Integrating QSAR and read-across for environmental assessment

Benfenati, Emilio; Roncaglioni, Alessandra; Petoumenou, Maria I.; Cappelli, Claudia Ileana; Gini, Giuseppina

doi:10.1080/1062936x.2015.1078408

Cited by 20 publications

(5 citation statements)

References 10 publications

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“…Recently, integrative approaches of QSAR and read-across have been proposed by our 28 and other groups. [29][30][31] Specifically, we have proposed chemical-biological read-across (CBRA), which combines quantitative toxicity prediction with a visualization methodology, ensuring both high predictivity and interpretability of models. 28 In this paper, we have compared, contrasted, and attempted to consolidate the two major modern approaches to toxicity assessment.…”

Section: Introductionmentioning

confidence: 99%

Alarms about structural alerts

et al. 2016

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Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple and transparent means to flag potential chemical hazards or group compounds into categories for read-across. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. Conversely, the rigorously developed and properly validated statistical QSAR models can accurately and reliably predict the toxicity of a chemical; however, their use in regulatory toxicology has been hampered by the lack of transparency and interpretability. We demonstrate that contrary to the common perception of QSAR models as “black boxes” they can be used to identify statistically significant chemical substructures (QSAR-based alerts) that influence toxicity. We show through several case studies, however, that the mere presence of structural alerts in a chemical, irrespective of the derivation method (expert-based or QSAR-based), should be perceived only as hypotheses of possible toxicological effect. We propose a new approach that synergistically integrates structural alerts and rigorously validated QSAR models for a more transparent and accurate safety assessment of new chemicals.

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Section: Introductionmentioning

confidence: 99%

Alarms about structural alerts

et al. 2016

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“…VEGA already explores read-across since information on the most similar compounds is displayed in the detailed output report. However, ToxRead is a tool that is specific for read-across, 17,18 and thus, it can be used as a tool to gain deeper insight into a read-across prediction when the information obtained by using VEGA is not sufficient. ToxRead currently covers several endpoints, and this number is steadily increasing.…”

Section: Toxread: a Closer Look To Enhance Read-acrossmentioning

confidence: 99%

The VEGAHUB Platform: The Philosophy and the Tools

Roncaglioni¹,

Lombardo

Benfenati

2022

Altern Lab Anim

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VEGAHUB ( www.vegahub.eu ) is a repository of freely available, downloadable tools based on computational toxicology methodologies. The main software tool available in VEGAHUB is VEGA QSAR software encoding more than 90 quantitative structure–activity relationship (QSAR) models for tens of endpoints for human toxicology, ecotoxicology, environmental, physico-chemical and toxicokinetic properties. However, beyond VEGA QSAR, VEGAHUB offers several other tools. Here, we present these resources, the possibilities to fully exploit them and the ways in which to integrate results provided by different VEGAHUB tools. Read-across and weight-of-evidence represent a major advantage of VEGAHUB. Integration between hazard and exposure is provided within innovative tools, which are specific for well-defined scenarios, such as those for cosmetic products. Prioritisation can be achieved by integrating results from 48 models. Finally, we highlight how some tools may not only fit predefined endpoints but also could be applied to general problems and research applications in the QSAR field. A couple of examples are provided, in which a critical assessment of the predictions and the documentation associated with the prediction are considered, in order to properly assess the quality of the results. These results may be associated with different levels of uncertainty or even be conflicting.

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“…and hence often difficult to replicate. Besides, the category approach is typically not so strictly formalized, depending on the similar chemicals data existing in internal database [44].…”

Section: Difference Between Classification and Quantitative Assessmentmentioning

confidence: 99%

Comparison of seven in silico tools for evaluating of daphnia and fish acute toxicity: case study on Chinese Priority Controlled Chemicals and new chemicals

et al. 2021

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Background A number of predictive models for aquatic toxicity are available, however, the accuracy and extent of easy to use of these in silico tools in risk assessment still need further studied. This study evaluated the performance of seven in silico tools to daphnia and fish: ECOSAR, T.E.S.T., Danish QSAR Database, VEGA, KATE, Read Across and Trent Analysis. 37 Priority Controlled Chemicals in China (PCCs) and 92 New Chemicals (NCs) were used as validation dataset. Results In the quantitative evaluation to PCCs with the criteria of 10-fold difference between experimental value and estimated value, the accuracies of VEGA is the highest among all of the models, both in prediction of daphnia and fish acute toxicity, with accuracies of 100% and 90% after considering AD, respectively. The performance of KATE, ECOSAR and T.E.S.T. is similar, with accuracies are slightly lower than VEGA. The accuracy of Danish Q.D. is the lowest among the above tools with which QSAR is the main mechanism. The performance of Read Across and Trent Analysis is lowest among all of the tested in silico tools. The predictive ability of models to NCs was lower than that of PCCs possibly because never appeared in training set of the models, and ECOSAR perform best than other in silico tools. Conclusion QSAR based in silico tools had the greater prediction accuracy than category approach (Read Across and Trent Analysis) in predicting the acute toxicity of daphnia and fish. Category approach (Read Across and Trent Analysis) requires expert knowledge to be utilized effectively. ECOSAR performs well in both PCCs and NCs, and the application shoud be promoted in both risk assessment and priority activities. We suggest that distribution of multiple data and water solubility should be considered when developing in silico models. Both more intelligent in silico tools and testing are necessary to identify hazards of Chemicals.

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Integrating QSAR and read-across for environmental assessment

Cited by 20 publications

References 10 publications

Alarms about structural alerts

Alarms about structural alerts

The VEGAHUB Platform: The Philosophy and the Tools

Comparison of seven in silico tools for evaluating of daphnia and fish acute toxicity: case study on Chinese Priority Controlled Chemicals and new chemicals

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