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Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy.

Publication ,  Journal Article
Maharaj, AR; Wu, H; Hornik, CP; Arrieta, A; James, L; Bhatt-Mehta, V; Bradley, J; Muller, WJ; Al-Uzri, A; Downes, KJ; Cohen-Wolkowiez, M
Published in: J Pharmacokinet Pharmacodyn
June 2020

Currently employed methods for qualifying population physiologically-based pharmacokinetic (Pop-PBPK) model predictions of continuous outcomes (e.g., concentration-time data) fail to account for within-subject correlations and the presence of residual error. In this study, we propose a new method for evaluating Pop-PBPK model predictions that account for such features. The approach focuses on deriving Pop-PBPK-specific normalized prediction distribution errors (NPDE), a metric that is commonly used for population pharmacokinetic model validation. We describe specific methodological steps for computing NPDE for Pop-PBPK models and define three measures for evaluating model performance: mean of NPDE, goodness-of-fit plots, and the magnitude of residual error. Utility of the proposed evaluation approach was demonstrated using two simulation-based study designs (positive and negative control studies) as well as pharmacokinetic data from a real-world clinical trial. For the positive-control simulation study, where observations and model simulations were generated under the same Pop-PBPK model, the NPDE-based approach denoted a congruency between model predictions and observed data (mean of NPDE =  - 0.01). In contrast, for the negative-control simulation study, where model simulations and observed data were generated under different Pop-PBPK models, the NPDE-based method asserted that model simulations and observed data were incongruent (mean of NPDE =  - 0.29). When employed to evaluate a previously developed clindamycin PBPK model against prospectively collected plasma concentration data from 29 children, the NPDE-based method qualified the model predictions as successful (mean of NPDE = 0). However, when pediatric subpopulations (e.g., infants) were evaluated, the approach revealed potential biases that should be explored.

Duke Scholars

Published In

J Pharmacokinet Pharmacodyn

DOI

EISSN

1573-8744

Publication Date

June 2020

Volume

47

Issue

3

Start / End Page

199 / 218

Location

United States

Related Subject Headings

  • Statistical Distributions
  • Software
  • Prospective Studies
  • Pharmacology & Pharmacy
  • Models, Biological
  • Male
  • Infant
  • Humans
  • Gestational Age
  • Female
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Maharaj, A. R., Wu, H., Hornik, C. P., Arrieta, A., James, L., Bhatt-Mehta, V., … Cohen-Wolkowiez, M. (2020). Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy. J Pharmacokinet Pharmacodyn, 47(3), 199–218. https://doi.org/10.1007/s10928-020-09684-2
Maharaj, Anil R., Huali Wu, Christoph P. Hornik, Antonio Arrieta, Laura James, Varsha Bhatt-Mehta, John Bradley, et al. “Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy.J Pharmacokinet Pharmacodyn 47, no. 3 (June 2020): 199–218. https://doi.org/10.1007/s10928-020-09684-2.
Maharaj AR, Wu H, Hornik CP, Arrieta A, James L, Bhatt-Mehta V, et al. Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy. J Pharmacokinet Pharmacodyn. 2020 Jun;47(3):199–218.
Maharaj, Anil R., et al. “Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy.J Pharmacokinet Pharmacodyn, vol. 47, no. 3, June 2020, pp. 199–218. Pubmed, doi:10.1007/s10928-020-09684-2.
Maharaj AR, Wu H, Hornik CP, Arrieta A, James L, Bhatt-Mehta V, Bradley J, Muller WJ, Al-Uzri A, Downes KJ, Cohen-Wolkowiez M. Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy. J Pharmacokinet Pharmacodyn. 2020 Jun;47(3):199–218.
Journal cover image

Published In

J Pharmacokinet Pharmacodyn

DOI

EISSN

1573-8744

Publication Date

June 2020

Volume

47

Issue

3

Start / End Page

199 / 218

Location

United States

Related Subject Headings

  • Statistical Distributions
  • Software
  • Prospective Studies
  • Pharmacology & Pharmacy
  • Models, Biological
  • Male
  • Infant
  • Humans
  • Gestational Age
  • Female