Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

Identification of Endocrine Disrupting Chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause Estrogen Receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous Quantitative Structure-Activity Relationships (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R2=0.71, STL R2=0.73). For ERβ binding affinity, MTL models were significantly more predictive (R2=0.53, p<0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation

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

DEVELOPMENT OF AN INTEGRATED IN SILICO STRATEGY FOR THE RISK ASSESSMENT OF CHEMICALS AND THEIR MIXTURES ON DIFFERENT TOXICOLOGICAL OUTCOMES.

Daily, we are exposed to a mixture of multiple chemicals via food intake, inhalation and dermal contact. The risk for health that may result from this depends on how the effects of different chemicals in the mixture combine, and whether there is any synergism or antagonism between them. The number of different combinations of chemicals in mixtures is infinite and an efficient test strategy for mixtures is lacking. Furthermore, there is social pressure to reduce animal testing, which is the current practice in safety testing of chemicals. In this context, computational biochemistry and, more in general, bioinformatics meets all the requirements, and provides the foundation for further in vitro or in vivo studies. Aim of this PhD thesis is the development of an in silico workflow able to prioritize and discriminate chemicals that act as endocrine active substances (EAS), interfere with the retinoic acid pathway during embryo development and/or may cause liver toxicity. From the observation of the molecular initiating event to the description of the adverse outcome pathway, both ligand- and structure-based approaches were integrated with systems biology. Within this framework, (Q)SAR and molecular docking results were mixed into a majority consensus score to rank chemicals and low-mode molecular dynamic simulations were used to study their intrinsic activity, with respect to a specific nuclear receptor. Moreover, a computational approach based on both the transition state and the density functional theories was used to try discriminating a subset of chemicals as inhibitors or substrates of particular enzymes involved in the retinoic acid pathway, computing also their binding free energy values. This information was also included both in the pharmaco-dynamics (PD) and in the physiological based pharmaco-kinetics (PBPK) models. This in silico pipeline, besides being faster, has economic and ethical advantages, reducing both the research costs and the number of involved animals, in agreement with the \u201c3R\u201d principles (Reduction, Refinement and Replacement)

Breast Cancer Progression and Phytoestrogen Interactions with Estrogen Receptors

Breast cancer is one of the most common diseases affecting women and approximately 1.3 million females are diagnosed each year with this disease worldwide. Breast cancer is a multi-factorial disease and it is difficult to predict or control the physiopathology, to date one of the major risk factors alongside the patient’s genetic background is life time exposure to estrogen. Understanding the estrogen receptor (ER) has been a milestone in elucidating breast cancer biology, leading to advances in disease management. Alongside this, evidence from epidemiological studies suggests that dietary consumption of phytoestrogens may modulate disease progression. This study hypothesises that the interaction between some phytoestrogens (present in the pre-diagnosis diet or in the new diet adopted by breast cancer patients) and specific ER isoforms displayed in breast tumours influences the action of synthetic and endogenous estrogen in breast cancer cells. This study aimed to understand the interaction between estrogen, hormone drugs and phytoestrogens on the ER. In silico modelling of the ER focused on the wild type isoforms ERα and ERβ and different ligands (SWISS MODEL and docked through AutoDock Vina). Subsequently, isoforms of ERα and ERβ and different ligands (E1, E2, E3, PE, Tamoxifen, ICI 182,780) were modelled and tested by docking against the same set of ligands (E1, E2, E3, PE, Tamoxifen, ICI 182,780). The system described here highlighted the main amino acid residues of the LBD of ERα and ERβ along with ligand interactions for both agonists and antagonists, described in previous X-ray crystallography experiments. All of the phytoestrogens studied using AutoDock Vina interacted with the hormone binding site of both ERα and ERβ, due the phenolic ring of the studied structure which favoured the interaction with the hydrophobic environment of LBD amino acids. All of the dietary phytoestrogens showed lower binding affinity (<9.1 Kcal/mol) compared with estradiol (-10.6 Kcal/mol) in all the isoforms and isotypes studied, suggesting that phytoestrogens should not displace estradiol from the LBD, however it remains unclear if PE can act as an agonist compound in the ER pathways. Also, some phytoestrogens appeared to have greater affinity to the ERα and ERβ than Tamoxifen (antagonist models), but it is uncertain as to whether the resulting structure will interfere with subsequent interactions. Further laboratory experiments will be necessary to understand the impact of the PE in the ERs structure and the respective role in the ER pathway. The data from this computer modelling approach has provided an insight into the interactions between endogenous estrogens, drugs, phytoestrogens and ER. The in silico studies generated a system that recapitulated data obtained by other research groups (experimentally) and will be of value as a screening tool for further studies of new drugs and exogenous estrogens and their potential role in ER-induced breast cancer pathophysiology

