TY - JOUR
T1 - Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy
AU - Maharaj, Anil R.
AU - Wu, Huali
AU - Hornik, Christoph P.
AU - Arrieta, Antonio
AU - James, Laura
AU - Bhatt-Mehta, Varsha
AU - Bradley, John
AU - Muller, William J.
AU - Al-Uzri, Amira
AU - Downes, Kevin J.
AU - Cohen-Wolkowiez, Michael
N1 - Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - 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.
AB - 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.
KW - Normalized prediction distribution errors
KW - Pediatric subpopulations
KW - Population physiologically-based pharmacokinetic modeling
KW - Potential biases
UR - http://www.scopus.com/inward/record.url?scp=85084128298&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084128298&partnerID=8YFLogxK
U2 - 10.1007/s10928-020-09684-2
DO - 10.1007/s10928-020-09684-2
M3 - Article
C2 - 32323049
AN - SCOPUS:85084128298
SN - 1567-567X
VL - 47
SP - 199
EP - 218
JO - Journal of Pharmacokinetics and Pharmacodynamics
JF - Journal of Pharmacokinetics and Pharmacodynamics
IS - 3
ER -