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Modeling the Exposure-Response Relationship of Etanercept in the Treatment of Patients With Chronic Moderate to Severe Plaque Psoriasis

From Pfizer, Inc, Ann Arbor, Michigan (Mr Hutmacher); ZymoGenetics, Seattle, Washington (Dr Nestorov); Globomax ICON, Hanover, Maryland (Dr Ludden); and Amgen, Inc, Thousand Oaks, California (Dr Zitnik, Dr Banfield).

Modeling exposure-response relationships adds significant value to comprehending and interpreting both efficacy and safety data. An exposure-response model was developed using generalized nonlinear mixed-effects methodologies to correlate etanercept exposure with a 75% or greater reduction from baseline in the psoriasis area and severity index (PASI75). Three randomized trials of psoriasis patients were pooled for analysis. Three empirical exposure measures—cumulative dose, predicted cumulative area under the curve, and predicted trough concentration—were evaluated for their predictive capabilities. The predicted cumulative area under the curve model demonstrated the best ability via simulation to reproduce the data and was used to assess the following covariates: age, baseline psoriasis area and severity index, duration of psoriasis disease, prior systemic or phototherapy, race, sex, and weight. The final model was composed by scrutinizing the confidence intervals of a nonparametric bootstrap and included race and sex effects on baseline logit, baseline psoriasis area and severity index and prior systemic or phototherapy effects on maximum drug effect, a weight effect on apparent potency, and an age effect on the rate of drug effect. The model identified covariates predictive of data trends and adequately characterized by simulation the PASI75 over the entire clinical trial design space. In combination with a statistical subgroup analysis, the exposure-response model indicated that dose adjustment was not necessary for etanercept in any patient subpopulation with moderate to severe plaque psoriasis.

Key Words: Population pharmacokinetic/pharmacodynamic (PK/PD) modeling • exposure-response modeling • generalized nonlinear mixed-effects models • logistic regression • NONMEM

Address for reprints: Address for correspondence: Christopher Banfield, PhD, Amgen, Inc, One Amgen Center Drive, M.S. 38-3-A, Thousand Oaks, CA 91320; e-mail: banfield{at}amgen.com.

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