Literature DB >> 15669774

Grey-box modelling of pharmacokinetic/pharmacodynamic systems.

Christoffer W Tornøe1, Judith L Jacobsen, Oluf Pedersen, Torben Hansen, Henrik Madsen.   

Abstract

Grey-box pharmacokinetic/pharmacodynamic (PK/PD) modelling is presented as a promising way of modelling PK/PD systems. The concept behind grey-box modelling is based on combining physiological knowledge along with information from data in the estimation of model parameters. Grey-box modelling consists of using stochastic differential equations (SDEs) where the stochastic term in the differential equations represents unknown or incorrectly modelled dynamics of the system. The methodology behind the grey-box PK/PD modelling framework for systematic model improvement is illustrated using simulated data and furthermore applied to Bergman's minimal model of glucose kinetics using clinical data from an intravenous glucose tolerance test (IVGTT). The grey-box estimates of the stochastic system noise parameters indicate that the glucose minimal model is too simple and should preferably be revised in order to describe the complicated in vivo system of insulin and glucose following an IVGTT.

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Year:  2004        PMID: 15669774     DOI: 10.1007/s10928-004-8323-8

Source DB:  PubMed          Journal:  J Pharmacokinet Pharmacodyn        ISSN: 1567-567X            Impact factor:   2.745


  7 in total

1.  Non-linear mixed-effects models with stochastic differential equations: implementation of an estimation algorithm.

Authors:  Rune V Overgaard; Niclas Jonsson; Christoffer W Tornøe; Henrik Madsen
Journal:  J Pharmacokinet Pharmacodyn       Date:  2005-02       Impact factor: 2.745

2.  Exact Gradients Improve Parameter Estimation in Nonlinear Mixed Effects Models with Stochastic Dynamics.

Authors:  Helga Kristin Olafsdottir; Jacob Leander; Joachim Almquist; Mats Jirstrand
Journal:  AAPS J       Date:  2018-08-01       Impact factor: 4.009

3.  Population pharmacokinetic/pharmacodynamic (PK/PD) modelling of the hypothalamic-pituitary-gonadal axis following treatment with GnRH analogues.

Authors:  Christoffer W Tornøe; Henrik Agersø; Thomas Senderovitz; Henrik A Nielsen; Henrik Madsen; Mats O Karlsson; E Niclas Jonsson
Journal:  Br J Clin Pharmacol       Date:  2006-11-10       Impact factor: 4.335

4.  Stochastic differential equations in NONMEM: implementation, application, and comparison with ordinary differential equations.

Authors:  Christoffer W Tornøe; Rune V Overgaard; Henrik Agersø; Henrik A Nielsen; Henrik Madsen; E Niclas Jonsson
Journal:  Pharm Res       Date:  2005-08-03       Impact factor: 4.200

5.  Predictive performance for population models using stochastic differential equations applied on data from an oral glucose tolerance test.

Authors:  Jonas B Møller; Rune V Overgaard; Henrik Madsen; Torben Hansen; Oluf Pedersen; Steen H Ingwersen
Journal:  J Pharmacokinet Pharmacodyn       Date:  2009-12-16       Impact factor: 2.745

6.  Stochastic modeling of systems mapping in pharmacogenomics.

Authors:  Zuoheng Wang; Jiangtao Luo; Guifang Fu; Zhong Wang; Rongling Wu
Journal:  Adv Drug Deliv Rev       Date:  2013-03-22       Impact factor: 15.470

7.  Stochastic nonlinear mixed effects: a metformin case study.

Authors:  Brett Matzuka; Jason Chittenden; Jonathan Monteleone; Hien Tran
Journal:  J Pharmacokinet Pharmacodyn       Date:  2015-11-19       Impact factor: 2.745

  7 in total

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