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Impact of Demographics, Organ Impairment, Disease, Formulation, and Food on the Pharmacokinetics of the Selective S1P1 Receptor Modulator Ponesimod Based on 13 Clinical Studies.
Lott, Dominik; Lehr, Thorsten; Dingemanse, Jasper; Krause, Andreas.
Afiliação
  • Lott D; Department of Clinical Pharmacy, Saarland University, 66123, Saarbrücken, Germany. Dominik.Lott@uni-saarland.de.
  • Lehr T; Department of Clinical Pharmacology, Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123, Allschwil, Switzerland. Dominik.Lott@uni-saarland.de.
  • Dingemanse J; Department of Clinical Pharmacy, Saarland University, 66123, Saarbrücken, Germany.
  • Krause A; Department of Clinical Pharmacology, Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123, Allschwil, Switzerland.
Clin Pharmacokinet ; 56(4): 395-408, 2017 04.
Article em En | MEDLINE | ID: mdl-27638335
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Tiazóis / Ensaios Clínicos como Assunto / Interações Alimento-Droga / Receptores de Lisoesfingolipídeo / Modelos Biológicos Tipo de estudo: Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Tiazóis / Ensaios Clínicos como Assunto / Interações Alimento-Droga / Receptores de Lisoesfingolipídeo / Modelos Biológicos Tipo de estudo: Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article