ArticlePharmaceutical research1998
Bootstrapping for pharmacokinetic models: visualization of predictive and parameter uncertainty.
Article in Pharmaceutical research, 1998. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
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- Recommended approaches for integration of population pharmacokinetic modelling with precision dosing in clinical practice.British journal of clinical pharmacology · 2025Review
- A phase I/IIa trial of atorvastatin in Japanese patients with acute Kawasaki disease with coronary artery aneurysm: Study protocol of a multicenter, single-arm, open-label trial.Contemporary clinical trials communications · 2022Article
- Quantification of Radiation Injury on Neutropenia and the Link between Absolute Neutrophil Count Time Course and Overall Survival in Nonhuman Primates Treated with G-CSF.Pharmaceutical research · 2020Article
- The h subunit of eIF3 promotes reinitiation competence during translation of mRNAs harboring upstream open reading frames.RNA (New York, N.Y.) · 2010Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
purposeWe explore use of "bootstrapping" methods to obtain a measure of reliability of predictions made in part from fits of individual drug level data with a pharmacokinetic (PK) model, and to help clarify parameter identifiability for such models.
methodsSimulation studies use four sets (A-D) of drug concentration data obtained following a single oral dose. Each set is fit with a two compartment PK model, and the "bootstrap" is employed to examine the potential predictive variation in estimates of parameter sets. This yields an empirical distribution of plausible steady state (SS) drug concentration predictions that can be used to form a confidence interval for a prediction.
resultsA distinct, narrow confidence region in parameter space is identified for subjects A and B. The bootstrapped sets have a relatively large coefficient of variation (CV) (35-90% for A), yet the corresponding SS drug levels are tightly clustered (CVs only 2-9%). The results for C and D are dramatically different. The CVs for both the parameters and predicted drug levels are larger by a factor of 5 and more. The results reveal that the original data for C and D, but not A and B, can be represented by at least two different PK model manifestations, yet only one provides reliable predictions.
conclusionsThe insights gained can facilitate making decisions about parameter identifiability. In particular, the results for C and D have important implications for the degree of implicit overparameterization that may exist in the PK model. In cases where the data support only a single model manifestation, the "bootstrap" method provides information needed to form a confidence interval for a prediction.
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