Binary classification models for endocrine disrupter effects mediated through the estrogen receptor†

†Presented at the 13^th International Workshop on QSARs in the Environmental Sciences (QSAR 2008), 8–12 June 2008, Syracuse, USA.

Abstract

Endocrine disrupters (EDs) form an interesting field of application attracting great attention in the recent years. They represent a number of exogenous substances interfering with the function of the endocrine system, including the interfering with developmental processes. In particular EDs are mentioned as substances requiring a more detailed control and specific authorization within REACH, the new European legislation on chemicals, together with other groups of chemicals of particular concern. QSAR represents a challenging method to approach data gap which is foreseen by REACH. The aim of this study was to provide an insight into the use of QSAR models to address ED effects mediated through the estrogen receptor (ER). New predictive models were derived to assess estrogenicity for a very large and heterogeneous dataset of chemical compounds. QSAR binary classifiers were developed based on different data mining techniques such as classification trees, decision forest, fuzzy logic, neural networks and support vector machines. The focus was given to multiple endpoints to better characterize the effects of EDs evaluating both binding (RBA) and transcriptional activity (RA). A possible combination of the models was also explored. A very good accuracy was reached for both RA and RBA models (higher than 80%).

†Presented at the 13^th International Workshop on QSARs in the Environmental Sciences (QSAR 2008), 8–12 June 2008, Syracuse, USA.

Keywords:

• endocrine disrupters
• estrogen receptor
• classification tree
• SVM
• fuzzy logic
• neural network

Acknowledgments

The European Commission is gratefully acknowledged for support through CAESAR (contract no. SSPI-022674-CAESAR) and OSIRIS (contract no. GOCE-CT-2007-037017) projects. The authors gratefully acknowledge Prof. Jacques R. Chrétien for helpful discussions.

Notes

†Presented at the 13^th International Workshop on QSARs in the Environmental Sciences (QSAR 2008), 8–12 June 2008, Syracuse, USA.