Literature DB >> 8877017

Estimating impossible curves using NONMEM.

R C Schoemaker1, A F Cohen.   

Abstract

1. On fitting model equations to experimental data, the situation may arise that individual subjects provide insufficient information to obtain adequate parameter estimates due to the fact that not all aspects are exhibited by all subjects or that the models are simply too complex. This may be solved by applying nonlinear mixed effect modelling to the data, which integrates the information provided by different subjects. 2. We aim to provide insight into the methodology and its use in these situations, illustrated by three examples: determination of pharmacokinetics in a rising dose design, where the lower doses provide insufficient information (due to assay limitations) to estimate terminal half-life; determination of the kinetics of the low molecular weight heparin enoxaparine (Clexane) using anti-Xa activity, effectively dealing with lingering low/basal activity; simultaneous estimation of the pharmacokinetics and pharmacodynamics of the low molecular weight heparin dalteparin (Fragmin) after subcutaneous and intravenous administration.

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Year:  1996        PMID: 8877017      PMCID: PMC2042675          DOI: 10.1046/j.1365-2125.1996.04231.x

Source DB:  PubMed          Journal:  Br J Clin Pharmacol        ISSN: 0306-5251            Impact factor:   4.335


  33 in total

1.  Estimating potency for the Emax-model without attaining maximal effects.

Authors:  R C Schoemaker; J M van Gerven; A F Cohen
Journal:  J Pharmacokinet Biopharm       Date:  1998-10

2.  Nonlinearity detection: advantages of nonlinear mixed-effects modeling.

Authors:  E N Jonsson; J R Wade; M O Karlsson
Journal:  AAPS PharmSci       Date:  2000

3.  Central nervous system effects of moxonidine experimental sustained release formulation in patients with mild to moderate essential hypertension.

Authors:  Michiel J B Kemme; Jeroen P vd Post; Rik C Schoemaker; Matthias Straub; Adam F Cohen; Joop M A van Gerven
Journal:  Br J Clin Pharmacol       Date:  2003-06       Impact factor: 4.335

4.  Estimating inestimable standard errors in population pharmacokinetic studies: the bootstrap with Winsorization.

Authors:  Ene I Ette; Leonard C Onyiah
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2002 Jul-Sep       Impact factor: 2.441

Review 5.  Interpreting population pharmacokinetic-pharmacodynamic analyses - a clinical viewpoint.

Authors:  Stephen B Duffull; Daniel F B Wright; Helen R Winter
Journal:  Br J Clin Pharmacol       Date:  2011-06       Impact factor: 4.335

6.  Population pharmacokinetics of darbepoetin alfa in haemodialysis and peritoneal dialysis patients after intravenous administration.

Authors:  Hirotaka Takama; Hideji Tanaka; Daisuke Nakashima; Hiroyasu Ogata; Eiji Uchida; Tadao Akizawa; Shozo Koshikawa
Journal:  Br J Clin Pharmacol       Date:  2006-08-31       Impact factor: 4.335

7.  Population pharmacokinetic analysis for simultaneous determination of B (max) and K (D) in vivo by positron emission tomography.

Authors:  Lia C Liefaard; Bart A Ploeger; Carla F M Molthoff; Ronald Boellaard; Adriaan A Lammertsma; Meindert Danhof; Rob A Voskuyl
Journal:  Mol Imaging Biol       Date:  2005 Nov-Dec       Impact factor: 3.488

8.  Pharmacokinetics of amoxicillin in maternal, umbilical cord, and neonatal sera.

Authors:  Anouk E Muller; Paul M Oostvogel; Joost DeJongh; Johan W Mouton; Eric A P Steegers; P Joep Dörr; Meindert Danhof; Rob A Voskuyl
Journal:  Antimicrob Agents Chemother       Date:  2009-01-21       Impact factor: 5.191

9.  Dosing strategy for enoxaparin in patients with renal impairment presenting with acute coronary syndromes.

Authors:  B Green; M Greenwood; D Saltissi; J Westhuyzen; L Kluver; J Rowell; J Atherton
Journal:  Br J Clin Pharmacol       Date:  2005-03       Impact factor: 4.335

10.  Development of a dosing strategy for enoxaparin in obese patients.

Authors:  Bruce Green; Stephen B Duffull
Journal:  Br J Clin Pharmacol       Date:  2003-07       Impact factor: 4.335

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