Evidence map›Paper›PMID 36293410›Full record

ReviewInternational journal of molecular sciences2022

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools.

Elena O Kutumova, Ilya R Akberdin, Ilya N Kiselev, Ruslan N Sharipov, Vera S Egorova, Anastasiia O Syrocheva, Alessandro Parodi, Andrey A Zamyatnin, Fedor A Kolpakov

Open access · goldAbstract readReview
In one paragraph

Review in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.1field-weighted citation impact, top 13% of its field
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

16 citing papers in PubMed, 30 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Review
  6. Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Review
  12. Article
  13. Article
  14. Review
  15. Rational nanoparticle design: Optimization using insights from experiments and mathematical models.Journal of controlled release : official journal of the Controlled Release Society · 2023
    Review
  16. 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 at 3 institutions in 1 country.

Elena O KutumovaScientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia.ORCID 0000-0001-7746-5760
Ilya R AkberdinScientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia.
Ilya N KiselevScientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia.
Ruslan N SharipovScientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia.ORCID 0000-0003-2182-5493
Vera S EgorovaScientific Center for Translational Medicine, Sirius University of Science and Technology, 354340 Sochi, Russia.
Anastasiia O SyrochevaScientific Center for Translational Medicine, Sirius University of Science and Technology, 354340 Sochi, Russia.
Alessandro ParodiScientific Center for Translational Medicine, Sirius University of Science and Technology, 354340 Sochi, Russia.ORCID 0000-0003-0985-6545
Andrey A ZamyatninScientific Center for Translational Medicine, Sirius University of Science and Technology, 354340 Sochi, Russia.ORCID 0000-0002-3046-4565
Fedor A KolpakovScientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia.
Sirius University of Science and Technology · RUNovosibirsk State University · RUSechenov University · RU

Funding

Russian Science Foundation 21-75-30020
6 · The paper itself

Abstract

Cancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. However, the ability of our body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the "cords" of traditional medical or material researchers. The goal of this review was to highlight the advantages that computational modeling could provide to nanomedicine and bring together scientists with different background by portraying in the most simple way the work of computational developers through the description of the tools that they use to predict nanoparticle transport and tumor targeting in our body.

Indexed as

Biological ProductsNanoparticlesNeoplasmsArtificial IntelligenceComputer SimulationData AnalysisHumansModels, BiologicalSoftwareTissue DistributionBiological ProductsBioUMLnanoparticlesphysiologically based pharmacokinetic modelingsimulation software

Identifiers

PMID36293410
PMCPMC9604366
OpenAlexW4306850130

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.