ArticleFrontiers in pharmacology2018
Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling.
Article in Frontiers in pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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.
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Who cites it
29 citing papers in PubMed, 82 citations in OpenAlex.
- From bench to bedside: Overcoming translational hurdles in nanoparticle research with pharmacokinetic modeling.Bioengineering & translational medicine · 2026Review
- SensX: Model-Agnostic Local Feature Attribution via Calibrated Global Sensitivity Analysis.Transactions on machine learning research · 2026Article
- Validating Physiologically-Based Pharmacokinetic Models Using the Continuous Ranked Probability Score: Beyond Being Correct on Average.CPT: pharmacometrics & systems pharmacology · 2026Article
- Scientific Opinion on the application of physiologically based kinetic (PBK) modelling for the quantitative in vitro to in vivo extrapolation (QIVIVE) of developmental neurotoxicity in vitro battery (DNT IVB) data for pesticide active substances.EFSA journal. European Food Safety Authority · 2025Article
- Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling.Advanced drug delivery reviews · 2025Review
- Applications of PBPK Models to Predict Tissue Residues and Extralabel Withdrawal Times of Drugs in Food Animals: Perspectives from the Food Animal Residue Avoidance Databank (FARAD) Program.The AAPS journal · 2025Review
- Bayesian refinement of a physiologically based pharmacokinetic model for ethylbenzene pharmacokinetics in mice, rats, and humans.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- Development of a Physiologically Based Pharmacokinetic Model for Inorganic Mercury Salts in Rodents and Humans and Its Application in the Derivation of Health-Based Toxicity Metrics.Environmental science & technology · 2025Article
- A Framework for Quantitative Systems Pharmacology Model Execution.Handbook of experimental pharmacology · 2025Review
- Real-World Application of a Quantitative Systems Pharmacology (QSP) Model to Predict Potassium Concentrations from Electronic Health Records: A Pilot Case towards Prescribing Monitoring of Spironolactone.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Parameter grouping and co-estimation in physiologically based kinetic models using genetic algorithms.Toxicological sciences : an official journal of the Society of Toxicology · 2024Article
- The Use of Global Sensitivity Analysis to Assess the Oral Absorption of Weakly Basic Compounds: A Case Example of Dipyridamole.Pharmaceutical research · 2024Article
- A two-stages global sensitivity analysis by using the δ sensitivity index in presence of correlated inputs: application on a tumor growth inhibition model based on the dynamic energy budget theory.Journal of pharmacokinetics and pharmacodynamics · 2023Article
- Accelerating robust plausible virtual patient cohort generation by substituting ODE simulations with parameter space mapping.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- Physiologically-Based Pharmacokinetic Model Development, Validation, and Application for Prediction of Eliglustat Drug-Drug Interactions.Clinical pharmacology and therapeutics · 2022Article
- Physiologically Based Pharmacokinetic Modeling of Vitamin DDrug metabolism and disposition: the biological fate of chemicals · 2022Article
- An Interactive Generic Physiologically Based Pharmacokinetic (igPBPK) Modeling Platform to Predict Drug Withdrawal Intervals in Cattle and Swine: A Case Study on Flunixin, Florfenicol, and Penicillin G.Toxicological sciences : an official journal of the Society of Toxicology · 2022Article
- Anti-Inflammatory and Anti-Rheumatic Potential of Selective Plant Compounds by Targeting TLR-4/AP-1 Signaling: A Comprehensive Molecular Docking and Simulation Approaches.Molecules (Basel, Switzerland) · 2022Article
- Modelling the performance of an integrated fixed-film activated sludge (IFAS) system: a systematic approach to automated calibration.Scientific reports · 2022Article
- Streamlining physiologically-based pharmacokinetic model design for intravenous delivery of nanoparticle drugs.CPT: pharmacometrics & systems pharmacology · 2022Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Traditionally, the solution to reduce parameter dimensionality in a physiologically-based pharmacokinetic (PBPK) model is through expert judgment. However, this approach may lead to bias in parameter estimates and model predictions if important parameters are fixed at uncertain or inappropriate values. The purpose of this study was to explore the application of global sensitivity analysis (GSA) to ascertain which parameters in the PBPK model are non-influential, and therefore can be assigned fixed values in Bayesian parameter estimation with minimal bias. We compared the elementary effect-based Morris method and three variance-based Sobol indices in their ability to distinguish "influential" parameters to be estimated and "non-influential" parameters to be fixed. We illustrated this approach using a published human PBPK model for acetaminophen (APAP) and its two primary metabolites APAP-glucuronide and APAP-sulfate. We first applied GSA to the original published model, comparing Bayesian model calibration results using all the 21
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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.