Literature DB >> 30360949

Characterization of changes in HbA1c in patients with and without secondary failure after metformin treatments by a population pharmacodynamic analysis using mixture models.

Yoko Tamaki1, Kunio Maema2, Makoto Kakara1, Masato Fukae1, Ryoko Kinoshita1, Yushi Kashihara1, Shota Muraki1, Takeshi Hirota1, Ichiro Ieiri3.   

Abstract

The objective of the present study was to develop a population pharmacodynamic (PPD) model to describe the glycated hemoglobin (HbA1c)-lowering effects of metformin in type 2 diabetes mellitus patients with and without secondary failure and to characterize changes in HbA1c levels in the two subpopulations using a mixture model. Information on patients was collected retrospectively from electronic medical records. In this study, the mixture model was used to characterize the bimodal effects of metformin. A PPD analysis was performed using NONMEM 7.3.0. A physiological indirect response model, based on 829 HbA1c levels of 69 patients, described the time course for the HbA1c-lowering effects of metformin. Evidence for the different effectiveness of metformin subpopulations was provided using the mixture model. In the final PPD model, the inhibition effect was constant over a study duration in a patient subpopulation without secondary failure. In contrast, the inhibition effect decreased as a function of time after start of metformin treatment in a subpopulation with secondary failure. These results indicated that HbA1c improvements appeared to deteriorate over time in patients with secondary failure. In a PPD analysis of metformin, it was possible to assign patients with secondary failure using the mixture model.
Copyright © 2018 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

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Keywords:  Electronic medical record; Glycated hemoglobin; Metformin; Mixture model; Population pharmacodynamics; Secondary failure

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Year:  2018        PMID: 30360949     DOI: 10.1016/j.dmpk.2018.08.002

Source DB:  PubMed          Journal:  Drug Metab Pharmacokinet        ISSN: 1347-4367            Impact factor:   3.614


  1 in total

1.  Development of visual predictive checks accounting for multimodal parameter distributions in mixture models.

Authors:  Usman Arshad; Estelle Chasseloup; Rikard Nordgren; Mats O Karlsson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2019-04-09       Impact factor: 2.745

  1 in total

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