Literature DB >> 26142076

The method of averaging applied to pharmacokinetic/pharmacodynamic indirect response models.

Adrian Dunne1, Willem de Winter, Chyi-Hung Hsu, Shiferaw Mariam, Martine Neyens, José Pinheiro, Xavier Woot de Trixhe.   

Abstract

The computational effort required to fit the pharmacodynamic (PD) part of a pharmacokinetic/pharmacodynamic (PK/PD) model can be considerable if the differential equations describing the model are solved numerically. This burden can be greatly reduced by applying the method of averaging (MAv) in the appropriate circumstances. The MAv gives an approximate solution, which is expected to be a good approximation when the PK profile is periodic (i.e. repeats its values in regular intervals) and the rate of change of the PD response is such that it is approximately constant over a single period of the PK profile. This paper explains the basis of the MAv by means of a simple mathematical derivation. The NONMEM® implementation of the MAv using the abbreviated FORTRAN function FUNCA is described and explained. The application of the MAv is illustrated by means of an example involving changes in glycated hemoglobin (HbA1c%) following administration of canagliflozin, a selective sodium glucose co-transporter 2 inhibitor. The PK/PD model applied to these data is fitted with NONMEM® using both the MAv and the standard method using a numerical differential equation solver (NDES). Both methods give virtually identical results but the NDES method takes almost 8 h to run both the estimation and covariance steps, whilst the MAv produces the same results in less than 30 s. An outline of the NONMEM® control stream and the FORTRAN code for the FUNCA function is provided in the appendices.

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Year:  2015        PMID: 26142076     DOI: 10.1007/s10928-015-9426-0

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


  5 in total

1.  Simultaneous vs. sequential analysis for population PK/PD data I: best-case performance.

Authors:  Liping Zhang; Stuart L Beal; Lewis B Sheiner
Journal:  J Pharmacokinet Pharmacodyn       Date:  2003-12       Impact factor: 2.745

2.  Mathematical formalism for the properties of four basic models of indirect pharmacodynamic responses.

Authors:  W Krzyzanski; W J Jusko
Journal:  J Pharmacokinet Biopharm       Date:  1997-02

3.  Characterization of four basic models of indirect pharmacodynamic responses.

Authors:  A Sharma; W J Jusko
Journal:  J Pharmacokinet Biopharm       Date:  1996-12

4.  Population Pharmacokinetic Modeling of Canagliflozin in Healthy Volunteers and Patients with Type 2 Diabetes Mellitus.

Authors:  Eef Hoeben; Willem De Winter; Martine Neyens; Damayanthi Devineni; An Vermeulen; Adrian Dunne
Journal:  Clin Pharmacokinet       Date:  2016-02       Impact factor: 6.447

5.  Comparison of four basic models of indirect pharmacodynamic responses.

Authors:  N L Dayneka; V Garg; W J Jusko
Journal:  J Pharmacokinet Biopharm       Date:  1993-08
  5 in total
  1 in total

1.  Dynamic population pharmacokinetic-pharmacodynamic modelling and simulation supports similar efficacy in glycosylated haemoglobin response with once or twice-daily dosing of canagliflozin.

Authors:  Willem de Winter; Adrian Dunne; Xavier Woot de Trixhe; Damayanthi Devineni; Chyi-Hung Hsu; Jose Pinheiro; David Polidori
Journal:  Br J Clin Pharmacol       Date:  2017-01-31       Impact factor: 4.335

  1 in total

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