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Design and Optimization Strategies of a High-Performance Vented Box
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Inter occasion variability in individual optimal design.

Anders N Kristoffersson1, Lena E Friberg2, Joakim Nyberg2

  • 1Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 75124, Uppsala, Sweden. Anders.Kristoffersson@farmbio.uu.se.

Journal of Pharmacokinetics and Pharmacodynamics
|October 11, 2015
PubMed
Summary
This summary is machine-generated.

Accounting for inter occasion variability (IOV) in optimal design improves the precision of individual pharmacokinetic/pharmacodynamic parameter estimation. Two methods, MAPocc and POPocc, accurately predicted precision and shrinkage, with MAPocc showing slight superiority.

Keywords:
BayesianFisher informationInter occasion variability (IOV)Maximum a posteriori (MAP)Optimal design (OD)PharmacometricsShrinkage

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Area of Science:

  • Pharmacometrics
  • Pharmacokinetic/Pharmacodynamic (PK/PD) Modeling

Background:

  • Inter occasion variability (IOV) is crucial in developing designs for estimating individual pharmacokinetic or pharmacodynamic parameters.
  • IOV can negatively impact the precision of maximum a posteriori (MAP) estimated individual parameters.
  • The influence of IOV inclusion in optimal design for individual parameter estimation remains under-investigated.

Purpose of the Study:

  • To evaluate methods for incorporating IOV into the maximum a posteriori Fisher information matrix (FIMMAP) for optimal design.
  • To compare designs that account for IOV against those that ignore it.

Main Methods:

  • Two methods for including IOV in FIMMAP were assessed: MAPocc (IOV as a fixed effect deviation) and POPocc (IOV as an occasion random effect).
  • Sparse sampling schedules were designed for two test models.
  • Designs were compared to scenarios ignoring IOV (Omit) or inflating inter-individual variability (Inflate).

Main Results:

  • Accounting for IOV in FIMMAP significantly altered designs compared to ignoring it.
  • Designs incorporating IOV demonstrated superior precision in individual parameter estimation via stochastic simulation and estimation.
  • Both MAPocc and POPocc methods accurately predicted parameter precision and shrinkage.
  • The MAPocc method was slightly superior to POPocc on average and less computationally demanding.

Conclusions:

  • Incorporating IOV into optimal design markedly improves the precision of individual parameter estimation.
  • MAPocc and POPocc are effective methods for accounting for IOV in optimal design, with MAPocc being slightly more efficient.
  • These findings are critical for developing robust PK/PD models where individual variability is a key focus.