Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental contaminants known for their photoinduced toxicity. There are two mechanisms through which this may occur: photosensitization and photomodification. Photosensitization generally leads to the production of singlet oxygen, a reactive oxygen species (ROS), which is highly damaging to biological molecules. Photomodification of PAHs, usually via oxygenation, results in the formation of new compounds (oxyPAHs), and can occur under environmentally relevant levels of actinic radiation. <br /><br /> PAHs and oxyPAHs readily adsorb to the organic phase of particulate matter in the environment such as sediments. It is logical to conclude that sediment transport will also facilitate the transport of these contaminants, and it has been shown that in the course of transport, degradative processes evoke a change in the profile of the PAHs present. Sediment samples taken along a transect from Hamilton Harbour were fractionated, and analyzed using a 2D HPLC method. All sediments contained intact and modified PAHs, although a marked change was noted in the profile of compounds present in the samples, which differ in distance from shore. Fractions of sediment extract were tested for toxicity using a bacterial respiration assay. Toxicity was observed in fractions containing modified PAHs, and was similar to that of intact PAH-containing fractions. <br /><br /> Subsequently, the toxicities of 16 intact PAHs were assessed to <i>Daphnia magna</i> under two ultraviolet radiation (UV) conditions. The toxicity of intact PAHs generally increased in the presence of full spectrum simulated solar radiation (SSR), relative to visible light plus UVA only. To expand the existing data on the effects of PAH photoproducts to animals, fourteen oxyPAHs were also assayed with <i>D. magna</i>, most of which were highly toxic without further photomodification. The data presented highlight the effects of UV radiation on mediating PAH toxicity. The importance of the role of photomodification is also stressed, as several oxyPAHs were highly toxic to <i>D. magna</i>, a key bioindicator species in aquatic ecosystems. <br /><br /> A QSAR model previously developed for <i>Lemna gibba</i> showed that a photosensitization factor (PSF) and a photomodification factor (PMF) could be combined to describe toxicity. To determine whether it was predictive for <i>D. magna</i>, toxicity was assessed as both EC50 and ET50. As with <i>L. gibba</i> and <i>Vibrio fischeri</i>, neither the PSF nor the PMF alone correlated to D. magna toxicity. However, a PSF modified for <i>D. magna</i> did in fact exhibit correlation with toxicity, which was further improved when summed with a modified PMF. The greatest correlation was observed with EC50 toxicity data. This research provides further evidence that models that include factors for photosensitization and photomodification will likely be applicable across a broad range of species. To gain further knowledge of the roles that the variables contributing to the photosensitization and photomodification, a structural equation model was constructed based on the <i>D. magna</i> QSAR. This model accounted for a high amount of variance in six sets of toxicity data, as well as insight into the mechanisms of phototoxicity affecting different aquatic organisms.

The biodegradability and physicochemical properties of unsubstituted and methylated polycyclic aromatic hydrocarbons (PAHs) were investigated. The focus was on the development of models expressing the influence of molecular structure and properties on observed behavior. Linear free energy relationships (LFERs) were developed for the estimation of aqueous solubilities, octanol/water partition coefficients, and vapor pressures as functions of chromatographic retention time. LFERs were tested in the estimation of physicochemical properties for twenty methylated naphthalenes containing up to four methyl substituents. It was determined that LFERs can accurately estimate physicochemical properties for methylated naphthalenes. Twenty unsubstituted and methylated PAHs containing up to four aromatic rings were biodegraded individually by Sphingomonas paucimobilis strain EPA505, and Monod-type kinetic coefficients were estimated for each PAH using the integral method. Estimated extant kinetic parameters included the maximal specific biodegradation rate, the affinity coefficient, and the inhibition coefficient. The generic Andrews model adequately simulated kinetic data. The ability of PAHs to serve as sole energy and carbon sources was also evaluated. Quantitative structure-biodegradability relationships (QSBRs) were developed based on the estimates of the kinetic and growth parameters. A genetic algorithm was used for QSBR development. Statistical analysis and validation demonstrated the predictive value of the QSBRs. Spatial and topological molecular descriptors were essential in explaining biodegradability. Mechanistic interpretation of the kinetic data and the QSBRs provided evidence that simple or facilitated diffusion through the cell membranes is the rate-determining step in PAH biodegradation by strain EPA505. A kinetic experiment was conducted to investigate biodegradation of PAH mixtures by strain EPA505. The investigation focused on 2-methylphenanthrene, fluoranthene, and pyrene, and their mixtures. Integrated material balance equations describing different interaction types were fitted to the depletion data and evaluated on a statistical and probabilistic basis. Mixture degradation was most adequately described by a pure competitive interaction model with mutual substrate exclusivity, a fully predictive model utilizing parameters estimated in the sole-PAH experiments only. The models developed in this research provide insight into how molecular structure and properties influence physicochemical properties and biodegradability of PAHs. The models have considerable predictive value and could reduce the need for laboratory testing.

