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Modeling the Interaction Space of Biological Macromolecules: A Proteochemometric Approach : Applications for Drug Discovery and DevelopmentKontijevskis, Aleksejs January 2008 (has links)
Molecular interactions lie at the heart of myriad biological processes. Knowledge of molecular recognition processes and the ability to model and predict interactions of any biological molecule to any chemical compound are the key for better understanding of cell functions and discovery of more efficacious medicines. This thesis presents contributions to the development of a novel chemo-bioinformatics approach called proteochemometrics; a general method for interaction space analysis of biological macromolecules and their ligands. In this work we explore proteochemometrics-based interaction models over broad groups of protein families, evaluate their validity and scope, and compare proteochemometrics to traditional modeling approaches. Through the proteochemometric analysis of large interaction data sets of multiple retroviral proteases from various viral species we investigate complex mechanisms of drug resistance in HIV-1 and discover general physicochemical determinants of substrate cleavage efficiency and binding in retroviral proteases. We further demonstrate how global proteochemometric models can be used for design of protease inhibitors with broad activity on drug-resistant viral mutants, for monitoring drug resistance mechanisms in the physicochemical sense and prediction of potential HIV-1 evolution trajectories. We provide novel insights into the complexity of HIV-1 protease specificity by constructing a generalized IF-THEN rule model based on bioinformatics analysis of the largest set of HIV-1 protease substrates and non-substrates. We discuss how proteochemometrics can be used to map recognition sites of entire protein families in great detail and demonstrate how it can incorporate target variability into drug discovery process. Finally, we assess the utility of the proteochemometric approach in evaluation of ADMET properties of drug candidates with a special focus on inhibition of cytochrome P450 enzymes and investigate application of the approach in the pharmacogenomics field.
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Nonionic surfactants : A multivariate studyUppgård, Lise-Lott January 2002 (has links)
In this thesis technical nonionic surfactants are studied using multivariate techniques. The surfactants studied were alkyl ethoxylates (AEOs) and alkyl polyglucosides (APGs). The aquatic toxicity of the surfactants towards two organisms, a shrimp and a rotifer, was examined. The specified effect was lethality, LC50, as indicated by immobilisation. In a comparative study, the LC50 values obtained were used to develop two different types of model. In the log P model the toxicity was correlated to log P alone, while in the multivariate model several physicochemical variables, including log P, were correlated to the toxicity. The multivariate model gave smaller prediction errors than the log P model. Further, the change in reactivity when a surfactant mixture was added to dissolving pulp under alkaline conditions was studied, using the amount of residual cellulose as a measure of the reactivity. Ten AEO/APG mixtures were tested, and the mixture with greatest potential was studied in more detail. An optimum in the amount of added surfactant was found that seems to coincide, according to surface tension measurements, with the CMC.
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Design and Synthesis of Novel Serotonin Receptor LigandsKlenc, Jeffrey D 18 August 2010 (has links)
Novel and potent ligands to the serotonin7 (5-HT7) receptor have been synthesized. The synthesized compounds include a set of substituted pyrimidines which show high affinity to the 5-HT7 receptor, synthesized by previously described methods [1,2] in high yield. Comparing the affinities of substituted pyrimidines to previously calculated models [3,4] yielded new hypotheses about the nature of interaction between the pyrimidine ligands and the 5-HT7 binding site. Several new series of compounds were synthesized by various methods to validate these hypotheses, including a conjugate addition to vinylpyrimidines [5]. These compounds include benzofurans, oximes, hydrazones, as well as a group of substituted piperazines. All series of compounds show affinity to the 5-HT7 receptor comparable to previously synthesized 5-HT7 ligands. Several of the synthesized ligands show affinity which exceeds that of currently available ligands. The synthesized compounds were evaluated quantitatively by calculating a three-dimensional quantitative structure-affinity relationship (3D-QSAR) for the 5-HT7 receptor. Evaluation of the calculated model validated qualitative assumptions about the data set as well as described regions of interaction in greater detail than previously available. These observations give further insight on the nature of ligand-binding site interactions with highly potent ligands such as 4-(3-furyl)-2-(N-methylpiperazino)pyrimidine which will lead to more potent 5-HT7 receptor ligands. Additionally, a model was calculated for affinity to the 5-HT2a receptor. Comparing this model to that calculated for affinity to the 5-HT7 receptor identified two regions which may be exploited in future sets of ligands to increase selectivity to the 5HT7 receptor.
