Evidence map›Paper›PMID 38555325›Full record

ArticleArchives of toxicology2024

A problem formulation framework for the application of in silico toxicology methods in chemical risk assessment.

Jerry Achar, Mark T D Cronin, James W Firman, Gunilla Öberg

Abstract read
In one paragraph

Article in Archives of toxicology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Jerry AcharInstitute for Resources Environment, and Sustainability, The University of British Columbia, 2202 Main Mall, Vancouver, BC, V6T 1Z4, Canada.
Mark T D CroninSchool of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK. M.T.Cronin@ljmu.ac.uk.ORCID http://orcid.org/0000-0002-6207-4158
James W FirmanSchool of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK.
Gunilla ÖbergInstitute for Resources Environment, and Sustainability, The University of British Columbia, 2202 Main Mall, Vancouver, BC, V6T 1Z4, Canada.

Funding

Social Science and Humanities Research Council (CA) 435-2019-0465
6 · The paper itself

Abstract

The first step in the hazard or risk assessment of chemicals should be to formulate the problem through a systematic and iterative process aimed at identifying and defining factors critical to the assessment. However, no general agreement exists on what components an in silico toxicology problem formulation (PF) should include. The present work aims to develop a PF framework relevant to the application of in silico models for chemical toxicity prediction. We modified and applied a PF framework from the general risk assessment literature to peer reviewed papers describing PFs associated with in silico toxicology models. Important gaps between the general risk assessment literature and the analyzed PF literature associated with in silico toxicology methods were identified. While the former emphasizes the need for PFs to address higher-level conceptual questions, the latter does not. There is also little consistency in the latter regarding the PF components addressed, reinforcing the need for a PF framework that enable users of in silico toxicology models to answer the central conceptual questions aimed at defining components critical to the model application. Using the developed framework, we highlight potential areas of uncertainty manifestation in in silico toxicology PF in instances where particular components are missing or implicitly described. The framework represents the next step in standardizing in silico toxicology PF component. The framework can also be used to improve the understanding of how uncertainty is apparent in an in silico toxicology PF, thus facilitating ways to address uncertainty.

Indexed as

Computer SimulationToxicologyAnimalsHumansRisk AssessmentToxicity TestsUncertaintyIn silico methodsProblem formulationRisk assessmentUncertainty

Identifiers

PMID38555325
PMCPMC11106140

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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.