Evidence map›Paper›PMID 35188648›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives.

Maria Chiara Astuto, Matteo R Di Nicola, José V Tarazona, A Rortais, Yann Devos, A K Djien Liem, George E N Kass, Maria Bastaki, Reinhilde Schoonjans, Angelo Maggiore and 9 more

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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

19 authors.

Maria Chiara Astuto *European Food Safety Authority, Parma, Italy.
Matteo R Di Nicola *Unit of Dermatology, IRCCS San Raffaele Hospital, Milan, Italy.
José V TarazonaEuropean Food Safety Authority, Parma, Italy.
A RortaisEuropean Food Safety Authority, Parma, Italy.
Yann DevosEuropean Food Safety Authority, Parma, Italy.
A K Djien LiemEuropean Food Safety Authority, Parma, Italy.
George E N KassEuropean Food Safety Authority, Parma, Italy.
Maria BastakiEuropean Food Safety Authority, Parma, Italy.
Reinhilde SchoonjansEuropean Food Safety Authority, Parma, Italy.
Angelo MaggioreEuropean Food Safety Authority, Parma, Italy.
Sandrine CharlesUniversity of Lyon, Lyon, France.
Aude RatierUniversity of Lyon, Lyon, France.
Christelle LopesUniversity of Lyon, Lyon, France.
Ophelia GestinUniversity of Lyon, Lyon, France.
Tobin RobinsonEuropean Food Safety Authority, Parma, Italy.
Antony WilliamsCenter for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, NC, USA.
Nynke KramerInstitute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands.
Edoardo CarnesecchiInstitute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands.
Jean-Lou C M DorneEuropean Food Safety Authority, Parma, Italy. Jean-Lou.DORNE@efsa.europa.eu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This chapter aims to introduce the reader to the basic principles of environmental risk assessment of chemicals and highlights the usefulness of tiered approaches within weight of evidence approaches in relation to problem formulation i.e., data availability, time and resource availability. In silico models are then introduced and include quantitative structure-activity relationship (QSAR) models, which support filling data gaps when no chemical property or ecotoxicological data are available. In addition, biologically-based models can be applied in more data rich situations and these include generic or species-specific models such as toxicokinetic-toxicodynamic models, dynamic energy budget models, physiologically based models, and models for ecosystem hazard assessment i.e. species sensitivity distributions and ultimately for landscape assessment i.e. landscape-based modeling approaches. Throughout this chapter, particular attention is given to provide practical examples supporting the application of such in silico models in real-world settings. Future perspectives are discussed to address environmental risk assessment in a more holistic manner particularly for relevant complex questions, such as the risk assessment of multiple stressors and the development of harmonized approaches to ultimately quantify the relative contribution and impact of single chemicals, multiple chemicals and multiple stressors on living organisms.

Indexed as

EcosystemEcotoxicologyComputer SimulationQuantitative Structure-Activity RelationshipRisk AssessmentDynamic energy budget modelsEnvironmental risk assessmentLandscape modelingPhysiologically based modelsQuantitative structure–activity relationship models

Identifiers

PMID35188648

What OpenQuestion holds

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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.