once daily (QD). The data included were from a Phase 2, randomized, double-blind, parallel-group study in 323 subjects with active rheumatoid arthritis on a background of methotrexate. Subjects received DAGR 1, 5, 10 or 15 mg, prednisone 5 or 10 mg, or placebo QD for 8 weeks. The Disease Activity Score 28-4 calculated using C-Reactive Protein (DAS28-4 CRP) was the efficacy endpoint utilized in this dose–response model. For DAGR, the maximum effect (Emax) on DAS28-4 CRP was estimated to be −1.2 points (95 % CI −1.7, −0.84), and the evaluated dose range provided 31–87 % of the Emax; for prednisone 5 and 10 mg, the estimated effects were −0.27 (95 % CI −0.55, 0.006) and −0.94 point (95 % CI −1.3, −0.59), respectively. Stochastic simulations indicated that the DAGR 1, 5, 10 and 15 mg have probabilities of 0.9, 29, 54 and 62 %, respectively, to achieve efficacy greater than prednisone 10 mg at week 8. DAGR 9 mg estimated probability was 50 % suggesting that DAGR ≥9 mg QD has an effect on DAS28-4 CRP comparable to or greater than prednisone 10 mg QD. This work informs dose selection for late-stage confirmatory trials.


Dissociated agonist of glucocorticoid receptor (DAGR) Rheumatoid arthritis (RA) Longitudinal dose–response Stochastic simulations Disease activity score 28-4 C-reactive protein (DAS28-4 CRP) Phase 2 randomized double-blind clinical trial 



DJC would like to acknowledge Gregory J. Hather (Pfizer Inc.) for his insightful comments on the statistical aspects. DJC also acknowledges William S. Denney (Pfizer Inc.) for his assistance with the R code. In addition, the authors would like to thank Jack Cook (Pfizer Inc.) for his valuable input to the manuscript.

Compliance with ethical standards

Conflict of interest

All authors are employees of Pfizer Inc. This study was sponsored by Pfizer Inc.

Supplementary material

10928_2016_9475_MOESM1_ESM.docx (199 kb)
Supplementary material 1 (DOCX 199 kb)


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Daniela J. Conrado
    • 1
    • 5
  • Sriram Krishnaswami
    • 1
  • Satoshi Shoji
    • 2
  • Sheela Kolluri
    • 3
  • Judith Hey-Hadavi
    • 3
  • Dorothy McCabe
    • 3
  • Ricardo Rojo
    • 1
  • Brinda K. Tammara
    • 4
  1. 1.Pfizer Global Innovative Pharma BusinessGrotonUSA
  2. 2.Pfizer Japan Inc. Development JapanTokyoJapan
  3. 3.Pfizer Global Innovative Pharma BusinessPfizerUSA
  4. 4.Pfizer Global Innovative Pharma BusinessCollegevilleUSA
  5. 5.Metrum Research Group LLCWellesleyUSA

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