Evidence map›Paper›PMID 26929620›Full record

ArticleInternational journal of nanomedicine2016

Toward a general physiologically-based pharmacokinetic model for intravenously injected nanoparticles.

Ulrika Carlander, Dingsheng Li, Olivier Jolliet, Claude Emond, Gunnar Johanson

Open access · goldAbstract read
In one paragraph

Article in International journal of nanomedicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 1 pooled it
5.7field-weighted citation impact, top 4% 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

43 citing papers in PubMed, 1 synthesis or guideline pooled it, 91 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Materials today. Bio · 2025
    Review
  5. Article
  6. Review
  7. Parameter grouping and co-estimation in physiologically based kinetic models using genetic algorithms.Toxicological sciences : an official journal of the Society of Toxicology · 2024
    Article
  8. Article
  9. Article
  10. An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice.Journal of controlled release : official journal of the Controlled Release Society · 2023
    Article
  11. Rational nanoparticle design: Optimization using insights from experiments and mathematical models.Journal of controlled release : official journal of the Controlled Release Society · 2023
    Review
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  15. Review
  16. Article
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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

5 authors at 4 institutions in 3 countries.

Ulrika CarlanderInstitute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
Dingsheng LiDepartment of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Olivier JollietDepartment of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Claude EmondBioSimulation Consulting Inc., Newark, DE, USA; Department of Environmental and Occupational Health, School of Public Health, University of Montreal, QC, Canada.
Gunnar JohansonInstitute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
Karolinska Institutet · SEBiostatistical Consulting (United States) · USCenter for Environmental Health · USUniversity of Michigan–Ann Arbor · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To assess the potential toxicity of nanoparticles (NPs), information concerning their uptake and disposition (biokinetics) is essential. Experience with industrial chemicals and pharmaceutical drugs reveals that biokinetics can be described and predicted accurately by physiologically-based pharmacokinetic (PBPK) modeling. The nano PBPK models developed to date all concern a single type of NP. Our aim here was to extend a recent model for pegylated polyacrylamide NP in order to develop a more general PBPK model for nondegradable NPs injected intravenously into rats. The same model and physiological parameters were applied to pegylated polyacrylamide, uncoated polyacrylamide, gold, and titanium dioxide NPs, whereas NP-specific parameters were chosen on the basis of the best fit to the experimental time-courses of NP accumulation in various tissues. Our model describes the biokinetic behavior of all four types of NPs adequately, despite extensive differences in this behavior as well as in their physicochemical properties. In addition, this simulation demonstrated that the dose exerts a profound impact on the biokinetics, since saturation of the phagocytic cells at higher doses becomes a major limiting step. The fitted model parameters that were most dependent on NP type included the blood:tissue coefficients of permeability and the rate constant for phagocytic uptake. Since only four types of NPs with several differences in characteristics (dose, size, charge, shape, and surface properties) were used, the relationship between these characteristics and the NP-dependent model parameters could not be elucidated and more experimental data are required in this context. In this connection, intravenous biodistribution studies with associated PBPK analyses would provide the most insight.

Indexed as

Cell Membrane PermeabilityModels, BiologicalAcrylic ResinsAnimalsGoldInjections, IntravenousNanoparticlesPhagocytosisPolyacrylamidesRatsSurface PropertiesTissue DistributionTitaniumAcrylic ResinsGoldPolyacrylamidesTitaniumtitanium dioxidegoldintravenous administrationnanorodsnondegradablePBPKphagocytosispolyacrylamidepolyethylene glycol coatingratstitanium dioxide

Identifiers

PMID26929620
PMCPMC4755468
OpenAlexW2266913154

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.