Evidence map›Paper›PMID 39494970›Full record

ArticleBriefings in bioinformatics2024

Making PBPK models more reproducible in practice.

Elena Domínguez-Romero, Stanislav Mazurenko, Martin Scheringer, Vítor A P Martins Dos Santos, Chris T Evelo, Mihail Anton, John M Hancock, Anže Županič, Maria Suarez-Diez

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Elena Domínguez-RomeroDepartment of Bioinformatics-BiGCaT, School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.ORCID 0000-0001-9795-5628
Stanislav MazurenkoLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic.ORCID 0000-0003-3659-4819
Martin ScheringerRECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 611 37, Brno, Czech Republic.ORCID 0000-0002-0809-7826
Vítor A P Martins Dos SantosLaboratory of Bioprocess Engineering, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands.ORCID 0000-0002-2352-9017
Chris T EveloDepartment of Bioinformatics-BiGCaT, School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.ORCID 0000-0002-5301-3142
Mihail AntonELIXIR, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom.ORCID 0000-0002-7753-9042
John M HancockInstitute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Zaloška 4, 1000 Ljubljana, Slovenia.ORCID 0000-0003-2991-2217
Anže ŽupaničDepartment of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia.ORCID 0000-0003-3303-9086
Maria Suarez-DiezLaboratory of Systems and Synthetic Biology (SSB), Wageningen University and Research, Agrotechnology and Food Sciences, Stippeneng 4, 6708 WE Wageningen, The Netherlands.ORCID 0000-0001-5845-146X

Funding

Czech Ministry of Education, Youth and Sports ESFRI ELIXIR LM2023055European Union's Horizon 2020 HBM4EUEuropean Union's Horizon 2020 Research and Innovation Programme 857560Netherlands Research Council NWA-ORC 1292.19.272
6 · The paper itself

Abstract

Systems biology aims to understand living organisms through mathematically modeling their behaviors at different organizational levels, ranging from molecules to populations. Modeling involves several steps, from determining the model purpose to developing the mathematical model, implementing it computationally, simulating the model's behavior, evaluating, and refining the model. Importantly, model simulation results must be reproducible, ensuring that other researchers can obtain the same results after writing the code de novo and/or using different software tools. Guidelines to increase model reproducibility have been published. However, reproducibility remains a major challenge in this field. In this paper, we tackle this challenge for physiologically-based pharmacokinetic (PBPK) models, which represent the pharmacokinetics of chemicals following exposure in humans or animals. We summarize recommendations for PBPK model reporting that should apply during model development and implementation, in order to ensure model reproducibility and comprehensibility. We make a proposal aiming to harmonize abbreviations used in PBPK models. To illustrate these recommendations, we present an original and reproducible PBPK model code in MATLAB, alongside an example of MATLAB code converted to Systems Biology Markup Language format using MOCCASIN. As directions for future improvement, more tools to convert computational PBPK models from different software platforms into standard formats would increase the interoperability of these models. The application of other systems biology standards to PBPK models is encouraged. This work is the result of an interdisciplinary collaboration involving the ELIXIR systems biology community. More interdisciplinary collaborations like this would facilitate further harmonization and application of good modeling practices in different systems biology fields.

Indexed as

Models, BiologicalPharmacokineticsSoftwareSystems BiologyAnimalsComputer SimulationHumansReproducibility of ResultsMATLABmodel codepharmacokineticsreproducibilitySBMLsystems biology

Identifiers

PMID39494970
PMCPMC11533111

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.