Risk Analysis · 1994 · 78 citations · 9 references
PharmacyPharmacokineticsCompartmental ModelingDose-response ModelingPharmacokinetic ModelingModel BuildingModel EvaluationPharmacokinetic SimulationPhysiologically Based Pharmacokinetic ModelingModel Structure AnalysisModel Structure IdentificationParameter IdentifiabilityPharmacokinetic Parameter AnalysisPharmacokinetic ParametersPhysiological-based Pharmacokinetic ModelingSensitivity Analysis Of Model Outputs
The study aims to rank PBPK model parameters by their impact on methylene chloride predictions and to use time‑dependent sensitivity analysis to guide experimental design for optimal parameter estimation. Parameters were systematically varied in a PBPK model for mice and humans across exposure concentrations, with outputs tracked over time while ensuring mass‑balance conservation, and sensitivity analysis was employed to identify conditions that maximize parameter identifiability. Parameter importance depended on concentration, species, and output, model structure had a significant effect, and normalized sensitivity coefficients ranged from –1.12 to 1, mostly below 1, indicating limited amplification of input errors.
The parameters in a physiologically based pharmacokinetic (PBPK) model of methylene chloride were varied systematically, and the resulting variation in a number of model outputs was determined as a function of time for mice and humans at several exposure concentrations. The importance of the various parameters in the model was highly dependent on the conditions (concentration, species) for which the simulation was performed and the model output (dose surrogate) being considered. Model structure also had a significant impact on the results. For sensitivity analysis, particular attention must be paid to conservation equations to ensure that the variational calculations do not alter mass balance, introducing extraneous effects into the model. All of the normalized sensitivity coefficients calculated in this study ranged between −1.12 and 1, and most were much less than 1 in absolute value, indicating that individual input errors are not greatly amplified in the outputs. In addition to ranking parameters in terms of their impact on model predictions, time‐dependent sensitivity analysis can also be used as an aid in the design of experiments to estimate parameters by predicting the experimental conditions and sampling points which will maximize parameter identifiability.
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<i>Numerical Mathematical Analysis</i>
J. B. Scarborough, Joseph Hilsenrath · Physics Today · 1963 · 641 citations
Numerical Mathematical Analysis
H. P., J. B. Scarborough · Mathematics of Computation · 1963 · 156 citations