Xiao Hu (1), Yue Cui (1), Yaming Hang (1), Shifang Liu (1), Serena Hung (1), Aaron Deykin (1), Ivan Nestorov (1)
(1) Biogen Idec Inc
Objectives: To establish a population pharmacokinetic (PK) model of peginterferon beta-1a (PEGIFN) in relapsing remitting multiple sclerosis (RRMS) patients and establish the relationship between PEGIFN exposure and annualized relapse rate (ARR).
Methods: PK and ARR data were obtained from a double-blind placebo-controlled Phase 3 study in RRMS patients (n=1512), in which 125 µg subcutaneous PEGIFN every 2 (Q2W) or 4 (Q4W) weeks reduced ARR (primary endpoint) significantly, compared with placebo treatment. PEGIFN serum concentrations were fitted to a PK model using non-linear mixed-effects modelling implemented in NONMEM V7.2.0 [1]. BIIB017 exposure was represented by monthly cumulative AUC for each subject, which was derived from individual post hoc PK parameters. The relationship between cumulative AUC and mean ARR was described by a log-linear model, assuming Poisson-gamma mixture model (negative binomial model) for ARR. Parameters were estimated using Bayesian analysis with Gibbs sampling in WinBUGS v1.4.3 [2]. Non-informative priors were used for parameter estimates.
Results: The PK of PEGIFN was described by a one-compartment model with first-order absorption rate. BMI was identified as a significant covariate in the final PK model (p<0.001). Derived from the population PK analysis, cumulative median AUC of the Q2W group (76.8 ng/mL*hr) was approximately two times cumulative median AUC of the Q4W group (39.2 ng/mL*hr). The relationship between monthly cumulative AUC and ARR was well described by the log-linear model. In general, the ARR decreased as cumulative AUC increased. The slope for ARR reduction was steep in the Q4W AUC range, especially at below median AUC. In contrast, the slope started to level off in the Q2W AUC range. Based on model extrapolation, a higher AUC than that of the Q2W group might achieve a greater reduction in ARR. Although the model showed that Q2W dosing may not have achieved maximal ARR reduction, it demonstrated that the better efficacy of the Q2W dosing regimen as compared with the Q4W dosing regimen was driven by its greater PEGIFN exposure.
Conclusions: The model suggested that greater PEGIFN exposure in the Q2W group explain the enhanced efficacy, as accounted by ARR, observed for the Q2W group, as compared to the Q4W group.
References:
[1] Beal SL, Sheiner LB, Boeckmann AJ & Bauer RJ (Eds.) NONMEM Users Guides. 1989-2011. Icon Development Solutions, Ellicott City, Maryland, USA.
[2] Lunn DJ, Thomas A, Best N, and Spiegelhalter D, WinBUGS – a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing (2000), 10:325-337.
Reference: PAGE 23 () Abstr 3309 [www.page-meeting.org/?abstract=3309]
Poster: Drug/Disease modeling - CNS