Information contributed by meta-analysis in exposure–response modeling: application to phase 2 dose selection of guselkumab in patients with moderate-to-severe psoriasis

  • Chuanpu Hu
  • Yasmine Wasfi
  • Yanli Zhuang
  • Honghui Zhou
Original Paper

DOI: 10.1007/s10928-014-9360-6

Cite this article as:
Hu, C., Wasfi, Y., Zhuang, Y. et al. J Pharmacokinet Pharmacodyn (2014) 41: 239. doi:10.1007/s10928-014-9360-6

Abstract

Ustekinumab, a human immunoglobulin G1 kappa (IgG1κ) monoclonal antibody that binds with high affinity to human interleukin (IL)-12 and IL-23, has been approved to treat patients with psoriasis. Guselkumab is a related human IgG1 monoclonal antibody in clinical development which specifically blocks IL-23. The objective of this study was to study the exposure–response relationship of guselkumab to guide dose selection for a Phase 2 study in patients with moderate-to-severe psoriasis. Data were available from a Phase 1 study of 47 healthy subjects and 24 patients with psoriasis who received various doses of guselkumab. Disease severity was assessed using Psoriasis Area and Severity Index (PASI) scores in all studies. Individual pharmacokinetic parameters were derived from population pharmacokinetics modeling for the purpose of exposure–response modeling to guide dosing regimen selection. A population mechanism-based exposure–response model of guselkumab was developed to evaluate the association of guselkumab dosing with PASI scores using a Type I indirect response model, with placebo effect empirically modeled. The model was subsequently updated, first by incorporating data from psoriasis patients who received placebo (n = 765) and from patients actively treated with ustekinumab 45 or 90 mg (n = 1,230) in two ustekinumab Phase 3 trials. Inclusion of this additional ustekinumab data and the consequent contributions to specific model components substantially reduced uncertainties in all model components except for one parameter. Additional sensitivity analyses showed that the dose selection decision was robust to this remaining uncertainty. The described approach underscores the importance of utilizing all available sources of information in dose selection decisions, along with the importance of effective development team interaction.

Keywords

Population pharmacokinetic/pharmacodynamic modeling NONMEM Clinical drug development 

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Chuanpu Hu
    • 1
  • Yasmine Wasfi
    • 2
  • Yanli Zhuang
    • 3
  • Honghui Zhou
    • 3
  1. 1.Model Based Drug DevelopmentJanssen Research & Development, LLCSpring HouseUSA
  2. 2.Clinical ImmunologyJanssen Research & Development, LLCSpring HouseUSA
  3. 3.Pharmacokinetics and Pharmacodynamics, Biologics Clinical PharmacologyJanssen Research & Development, LLCSpring HouseUSA

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