Laure Deyme (1), Jean-Marie Martinez (1), David Fabre (1), Binfeng Xia* (2), Kissell, Julie (3), Christine Xu (2)
(1) Sanofi R&D, Montpellier; (2) Sanofi, Bridgewater, NJ USA (3) Sanofi, Cambridge MA USA ; * Former employee, employed at the time of study.
Introduction: Gaucher disease (GD) is an autosomal recessive lysosomal storage disease that results from a deficiency of acid β-glucosidase. The main natural substrate for this enzyme is glucosylceramide, an intermediate metabolite in the synthesis and catabolism of more complex glycosphingolipids. Eliglustat is a member of a class of glucosylceramide synthase inhibitors developed by Sanofi and approved as first line therapy for the long-term treatment of adults with GD type 1 who are CYP2D6 extensive metabolizers (EMs), intermediate metabolizers (IMs), or poor metabolizers (PMs).
Objectives: The objectives of this analysis were to develop a population pharmacokinetic model to characterize the PK of pediatric patients enrolled in an ongoing pediatric phase 3 clinical study (NCT03485677) and to support the dose recommendation in pediatric population depending on the CYP2D6 phenotype and the body weight (BW) group.
Methods: Pediatric data were pooled with adult data for model development. A population PK (popPK) model was developed in NONMEM 7.5.1 [1] using FOCEI algorithm for optimization. The qualification step was assessed using prediction-corrected Visual Predictive Check (pcVPC) [2] and bootstrap methods. The popPK model was used to estimate the individual PK parameters. The mrgsolve package [3] was used to derive exposure parameters in pediatric patients.
Results: Two allometric scaled PopPK models were developed based on the CYP2D6 phenotype, because a single popPK model could not adequately describe the eliglustat PK due to the large impact of the CYP2D6 phenotype on eliglustat PK.
For the CYP2D6 EMs and IMs population, the exclusive dataset was composed of 606 subjects (193 healthy volunteers, 357 adult patients and 56 pediatric patients) enrolled into phase 1 to phase 3 clinical studies. The structural model of the CYP2D6 EMs and IMs population was composed of a sequential zero and first order absorption, a two-compartment model and a linear clearance. The model was allometrically scaled with fixed exponents as the data of adult and pediatric population were pooled and an inter-occasion variability was added. In the covariate analysis, the statistically significant covariates included the CYP2D6 phenotype on the bioavailability (F1), the dose on the clearance, the chronic dosing effect on F1 and the formulation on F1.
For the CYP2D6 PMs population, the other exclusive dataset was composed of 35 subjects (20 healthy volunteers, 14 adult patients and 1 pediatric patient). The structural model was composed of a sequential zero and first order absorption and a delay for the absorption, a two-compartment model and a linear clearance. No statistically significant covariate was included, except the allometric scaled parameters with fixed exponents.
Both models reasonably well described the data. The pcVPC showed an overall good model performance and indicated that the models could be used for simulation purposes.
Conclusions: The two allometric scaled popPK models described the PK of eliglustat in pediatric patients with GD accros all 3 CYP2D6 phenotypes. The main source of intrinsic PK variability identified in pediatric participants is CYP2D6 phenotype, thus support the dose recommendation depending on the CYP2D6 phenotype and the BW group.
References:
[1] Beal S., Sheiner L.B., Boeckmann A., & Bauer R.J., NONMEM User’s Guides. (1989-2017), Icon Development Solutions, Ellicott City, MD, USA, 2017.
[2]. Bergstrand M, Hooker AC, Wallin JE, Karlsson MO. Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J. 2011 Jun;13(2):143–51.
[3]. Baron KT. mrgsolve: Simulate from ode-based population pk/pd and systems
pharmacology models. 2019
Reference: PAGE 32 (2024) Abstr 11005 [www.page-meeting.org/?abstract=11005]
Poster: Drug/Disease Modelling - Paediatrics