Evidence map›Paper›PMID 41806284›Full record

ArticlePharmaceutical statistics

Leveraging Two-Stage δ Global Sensibility Analysis Method to Inform Parameter Estimation in PBPK Models.

Marina Cuquerella-Gilabert, Alessandro De Carlo, Sergio Sánchez Herrero, Javier Reig-López, Matilde Merino-Sanjuan, Elena M Tosca, Víctor Mangas-Sanjuan, Paolo Magni

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In one paragraph

Article in Pharmaceutical statistics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Marina Cuquerella-GilabertDepartment of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, Valencia, Spain.
Alessandro De CarloDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
Sergio Sánchez HerreroSimulation Department, Empresarios Agrupados Internacional S.A., Madrid, Spain.
Javier Reig-LópezDepartment of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, Valencia, Spain.
Matilde Merino-SanjuanDepartment of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, Valencia, Spain.
Elena M ToscaDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
Víctor Mangas-SanjuanDepartment of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, Valencia, Spain.ORCID 0000-0002-3388-5023
Paolo MagniDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.

Funding

Ministerio de Ciencia e Innovación DIN2021-012259
6 · The paper itself

Abstract

Appropriately performing Global Sensitivity Analysis (GSA) and refining models through the estimation of key parameters on individual data are fundamental steps in PBPK modeling, yet they remain insufficiently addressed in current practice. The main aim of this work is to establish a computational framework linking PhysPK with Python, enabling the application of the Two-stage δ GSA and individual parameter estimation via the iterative two-stage (ITS) method to the semi-mechanistic PBPK model Phys-DAT. The Two-Stage δ GSA was implemented to assess the impact of parameter uncertainty and correlations on key pharmacokinetic (PK) endpoints, AUC, C

Indexed as

Computer SimulationModels, BiologicalPharmacokineticsAlgorithmsAnimalsArea Under CurveHumansUncertaintyestimationglobal sensitivity analysisparameter estimationPBPKsampling strategiestwo‐stage δ GSAuncertainty

Identifiers

PMID41806284
PMCPMC12974557

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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 OpenQuestion graph.