In Silico Prediction of Estrogen Receptor Subtype Binding Affinity and Selectivity Using Statistical Methods and Molecular Docking with 2-Arylnaphthalenes and 2-Arylquinolines

Over the years development of selective estrogen receptor (ER) ligands has been of great concern to researchers involved in the chemistry and pharmacology of anticancer drugs, resulting in numerous synthesized selective ER subtype inhibitors. In this work, a data set of 82 ER ligands with ERα and ERβ inhibitory activities was built, and quantitative structure-activity relationship (QSAR) methods based on the two linear (multiple linear regression, MLR, partial least squares regression, PLSR) and a nonlinear statistical method (Bayesian regularized neural network, BRNN) were applied to investigate the potential relationship of molecular structural features related to the activity and selectivity of these ligands. For ERα and ERβ, the performances of the MLR and PLSR models are superior to the BRNN model, giving more reasonable statistical properties (ERα: for MLR, Rtr2 = 0.72, Qte2 = 0.63; for PLSR, Rtr2 = 0.92, Qte2 = 0.84. ERβ: for MLR, Rtr2 = 0.75, Qte2 = 0.75; for PLSR, Rtr2 = 0.98, Qte2 = 0.80). The MLR method is also more powerful than other two methods for generating the subtype selectivity models, resulting in Rtr2 = 0.74 and Qte2 = 0.80. In addition, the molecular docking method was also used to explore the possible binding modes of the ligands and a relationship between the 3D-binding modes and the 2D-molecular structural features of ligands was further explored. The results show that the binding affinity strength for both ERα and ERβ is more correlated with the atom fragment type, polarity, electronegativites and hydrophobicity. The substitutent in position 8 of the naphthalene or the quinoline plane and the space orientation of these two planes contribute the most to the subtype selectivity on the basis of similar hydrogen bond interactions between binding ligands and both ER subtypes. The QSAR models built together with the docking procedure should be of great advantage for screening and designing ER ligands with improved affinity and subtype selectivity property

The Development of New Concepts for Assessing Reproductive Toxicity Applicable to Large Scale Toxicological Programmes

Large scale toxicological testing programmes which are currently ongoing such as the new European chemical legislation REACH require the development of new integrated testing strategies rather than applying traditional testing schemes to thousands of chemicals. The current practice of requiring in vivo testing for every possible adverse effect endanger the success of these programmes due (i) to limited testing facilities and sufficient capacity of scientific/technical knowledge for reproductive toxicity; (ii) an unacceptable number of laboratory animals involved (iii) an intolerable number of chemicals classified as false positive. A key aspect of the implementation of new testing strategies is the determination of prevalence of reproductive toxicity in the universe of industrial chemicals. Prevalences are relevant in order to be aware on the expected rate of false classification during the toxicological testing and to implement appropriate measures for their avoidance. Furthermore, a detailed understanding on the subendpoints affected by reproductive toxicants and the underlying mechanisms will lead to more science based testing strategies integrating alternative methods without compromising the protection of consumers

Focal Spot, Summer 2002

https://digitalcommons.wustl.edu/focal_spot_archives/1091/thumbnail.jp

Data exploration and knowledge extraction: their application to the study of endocrine disrupting chemicals

Interest in computer-aided methods for investigating the biological field has increased significantly. One method is Quantitative Structure-Activity Relationships (QSAR), a valuable technique for predicting the effects of a substance from its chemical structure. A challenging application of QSAR is in characterizing the (bio)activity profiles of chemicals. Endocrine disrupters (EDs) are exogenous substances interfering with the function of the endocrine system and represent an interesting field of application for in silico methods. EDs targets include nuclear receptors, particularly effects mediated by the oestrogen receptor (ER). They are also mentioned as substances requiring a more detailed control and specific authorisation within REACH, the new European legislation on chemicals. QSAR represents a challenging method to approach data gap about EDs since REACH substantially boosted interest on computational chemistry to replace experimental testing. This work: aimed to explore the status, availability and reliability of non-testing methods applied to endocrine disruption via oestrogen receptors and eventually to propose new models easily exploitable in regulatory contexts. The work evaluated existing QSAR models present in literature to assess their validity on the basis of the OECD principles for QSAR validation. Different kinds of models have been analysed and they were externally validated with new data found in the literature. Furthermore, new QSAR binary classifiers have been developed using different data mining techniques (e.g.: classification trees, fuzzy logic, neural networks) based on a very large and heterogeneous dataset of chemical compounds. The focus was given to both binding (RBA) and transcriptional activity (RA) better to characterize the effects of EDs. A possible combination of the models was also explored. A very good accuracy was achieved for both RA and RBA (>85%). These models can be a valuable complement to in vivo and in vitro studies in the toxicological characterisation of chemical compounds

