Ecotoxicological QSAR modeling of endocrine disruptor chemicals
Copyright © 2019 Elsevier B.V. All rights reserved..
This study reports highly robust externally predictive quantitative structure-toxicity relationship (QSTR) and interspecies quantitative structure-toxicity-toxicity (i-QSTTR) models developed using toxicity data of endocrine disruptor chemicals (EDCs) towards 14 different species falling in four different trophic levels. Genetic algorithm followed by Partial Least Squares (PLS) regression was used in model development following the strict OECD guidelines. The models were developed using 2D descriptors having definite physicochemical meaning and validated by several internationally accepted validation metrics. The scope of predictions was defined by estimating applicability domain of the models. Presence of halogens, sulfur and phosphorus in the molecules greatly influenced the toxicity of EDCs as suggested by continuous repetition of 2D atom pair descriptors. Lipophilic contributions as calculated by logP terms (mainly ALOGP2 and XlogP) were the second most important feature controlling the EDC hazards. Hydrophilic moiety such as functionalities like esters, aliphatic ethers, branching and higher oxygen content reduced the EDC toxicity. Interspecies models were employed in data gap filling following the hierarchy of different species. The reliability of predictions was calculated by the "prediction reliability indicator" tool.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:369 |
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| Enthalten in: |
Journal of hazardous materials - 369(2019) vom: 05. Mai, Seite 707-718 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Khan, Kabiruddin [VerfasserIn] |
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| Links: |
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| Themen: |
Endocrine Disruptors |
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| Anmerkungen: |
Date Completed 20.07.2020 Date Revised 20.07.2020 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jhazmat.2019.02.019 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM294557156 |
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| 245 | 1 | 0 | |a Ecotoxicological QSAR modeling of endocrine disruptor chemicals |
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| 500 | |a Date Revised 20.07.2020 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2019 Elsevier B.V. All rights reserved. | ||
| 520 | |a This study reports highly robust externally predictive quantitative structure-toxicity relationship (QSTR) and interspecies quantitative structure-toxicity-toxicity (i-QSTTR) models developed using toxicity data of endocrine disruptor chemicals (EDCs) towards 14 different species falling in four different trophic levels. Genetic algorithm followed by Partial Least Squares (PLS) regression was used in model development following the strict OECD guidelines. The models were developed using 2D descriptors having definite physicochemical meaning and validated by several internationally accepted validation metrics. The scope of predictions was defined by estimating applicability domain of the models. Presence of halogens, sulfur and phosphorus in the molecules greatly influenced the toxicity of EDCs as suggested by continuous repetition of 2D atom pair descriptors. Lipophilic contributions as calculated by logP terms (mainly ALOGP2 and XlogP) were the second most important feature controlling the EDC hazards. Hydrophilic moiety such as functionalities like esters, aliphatic ethers, branching and higher oxygen content reduced the EDC toxicity. Interspecies models were employed in data gap filling following the hierarchy of different species. The reliability of predictions was calculated by the "prediction reliability indicator" tool | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Endocrine disruptors | |
| 650 | 4 | |a Modeling | |
| 650 | 4 | |a QSAR | |
| 650 | 4 | |a Validation | |
| 650 | 4 | |a i-QSTTR | |
| 650 | 7 | |a Endocrine Disruptors |2 NLM | |
| 650 | 7 | |a Environmental Pollutants |2 NLM | |
| 700 | 1 | |a Roy, Kunal |e verfasserin |4 aut | |
| 700 | 1 | |a Benfenati, Emilio |e verfasserin |4 aut | |
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