Development of a Nicotinic Acetylcholine Receptor nAChR α7 Binding Activity Prediction Model

Despite the well-known adverse health effects associated with tobacco use, addiction to nicotine found in tobacco products causes difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the physiological targets of nicotine and facilitate addiction to tobacco products. The nAChR-α7 subtype plays an important role in addiction; therefore, predicting the binding activity of tobacco constituents to nAChR-α7 is an important component for assessing addictive potential of tobacco constituents. We developed an α7 binding activity prediction model based on a large training data set of 843 chemicals with human α7 binding activity data extracted from PubChem and ChEMBL. The model was tested using 1215 chemicals with rat α7 binding activity data from the same databases. Based on the competitive docking results, the docking scores were partitioned to the key residues that play important roles in the receptor−ligand binding. A decision forest was used to train the human α7 binding activity prediction model based on the partition of docking scores. Five-fold cross validations were conducted to estimate the performance of the decision forest models. The developed model was used to predict the potential human α7 binding activity for 5275 tobacco constituents. The human α7 binding activity data for 84 of the 5275 tobacco constituents were experimentally measured to confirm and empirically validate the prediction results. The prediction accuracy, sensitivity, and specificity were 64.3, 40.0, and 81.6%, respectively. The developed prediction model of human α7 may be a useful tool for high-throughput screening of potential addictive tobacco constituents

Development of Virtual Screening and In Silico Biomarker Identification Model for Pharmaceutical Agents

Ph.DDOCTOR OF PHILOSOPH

Evaluation of in silico and in vitro screening methods for characterising endocrine disrupting chemical hazards

Anthropogenic activities have drastically altered chemical exposure, with traces of synthetic chemicals detected ubiquitously in the environment. Many of these chemicals are thought to perturb endocrine function, leading to declines in reproductive health and fertility, and increases in the incidence of cancer, metabolic disorders and diabetes. There are over 90 million unique chemicals registered under the Chemical Abstracts Service (CAS), of which only 308,000 were subject to inventory and/or regulation, in September 2013. However, as a specific aim of the EU REACH regulations, the UK is obliged to reduce the chemical safety initiatives reliance on in vivo apical endpoints, promoting the development and validation of alternative mechanistic methods. The human health cost of endocrine disrupting chemical (EDC) exposure in the EU, has been estimated at €31 billion per annum. In light of the EU incentives, this study aims to evaluate current in silico and in vitro tools for EDC screening and hazard characterisation; testing the hypothesis that in silico virtual screening accurately predicts in vitro mechanistic assays. Nuclear receptor binding interactions are the current focus of in silico and in vitro tools to predict EDC mechanisms. To the author’s knowledge, no single study has quantitatively assessed the relationship between in silico nuclear receptor binding and in vitro mechanistic assays, in a comprehensive manner. Tripos ® SYBYL software was used to develop 3D-molecular models of nuclear receptor binding domains. The ligand binding pockets of estrogen (ERα and ERβ), androgen (AR), progesterone (PR) and peroxisome proliferator activated (PPARγ) receptors were successfully modelled from X-ray crystal structures. A database of putative-EDC ligands (n= 378), were computationally ‘docked’ to the pseudo-molecular targets, as a virtual screen for nuclear receptor activity. Relative to in vitro assays, the in silico screen demonstrated a sensitivity of 94.5%. The SYBYL Surflex-Dock method surpassed the OECD Toolbox ER-Profiler, DfW and binary classification models, in correctly identifying endocrine active substances (EAS). Aiming to evaluate the current in vitro tools for endocrine MoA, standardised ERα transactivation (HeLa9903), stably transfected AR transactivation (HeLa4-11) assays in addition to novel transiently transfected reporter gene assays, predicted the mechanism and potency of test substances prioritised from the in silico results (n = 10 potential-EDCs and 10 hormone controls). In conclusion, in silico SYBYL molecular modelling and Surflex-Dock virtual screening sensitively predicted the binding of ERα/β, AR, PR and PPARγ potential EDCs, and was identified as a potentially useful regulatory tool, to support EAS hazard identification