Considering ionic state in modeling sorption of pharmaceuticals to sewage sludge.

Information on the partitioning of chemicals between particulate matter and water in sewage treatment plants (STPs) can be used to predict their subsequent environmental fate. However, this information can be challenging to acquire, especially for pharmaceuticals that are frequently present in ionized forms. This study investigated the relationship between the ionization state of active pharmaceutical ingredients (APIs) and their partitioning between water and sludge in STPs.

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project.

Background: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER).

Identifying potential endocrine disruptors among industrial chemicals and their metabolites--development and evaluation of in silico tools.

The aim of this study was to improve the identification of endocrine disrupting chemicals (EDCs) by developing and evaluating in silico tools that predict interactions at the estrogen (E) and androgen (A) receptors, and binding to transthyretin (T). In particular, the study focuses on evaluating the use of the EAT models in combination with a metabolism simulator to study the significance of bioactivation for endocrine disruption. Balanced accuracies of the EAT models ranged from 77-87%, 62-77%, and 65-89% for E-, A-, and T-binding respectively.

On the use of in silico tools for prioritising toxicity testing of the low-volume industrial chemicals in REACH.

This study was conducted to evaluate the utility of a selection of commercially and freely available non-testing tools and to analyse how REACH registrants can apply these as prioritisation tool for low-volume chemicals. The analysis was performed on a set of organic industrial chemicals and pesticides with extensive peer-reviewed risk assessment data. Analysed in silico model systems included Derek Nexus, Toxtree, QSAR Toolbox, LAZAR, TEST and VEGA, and results from these were compared with expert-judged risk classification according to the classifying, labelling and packaging (CLP) regulation.

Are chemicals in articles an obstacle for reaching environmental goals? - Missing links in EU chemical management.

It is widely acknowledged that the management of risks associated with chemicals in articles needs to be improved. The EU environmental policy states that environmental damage should be rectified at source. It is therefore motivated that the risk management of substances in articles also takes particular consideration to those substances identified as posing a risk in different environmental compartments.