ArticleCPT: pharmacometrics & systems pharmacology2022
Population PBPK modeling using parametric and nonparametric methods of the Simcyp Simulator, and Bayesian samplers.
Article in CPT: pharmacometrics & systems pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Scientific Opinion on waiving of the dog studies in the regulatory process of agrochemicals approval.EFSA journal. European Food Safety Authority · 2026Article
- Age-Driven Physiologically Based Pharmacokinetic Modeling of Empagliflozin: Toward Precision Dosing in Youth.ACS pharmacology & translational science · 2026Article
- Quantitative estimates of inter-individual variability for new approach methodologies-based systemic safety toolbox using a population-based human in vitro model.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- Virtual Bioequivalence Assessment of Long-acting Injectable Suspensions Using PBPK Modeling: Part 1. Impact of particle Size on Formulation Variability.The AAPS journal · 2026Article
- Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development.CPT: pharmacometrics & systems pharmacology · 2026Review
- Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling.Advanced drug delivery reviews · 2025Review
- Integrative human intervention study for mycotoxin biomarker discovery and toxicokinetic characterization.Scientific reports · 2025Article
- Modeling Whole-Body Dynamic PET Microdosing Data to Predict the Whole-Body Pharmacokinetics of Glyburide in Humans.Clinical pharmacokinetics · 2025Article
- A Comprehensive CYP2D6 Drug-Drug-Gene Interaction Network for Application in Precision Dosing and Drug Development.Clinical pharmacology and therapeutics · 2025Article
- Comparing computational times for simulations when using PBPK model template and stand-alone implementations of PBPK models.Frontiers in toxicology · 2025Article
- Static Versus Dynamic Model Predictions of Competitive Inhibitory Metabolic Drug-Drug Interactions via Cytochromes P450: One Step Forward and Two Steps Backwards.Clinical pharmacokinetics · 2025Article
- Invited Perspective: Uneven Progress Addressing Population Variability in Human Health Risk Assessment.Environmental health perspectives · 2024Article
- Mapping Interindividual Variability of Toxicodynamics Using High-Throughput Transcriptomics and Primary Human Hepatocytes from Fifty Donors.Environmental health perspectives · 2024Article
- Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare.Pharmaceutics · 2024Review
- Virtual Bioequivalence Assessment of Ritlecitinib Capsules with Incorporation of Observed Clinical Variability Using a Physiologically Based Pharmacokinetic Model.The AAPS journal · 2024Article
- A Bayesian framework for virtual comparative trials and bioequivalence assessments.Frontiers in pharmacology · 2024Article
- Computer modeling of digestive processes in the alimentary tract and their physiological regulation mechanisms: closing the gap between digestion models andFrontiers in nutrition · 2024Article
- Population PBPK modeling using parametric and nonparametric methods of the Simcyp Simulator, and Bayesian samplers.CPT: pharmacometrics & systems pharmacology · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Physiologically-based pharmacokinetic (PBPK) models usually include a large number of parameters whose values are obtained using in vitro to in vivo extrapolation. However, such extrapolations can be uncertain and may benefit from inclusion of evidence from clinical observations via parametric inference. When clinical interindividual variability is high, or the data sparse, it is essential to use a population pharmacokinetics inferential framework to estimate unknown or uncertain parameters. Several approaches are available for that purpose, but their relative advantages for PBPK modeling are unclear. We compare the results obtained using a minimal PBPK model of a canonical theophylline dataset with quasi-random parametric expectation maximization (QRPEM), nonparametric adaptive grid estimation (NPAG), Bayesian Metropolis-Hastings (MH), and Hamiltonian Markov Chain Monte Carlo sampling. QRPEM and NPAG gave consistent population and individual parameter estimates, mostly agreeing with Bayesian estimates. MH simulations ran faster than the others methods, which together had similar performance.
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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 Socratic graph.