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Pitfalls of using numerical predictive checks for population physiologically-based pharmacokinetic model evaluation.

Publication ,  Journal Article
Maharaj, AR; Wu, H; Hornik, CP; Cohen-Wolkowiez, M
Published in: J Pharmacokinet Pharmacodyn
June 2019

Comparisons between observed data and model simulations represent a critical component for establishing confidence in population physiologically-based pharmacokinetic (Pop-PBPK) models. Numerical predictive checks (NPC) that assess the proportion of observed data that correspond to Pop-PBPK model prediction intervals (PIs) are frequently used to qualify such models. We evaluated the effects of three components on the performance of NPC for qualifying Pop-PBPK model concentration-time predictions: (1) correlations (multiple samples per subject), (2) residual error, and (3) discrepancies in the distribution of demographics between observed and virtual subjects. Using a simulation-based study design, we artificially created observed pharmacokinetic (PK) datasets and compared them to model simulations generated under the same Pop-PBPK model. Observed datasets containing uncorrelated and correlated observations (± residual error) were formulated using different random-sampling techniques. In addition, we created observed datasets where the distribution of subject body weights differed from that of the virtual population used to generate model simulations. NPC for each observed dataset were computed based on the Pop-PBPK model's 90% PI. NPC were associated with inflated type-I-error rates (> 0.10) for observed datasets that contained correlated observations, residual error, or both. Additionally, the performance of NPC were sensitive to the demographic distribution of observed subjects. Acceptable use of NPC was only demonstrated for the idealistic case where observed data were uncorrelated, free of residual error, and the demographic distribution of virtual subjects matched that of observed subjects. Considering the restricted applicability of NPC for Pop-PBPK model evaluation, their use in this context should be interpreted with caution.

Duke Scholars

Published In

J Pharmacokinet Pharmacodyn

DOI

EISSN

1573-8744

Publication Date

June 2019

Volume

46

Issue

3

Start / End Page

263 / 272

Location

United States

Related Subject Headings

  • Pharmacology & Pharmacy
  • Pharmacokinetics
  • Models, Biological
  • Humans
  • Computer Simulation
  • 3214 Pharmacology and pharmaceutical sciences
  • 1115 Pharmacology and Pharmaceutical Sciences
 

Citation

APA
Chicago
ICMJE
MLA
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Maharaj, A. R., Wu, H., Hornik, C. P., & Cohen-Wolkowiez, M. (2019). Pitfalls of using numerical predictive checks for population physiologically-based pharmacokinetic model evaluation. J Pharmacokinet Pharmacodyn, 46(3), 263–272. https://doi.org/10.1007/s10928-019-09636-5
Maharaj, Anil R., Huali Wu, Christoph P. Hornik, and Michael Cohen-Wolkowiez. “Pitfalls of using numerical predictive checks for population physiologically-based pharmacokinetic model evaluation.J Pharmacokinet Pharmacodyn 46, no. 3 (June 2019): 263–72. https://doi.org/10.1007/s10928-019-09636-5.
Maharaj AR, Wu H, Hornik CP, Cohen-Wolkowiez M. Pitfalls of using numerical predictive checks for population physiologically-based pharmacokinetic model evaluation. J Pharmacokinet Pharmacodyn. 2019 Jun;46(3):263–72.
Maharaj, Anil R., et al. “Pitfalls of using numerical predictive checks for population physiologically-based pharmacokinetic model evaluation.J Pharmacokinet Pharmacodyn, vol. 46, no. 3, June 2019, pp. 263–72. Pubmed, doi:10.1007/s10928-019-09636-5.
Maharaj AR, Wu H, Hornik CP, Cohen-Wolkowiez M. Pitfalls of using numerical predictive checks for population physiologically-based pharmacokinetic model evaluation. J Pharmacokinet Pharmacodyn. 2019 Jun;46(3):263–272.
Journal cover image

Published In

J Pharmacokinet Pharmacodyn

DOI

EISSN

1573-8744

Publication Date

June 2019

Volume

46

Issue

3

Start / End Page

263 / 272

Location

United States

Related Subject Headings

  • Pharmacology & Pharmacy
  • Pharmacokinetics
  • Models, Biological
  • Humans
  • Computer Simulation
  • 3214 Pharmacology and pharmaceutical sciences
  • 1115 Pharmacology and Pharmaceutical Sciences