Ce travail s'inscrit dans un programme de recherche visant à synthétiser de nouveaux composés organiques et phosphorés possédant un rapport activité/toxicité convenable. Pour cela, nous avons réalisé la synthèse de nouveaux motifs N-substitués du benzothiazole et du thiadiazole tels que des thiols, aminothiols, acides thiosulfoniques et phosphorothioates. Tous ces composés ont été caractérisés physico-chimiquement par spectroscopie RMN (proton, carbone, phosphore, 2D), par spectrométrie de masse, analyse élémentaire et pour certains d'entre eux par diffraction des rayons X. L'activité de la plupart des composés a été évaluée par des tests in vitro. Les résultats expérimentaux ont été confirmés par des calculs théoriques de DFT visant à étudier le mécanisme de capture des radicaux libres par nos composés. D'autre part, une étude de relation structure activité (QSAR) a été réalisée. Les résultats nous ont permis d'élaborer un modèle permettant d'établir une relation structure-activité.

Désireux de mettre au point de nouvelles substances pouvant présenter une activité biologique potentielle au niveau du Système Nerveux Central, nous avons envisagé d'étudier les Relations Quantitatives Structure-Activité (QSAR) sur plusieurs familles de composés 5-HT1A. L'objectif était de prédire l'activité et la sélectivité biologique de nouveaux ligands 5-HT1A vis-à-vis de certains neurorécepteurs transmembranaires (5-HT1A, alpha1, alpha2, D2). Pour ce faire, 382 composés issus de différentes familles chimiques (indole, tétraline, chromane, thiochromane, pipérazine, etc...) ont été modélisés et superposés aux pharmacophores 5-HT1A. Nous avons ensuite, pour chaque molécule, calculé et comparé différents descripteurs moléculaires représentatifs de l'aspect électrostatique, lipophilique, stérique et topologique. Les différentes tables constituées des composés et des descripteurs ont ensuite été étudiées par des analyses statistiques comme l'analyse en composantes principales, l'analyse discriminante, la régression multiple et l'analyse Partial Least Square. A partir des meilleurs modèles statistiques, l'activité biologique de nouveaux ligands a été prédite. Après la synthèse et les tests pharmacologiques, ces prédictions se sont révélées précises (écart moyen de 1 unité de pIC50). En parallèle, une étude de dynamique moléculaire du récepteur sérotoninergique transmembranaire 5-HT1A a été entreprise afin de rendre compte de l'importance fonctionnelle de ces ligands au niveau des interactions électrostatiques, stériques et lipophiliques mises en jeu au sein du site actif du récepteur. Ces études sur un nombre limité de complexes ligand/récepteur ont aussi été menées afin de comprendre le rôle de certaines parties fonctionnelles de ces ligands, rôle mal défini jusqu'alors par les autres techniques. Nous avons aussi obtenu des informations qualitatives sur les interactions et la mobilité de certaines parties du récepteur, entre autres sur les hélices transmembranaires TMH5, 6 et 7.

The emergence of pathogens resistant to available drug therapies is a pressing global health problem. Antimicrobial peptides (AMPs) may potentially form new therapeutics to counter these pathogens. AMPs are key components in the mammalian innate immune system and are responsible for both direct killing and immunomodulatory effects in host defense against pathogenic organisms. This thesis describes computational methods for the identification of novel natural and synthetic AMPs. A bioinformatic resource was constructed for classification and discovery of gene- coded AMPs, consisting of a database of clustered known AMPs and a set of hidden Markov models (HMMs). One set of 146 clusters was based on the mature peptide sequence, and one set of 40 clusters was based on propeptide sequence. The bovine genome was analyzed using the AMPer resources, and 27 of the 34 known bovine AMPs were identified with high confidence and up to 69 AMPs were predicted to be novel peptides. One novel cathelicidin AMP was experimentally verified as up-regulated in response to infection in bovine intestinal tissue. A chemoinformatic analysis was performed to model the antibacterial activity of short synthetic peptides. Using high-throughput screening data for the activities of over 1400 peptides of diverse sequence, quantitative structure-activity relation (QSAR) models were created using artificial neural networks and physical characteristics of the peptide that included three-dimensional atomic structure. The models were used to predict the activity of a set of approximately 100,000 peptide sequence variants. After ranking the predicted activity, the models were shown to be very accurate. When 200 peptides were synthesized and screened using four levels of expected activity, 94% of the top 50 peptides expected to have the highest level of activity were found to be highly active. Several promising candidates were synthesized with high quality and tested against several multi- antibiotic-resistant pathogens including clinical strains of Pseudomonas aeruginosa, Staphylococcus aureus, Enterococcus faecalis and Escherichia coli. These peptides were found to be highly active against these pathogens as determined by minimal inhibitory concentration; this serves as independent confirmation of the effectiveness of high-throughput screening and in silico analysis for identifying peptide antibiotic drug leads.

