Structural features of diverse ligands influencing binding affinities to Estrogen α and Estrogen β receptors. Part I: molecular descriptors calculated from minimal energy conformation of isolated ligands

Evaluation of the OECD (Q)SAR Application Toolbox for the profiling of estrogen receptor binding affinities

The determination of binding affinities for the estrogen receptor (ER) is used extensively to assess potential hazards to human health and the environment arising from chemicals that can interfere with natural hormone homeostasis. Given the great number of chemicals to which humans and wildlife are exposed, (quantitative) structure-activity relationship (Q)SAR models for the characterization of ER disruptors represent a fast and cost-efficient alternative to experimental testing. In this toxicological context, the freely available Organisation for Economic Co-operation and Development (OECD) (Q)SAR Application Toolbox provides a profiler for the categorical profiling of chemicals according to their ER binding propensities. The aim of this study was to evaluate the predictive performances of this profiler. To achieve such a purpose, prediction results with the ER-profiler were compared with experimental binding affinities relative to two large datasets of chemicals (rat and human). The resulting Cooper statistics indicated that the binding affinities of the majority of chemicals included in the retained datasets could be correctly predicted.

Descriptor analysis of estrogen receptor β-selective ligands using 2-phenylquinoline, tetrahydrofluorenone and 3-hydroxy 6H-benzo[c]chromen-6-one scaffolds

Estrogen receptor beta (ERβ) selective ligands have attracted much attention recently in the design of anti-cancer drugs that are devoid of the common side effects of estrogen. Structural studies of estrogen receptor alpha (ERα) and β revealed that there were considerable differences in their ligand-binding cavity and in their volume. Hence, the present study has hypothesized that size and shape descriptors can influence the affinity/selectivity of the ligands towards ERβ. To prove the same, quantitative structure-activity relationship (QSAR) analyses were carried out using multiple regression analysis on 2-phenylquinoline, tetrahydrofluorenone and 3-hydroxy-6H-benzo[c]chromen-6-one series. Results indicate that increased lipophilicity, decrease in ellipsoidal volume and width of substituents, presence of halogen atoms was essential for the ligands to have high affinity/selectivity towards ERβ. QSAR models obtained were both internally and externally validated. The study delineates that the size and shape descriptors are best modulators of ERβ affinity/selectivity. Docking studies were performed to support our QSAR results.

QSAR models for reproductive toxicity and endocrine disruption activity

Reproductive toxicity is an important regulatory endpoint, which is required in registration procedures of chemicals used for different purposes (for example pesticides). The in vivo tests are expensive, time consuming and require large numbers of animals, which must be sacrificed. Therefore an effort is ongoing to develop alternative In vitro and in silico methods to evaluate reproductive toxicity. In this review we describe some modeling approaches. In the first example we describe the CAESAR model for prediction of reproductive toxicity; the second example shows a classification model for endocrine disruption potential based on counter propagation artificial neural networks; the third example shows a modeling of relative binding affinity to rat estrogen receptor, and the fourth one shows a receptor dependent modeling experiment.

Assessing the reproductive toxicity of some (con)azole compounds using a structure-activity relationship approach

The present research investigates the study of a set of 27 (con)azoles and their reproductive toxicity. (Con)azoles are used as fungicides and herbicides in agriculture for treatment of fruits, vegetables, cereals, and seeds, or as human antimycotic therapeutics. According to EEC Directive 91/414, active substances used in plant protection products must undergo reproductive toxicity testing. Reproductive toxicity is a complex biological endpoint, which includes many different biological processes and, therefore, it can only to a limited extent be assessed by a single quantitative structure-activity relationship (QSAR) model. The proposed SAR models are built using unsupervised methods, such as hierarchical clustering, principal component analysis and self-organizing maps, with the aim of studying the similarity relationships between structures. The molecular structures are represented with a set of topological and structural descriptors. The models showing clusters, closest neighbours or outliers may support the categorization and the classification of (con)azoles as potential reproductive toxicants.