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Related Experiment Videos

Application of NONMEM to routine bioavailability data.

D A Graves1, I Chang

  • 1Department of Clinical Research and Development, Fisons Pharmaceuticals, Rochester, New York 14603.

Journal of Pharmacokinetics and Biopharmaceutics
|April 1, 1990
PubMed
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NONMEM modeling of extended-release pseudoephedrine pharmacokinetics revealed it provides more meaningful estimates than traditional methods. This approach offers enhanced insights into residual error, bioinequivalence, and dose-dumping, even with abundant data.

Area of Science:

  • Pharmacokinetics
  • Pharmacometrics
  • Drug Development

Background:

  • NONMEM is a modeling tool often used for sparse data.
  • Its application to dense pharmacokinetic data, amenable to traditional analysis, is less explored.
  • Extended-release (ER) pseudoephedrine product development generated relevant data.

Purpose of the Study:

  • To evaluate the application of NONMEM for analyzing pharmacokinetic data from extended-release (ER) pseudoephedrine products.
  • To compare NONMEM's capabilities against traditional evaluation methods for dense pharmacokinetic datasets.
  • To assess NONMEM's utility in providing additional pharmacokinetic insights.

Main Methods:

  • Analysis of plasma concentration data from four pseudoephedrine ER studies (liquid and capsule, single dose and steady-state) using NONMEM.

Related Experiment Videos

  • Inclusion of immediate-release (IR) controls in the study designs.
  • Comparison of NONMEM results with traditional two-stage analysis approaches.

    • Main Results:

    • NONMEM provided estimates of residual error for both single dose and steady-state studies.
    • It offered stochastic measures of bioinequivalence and dose-dumping.
    • NONMEM enabled hypothesis testing concurrently with pharmacokinetic parameter estimation, such as comparing absorption rates between formulations.
    • Less biased absorption rate estimates were obtained for ER formulations using IR runs.

    Conclusions:

    • NONMEM offers valuable insights beyond traditional methods, even for datasets with ample data.
    • It provides more meaningful estimates less susceptible to assay or residual variability.
    • Fundamental differences between population (NONMEM) and two-stage analysis approaches were discussed.