Dominik Lott (1,2), Jasper Dingemanse (1), Thorsten Lehr (2), Andreas Krause (1)
(1) Actelion Pharmaceuticals Ltd, Department of Clinical Pharmacology, Modeling and Simulation, Allschwil, Switzerland, (2) Saarland University, Department of Clinical Pharmacy, Saarbrücken, Germany
Objectives: Development of a population pharmacokinetic (PK) model for the characterization of the reversible, orally active, selective S1P1 receptor modulator ponesimod, including the influence of different formulations (capsule, tablet), disease (psoriasis, multiple sclerosis, hepatic impairment), and demographics (body weight, age, race).
Methods: Plasma concentration-time data from 680 individuals in 13 clinical studies were pooled. The data set comprises single doses up to 75 mg, multiple once-daily doses up to 100 mg as well as different up-titration regimens. In total, 13700 ponesimod concentration measurements were available.
Different structural models, i.e., 1-, 2-, and 3-compartment models with different absorption models were assessed. Following the selection of the structural model, the influence of covariates was assessed by using forward inclusion-backward deletion. The adequacy of the model was evaluated based on visual predictive checks, goodness-of-fit plots, and parameter variability.
Results: The PK characteristics of ponesimod were accurately described by a two-compartment model with sequential zero/first-order absorption, inter-compartmental drug flow (Q/F=21 L/h), and linear apparent clearance (CL/F=6.6 L/h). The estimated apparent volumes of distribution were 165 L and 107 L for the central the peripheral compartment, respectively.
Higher body weights (100 kg), psoriasis, and multiple sclerosis were identified to significantly increase the central volume of distribution by 28%, 45%, and 21%, respectively. Clearance was significantly lower in subjects with mild (-30%), moderate (-52%), and severe (-68%) hepatic impairment, subjects of race ‘Black’ (-15%), and affected by body weight (higher clearance with increased body weight).
The impact of the identified covariates on ponesimod steady-state exposure largely lies within the margins of the inter-subject variability with the exception of hepatic impairment. Increases of up to 212% were predicted for severe cases of liver dysfunction.
Conclusions: The analysis shows that the inter-individual variability in the PK of ponesimod can partially be explained by covariates that were identified as statistically significant. However, the net effect on steady-state exposure is small and considered as not clinically relevant with the exception of hepatic impairment.
The diversity of the underlying data, the inclusion of a large variety of studies as well as the number of concentration measurements included make this analysis a robust and valuable tool to support dosing strategies for ponesimod.
Reference: PAGE 25 () Abstr 5927 [www.page-meeting.org/?abstract=5927]
Poster: Drug/Disease modeling - Safety