A comprehensive evaluation of exposure–response relationships in clinical trials: application to support guselkumab dose selection for patients with psoriasis
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Guselkumab, a human IgG1 monoclonal antibody that blocks interleukin-23, has been evaluated in one Phase 2 and two Phase 3 trials in patients with moderate-to-severe psoriasis, in which disease severity was assessed using Psoriasis Area and Severity Index (PASI) and Investigator’s Global Assessment (IGA) scores. Through the application of landmark and longitudinal exposure–response (E–R) modeling analyses, we sought to predict the guselkumab dose–response (D–R) relationship using data from 1459 patients who participated in these trials. A recently developed novel latent-variable Type I Indirect Response joint model was applied to PASI75/90/100 and IGA response thresholds, with placebo effect empirically modeled. An effect of body weight on E–R, independent of pharmacokinetics, was identified. Thorough landmark analyses also were implemented using the same dataset. The E–R models were combined with a population pharmacokinetic model to generate D–R predictions. The relative merits of longitudinal and landmark analysis also are discussed. The results provide a comprehensive and robust evaluation of the D–R relationship.
KeywordsExposure–response modeling NONMEM Ordered categorical endpoints Joint modeling Latent variable IDR modeling Clinical drug development
The authors thank Michelle L. Perate MS, a professional medical writer funded by Janssen Scientific Affairs., LLC, for editorial support.
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