ArticleJournal of pharmacokinetics and pharmacodynamics2013
The design and analysis of parallel experiments to produce structurally identifiable models.
Article in Journal of pharmacokinetics and pharmacodynamics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Structural identifiability for mathematical pharmacology: models of myelosuppression.Journal of pharmacokinetics and pharmacodynamics · 2018Pooled it
- Dosing of Ceftriaxone and Metronidazole for Children With Severe Acute Malnutrition.Clinical pharmacology and therapeutics · 2018Trial
- Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis and prediction in the life sciences.Journal of the Royal Society, Interface · 2024Review
- Quality Assurance of PBPK Modeling Platforms and Guidance on Building, Evaluating, Verifying and Applying PBPK Models Prudently under the Umbrella of Qualification: Why, When, What, How and By Whom?Pharmaceutical research · 2022Review
- A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations.CPT: pharmacometrics & systems pharmacology · 2020Article
- Physiologically Based Pharmacokinetic Model Qualification and Reporting Procedures for Regulatory Submissions: A Consortium Perspective.Clinical pharmacology and therapeutics · 2018Review
- A confidence building exercise in data and identifiability: Modeling cancer chemotherapy as a case study.Journal of theoretical biology · 2017Article
- Deterministic identifiability of population pharmacokinetic and pharmacokinetic-pharmacodynamic models.Journal of pharmacokinetics and pharmacodynamics · 2017Article
- Parameter Identifiability of Fundamental Pharmacodynamic Models.Frontiers in physiology · 2016Article
- Determination of parameter identifiability in nonlinear biophysical models: A Bayesian approach.The Journal of general physiology · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pharmacokinetic analysis in humans using compartmental models is restricted with respect to the estimation of parameter values. This is because the experimenter usually is only able to apply inputs and observations in a very small number of compartments in the system. This has implications for the structural identifiability of such systems and consequently limits the complexity and mechanistic relevance of the models that may be applied to such experiments. A number of strategies are presented whereby models are rendered globally identifiable by considering a series of experiments in parallel. Examples are taken from the pharmacokinetic literature and analysed using this parallel experiment methodology. It is concluded that considering a series of pharmacokinetic experiments where some, but not all, of the parameters may be shared across the experiments can improve the identifiability of some compartmental models.
Indexed as
Identifiers
23300030What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.