Pesticides as Estrogen Disruptors: QSAR for Selective ERα and ERβ Binding of Pesticides

Title: Pesticides as Estrogen Disruptors: QSAR for Selective ERα and ERβ Binding of Pesticides

Volume: 14 Issue: 2

Author(s): Snezana Agatonovic-Kustrin, Marliese Alexander, David W. Morton and Joseph V. Turner

Affiliation:

Keywords: Artificial neural network, endocrine disruptor, estrogen, pesticides, receptors, quantitative structure activity relationship, EDSTAC, QSAR, ANNs, bioaccumulation, REACH, RBA, Multi Layer Perception, MLP, ER model, MCPP, NRRR, lipophilicity, Genistein, teleost fish

Abstract: Evidence suggests that environmental exposure to estrogen-like compounds can cause adverse effects in humans and wildlife. The Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC) has advised screening of 87,000 compounds in the interest of human safety. This may best be accomplished by pre-screening using quantitative structure-activity relationship (QSAR) modelling. The present study aimed to develop in silico QSARs based on natural, semi-synthetic, synthetic, and phytoestrogens, to predict the potential estrogenic toxicity of pesticides. A diverse set of 170 compounds including steroidal-, synthetic- and phytoestrogens, as well as pesticides was used to construct the QSAR models using artificial neural networks (ANNs). Mean correlation coefficients between experimentally measured and predicted binding affinities were all greater than 0.7 and models had few false negative results, an important consideration for screening tools. This study demonstrated the utility of ANNs as QSAR models for pre-screening of potential endocrine disruptors.

Cite this article as:

Agatonovic-Kustrin Snezana, Alexander Marliese, W. Morton David and V. Turner Joseph, Pesticides as Estrogen Disruptors: QSAR for Selective ERα and ERβ Binding of Pesticides, Combinatorial Chemistry & High Throughput Screening 2011; 14 (2) . https://dx.doi.org/10.2174/138620711794474097