American College of Clinical Pharmacy
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Sun-118 - Interoccasion Variability, Estimation Methods and Their Impact on Pharmacokinetic Estimates

Scientific Poster Session II - Original Research

Original Research
  Sunday, November 12, 2023
  12:45 PM–02:15 PM

Abstract

Introduction:

Population pharmacokinetic (PPK) analyses are crucial for better understanding of drug PK, safety, efficacy, and its proposed optimal dosing regimen. Interoccasion variability (IOV) has been proposed to be an essential component of PPK estimation approximately 30 years ago, but only 1 Method1 and limited research regarding its coding method has been conducted or published to date.

Research Question or Hypothesis:

Which IOV coding method and estimation algorithm may be used to provide the most accurate PK results.

Study Design:

Thirty studies of 40 subjects each who received 1 dose on 2 separate occasions were simulated by a blinded researcher using an oral 2-cpt PK linear model.

Methods:

The 30 studies were fitted by another blinded researcher who only knew which parameters had IOV. The studies were fitted using 3 different IOV coding methods and 4 different estimation algorithms (NONMEM® FOCE, FOCEI, and MCISEM; and ADAPT5® MLEM). Population and individual IOV, population and individual parameters, their variability and residual variability results were compared between algorithms in terms of absolute bias and imprecision. Statistical significance was set a priori at p<0.05.

Results:

For 30 studies:

Method #11:

  • No apparent difference detected between FOCE and FOCEI.

Method #2:

  • MLEM appeared to have a lower bias than FOCE and FOCEI (p<0.05) for CL (individual values and population interCV%) and residual variability.

Method #3:

  • MLEM appeared to have a lower bias than FOCE and FOCEI (p<0.05) for CL (population value and interCV%, individual values (FOCE only)) and residual variability
  • FOCE appeared to have a lower bias than FOCEI (p<0.05) for individual IOV

Conclusion:

IOV coding Method #3 seems to be better than Method #1 and #2. MLEM appears to perform slightly better overall than the other tested algorithms.

References:

[1] Karlsson M, Sheiner L: The Importance of Modeling Interoccasion Variability in Population Pharmacokinetic Analyses, Journal of Pharmacokinetics and Biopharmaceutics, 21(6): 735-750, 1993.

Presenting Author

Dana Bakir BSc., MSc.
Universite de Montreal

Authors

Murray Ducharme PharmD, FCCP, FCP
Learn and Confirm Inc.