A multicomponent similarity approach to identify potential substances of very high concern

Abstract

The number of chemicals being placed on the market is increasing. As such, there is an increased need for screening and evaluation of chemical hazards and risks. Particularly, chemicals with intrinsic properties that are considered of very high concern are ideally identified and regulated before wide-spread use and exposure. The use of in silico tools can help to identify potential substances of very high concern (SVHCs).

Earlier, predictive models have been developed that identify potential SVHCs based on global structural similarity to known SVHCs. Here in this study, these read-across similarity models have been extended with other similarity modules, including toxicophore, biological and physicochemical similarity.

The newly developed SVHC similarity profiles do individually not outperform the existing global similarity model. However, combining these new modules in an extended similarity approach results in more comprehensive predictions and allows for improved interpretability and applicability to the broader chemical universe. As such, this new approach is thought to support model users in interpretation of the model-prediction, and can thereby contribute to better screening and prioritization of potential SVHCs.