A new integrated in silico strategy for the assessment and prioritization of persistence of chemicals under REACH. (March 2016)

• Record Type: Journal Article
• Authors: Pizzo, Fabiola; Lombardo, Anna; Brandt, Marc; Manganaro, Alberto; Benfenati, Emilio
• Is Part Of: Environment international. Volume 88(2016:Mar.)
• Page Start: 250
• Page End: 260
• Publication Date: 2016-03
• Subjects: REACH Registration, Evaluation, Authorisation and restriction of CHemicals -- P persistent -- nP non-persistent -- vP very persistent -- nP/P non-persistent/persistent -- P/vP persistent/very persistent -- HL half-lives -- DT50 disappearance time 50 -- PBT persistent, bioaccumulative, toxic -- PBT/vPvB persistent, bioaccumulative, toxic/very persistent, very bioaccumulative -- POPs persistent organic pollutants -- RB readily biodegradable -- nRB non-readily biodegradable -- k-NN k-nearest neighbor algorithm -- (Q)SAR (quantitative) structure–activity relationship -- SAs structural alerts -- TP true positive -- TN true negative -- FP false positive -- FN false negative -- MCC Matthew's correlation coefficient -- AD applicability domain -- ADI applicability domain index -- EChA European Chemicals Agency -- US EPA United States Environmental Protection Agency; Persistence -- k-NN -- Structural alerts -- Chemical classes -- PBT -- In silico; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2015.12.019
• Languages: English
• ISSNs: 0160-4120

Abstract

Abstract: The fact that chemicals can be recalcitrant and persist in the environment arouses concern since their effects may seriously harm human and environmental health. We compiled three datasets containing half-life (HL) data on sediment, soil and water compartments in order to build in silico models and, finally, an integrated strategy for predicting persistence to be used within the EU legislation Registration, Evaluation, Authorisation and restriction of CHemicals (REACH). After splitting the datasets into training (80%) and test sets (20%), we developed models for each compartment using the k -nearest neighbor algorithm ( k- NN). Accuracy was higher than 0.79 and 0.76 respectively in the training and test sets for all three compartments. To support the k- NN predictions, we identified some structural alerts, using SARpy software, with a high-true positive percentage in the test set and some chemical classes related to persistence using the software IstChemFeat. All these results were combined to build an integrated model and to reach to an overall conclusion (based on assessment and reliability) on the persistence of the substance. The results on the external validation set were very encouraging and support the idea that this tool can be used successfully for regulatory purposes and to prioritize substances. Highlights: We developed an integrated in silico strategy for the assessment of persistence. Data on sediment, soil and water compartments were considered for the analysis. We built k -NN models and identified structural alerts and chemical classes for all compartments. We obtained good results both on single and integrated models. Considering the results, this approach can be used for regulatory purposes.