Li Zhang (1), Christine Xu (1), John D. Davis (2), Pavel Kovalenko (2), A Thomas DiCioccio (2), Vanaja Kanamaluru (1), Qiang Lu (1)
(1) Sanofi, Bridgewater, NJ, USA; (2) Regeneron Pharmaceuticals, Inc., Tarrytown, NY, USA
Objectives: Dupilumab is a human monoclonal antibody of the IgG4 subclass that binds to the IL-4Rα subunit and inhibits IL-4 and IL-13 signaling which are key proximal drivers of type 2 inflammation [1]. Dupilumab has demonstrated significant clinical efficacy in multiple type 2 inflammatory diseases, namely atopic dermatitis (AD), asthma, and chronic rhinosinusitis with nasal polyposis (CRSwNP). Taking into account the similarity in PK profiles across adult healthy subjects (HV), AD and asthma populations, a Pop PK base model developed based upon data pooled from these populations holds great potential to characterize the pharmacokinetic profile of dupilumab across multiple type 2 inflammatory disease populations. Therefore, this meta-analysis aimed to develop and qualify a global Pop PK base model for dupilumab in adult HVs and AD and asthma patients.
Methods: Concentrations of functional dupilumab in serum from 20 Phase 1, 2 and 3 studies were used. These included Phase 1 studies in HV after a single intravenous (IV) or subcutaneous (SC) administration of dupilumab and Phase 2 and 3 studies in AD and asthma patients after repeated SC administration of dupilumab once every week (qw), two weeks (q2w), or four weeks (q4w), were included for the Pop PK base model development. Based on the similarity in dupilumab PK profiles between adult HV, asthma and AD populations, the base model structure of a previously developed asthma Pop PK model (two-compartment model with parallel linear and Michaelis-Menten [M-M] elimination)[2] served as the starting point. Given the well characterized body weight effect on dupilumab PK in previous AD and asthma Pop PK models [2, 3], the relationship between body weight and selected PK parameters was evaluated in the current analysis using forward selection followed by a backward elimination procedure.
Results: Final model development dataset included 30557 dupilumab concentrations from 4056 subjects consisting of 202 HV, 1839 AD patients, and 2015 asthma patients (including 69 adolescents) across a wide dose range of 100 mg q4w to 300 mg qw. The PK of dupilumab was well described by a 2-compartment model with first order absorption kinetics and parallel linear and nonlinear M-M elimination. The precision of PK parameter estimates from this global base model was high throughout (%RSE < 40%). Notably, key PK parameter estimates (e.g. bioavailability of 60.9%, distribution volume at steady-state (Vss) of 4.37 L, linear clearance of 0.12 L/day) were consistent with those from prior AD and asthma Pop PK models [2, 3]. Similar to prior AD and asthma models, weight was included as a covariate in the final Pop PK base model, where volume of central compartment (V2), maximum target-mediated rate of elimination (Vmax), and linear elimination rate constant (Ke) were significantly related to body weight with higher V2, Vmax, and Ke in patients with higher body weight. After inclusion of weight effect, inter-individual variability estimates for key PK parameters (i.e. Ke, V2 and Vmax) decreased approximately 4.0% – 6.21% compared to the base model. Moreover, model-simulated PK profiles were in good agreement with observed PK profiles (including the target-mediated elimination phase) in AD and asthma populations, supporting the predictability of this base model for other type 2 inflammatory disease populations (e.g. CRSwNP).
Conclusions: A population pharmacokinetic meta-analysis was performed to develop a Pop PK base model, which described the PK of functional dupilumab in HVs and AD and asthma patients across a wide SC dose range of 100 mg q4w to 300 mg qw. Consistent with the prior AD and asthma Pop PK models, weight exerted a notable effect explaining between-subject variability of steady-state exposure of functional dupilumab concentration in AD and asthma patients. This Pop PK base model, which is based on a large dataset derived from 3 different populations, may serve as a robust starting point for predictions of dupilumab concentrations or subsequent Pop PK analyses in other populations with type 2 inflammatory disease.
References:
[1] Gandhi NA, Bennett BL, Graham NMH, Pirozzi G, Stahl N, Yancopoulos GD. Targeting key proximal drivers of type 2 inflammation in disease. Nat Rev Drug Discov (2016) 15(1): 35-50.
[2] Zhang L, Gao Y, Li M, Davis JD, Kanamaluru V, Lu Q. Population pharmacokinetic analysis of dupilumab in adult and adolescent patients with asthma. PAGE 27 (2018) Abstr 8652 [www.page-meeting.org/?abstract=8652]
[3] Kovalenko P, Davis JD, Li M, DiCioccio AT. Population pharmacokinetic analysis of dupilumab using early Phase and Phase 3 Data. J Pharmacokinet Pharmacodyn (2017) 44: S69.
Reference: PAGE 28 (2019) Abstr 9160 [www.page-meeting.org/?abstract=9160]
Poster: Drug/Disease Modelling - Other Topics