Reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrations
A reliable quantification of the potential effects of chemicals on freshwater ecosystems requires ecotoxicological response data for a large set of species which is typically not available in practice. In this study, we propose a method to estimate hazardous concentrations (HCs) of chemicals on fres...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-01-01
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| Series: | Environment International |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412019329113 |
| _version_ | 1818322250392141824 |
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| author | Mélanie Douziech Ad M.J. Ragas Rosalie van Zelm Rik Oldenkamp A. Jan Hendriks Henry King Rafika Oktivaningrum Mark A.J. Huijbregts |
| author_facet | Mélanie Douziech Ad M.J. Ragas Rosalie van Zelm Rik Oldenkamp A. Jan Hendriks Henry King Rafika Oktivaningrum Mark A.J. Huijbregts |
| author_sort | Mélanie Douziech |
| collection | DOAJ |
| description | A reliable quantification of the potential effects of chemicals on freshwater ecosystems requires ecotoxicological response data for a large set of species which is typically not available in practice. In this study, we propose a method to estimate hazardous concentrations (HCs) of chemicals on freshwater ecosystems by combining two in silico approaches: quantitative structure activity relationships (QSARs) and interspecies correlation estimation (ICE) models. We illustrate the principle of our QSAR-ICE method by quantifying the HCs of 51 chemicals at which 50% and 5% of all species are exposed above the concentration causing acute effects. We assessed the bias of the HCs, defined as the ratio of the HC based on measured ecotoxicity data and the HC based on in silico data, as well as the statistical uncertainty, defined as the ratio of the 95th and 5th percentile of the HC. Our QSAR-ICE method resulted in a bias that was comparable to the use of measured data for three species, as commonly used in effect assessments: the average bias of the QSAR-ICE HC50 was 1.2 and of the HC5 2.3 compared to 1.2 when measured data for three species were used for both HCs. We also found that extreme statistical uncertainties (>105) are commonly avoided in the HCs derived with the QSAR-ICE method compared to the use of three measurements with statistical uncertainties up to 1012. We demonstrated the applicability of our QSAR-ICE approach by deriving HC50s for 1,223 out of the 3,077 organic chemicals of the USEtox database. We conclude that our QSAR-ICE method can be used to determine HCs without the need for additional in vivo testing to help prioritise which chemicals with no or few ecotoxicity data require more thorough assessment. Keywords: Prioritization, Uncertainty, Variability, ICE, QSAR, Species sensitivity distribution |
| first_indexed | 2024-12-13T10:53:49Z |
| format | Article |
| id | doaj.art-aa9c0c0012b449a9ba62c85ce335f720 |
| institution | Directory Open Access Journal |
| issn | 0160-4120 |
| language | English |
| last_indexed | 2024-12-13T10:53:49Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj.art-aa9c0c0012b449a9ba62c85ce335f7202022-12-21T23:49:43ZengElsevierEnvironment International0160-41202020-01-01134Reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrationsMélanie Douziech0Ad M.J. Ragas1Rosalie van Zelm2Rik Oldenkamp3A. Jan Hendriks4Henry King5Rafika Oktivaningrum6Mark A.J. Huijbregts7Department of Environmental Science, Institute for Water and Wetland Research, Radboud University Nijmegen, P.O. Box 9010, 6500 GL Nijmegen, the Netherlands; Corresponding author.Department of Environmental Science, Institute for Water and Wetland Research, Radboud University Nijmegen, P.O. Box 9010, 6500 GL Nijmegen, the Netherlands; Open University, Faculty of Management Science & Technology, Valkenburgerweg 177, NL-6419 AT Heerlen, the NetherlandsDepartment of Environmental Science, Institute for Water and Wetland Research, Radboud University Nijmegen, P.O. Box 9010, 6500 GL Nijmegen, the NetherlandsAmsterdam Institute for Global Health & Development, AHTC Tower C4, Paasheuvelweg 25, 1105 BP Amsterdam, the NetherlandsDepartment of Environmental Science, Institute for Water and Wetland Research, Radboud University Nijmegen, P.O. Box 9010, 6500 GL Nijmegen, the NetherlandsSafety & Environmental Assurance Centre, Unilever, Colworth Science Park, Bedfordshire MK441LQ, UKDepartment of Environmental Science, Institute for Water and Wetland Research, Radboud University Nijmegen, P.O. Box 9010, 6500 GL Nijmegen, the NetherlandsDepartment of Environmental Science, Institute for Water and Wetland Research, Radboud University Nijmegen, P.O. Box 9010, 6500 GL Nijmegen, the NetherlandsA reliable quantification of the potential effects of chemicals on freshwater ecosystems requires ecotoxicological response data for a large set of species which is typically not available in practice. In this study, we propose a method to estimate hazardous concentrations (HCs) of chemicals on freshwater ecosystems by combining two in silico approaches: quantitative structure activity relationships (QSARs) and interspecies correlation estimation (ICE) models. We illustrate the principle of our QSAR-ICE method by quantifying the HCs of 51 chemicals at which 50% and 5% of all species are exposed above the concentration causing acute effects. We assessed the bias of the HCs, defined as the ratio of the HC based on measured ecotoxicity data and the HC based on in silico data, as well as the statistical uncertainty, defined as the ratio of the 95th and 5th percentile of the HC. Our QSAR-ICE method resulted in a bias that was comparable to the use of measured data for three species, as commonly used in effect assessments: the average bias of the QSAR-ICE HC50 was 1.2 and of the HC5 2.3 compared to 1.2 when measured data for three species were used for both HCs. We also found that extreme statistical uncertainties (>105) are commonly avoided in the HCs derived with the QSAR-ICE method compared to the use of three measurements with statistical uncertainties up to 1012. We demonstrated the applicability of our QSAR-ICE approach by deriving HC50s for 1,223 out of the 3,077 organic chemicals of the USEtox database. We conclude that our QSAR-ICE method can be used to determine HCs without the need for additional in vivo testing to help prioritise which chemicals with no or few ecotoxicity data require more thorough assessment. Keywords: Prioritization, Uncertainty, Variability, ICE, QSAR, Species sensitivity distributionhttp://www.sciencedirect.com/science/article/pii/S0160412019329113 |
| spellingShingle | Mélanie Douziech Ad M.J. Ragas Rosalie van Zelm Rik Oldenkamp A. Jan Hendriks Henry King Rafika Oktivaningrum Mark A.J. Huijbregts Reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrations Environment International |
| title | Reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrations |
| title_full | Reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrations |
| title_fullStr | Reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrations |
| title_full_unstemmed | Reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrations |
| title_short | Reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrations |
| title_sort | reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrations |
| url | http://www.sciencedirect.com/science/article/pii/S0160412019329113 |
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