Literature DB >> 18388881

Models for plasma glucose, HbA1c, and hemoglobin interrelationships in patients with type 2 diabetes following tesaglitazar treatment.

B Hamrén1, E Björk, M Sunzel, Mo Karlsson.   

Abstract

Pharmacokinetic (PK) pharmacodynamic (PD) modeling was applied to understand and quantitate the interplay between tesaglitazar (a peroxisome proliferator-activated receptor alpha/gamma agonist) exposure, fasting plasma glucose (FPG), hemoglobin (Hb), and glycosylated hemoglobin (HbA1c) in type 2 diabetic patients. Data originated from a 12-week dose-ranging study with tesaglitazar. The primary objective was to develop a mechanism-based PD model for the FPG-HbA1c relationship. The secondary objective was to investigate possible mechanisms for the tesaglitazar effect on Hb. Following initiation of tesaglitazar therapy, time to new FPG steady state was approximately 9 weeks, and tesaglitazar potency in females was twice that in males. The model included aging of red blood cells (RBCs) using a transit compartment approach. The RBC life span was estimated to 135 days. The transformation from RBC to HbA1c was modeled as an FPG-dependent process. The model indicated that the tesaglitazar effect on Hb was caused by hemodilution of RBCs.

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Year:  2008        PMID: 18388881     DOI: 10.1038/clpt.2008.2

Source DB:  PubMed          Journal:  Clin Pharmacol Ther        ISSN: 0009-9236            Impact factor:   6.875


  32 in total

1.  Rapid sample size calculations for a defined likelihood ratio test-based power in mixed-effects models.

Authors:  Camille Vong; Martin Bergstrand; Joakim Nyberg; Mats O Karlsson
Journal:  AAPS J       Date:  2012-02-17       Impact factor: 4.009

2.  Comparative performance of cell life span and cell transit models for describing erythropoietic drug effects.

Authors:  Nageshwar R Budha; Andreas Kovar; Bernd Meibohm
Journal:  AAPS J       Date:  2011-10-18       Impact factor: 4.009

3.  Transforming parts of a differential equations system to difference equations as a method for run-time savings in NONMEM.

Authors:  K J F Petersson; L E Friberg; M O Karlsson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2010-09-29       Impact factor: 2.745

4.  Hemoglobin A1c and Self-Monitored Average Glucose: Validation of the Dynamical Tracking eA1c Algorithm in Type 1 Diabetes.

Authors:  Boris P Kovatchev; Marc D Breton
Journal:  J Diabetes Sci Technol       Date:  2015-11-09

Review 5.  A Comprehensive Review of Novel Drug-Disease Models in Diabetes Drug Development.

Authors:  Puneet Gaitonde; Parag Garhyan; Catharina Link; Jenny Y Chien; Mirjam N Trame; Stephan Schmidt
Journal:  Clin Pharmacokinet       Date:  2016-07       Impact factor: 6.447

6.  Efficacy of DPP-4 inhibitors, GLP-1 analogues, and SGLT2 inhibitors as add-ons to metformin monotherapy in T2DM patients: a model-based meta-analysis.

Authors:  Hiroyuki Inoue; Yoko Tamaki; Yushi Kashihara; Shota Muraki; Makoto Kakara; Takeshi Hirota; Ichiro Ieiri
Journal:  Br J Clin Pharmacol       Date:  2018-12-06       Impact factor: 4.335

7.  Semiparametric distributions with estimated shape parameters.

Authors:  Klas J F Petersson; Eva Hanze; Radojka M Savic; Mats O Karlsson
Journal:  Pharm Res       Date:  2009-07-01       Impact factor: 4.200

8.  Importance of shrinkage in empirical bayes estimates for diagnostics: problems and solutions.

Authors:  Radojka M Savic; Mats O Karlsson
Journal:  AAPS J       Date:  2009-08-01       Impact factor: 4.009

9.  A fast method for testing covariates in population PK/PD Models.

Authors:  Akash Khandelwal; Kajsa Harling; E Niclas Jonsson; Andrew C Hooker; Mats O Karlsson
Journal:  AAPS J       Date:  2011-07-02       Impact factor: 4.009

Review 10.  Pharmacokinetic/pharmacodynamic modelling in diabetes mellitus.

Authors:  Cornelia B Landersdorfer; William J Jusko
Journal:  Clin Pharmacokinet       Date:  2008       Impact factor: 6.447

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