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

Anil R. Maharaj, Huali Wu, Christoph P. Hornik, Antonio Arrieta, Laura James, Varsha Bhatt-Mehta, John Bradley, William J. Muller, Amira Al-Uzri, Kevin J. Downes, Michael Cohen-Wolkowiez*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)199-218
Number of pages20
JournalJournal of Pharmacokinetics and Pharmacodynamics
Issue number3
StatePublished - Jun 1 2020


  • Normalized prediction distribution errors
  • Pediatric subpopulations
  • Population physiologically-based pharmacokinetic modeling
  • Potential biases

ASJC Scopus subject areas

  • Pharmacology


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