The role of trust in the use of artificial intelligence for chemical risk assessment.

Risk assessment of chemicals is a time-consuming process and needs to be optimized to ensure all chemicals are timely evaluated and regulated. This transition could be stimulated by valuable applications of in silico Artificial Intelligence (AI)/Machine Learning (ML) models. However, implementation of AI/ML models in risk assessment is lagging behind.

ZZS similarity tool: The online tool for similarity screening to identify chemicals of potential concern.

Screening and prioritization of chemicals is essential to ensure that available evaluation capacity is invested in those substances that are of highest concern. We, therefore, recently developed structural similarity models that evaluate the structural similarity of substances with unknown properties to known Substances of Very High Concern (SVHC), which could be an indication of comparable effects. In the current study the performance of these models is improved by (1) separating known SVHCs in more specific subgroups, (2) (re-)optimizing similarity models for the various SVHC-subgroups, and (3) improving interpretability of the predicted outcomes by providing a confidence score.

Persistence, bioaccumulation and toxicity-assessment of petroleum UVCBs: A case study on alkylated three-ring PAHs.

Substances with (very) persistent, (very) bioaccumulative, and/or toxic properties (PBT/vPvB) are of environmental concern and are identified via hazard-based PBT-assessment approaches. The PBT-assessment of well-defined substances is optimized over the past decades, but is under development for substances of unknown or variable composition, complex reaction products or biological materials (UVCBs). Particularly, the large number of constituents and variable composition complicate the PBT-assessment of UVCBs.

Evaluating chemical similarity as a measure to identify potential substances of very high concern.

Due to the large amount of chemical substances on the market, fast and reproducible screening is essential to prioritize chemicals for further evaluation according to highest concern. We here evaluate the performance of structural similarity models that are developed to identify potential substances of very high concern (SVHC) based on structural similarity to known SVHCs. These models were developed following a systematic analysis of the performance of 112 different similarity measures for varying SVHC-subgroups.

Variability in fish bioconcentration factors: Influences of study design and consequences for regulation.

The fish bioconcentration factor (BCF) is an important aspect within bioaccumulation assessments. Several factors have been suggested to influence BCF values - including species, developmental stage, mixture exposure, and calculation method. However, their exact contribution to variance in BCF values is unknown.

Risk management of hazardous substances in a circular economy.

The ambitions for a circular economy are high and unambiguous, but day-to-day experience shows that the transition still has many difficulties to overcome. One of the current hurdles is the presence of hazardous substances in waste streams that enter or re-enter into the environment or the technosphere. The key question is: do we have the appropriate risk management tools to control any risks that might arise from the re-using and recycling of materials? We present some recent cases that illustrate current practice and complexity in the risk management of newly-formed circular economy chains.

Pesticide Mixture Toxicity in Surface Water Extracts in Snails (Lymnaea stagnalis) by an in Vitro Acetylcholinesterase Inhibition Assay and Metabolomics.

Many chemicals in use end up in the aquatic environment. The toxicity of water samples can be tested with bioassays, but a metabolomic approach has the advantage that multiple end points can be measured simultaneously and the affected metabolic pathways can be revealed. A current challenge in metabolomics is the study of mixture effects.