QSAR Models for the Prediction of Dietary Biomagnification Factor in Fish
Xenobiotics released in the environment can be taken up by aquatic and terrestrial organisms and can accumulate at higher concentrations through the trophic chain. Bioaccumulation is therefore one of the PBT properties that authorities require to assess for the evaluation of the risks that chemicals...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Toxics |
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| Online Access: | https://www.mdpi.com/2305-6304/11/3/209 |
| _version_ | 1797608862199578624 |
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| author | Linda Bertato Nicola Chirico Ester Papa |
| author_facet | Linda Bertato Nicola Chirico Ester Papa |
| author_sort | Linda Bertato |
| collection | DOAJ |
| description | Xenobiotics released in the environment can be taken up by aquatic and terrestrial organisms and can accumulate at higher concentrations through the trophic chain. Bioaccumulation is therefore one of the PBT properties that authorities require to assess for the evaluation of the risks that chemicals may pose to humans and the environment. The use of an integrated testing strategy (ITS) and the use of multiple sources of information are strongly encouraged by authorities in order to maximize the information available and reduce testing costs. Moreover, considering the increasing demand for development and the application of new approaches and alternatives to animal testing, the development of in silico cost-effective tools such as QSAR models becomes increasingly important. In this study, a large and curated literature database of fish laboratory-based values of dietary biomagnification factor (BMF) was used to create externally validated QSARs. The quality categories (high, medium, low) available in the database were used to extract reliable data to train and validate the models, and to further address the uncertainty in low-quality data. This procedure was useful for highlighting problematic compounds for which additional experimental effort would be required, such as siloxanes, highly brominated and chlorinated compounds. Two models were suggested as final outputs in this study, one based on good-quality data and the other developed on a larger dataset of consistent Log BMF<sub>L</sub> values, which included lower-quality data. The models had similar predictive ability; however, the second model had a larger applicability domain. These QSARs were based on simple MLR equations that could easily be applied for the predictions of dietary BMF<sub>L</sub> in fish, and support bioaccumulation assessment procedures at the regulatory level. To ease the application and dissemination of these QSARs, they were included with technical documentation (as QMRF Reports) in the QSAR-ME Profiler software for QSAR predictions available online. |
| first_indexed | 2024-03-11T05:49:28Z |
| format | Article |
| id | doaj.art-9e789c7ace874c58b81436139b2bf39a |
| institution | Directory Open Access Journal |
| issn | 2305-6304 |
| language | English |
| last_indexed | 2024-03-11T05:49:28Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Toxics |
| spelling | doaj.art-9e789c7ace874c58b81436139b2bf39a2023-11-17T14:12:39ZengMDPI AGToxics2305-63042023-02-0111320910.3390/toxics11030209QSAR Models for the Prediction of Dietary Biomagnification Factor in FishLinda Bertato0Nicola Chirico1Ester Papa2Department of Theoretical and Applied Sciences, University of Insubria, 21100 Varese, ItalyDepartment of Theoretical and Applied Sciences, University of Insubria, 21100 Varese, ItalyDepartment of Theoretical and Applied Sciences, University of Insubria, 21100 Varese, ItalyXenobiotics released in the environment can be taken up by aquatic and terrestrial organisms and can accumulate at higher concentrations through the trophic chain. Bioaccumulation is therefore one of the PBT properties that authorities require to assess for the evaluation of the risks that chemicals may pose to humans and the environment. The use of an integrated testing strategy (ITS) and the use of multiple sources of information are strongly encouraged by authorities in order to maximize the information available and reduce testing costs. Moreover, considering the increasing demand for development and the application of new approaches and alternatives to animal testing, the development of in silico cost-effective tools such as QSAR models becomes increasingly important. In this study, a large and curated literature database of fish laboratory-based values of dietary biomagnification factor (BMF) was used to create externally validated QSARs. The quality categories (high, medium, low) available in the database were used to extract reliable data to train and validate the models, and to further address the uncertainty in low-quality data. This procedure was useful for highlighting problematic compounds for which additional experimental effort would be required, such as siloxanes, highly brominated and chlorinated compounds. Two models were suggested as final outputs in this study, one based on good-quality data and the other developed on a larger dataset of consistent Log BMF<sub>L</sub> values, which included lower-quality data. The models had similar predictive ability; however, the second model had a larger applicability domain. These QSARs were based on simple MLR equations that could easily be applied for the predictions of dietary BMF<sub>L</sub> in fish, and support bioaccumulation assessment procedures at the regulatory level. To ease the application and dissemination of these QSARs, they were included with technical documentation (as QMRF Reports) in the QSAR-ME Profiler software for QSAR predictions available online.https://www.mdpi.com/2305-6304/11/3/209QSARbiomagnificationbioaccumulationMLRalternatives to animal testingdata quality |
| spellingShingle | Linda Bertato Nicola Chirico Ester Papa QSAR Models for the Prediction of Dietary Biomagnification Factor in Fish Toxics QSAR biomagnification bioaccumulation MLR alternatives to animal testing data quality |
| title | QSAR Models for the Prediction of Dietary Biomagnification Factor in Fish |
| title_full | QSAR Models for the Prediction of Dietary Biomagnification Factor in Fish |
| title_fullStr | QSAR Models for the Prediction of Dietary Biomagnification Factor in Fish |
| title_full_unstemmed | QSAR Models for the Prediction of Dietary Biomagnification Factor in Fish |
| title_short | QSAR Models for the Prediction of Dietary Biomagnification Factor in Fish |
| title_sort | qsar models for the prediction of dietary biomagnification factor in fish |
| topic | QSAR biomagnification bioaccumulation MLR alternatives to animal testing data quality |
| url | https://www.mdpi.com/2305-6304/11/3/209 |
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