The Grouping and Assessment Strategy for Organic Pigments (GRAPE): Scientific evidence to facilitate regulatory decision-making

Critical aspects in dissolution testing of nanomaterials in the oro-gastrointestinal tract: the relevance of juice composition for hazard identification and grouping

The dissolution of a nanomaterial (NM) in an in vitro simulant of the oro-gastrointestinal (OGI) tract is an important predictor of its biodurability in vivo. The cascade addition of simulated digestive juices (saliva, stomach and intestine), including inorganic/organic biomacromolecules and digestive enzymes (complete composition, referred to as "Type 1 formulation"), strives for realistic representation of chemical composition of the OGI tract. However, the data robustness requires consideration of analytical feasibility, such as the use of simplified media. Here we present a systematic analysis of the effects exerted by different digestive juice formulations on the dissolution% (or half-life values) of benchmark NMs (e.g., zinc oxide, titanium dioxide, barium sulfate, and silicon dioxide). The digestive juices were progressively simplified by removal of components such as organic molecules, enzymes, and inorganic molecules (Type 2, 3 and 4). The results indicate that the "Type 1 formulation" augments the dissolution via sequestration of ions by measurable factors compared to formulations without enzymes (i.e., Type 3 and 4). Type 1 formulation is thus regarded as a preferable option for predicting NM biodurability for hazard assessment. However, for grouping purposes, the relative similarity among diverse nanoforms (NFs) of a NM is decisive. Two similarity algorithms were applied, and additional case studies comprising NFs and non NFs of the same substance were included. The results support the grouping decision by simplified formulation (Type 3) as a robust method for screening and grouping purposes.

In Silico Identification of Potential Thyroid Hormone System Disruptors among Chemicals in Human Serum and Chemicals with a High Exposure Index

Data on toxic effects are at large missing the prevailing understanding of the risks of industrial chemicals. Thyroid hormone (TH) system disruption includes interferences of the life cycle of the thyroid hormones and may occur in various organs. In the current study, high-throughput screening data available for 14 putative molecular initiating events of adverse outcome pathways, related to disruption of the TH system, were used to develop 19 in silico models for identification of potential thyroid hormone system-disrupting chemicals. The conformal prediction framework with the underlying Random Forest was used as a wrapper for the models allowing for setting the desired confidence level and controlling the error rate of predictions. The trained models were then applied to two different databases: (i) an in-house database comprising xenobiotics identified in human blood and ii) currently used chemicals registered in the Swedish Product Register, which have been predicted to have a high exposure index to consumers. The application of these models showed that among currently used chemicals, fewer were overall predicted as active compared to chemicals identified in human blood. Chemicals of specific concern for TH disruption were identified from both databases based on their predicted activity.