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

AbstractComparisons 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 createdobserved pharmacokinetic (PK) datasets and compared them to model simulations generated under the same Pop-PBPK model.Observed datasets containing uncorrelated and correlatedobservations ( ± residual error) were formulated using different random-sampling techniques. In addition, we createdobserved datasets where the distribution of subject body weights differed from that of the virtual population used to generate model simulations. NPC for eachobserved dataset were computed based on the Pop-PBPK model ’s 90% PI. NPC were associated with inflated type-I-error rates (>  0.10) forobserved datasets that contained correlatedobservations, residual error, or both. Additionally, the performance of NPC were sensitive to the ...
Source: Journal of Pharmacokinetics and Pharmacodynamics - Category: Drugs & Pharmacology Source Type: research

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