Evidence map›Paper›PMID 32874922›Full record

ArticleToxicology reports2020

Predicting PBT and CMR properties of substances of very high concern (SVHCs) using QSAR models, and application for K-REACH.

Joonsik Moon, Byongcheun Lee, Jin-Sung Ra, Ki-Tae Kim

Abstract read
In one paragraph

Article in Toxicology reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Toxicology research · 2024
    Article
  4. Article
  5. 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

4 authors.

Joonsik MoonDepartment of Environmental Energy Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea.
Byongcheun LeeRisk Assessment Division, National Institute of Environmental Research, Incheon, 22689, Republic of Korea.
Jin-Sung RaEco-testing and Risk Assessment Center, Korea Institute of Industrial Technology (KITECH), Ansan, 15588, Republic of Korea.
Ki-Tae KimDepartment of Environmental Energy Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantitative structure-activity relationship (QSAR) models have been applied to predict a variety of toxicity endpoints. Their performance needs to be validated, in a variety of cases, to increase their applicability to chemical regulation. Using the data set of substances of very high concern (SVHCs), the performance of QSAR models were evaluated to predict the persistence and bioaccumulation of PBT, and the carcinogenicity and mutagenicity of CMR. BIOWIN and Toxtree showed higher sensitivity than other QSAR models - the former for persistence and bioaccumulation, the latter for carcinogenicity. In terms of mutagenicity, the sensitivities of QSAR models were underestimated, Toxtree was more accurate and specific than lazy structure-activity relationships (LAZARs) and Computer Assisted Evaluation of industrial chemical Substances According to Regulations (CAESAR). Using the weight of evidence (WoE) approach, which integrates results of individual QSAR models, enhanced the sensitivity of each toxicity endpoint. On the basis of obtained results, in particular the prediction of persistence and bioaccumulation by KOWWIN, a conservative criterion is recommended of log Kow greater than 4.5 in K-REACH, without an upper limit. This study suggests that reliable production of toxicity data by QSAR models is facilitated by a better understanding of the performance of these models.

Indexed as

AD, applicability domainAFC, atom/fragment contributionBCF, bioconcentration factorCAESAR, Computer Assisted Evaluation of industrial chemical Substances According to RegulationsCAS, chemicals abstracts serviceCMRCMR, carcinogenic, mutagenic or toxic for reproductionDSSTox, distributed structure-searchable toxicityECHA, European Chemical AgencyEDC, endocrine disrupting chemicalsEPI, estimation programs interfaceFN, false negativeFP, false positiveGHS, globally harmonized system of classification and labelling of chemicalsKow, octanol-water coefficientK-REACHLAZAR, lazy structure–activity relationshipsPBTPBT, persistent, bioaccumulative and toxicPFCAs, perfluorinated carboxylic acidsPFDA, nonadecafluorodecanoic acidQMRF, QSAR model reporting formatQPRF, QSAR prediction reporting formatQSARQSAR, quantitative structure-activity relationshipREACH, registration, evaluation, authorization and restriction of chemicalsSA, structure altersSMILES, simplified molecular-input line-entry systemSVHCsSVHCs, substances of very high concernTN, ture negativeTP, ture positiveUS EPA, United States Environmental Protection AgencyUVCBs, complex reaction products or biological materialsWoE, weight of evidence

Identifiers

PMID32874922
PMCPMC7451722

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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.