Chemicals that persist in the environment can harm humans and wildlife.
This study describes a computer modelling-based approach to predict which chemical compounds are likely to be persistent.
The models were correctly able to predict persistence for 11 of 12 chemicals tested and could provide a cost-effective alternative to laboratory testing.
A new integrated in silico strategy for the assessment and prioritization of persistence of chemicals under REACH, science direct, pii/S0160412015301240, 2013.
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.