Literature DB >> 16353905

Pharmacodynamic parameter estimation: population size versus number of samples.

Suzette Girgis1, Sudhakar M Pai, Ihab G Girgis, Vijay K Batra.   

Abstract

The purpose of this study was to evaluate the effects of population size, number of samples per individual, and level of interindividual variability (IIV) on the accuracy and precision of pharmacodynamic (PD) parameter estimates. Response data were simulated from concentration input data for an inhibitory sigmoid drug efficacy (E(max)) model using Nonlinear Mixed Effect Modeling, version 5 (NONMEM). Seven designs were investigated using different concentration sampling windows ranging from 0 to 3 EC(50) (EC(50) is the drug concentration at 50% of the E(max)) units. The response data were used to estimate the PD and variability parameters in NONMEM. The accuracy and precision of parameter estimates after 100 replications were assessed using the mean and SD of percent prediction error, respectively. Four samples per individual were sufficient to provide accurate and precise estimate of almost all of the PD and variability parameters, with 100 individuals and IIV of 30%. Reduction of sample size resulted in imprecise estimates of the variability parameters; however, the PD parameter estimates were still precise. At 45% IIV, designs with 5 samples per individual behaved better than those designs with 4 samples per individual. For a moderately variable drug with a high Hill coefficient, sampling from the 0.1 to 1, 1 to 2, 2 to 2.5, and 2.5 to 3 EC(50) window is sufficient to estimate the parameters reliably in a PD study.

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Year:  2005        PMID: 16353905      PMCID: PMC2750983          DOI: 10.1208/aapsj070246

Source DB:  PubMed          Journal:  AAPS J        ISSN: 1550-7416            Impact factor:   4.009


  8 in total

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Authors:  E I Ette; C A Howie; A W Kelman; B Whiting
Journal:  Pharm Res       Date:  1995-05       Impact factor: 4.200

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Authors:  Y Hashimoto; L B Sheiner
Journal:  J Pharmacokinet Biopharm       Date:  1991-06
  8 in total
  8 in total

1.  Population pharmacodynamic parameter estimation from sparse sampling: effect of sigmoidicity on parameter estimates.

Authors:  Sudhakar M Pai; Suzette Girgis; Vijay K Batra; Ihab G Girgis
Journal:  AAPS J       Date:  2009-07-24       Impact factor: 4.009

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Authors:  Thierry Busso; Martin Flück
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3.  Feasibility of Exposure-Response Analyses for Clinical Dose-Ranging Studies of Drug Combinations.

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Journal:  AAPS J       Date:  2018-04-23       Impact factor: 4.009

4.  Predicting Antibiotic Effect of Vancomycin Using Pharmacokinetic/Pharmacodynamic Modeling and Simulation: Dense Sampling versus Sparse Sampling.

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5.  Modeling the responses to resistance training in an animal experiment study.

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6.  Impact of Sampling Period on Population Pharmacokinetic Analysis of Antibiotics: Why do You Take Blood Samples Following the Fourth Dose?

Authors:  So Won Kim; Dong Jin Kim; Dae Young Zang; Dong-Hwan Lee
Journal:  Pharmaceuticals (Basel)       Date:  2020-09-16

7.  Population Pharmacodynamic Modeling Using the Sigmoid Emax Model: Influence of Inter-individual Variability on the Steepness of the Concentration-Effect Relationship. a Simulation Study.

Authors:  Johannes H Proost; Douglas J Eleveld; Michel M R F Struys
Journal:  AAPS J       Date:  2020-12-24       Impact factor: 4.009

8.  Combined Use of Etomidate and Dexmedetomidine Produces an Additive Effect in Inhibiting the Secretion of Human Adrenocortical Hormones.

Authors:  Hongbin Gu; Mazhong Zhang; Meihua Cai; Jinfen Liu
Journal:  Med Sci Monit       Date:  2015-11-16
  8 in total

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