Predicting PBT and CMR properties of substances of very high concern (SVHCs) using QSAR models, and application for K-REACH
| Autor: | Byongcheun Lee, Ki-Tae Kim, Jin-Sung Ra, Joonsik Moon |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
QMRF
QSAR model reporting format SVHCs substances of very high concern Health Toxicology and Mutagenesis 010501 environmental sciences Toxicology 01 natural sciences US EPA United States Environmental Protection Agency WoE weight of evidence AD applicability domain 0302 clinical medicine EPI estimation programs interface PBT persistent bioaccumulative and toxic CMR K-REACH REACH registration evaluation authorization and restriction of chemicals PBT Mathematics CAS chemicals abstracts service EDC endocrine disrupting chemicals FP false positive Toxicity data QSAR Regular Article PFCAs perfluorinated carboxylic acids Bioaccumulation CAESAR Computer Assisted Evaluation of industrial chemical Substances According to Regulations TP ture positive Quantitative structure–activity relationship BCF bioconcentration factor Kow octanol-water coefficient SVHCs SMILES simplified molecular-input line-entry system 03 medical and health sciences LAZAR lazy structure–activity relationships lcsh:RA1190-1270 GHS globally harmonized system of classification and labelling of chemicals FN false negative SA structure alters UVCBs complex reaction products or biological materials lcsh:Toxicology. Poisons 0105 earth and related environmental sciences Weight of evidence CMR carcinogenic mutagenic or toxic for reproduction PFDA nonadecafluorodecanoic acid TN ture negative ECHA European Chemical Agency QSAR quantitative structure-activity relationship AFC atom/fragment contribution DSSTox distributed structure-searchable toxicity Chemical regulation Biochemical engineering QPRF QSAR prediction reporting format 030217 neurology & neurosurgery |
| Zdroj: | Toxicology Reports Toxicology Reports, Vol 7, Iss, Pp 995-1000 (2020) |
| ISSN: | 2214-7500 |
| Popis: | Highlights • BIOWIN is effective for predicting persistence and bioaccumulation. • Toxtree is effective for predicting carcinogenicity and mutagenicity. • WoE approach enhances the sensitivity. • It is recommended to set a conservative criteria of log Kow more than 4.5 in K-REACH. 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. |
| Databáze: | OpenAIRE |
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