New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments.

To assess the impact of chemicals on an aquatic environment, toxicological data for three trophic levels are needed to address the chronic and acute toxicities. The use of non-testing methods, such as predictive computational models, was proposed to avoid or reduce the need for animal models and speed up the process when there are many substances to be tested. We developed predictive models for Raphidocelis subcapitata, Daphnia magna, and fish for acute and chronic toxicities. The random forest machine learning approach gave the best results. The models gave good statistical quality for all endpoints. These models are freely available for use as individual models in the VEGA platform and for prioritization in JANUS software.

Integrated in silico strategy for PBT assessment and prioritization under REACH.

Chemicals may persist in the environment, bioaccumulate and be toxic for humans and wildlife, posing great concern. These three properties, persistence (P), bioaccumulation (B), and toxicity (T) are the key targets of the PBT-hazard assessment. The European regulation for the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) requires assessment of PBT-properties for all chemicals that are produced or imported in Europe in amounts exceeding 10 tonnes per year, checking whether the criteria set out in REACH Annex XIII are met, so the substance should therefore be considered to have properties of very high concern. Considering how many substances can fall under the REACH regulation, there is a pressing need for new strategies to identify and screen large numbers fast and inexpensively. An efficient non-testing screening approach to identify PBT candidates is necessary, as a valuable alternative to money- and time-consuming laboratory tests and a good start for prioritization since few tools exist (e.g. the PBT profiler developed by US EPA). The aim of this work was to offer a conceptual scheme for identifying and prioritizing chemicals for further assessment and if appropriate further testing, based on their PBT-potential, using a non-testing screening approach. We integrated in silico models (using existing and developing new ones) in a final algorithm for screening and ranking PBT-potential, which uses experimental and predicted values as well as associated uncertainties. The Multi-Criteria Decision-Making (MCDM) theory was used to integrate the different values. Then we compiled a new set of data containing known PBT and non-PBT substances, in order to check how well our approach clearly differentiated compounds labeled as PBT from those labeled as non-PBT. This indicated that the integrated model distinguished between PBT from non-PBT compounds.