Evidence map›Paper›PMID 28168672›Full record

ReviewAdvances in experimental medicine and biology2017

Compilation of Data and Modelling of Nanoparticle Interactions and Toxicity in the NanoPUZZLES Project.

Andrea-Nicole Richarz, Aggelos Avramopoulos, Emilio Benfenati, Agnieszka Gajewicz, Nazanin Golbamaki Bakhtyari, Georgios Leonis, Richard L Marchese Robinson, Manthos G Papadopoulos, Mark Td Cronin, Tomasz Puzyn

Abstract readReview
PubMed Publisher
In one paragraph

Review in Advances in experimental medicine and biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Toxicity of metal-based nanoparticles: Challenges in the nano era.Frontiers in bioengineering and biotechnology · 2022
    Review
  3. Article
  4. In silico prediction of toxicity and its applications for chemicals at work.Toxicology and environmental health sciences · 2020
    Review
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

10 authors.

Andrea-Nicole RicharzSchool of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK.
Aggelos AvramopoulosInstitute of Biology, Pharmaceutical Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.
Emilio BenfenatiLaboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy.
Agnieszka GajewiczLaboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland.
Nazanin Golbamaki BakhtyariLaboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy.
Georgios LeonisInstitute of Biology, Pharmaceutical Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.
Richard L Marchese RobinsonSchool of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK.
Manthos G PapadopoulosInstitute of Biology, Pharmaceutical Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.
Mark Td CroninSchool of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK.
Tomasz PuzynLaboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The particular properties of nanomaterials have led to their rapidly increasing use in diverse fields of application. However, safety assessment is not keeping pace and there are still gaps in the understanding of their hazards. Computational models predicting nanotoxicity, such as (quantitative) structure-activity relationships ((Q)SARs), can contribute to safety evaluation, in line with general efforts to apply alternative methods in chemical risk assessment. Their development is highly dependent on the availability of reliable and high quality experimental data, both regarding the compounds' properties as well as the measured toxic effects. In particular, "nano-QSARs" should take the nano-specific characteristics into account. The information compiled needs to be well organized, quality controlled and standardized. Integrating the data in an overarching, structured data collection aims to (a) organize the data in a way to support modelling, (b) make (meta)data necessary for modelling available, and (c) add value by making a comparison between data from different sources possible.Based on the available data, specific descriptors can be derived to parameterize the nanomaterial-specific structure and physico-chemical properties appropriately. Furthermore, the interactions between nanoparticles and biological systems as well as small molecules, which can lead to modifications of the structure of the active nanoparticles, need to be described and taken into account in the development of models to predict the biological activity and toxicity of nanoparticles. The EU NanoPUZZLES project was part of a global cooperative effort to advance data availability and modelling approaches supporting the characterization and evaluation of nanomaterials.

Indexed as

Computer SimulationHumansNanoparticlesNanostructuresQuantitative Structure-Activity RelationshipRisk AssessmentData compilationData qualityData standardizationInteractionsNano-descriptorsNanomaterialNanoparticleNano-QSARToxicity

Identifiers

PMID28168672

What OpenQuestion holds

Textmetadata
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.