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Model-based development of a PPARγ agonist, rivoglitazone, to aid dose selection and optimize clinical trial designs

  • Shashank Rohatagi
  • , Timothy J. Carrothers
  • , Jin Yan Jin
  • , William J. Jusko
  • , Tatiana Khariton
  • , Joseph Walker
  • , Kenneth Truitt
  • , Daniel E. Salazar
  • Daiichi Sankyo Company, Limited
  • Pharsight Corporation
  • SUNY Buffalo

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

A model-based approach was implemented for the development of the proliferator-activated receptor gamma (PPARγ) agonist rivoglitazone. Population pharmacokinetic and pharmacodynamic models were developed using data collected from 2 phase I and 2 phase II studies in healthy volunteers and participants with type 2 diabetes mellitus. A 2-compartment model with first-order absorption and elimination and an absorption time lag best described rivoglitazone pharmacokinetics. Modified indirect-response models were used to characterize changes in fasting plasma glucose, HbA1c, and hemodilution as a function of rivoglitazone plasma concentrations. In addition, differences in hemodilution among participants correlated with the incidence of edema. Current use of oral antidiabetic medication was a significant covariate for the fasting plasma glucose-HbA1c exposure-response model. Using a learn-and-confirm process, models developed prior to the second phase II study were able to make valid predictions for exposures and response variables in that study. In future studies, seamless designs can be supported by models such as those developed here.

Original languageEnglish
Pages (from-to)1420-1429
Number of pages10
JournalJournal of Clinical Pharmacology
Volume48
Issue number12
DOIs
StatePublished - Dec 2008

Keywords

  • Exposure response
  • Learn and confirm
  • Pharmacodynamics
  • Population pharmacokinetics
  • Rivoglitazone

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