Dominik Lott (1,2), Jasper Dingemanse (1), Matthias Hoch (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 ponesimod and its two metabolites M12 and M13 in healthy and hepatically/renally impaired subjects.
Methods: Plasma concentration-time data from 153 individuals in 5 Phase 1 studies were available; data from single and multiple doses as well as an up-titration regimen, an intravenous (i.v.) and an oral (p.o.) formulation in the dose range from 5 to 100 mg were pooled. In total, 3735, 3548, and 3427 concentrations were available for ponesimod, M12, and M13, respectively.
A semi-mechanistic population PK model for parent and metabolites with metabolite formation in the gut/first liver passage and in the systemic circulation with incorporation of hepatic and renal function was implemented in Monolix version 4.3.1. After modeling the parent compound, metabolites were sequentially added to the model. Covariates, i.e., hepatic and renal function, were tested on the model parameters (clearance and metabolite formation) and added if statistically significant (p < 0.05). 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 first-order rate constants for absorption, metabolism, and elimination. Model diagnostics showed a good fit. Hepatic function was found to have a significant effect on the clearance of ponesimod and its metabolites.
Comparison of the PK profiles following i.v. and p.o. administration of ponesimod suggested metabolite formation in the gut/first liver passage prior to reaching the systemic circulation, described by rate constants characterizing metabolite formation and absorption. The combination of i.v. and p.o. data allowed estimation of bioavailability as well as the separation of metabolite formation at two sites.
Conclusions: The population PK model suggested relevant ponesimod metabolism prior to reaching the systemic circulation, e.g. , in the gut/first liver passage.
Hepatic function has a strong impact on the clearance of ponesimod as well as its primary metabolites. The model may serve as a valuable tool for the future drug development process and dose adaptation of ponesimod in hepatically impaired patients.
Reference: PAGE 24 (2015) Abstr 3537 [www.page-meeting.org/?abstract=3537]
Poster: Drug/Disease modeling - Other topics