Pesticides as estrogen disruptors: Qsar for selective eraα and erβ binding of pesticides

Agatonovic-Kustrin, Snezana, Alexander, Marliese, Morton, David W. and Turner, Joseph V. (2011) Pesticides as estrogen disruptors: Qsar for selective eraα and erβ binding of pesticides. Combinatorial Chemistry and High Throughput Screening, 14 2: 85-92. doi:10.2174/138620711794474097

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Author Agatonovic-Kustrin, Snezana
Alexander, Marliese
Morton, David W.
Turner, Joseph V.
Title Pesticides as estrogen disruptors: Qsar for selective eraα and erβ binding of pesticides
Journal name Combinatorial Chemistry and High Throughput Screening   Check publisher's open access policy
ISSN 1386-2073
Publication date 2011-02-01
Year available 2011
Sub-type Article (original research)
DOI 10.2174/138620711794474097
Open Access Status
Volume 14
Issue 2
Start page 85
End page 92
Total pages 8
Place of publication Bussum, AG, Netherlands
Publisher Bentham Science Publishers
Language eng
Formatted 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.
Keyword Biochemical Research Methods
Chemistry, Applied
Pharmacology & Pharmacy
Biochemistry & Molecular Biology
Pharmacology & Pharmacy
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Medicine Publications
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Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
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