Predicting estrogen receptor binding of chemicals using a suite of in silico methods - Complementary approaches of (Q)SAR, molecular docking and molecular dynamics.

With the aim of obtaining reliable estimates of Estrogen Receptor (ER) binding for diverse classes of compounds, a weight of evidence approach using estimates from a suite of in silico models was assessed. The predictivity of a simple Majority Consensus of (Q)SAR models was assessed using a test set of compounds with experimental Relative Binding Affinity (RBA) data. Molecular docking was also carried out and the binding energies of these compounds to the ERα receptor were determined. For a few selected compounds, including a known full agonist and antagonist, the intrinsic activity was determined using low-mode molecular dynamics methods. Individual (Q)SAR model predictivity varied, as expected, with some models showing high sensitivity, others higher specificity. However, the Majority Consensus (Q)SAR prediction showed a high accuracy and reasonably balanced sensitivity and specificity. Molecular docking provided quantitative information on strength of binding to the ERα receptor. For the 50 highest binding affinity compounds with positive RBA experimental values, just 5 of them were predicted to be non-binders by the Majority QSAR Consensus. Furthermore, agonist-specific assay experimental values for these 5 compounds were negative, which indicates that they may be ER antagonists. We also showed different scenarios of combining (Q)SAR results with Molecular docking classification of ER binding based on cut-off values of binding energies, providing a rational combined strategy to maximize terms of toxicological interest.

In silico assessment of toxicity of heat-generated food contaminants.

Since the discovery of acrylamide in heat-treated carbohydrate-rich foods, many more heat-generated food contaminants have been identified in a variety of foods and models systems. A database of these contaminants, generated as a result of either lipid oxidation or the Maillard reaction, has recently been compiled under the HEATOX project. A large majority of the compounds has not been tested for potential adverse effects on human health, which makes it difficult to carry out adequate assessment of risks to an average consumer. This study used two in silico toxicity Expert Systems (Topkat and Derek for Windows), as a preliminary screening tool to identify potential toxicants among the heat-generated contaminants in foods or model systems. The methodology enabled prioritisation of the compounds on the basis of predicted toxicities, and identification of potential toxicants for targeted testing by standard laboratory procedures. A comparison between the predicted toxicities of selected compounds and the available experimental data indicated that the methodology can be reliably used for assessing toxicity of untested food contaminants.

The physical demands of riding in National Hunt races.

Heart rate (fc) and post-competition blood lactate concentration ([La+]) were studied in seven male professional National Hunt jockeys over 30 races. The fc response for individual races followed a similar pattern for all subjects. The mean peak fc recorded during competition was 184 beats.min-1 (range 162-198 beats.min-1) with average fc during the races ranging from 136 to 188 beats.min-1. During consecutive races the recovery fc did not return to resting values. The mean [La+] was 7.1 mmol.l-1 (range 3.5-15.0 mmol.l-1). The conclusions of this study suggest that riding in National Hunt races is a physically demanding occupation. The muscular activity in this profession requires a high metabolic drive and produces a significant cardiorespiratory response.