Virtual screening is a fast, low cost method to identify potential small molecule therapeutics from large chemical databases for the vast amount of target proteins emerging from the life sciences and bioinformatics. In this work, we applied several conventional and newly developed virtual screening approaches to identify novel non-steroidal ligands for the human and zebrafish sex hormone binding globulin (SHBG). The ‘benchmark set of steroids’ is a set of steroids with known affinities for human SHBG that has been widely used for validation in the development of different virtual screening methods. We have updated this data set by including additional steroidal SHBG ligands and by modifying the predicted binding orientations of several benchmark steroids in the SHBG binding site based on the use of an improved docking protocol and information from recent crystallographic data. The new steroid binding orientations and the expanded version of the benchmark set was then used to create new in silico models which were applied in virtual screening to identify high-affinity non-steroidal human SHBG ligands from a large chemical database. Anthropogenic compounds with the capacity to interact with the steroid-binding site of SHBG pose health risks to humans and other vertebrates including fish. We constructed a homology model of SHBG from zebrafish and applied virtual screening to identify ligands for zebrafish SHBG from a set of 80 000 existing commercial substances, many of which can be exposed to the aquatic environment. Six hits from this in silico screen were tested experimentally for zebrafish SHBG binding and three of them, hexestrol, 4-tert-octylcatechol, dihydrobenzo(a)pyren-7(8H)-one demonstrated micromolar binding affinity for the zebrafish SHBG. These findings demonstrate the feasibility of using virtual screening to identify anthropogenic compounds that may disrupt or highjack functionally important protein:ligand interactions. Studies applying this new computational toxicology method could increase the awareness of hazards posed by existing commercial chemicals at relatively low cost.

No description available.

Drug discovery is a complicated and expensive process in the billion dollar range. One way of making the drug development process more efficient is better information handling, modelling and visualisation. The majority of todays drugs are small molecules, which interact with drug targets to cause an effect. Since the 1980s large amounts of compounds have been systematically tested by robots in so called high-throughput screening. Ligand-based drug discovery is based on modelling drug molecules. In the field known as Quantitative Structure–Activity Relationship (QSAR) molecules are described by molecular descriptors which are used for building mathematical models. Based on these models molecular properties can be predicted and using the molecular descriptors molecules can be compared for, e.g., similarity. Bioclipse is a workbench for the life sciences which provides ligand-based tools through a point and click interface.  The aims of this thesis were to research, and develop new or improved ligand-based methods and open source software, and to work towards making these tools available for users through the Bioclipse workbench. To this end, a series of molecular signature studies was done and various Bioclipse plugins were developed. An introduction to the field is provided in the thesis summary which is followed by five research papers. Paper I describes the Bioclipse 2 software and the Bioclipse scripting language. In Paper II the laboratory information system Brunn for supporting work with dose-response studies on microtiter plates is described. In Paper III the creation of a molecular fingerprint based on the molecular signature descriptor is presented and the new fingerprints are evaluated for target prediction and found to perform on par with industrial standard commercial molecular fingerprints. In Paper IV the effect of different parameter choices when using the signature fingerprint together with support vector machines (SVM) using the radial basis function (RBF) kernel is explored and reasonable default values are found. In Paper V the performance of SVM based QSAR using large datasets with the molecular signature descriptor is studied, and a QSAR model based on 1.2 million substances is created and made available from the Bioclipse workbench.

This research ranked the adsorbability of 115 emerging contaminants by granular activated carbon (GAC) from drinking water, mainly the organic chemicals identified on the Contaminant Candidate List 3 (CCL3), using classical and quantum quantitative structure activity relationships (QSAR). 80% of the investigated contaminants were classified as cost effectively treatable by GAC based on the models. A rapid small-scale column test (RSSCT) conducted with Lake Ontario water spiked with 8 selected emerging contaminants showed the modeling results were accurate. This research also tested the hypothesis that GAC exhaustion for geosmin and 2-methylisoborneol would be due entirely to natural organic matter, and would occur independently of the presence of these two compounds. RSSCT results confirmed this hypothesis. Mathematical modeling supported this observation by demonstrating that the ratio of the effluent concentration to the influent concentration of a trace organic contaminant is only dependent on the NOM loading state at any bed depth.

This research ranked the adsorbability of 115 emerging contaminants by granular activated carbon (GAC) from drinking water, mainly the organic chemicals identified on the Contaminant Candidate List 3 (CCL3), using classical and quantum quantitative structure activity relationships (QSAR). 80% of the investigated contaminants were classified as cost effectively treatable by GAC based on the models. A rapid small-scale column test (RSSCT) conducted with Lake Ontario water spiked with 8 selected emerging contaminants showed the modeling results were accurate. This research also tested the hypothesis that GAC exhaustion for geosmin and 2-methylisoborneol would be due entirely to natural organic matter, and would occur independently of the presence of these two compounds. RSSCT results confirmed this hypothesis. Mathematical modeling supported this observation by demonstrating that the ratio of the effluent concentration to the influent concentration of a trace organic contaminant is only dependent on the NOM loading state at any bed depth.