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Computational Studies of HIV-1 Protease InhibitorsSchaal, Wesley January 2002 (has links)
Human Immunodeficiency Virus (HIV) is the causative agent of the pandemic disease Acquired Immune Deficiency Syndrome (AIDS). HIV acts to disrupt the immune system which makes the body susceptible to opportunistic infections. Untreated, AIDS is generally fatal. Twenty years of research by countless scientists around the world has led to the discovery and exploitation of several targets in the replication cycle of HIV. Many lives have been saved, prolonged and improved as a result of this massive effort. One particularly successful target has been the inhibition of HIV protease. In combination with the inhibition of HIV reverse transcriptase, protease inhibitors have helped to reduce viral loads and partially restore the immune system. Unfortunately, viral mutations leading to drug resistance and harmful side-effects of the current medicines have identified the need for new drugs to combat HIV. This study presents computational efforts to understand the interaction of inhibitors to HIV protease. The first part of this study has used molecular modelling and Comparative Molecular Field Analysis (CoMFA) to help explain the structure-active relationship of a novel series of protease inhibitors. The inhibitors are sulfamide derivatives structurally similar to the cyclic urea candidate drug mozenavir (DMP-450). The central ring of the sulfamides twists to adopt a nonsymmetrical binding mode distinct from that of the cyclic ureas. The energetics of this twist has been studied with ab initio calculations to develop improved empirical force field parameters for use in molecular modelling. The second part of this study has focused on an analysis of the association and dissociation kinetics of a broad collection of HIV protease inhibitors. Quantitative models have been derived using CoMFA which relate the dissociation rate back to the chemical structures. Efforts have also been made to improve the models by systematically varying the parameters used to generate them.
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Molecular quantum similarity in QSAR: applications in computer-aided molecular designGallegos Saliner, Ana 29 June 2004 (has links)
La present tesi està centrada en l'ús de la Teoria de Semblança Quàntica per a calcular descriptors moleculars. Aquests descriptors s'utilitzen com a paràmetres estructurals per a derivar correlacions entre l'estructura i la funció o activitat experimental per a un conjunt de compostos. Els estudis de Relacions Quantitatives Estructura-Activitat són d'especial interès per al disseny racional de molècules assistit per ordinador i, en particular, per al disseny de fàrmacs. Aquesta memòria consta de quatre parts diferenciades. En els dos primers blocs es revisen els fonaments de la teoria de semblança quàntica, així com l'aproximació topològica basada en la teoria de grafs. Ambdues teories es fan servir per a calcular els descriptors moleculars. En el segon bloc, s'ha de remarcar la programació i implementació de programari per a calcular els anomenats índexs topològics de semblança quàntica. La tercera secció detalla les bases de les Relacions Quantitatives Estructura-Activitat i, finalment, el darrer apartat recull els resultats d'aplicació obtinguts per a diferents sistemes biològics. / The present thesis is centred in the use of the Quantum Similarity Theory to calculate molecular descriptors. These molecular descriptors are used as structural parameters to derive correlations between the structure and the function or experimental activity for a set of compounds. Quantitative Structure-Activity Relationship studies are of special interest for the rational Computer-Aided Molecular Design and, in particular, for Computer-Aided Drug Design. The memory has been structured in four differenced parts. The two first blocks revise the foundations of quantum similarity theory, as well as the topological approximation, based in classical graph theory. These theories are used to calculate the molecular descriptors. In the second block, the programming and implementation of Topological Quantum Similarity Indices must be remarked. The third section details the basis for Quantitative Structure-Activity Relationships and, finally, the last section gathers the application results obtained for different biological systems.
