Literature DB >> 7479560

Experimental design and efficient parameter estimation in preclinical pharmacokinetic studies.

E I Ette1, C A Howie, A W Kelman, B Whiting.   

Abstract

Monte Carlo simulation technique used to evaluate the effect of the arrangement of concentrations on the efficiency of estimation of population pharmacokinetic parameters in the preclinical setting is described. Although the simulations were restricted to the one compartment model with intravenous bolus input, they provide the basis of discussing some structural aspects involved in designing a destructive ("quantic") preclinical population pharmacokinetic study with a fixed sample size as is usually the case in such studies. The efficiency of parameter estimation obtained with sampling strategies based on the three and four time point designs were evaluated in terms of the percent prediction error, design number, individual and joint confidence intervals coverage for parameter estimates approaches, and correlation analysis. The data sets contained random terms for both inter- and residual intra-animal variability. The results showed that the typical population parameter estimates for clearance and volume were efficiently (accurately and precisely) estimated for both designs, while interanimal variability (the only random effect parameter that could be estimated) was inefficiently (inaccurately and imprecisely) estimated with most sampling schedules of the two designs. The exact location of the third and fourth time point for the three and four time point designs, respectively, was not critical to the efficiency of overall estimation of all population parameters of the model. However, some individual population pharmacokinetic parameters were sensitive to the location of these times.

Mesh:

Year:  1995        PMID: 7479560     DOI: 10.1023/a:1016267811074

Source DB:  PubMed          Journal:  Pharm Res        ISSN: 0724-8741            Impact factor:   4.200


  7 in total

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Authors:  M K al-Banna; A W Kelman; B Whiting
Journal:  J Pharmacokinet Biopharm       Date:  1990-08

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Journal:  J Pharmacokinet Biopharm       Date:  1987-02

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Journal:  J Pharmacokinet Biopharm       Date:  1983-06

5.  Interpretation of simulation studies for efficient estimation of population pharmacokinetic parameters.

Authors:  E I Ette; A W Kelman; C A Howie; B Whiting
Journal:  Ann Pharmacother       Date:  1993-09       Impact factor: 3.154

6.  Optimal sampling times for pharmacokinetic experiments.

Authors:  D Z D'Argenio
Journal:  J Pharmacokinet Biopharm       Date:  1981-12

7.  Optimized blood sampling protocols and sequential design of kinetic experiments.

Authors:  J J DiStefano
Journal:  Am J Physiol       Date:  1981-05
  7 in total
  17 in total

1.  Robust optimal design for the estimation of hyperparameters in population pharmacokinetics.

Authors:  M Tod; F Mentré; Y Merlé; A Mallet
Journal:  J Pharmacokinet Biopharm       Date:  1998-12

2.  Drug-drug pharmacodynamic interaction detection by a nonparametric population approach. Influence of design and of interindividual variability.

Authors:  Y Merlé; A Mallet; E Schmautz
Journal:  J Pharmacokinet Biopharm       Date:  1999-10

3.  Is mixed effects modeling or naïve pooled data analysis preferred for the interpretation of single sample per subject toxicokinetic data?

Authors:  J P Hing; S G Woolfrey; D Greenslade; P M Wright
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4.  Design and power of a population pharmacokinetic study.

Authors:  P I Lee
Journal:  Pharm Res       Date:  2001-01       Impact factor: 4.200

Review 5.  Whole body pharmacokinetic models.

Authors:  Ivan Nestorov
Journal:  Clin Pharmacokinet       Date:  2003       Impact factor: 6.447

Review 6.  A pragmatic approach to the design of population pharmacokinetic studies.

Authors:  Amit Roy; Ene I Ette
Journal:  AAPS J       Date:  2005-10-05       Impact factor: 4.009

Review 7.  Pharmacodynamic parameter estimation: population size versus number of samples.

Authors:  Suzette Girgis; Sudhakar M Pai; Ihab G Girgis; Vijay K Batra
Journal:  AAPS J       Date:  2005-10-05       Impact factor: 4.009

Review 8.  Pharmacokinetics/Pharmacodynamics and the stages of drug development: role of modeling and simulation.

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Journal:  AAPS J       Date:  2005-10-07       Impact factor: 4.009

9.  Optimal design for multivariate response pharmacokinetic models.

Authors:  Ivelina Gueorguieva; Leon Aarons; Kayode Ogungbenro; Karin M Jorga; Trudy Rodgers; Malcolm Rowland
Journal:  J Pharmacokinet Pharmacodyn       Date:  2006-03-21       Impact factor: 2.745

10.  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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