Dominik Lott1,2, Thorsten Lehr3, Jasper Dingemanse4, Andreas Krause4. 1. Department of Clinical Pharmacy, Saarland University, 66123, Saarbrücken, Germany. Dominik.Lott@uni-saarland.de. 2. Department of Clinical Pharmacology, Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123, Allschwil, Switzerland. Dominik.Lott@uni-saarland.de. 3. Department of Clinical Pharmacy, Saarland University, 66123, Saarbrücken, Germany. 4. Department of Clinical Pharmacology, Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123, Allschwil, Switzerland.
Abstract
BACKGROUND: Ponesimod is a selective, orally active sphingosine-1-phosphate receptor 1 modulator currently undergoing clinical evaluation for the treatment of multiple sclerosis (MS) in phase III clinical trials. Ponesimod dose-dependently reduces peripheral blood lymphocyte counts by blocking the egress of lymphocytes from lymphoid organs. METHODS: A population pharmacokinetic (PK) analysis was performed based on pooled data from 13 clinical studies. Interindividual variability (IIV) and the impact of key demographic variables and other covariates on ponesimod exposure were assessed quantitatively. RESULTS: A two-compartment model with sequential zero/first-order absorption, including lag time, intercompartmental drug flow, and first-order clearance, adequately described the PK of ponesimod. Body weight, race, MS, psoriasis, hepatic impairment, drug formulation, and food were identified to significantly affect the concentration-time profile. The inclusion of these covariates into the model explained approximately 25 % of the IIV in the PK of ponesimod. Model predictions indicated that the impact of the identified covariates on ponesimod steady-state exposure is within 20 % of exposure, and thus within the margins of the IIV, with the exception of hepatic impairment. Changes up to threefold were predicted for severe cases of liver dysfunction. CONCLUSION: The rich data set enabled building a comprehensive population PK model that accurately predicts the concentration-time data of ponesimod. Covariates other than hepatic impairment were considered not clinically relevant and thus do not require dose adjustment. A potential dose adaptation can be conducted based on the final model.
BACKGROUND:Ponesimod is a selective, orally active sphingosine-1-phosphate receptor 1 modulator currently undergoing clinical evaluation for the treatment of multiple sclerosis (MS) in phase III clinical trials. Ponesimod dose-dependently reduces peripheral blood lymphocyte counts by blocking the egress of lymphocytes from lymphoid organs. METHODS: A population pharmacokinetic (PK) analysis was performed based on pooled data from 13 clinical studies. Interindividual variability (IIV) and the impact of key demographic variables and other covariates on ponesimod exposure were assessed quantitatively. RESULTS: A two-compartment model with sequential zero/first-order absorption, including lag time, intercompartmental drug flow, and first-order clearance, adequately described the PK of ponesimod. Body weight, race, MS, psoriasis, hepatic impairment, drug formulation, and food were identified to significantly affect the concentration-time profile. The inclusion of these covariates into the model explained approximately 25 % of the IIV in the PK of ponesimod. Model predictions indicated that the impact of the identified covariates on ponesimod steady-state exposure is within 20 % of exposure, and thus within the margins of the IIV, with the exception of hepatic impairment. Changes up to threefold were predicted for severe cases of liver dysfunction. CONCLUSION: The rich data set enabled building a comprehensive population PK model that accurately predicts the concentration-time data of ponesimod. Covariates other than hepatic impairment were considered not clinically relevant and thus do not require dose adjustment. A potential dose adaptation can be conducted based on the final model.
Authors: Luca Piali; Sylvie Froidevaux; Patrick Hess; Oliver Nayler; Martin H Bolli; Eva Schlosser; Christopher Kohl; Beat Steiner; Martine Clozel Journal: J Pharmacol Exp Ther Date: 2011-02-23 Impact factor: 4.030
Authors: Tomas Olsson; Aaron Boster; Óscar Fernández; Mark S Freedman; Carlo Pozzilli; Doris Bach; Ouali Berkani; Markus S Mueller; Tatiana Sidorenko; Ernst-Wilhelm Radue; Maria Melanson Journal: J Neurol Neurosurg Psychiatry Date: 2014-03-21 Impact factor: 10.154
Authors: Dominik Lott; Andreas Krause; Christian A Seemayer; Daniel S Strasser; Jasper Dingemanse; Thorsten Lehr Journal: Pharm Res Date: 2016-12-27 Impact factor: 4.200