EU Science Hub

Computational modelling of estrogen active substances: results of a large-scale international research project

PDB 1A52 Estrogen receptor alpha ligand-binding domain complexed to estradiol
©EU, 2016
Nov 16 2016

The JRC contributed to a large-scale modelling project led by the US Environmental Protection Agency (EPA) that demonstrated the usefulness of computational models in screening thousands of environmental chemicals for their ability to bind to the estrogen receptor. Chemicals that bind to the estrogen receptor may interfere with the biological effects of the natural hormone estrogen, potentially leading to adverse effects on both humans and wildlife.

Along with 16 other groups in the USA and the EU, scientists from the JRC's EU Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM) participated in the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP), organised by the EPA's National Center for Computational Toxicology (NCCT). This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different computational modelling approaches. Some of the models used were existing software tools, while others were developed by using estrogen binding data generated by in vitro High Throughput Screening (HTS) assays. The JRC contribution was carried out in the context of a research collaboration established between the JRC and the NCCT in 2010.

Computational predictions of hormone receptor binding activity, such as those generated in the CERAPP project, can be used alongside information generated by in vitro tests to inform on the toxicological mode of action of chemicals. Another source of information on endocrine activity, containing experimental data, is the Endocrine Active Substances Information System (EASIS), a web-based application recently launched by the JRC.

Read more in: K. Mansouri et al.: "CERAPP: Collaborative Estrogen Receptor Activity Prediction Project", Toxicology in Vitro 36 (2016) 197–209, doi:10.1289/ehp.1510267