High-throughput simulation methods for protein-ligand docking

Mit dieser Arbeit haben wir versucht, einen Beitrag zum weiten Forschungsfeld der rechnerunterstützten Medikamentenentwicklung zu leisten. Wir versuchen mittels energetischer Kriterien potetielle Wirkstoffe zu identifizieren. Für jeden Liganden aus einer goßen Moleküldatenbank wird zuerst mittels stochastischer Simulationsmethoden die beste Protein-Ligandenkonformation ermittelt und damit dann die Affinität des Liganden zum Protein bestimmt. Zuerst wird die Zuverlässigkeit und die Genauigkeit unseres Ansatzes untersucht. Für ein Protein-Liganden Dockingsimulationsprogramm ist es wichtig, daß experimentell gemessene Bindungsorientierungen des Liganden zuverlässig reproduziert werden können; dies ist notwendig, um zuverlässig die Affinität eines Liganden zu bestimmen. In dieser Studie können wir zeigen, daß dies mit unserer Methode erfüllt ist. In einer weiteren Studie wird sich dem Thema der Proteinflexibilität zugewendet. Bindende Liganden können Konformationsänderungen der Proteinstruktur veranlassen. Besonders bei Protein-Liganden Dockingsimulationen stellen wir Nachteile fest, wenn die Proteinstruktur als unveränderliche drei-dimensionale Struktur angenommen wird. In unserem Ansatz werden die Proteinkonformationen des Proteins mittels Flexibilität in den Proteinseitenketten approximiert. In dieser Studie wird der Vorteil dieses Ansatzes nachgewiesen und aufgezeigt, daß auf diese Weise zuverlässiger und genauer gut-bindende Liganden erkannt werden können. In einer dritten Studie wird sich nun noch dem Problem gewidmet, daß häufig Proteine und Liganden mit den normalerweise verwendeten Methoden nur bis zu einem bestimmten Grade genau beschreiben lassen. Hier können wir zeigen, wie durch das Extrahieren von Parametern aus vorausgehenden quantenmechanischen Berechnungen die Genauigkeit der Affinitätsbestimmung erhöht werden kann. Für Protein-Liganden Dockingsimulationen konnten wir feststellen, daß es für diese Steigerung der Genauigkeit auch ausreicht, wenn nur das Protein mit solchen extrahierten Parametern beschrieben wird. Diese Methodik hat den Vorteil, daß langwierige quantenmechanischen Berechnungen nur einmal für das Protein und zwar nur zu Beginn der Simulationen mit großen Ligandendatenbanken durchgeführt werden müssen. Mit unserer Arbeit wurden also erfolgreich "high-throughput" Protein-Liganden Dockingsimulationsmethoden entwickelt. Wir haben die Zuverlässigkeit unseres Ansatzes, den Vorteil unseres flexiblen Proteinmodells aufgezeigt und desweiteren noch eine Methode vorgestellt, die eine höhere Genauigkeit zur Bestimmung der Affinität des Liganden erlaubt.With the work reported in this thesis, we aim to contribute to the field of computational drug discovery. We attempt to estimate the ligand affinity to a protein model by simulating the formation of protein-ligand complexes. In this approach, the affinity of a ligand to a protein is determined as the energetic difference between the energetically optimal protein-ligand conformation and the state in which protein and ligand are not interacting with each other. We show that our approach helps to identify good binding compounds in a large database of ligands. The structure of this thesis follows the chronology of our research efforts. We first started with testing the accuracy of our docking algorithm. To calculate binding energies in a good approximation, it is necessary to first determine a realistic protein-ligand conformation. In another study, we analyze the problem of protein flexibility and the shortcoming of using only one rigid protein structure for docking simulations. In large-scale database screens we compare the influence of rigid and flexible protein models with each other. We show that flexible protein models result in an increased reliability of the screen and in the identification of a higher number of good binding ligands. Receptor-ligand interactions are calculated using many approximations. In a further study we investigate, if the accuracy of binding energies could be improved by employing parameters obtained from quantum mechanical calculations. We show that by incorporating the results of quantum mechanical calculations for the receptor only, the overall accuracy of the whole simulation can be increased. This is an important result for high-throughput screening, because the time consuming quantum mechanical calculations can be done separately in advance. We have thus developed a high-throughput docking approach which allows us to identify good binding ligands in large databases. Including protein flexibility by allowing the side chains to alter their conformations results in a more realistic model of proteins. Applied to docking simulations of databases, our approach is less biased to the rigid, experimentally measured protein crystal structure which gives us the possibility to discover more diverse good binding ligands. In addition, the overall accuracy of our approach can be enhanced further by integrating quantum mechanical calculations into our description of the proteins