Predicting Biological Degradation and Toxicity of Steroidal Estrogens

This study was to construct a model to predict a variety of biological transformations of Ethinylestradiol (EE2) using electronic theory and to analyze the estrogenic potential of EE2 and its metabolites. As a secondary goal, Frontier Electron Density (FED) theory was applied to the natural steroidal estrogens, estrone (E1), estradiol (E2) and estriol (E3) to determine if similar initiating reactions could be expected. Electron density profiles were calculated for EE2 metabolites to determine possible metabolic pathways up to the cleavage of the first ring. The pathways predicted in this study assume that enzymes commonly found in wastewater treatment systems will be available to attack EE2 and each metabolite. Predictive pathways were generated for EE2 based on the electron density and well established degradation rules. A number of metabolites were shown to be consistent with FED theory. There are many methods available for effectively calculating the electron density of a given molecule. Calculations were carried out on the Pittsburgh Supercomputer (PSC) using the computational chemistry software Gaussian 03. Two molecular orbital theories available for use in Gaussian 03 were used and results compared to determine if the level of theory significantly affected the accuracy of the electron density calculations. In the beginning of this study only one theory was used but after studying the available theories in more detail I implemented a theory that was shown to be more accurate in literature. Using this information and well established degradation rules, metabolic pathways leading up to the first ring cleavage were predicted. Experimentally measured metabolites appear in the predicted pathways. In order to evaluate the environmental impacts of steroidal estrogens and their subsequent metabolites the estrogenic potential is calculated using chemaxon software. The estrogenic potential was estimated for EE2 and each of its metabolites both predicted and experimental as well as E1, E2 and E3 and known experimentally measured metabolites that are similar to EE2. In all cases the estrogenic potential of the metabolites indicate that they have a lower toxicity than the parent compounds but may still retain estrogenic potential after biotransformation

Artificial Intelligence-Based Drug Design and Discovery

The drug discovery process from hit-to-lead has been a challenging task that requires simultaneously optimizing numerous factors from maximizing compound activity, efficacy to minimizing toxicity and adverse reactions. Recently, the advance of artificial intelligence technique enables drugs to be efficiently purposed in silico prior to chemical synthesis and experimental evaluation. In this chapter, we present fundamental concepts of artificial intelligence and their application in drug design and discovery. The emphasis will be on machine learning and deep learning, which demonstrated extensive utility in many branches of computer-aided drug discovery including de novo drug design, QSAR (Quantitative Structure–Activity Relationship) analysis, drug repurposing and chemical space visualization. We will demonstrate how artificial intelligence techniques can be leveraged for developing chemoinformatics pipelines and presented with real-world case studies and practical applications in drug design and discovery. Finally, we will discuss limitations and future direction to guide this rapidly evolving field

Photodegradation of selected endocrine and pharmaceutically active compounds under environmentally relevant conditions - processes and products

2014 Spring.To view the abstract, please see the full text of the document

Applications and Improvements in the Molecular Modeling of Protein and Ligand Interactions

Understanding protein and ligand interactions is fundamental to treat disease and avoid toxicity in biological organisms. Molecular modeling is a helpful but imperfect tool used in computer-aided toxicology and drug discovery. In this work, molecular docking and structural informatics have been integrated with other modeling methods and physical experiments to better understand and improve predictions for protein and ligand interactions. Results presented as part of this research include: 1.) an application of single-protein docking for an intermediate state structure, specifically, modeling an intermediate state structure of alpha-1-antitrypsin and using the resulting model to virtually screen for chemical inhibitors that can treat alpha-1-antitrypsin deficiency, 2.) an application of multi-protein docking and metabolism prediction, specifically, modeling the cytochrome P450 metabolism and estrogen receptor activity of an environmental pollutant (PCB-30), and 3.) providing evidence to support the inclusion of anion-pi interactions in molecular modeling by demonstrating the biological roles of anion-pi interactions in stabilizing protein and protein-ligand structures. This work has direct applications for mitigating disease and toxicity, but it also demonstrates useful ways of integrating computational and experimental data to improve upon modeling protein and ligand interactions

2,3-cis-2R,3R-(−)-epiafzelechin-3-O-p-coumarate, a novel flavan-3-ol isolated from Fallopia convolvulus seed, is an estrogen receptor agonist in human cell lines

BACKGROUND: The plant genus Fallopia is well-known in Chinese traditional medicine and includes many species that contain bioactive compounds, namely phytoestrogens. Consumption of phytoestrogens may be linked to decreased incidence of breast and prostate cancers therefore discovery of novel phytoestrogens and novel sources of phytoestrogens is of interest. Although phytoestrogen content has been analyzed in the rhizomes of various Fallopia sp., seeds of a Fallopia sp. have never been examined for phytoestrogen presence. METHODS: Analytical chemistry techniques were used with guidance from an in vitro estrogen receptor bioassay (a stably transfected human ovarian carcinoma cell line) to isolate and identify estrogenic components from seeds of Fallopia convolvulus. A transiently transfected human breast carcinoma cell line was used to characterize the biological activity of the isolated compounds on estrogen receptors (ER) α and β. RESULTS: Two compounds, emodin and the novel flavan-3-ol, (−)-epiafzelechin-3-O-p-coumarate (rhodoeosein), were identified to be responsible for estrogenic activity of F. convolvulus seed extract. Absolute stereochemistry of rhodoeosein was determined by 1 and 2D NMR, optical rotation and circular dichroism. Emodin was identified by HPLC/DAD, LC/MS/MS, and FT/ICR-MS. When characterizing the ER specificity in biological activity of rhodoeosein and emodin, rhodoeosein was able to exhibit a four-fold greater relative estrogenic potency (REP) in breast cells transiently-transfected with ERβ as compared to those transfected with ERα, and emodin exhibited a six-fold greater REP in ERβ-transfected breast cells. Cell type-specific differences were observed with rhodoeosein but not emodin; rhodoeosein produced superinduction of reporter gene activity in the human ovarian cell line (> 400% of maximum estradiol [E2] induction) but not in the breast cell line. CONCLUSION: This study is the first to characterize the novel flavan-3-ol compound, rhodoeosein, and its ability to induce estrogenic activity in human cell lines. Rhodoeosein and emodin may have potential therapeutic applications as natural products activating ERβ, and further characterization of rhodoeosein is necessary to evaluate its selectivity as a cell type-specific ER agonist