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Performance of an Iterative Two-Stage Bayesian Technique for Population Pharmacokinetic Analysis of Rich Data Sets

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An Erratum to this article was published on 23 March 2007

Abstract

Purpose

To test the suitability of an Iterative Two-Stage Bayesian (ITSB) technique for population pharmacokinetic analysis of rich data sets, and to compare ITSB with Standard Two-Stage (STS) analysis and nonlinear Mixed Effect Modeling (MEM).

Materials and Methods

Data from a clinical study with rapacuronium and data generated by Monte Carlo simulation were analyzed by an ITSB technique described in literature, with some modifications, by STS, and by MEM (using NONMEM). The results were evaluated by comparing the mean error (accuracy) and root mean squared error (precision) of the estimated parameter values, their interindividual standard deviation, correlation coefficients, and residual standard deviation. In addition, the influence of initial estimates, number of subjects, number of measurements, and level of residual error on the performance of ITSB were investigated.

Results

ITSB yielded best results, and provided precise and virtually unbiased estimates of the population parameter means, interindividual variability, and residual standard deviation. The accuracy and precision of STS was poor, whereas ITSB performed better than MEM.

Conclusions

ITSB is a suitable technique for population pharmacokinetic analysis of rich data sets, and in the presented data set it is superior to STS and MEM.

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Abbreviations

AIC:

Akaike information criterion

ITSB:

iterative two-stage Bayesian

ME:

mean error

MEM:

(nonlinear) Mixed effect modeling

RMSE:

root mean squared error

STS:

standard two-stage

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Authors and Affiliations

  1. Research Group for Experimental Anesthesiology and Clinical Pharmacology, Department of Anesthesiology, University Medical Center Groningen, University of Groningen, PO Box 30001, 9700, RB, Groningen, The Netherlands

    Johannes H. Proost & Douglas J. Eleveld

  2. Department of Pharmacokinetics and Drug Delivery, Groningen University Institute for Drug Exploration (GUIDE), University of Groningen, Groningen, The Netherlands

    Johannes H. Proost

Authors
  1. Johannes H. Proost
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  2. Douglas J. Eleveld
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Corresponding author

Correspondence to Johannes H. Proost.

Additional information

An erratum to this article can be found at http://dx.doi.org/10.1007/s11095-007-9261-0

Appendix

Appendix

The following control file was used in the NONMEM analysis. For practical reasons parameters were expressed in liters and liters per minute for volume and clearance parameters, respectively.

  • $PROBMCRA

  • $DATA mcra.dat

    • IGNORE = C

  • $INPUT ID TIME WGT AMT RATE DV

  • $SUBROUTINES ADVAN11 TRANS4

  • $PK

    • CALLFL = 1

    • V1=THETA(1)*EXP(ETA(1))

    • V2=THETA(2)*EXP(ETA(2))

    • V3=THETA(3)*EXP(ETA(3))

    • CL=THETA(4)*EXP(ETA(4))

    • Q3=THETA(6)*EXP(ETA(6))

    • Q2=(THETA(2)*(THETA(6)/THETA(3) + THETA(5)))*EXP(ETA(5))

    • S1=V1

  • $ERROR

    • CALLFL = 0

    • Y=LOG(F)+ERR(1)

  • ; Starting at the exact values

  • $THETA (0, 3.64)(0, 3.01)(0, 6.44)(0, 0.51)(0, 0.048)(0, 0.051)

  • $OMEGA 0.0533 0.1217 0.1063 0.1245 0.2215 0.0650

  • $SIGMA 0.01

  • ;Without POSTHOC only typical values are in the table

  • $ESTIMATION MAX = 9999 SIG = 6 METHOD = COND NOABORT POSTHOC

  • $TABLE TIME V1 V2 V3 CL Q2 Q3 DV

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Proost, J.H., Eleveld, D.J. Performance of an Iterative Two-Stage Bayesian Technique for Population Pharmacokinetic Analysis of Rich Data Sets. Pharm Res 23, 2748–2759 (2006). https://doi.org/10.1007/s11095-006-9116-0

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  • DOI: https://doi.org/10.1007/s11095-006-9116-0

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