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Experimental Designs at the Crossroads of Drug DiscoveryOlsson, Ing-Marie January 2006 (has links)
New techniques and approaches for organic synthesis, purification and biological testing are enabling pharmaceutical industries to produce and test increasing numbers of compounds every year. Surprisingly, this has not led to more new drugs reaching the market, prompting two questions – why is there not a better correlation between their efforts and output, and can it be improved? One possible way to make the drug discovery process more efficient is to ensure, at an early stage, that the tested compounds are diverse, representative and of high quality. In addition the biological evaluation systems have to be relevant and reliable. The diversity of the tested compounds could be ensured and the reliability of the biological assays improved by using Design Of Experiments (DOE) more frequently and effectively. However, DOE currently offers insufficient options for these purposes, so there is a need for new, tailor-made DOE strategies. The aim of the work underlying this thesis was to develop and evaluate DOE approaches for diverse compound selection and efficient assay optimisation. This resulted in the publication of two new DOE strategies; D-optimal Onion Design (DOOD) and Rectangular Experimental Designs for Multi-Unit Platforms (RED-MUP), both of which are extensions to established experimental designs. D-Optimal Onion Design (DOOD) is an extension to D-optimal design. The set of possible objects that could be selected is divided into layers and D-optimal selection is applied to each layer. DOOD enables model-based, but not model-dependent, selections in discrete spaces to be made, since the selections are not only based on the D-optimality criterion, but are also biased by the experimenter’s prior knowledge and specific needs. Hence, DOOD selections provide controlled diversity. Assay development and optimisation can be a major bottleneck restricting the progress of a project. Although DOE is a recognised tool for optimising experimental systems, there has been widespread unwillingness to use it for assay optimisation, mostly because of the difficulties involved in performing experiments according to designs in 96-, 384- and 1536- well formats. The RED-MUP framework combines classical experimental designs orthogonally onto rectangular experimental platforms, which facilitates the execution of DOE on these platforms and hence provides an efficient tool for assay optimisation. In combination, these two strategies can help uncovering the crossroads between biology and chemistry in drug discovery as well as lead to higher information content in the data received from biological evaluations, providing essential information for well-grounded decisions as to the future of the project. These two strategies can also help researchers identify the best routes to take at the crossroads linking biological and chemical elements of drug discovery programs.
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The MHC-glycopeptide-T cell interaction in collagen induced arthritis : a study using glycopeptides, isosteres and statistical molecular design in a mouse model for rheumatoid arthritisHolm, Lotta January 2006 (has links)
Rheumatoid arthritis (RA) is an autoimmune disease affecting approximately 1% of the population in the western world. It is characterised by a tissue specific attack of cartilage in peripheral joints. Collagen induced arthritis (CIA) is one of the most commonly used animal models for (RA), with similar symptoms and histopathology. CIA is induced by immunisation of mice with type II collagen (CII), and the immunodominant part was previously found to be located between residues 256-270. This thesis describes the interaction between the MHC molecule, glycopeptide antigens from CII and the T cells that is essential in development of CIA. The glycopeptide properties for binding to the mouse MHC molecule Aq have been studied, as well as interaction points in the glycopeptide that are critical for stimulation of a T-cell response. The thesis is based on five studies. In the first paper the minimal glycopeptide core, that is required for binding to the Aq molecule while still giving a full T cell response was determined. The second paper studied the roles of amino acid side-chains and a backbone amide bond as T-cell contact points. In the third paper the hydrogen bond donor-acceptor characteristics of the 4-OH galactose hydroxyl group of the glycopeptide was studied in detail. In the fourth paper we established a structure activity relationship (QSAR model) for (glyco)peptide binding to the Aq molecule. Finally, the stereochemical requirements for glycopeptide binding to the Aq molecule and for T-cell recognition was studied in the fifth paper. The study was performed using collagen glycopeptide analogues, which were synthesised on solid phase. Amide bond and hydroxyl group isosteres were introduced for study of hydrogen bond donor-acceptor characteristics. Statistical methods were used to design a representative peptide test set and in establishing a QSAR model. The results give a deeper understanding of the interactions involved in the ternary MHC-glycopeptide-T cell complex. This information contributes to research directed towards finding new treatments for RA.
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A multivariate approach to QSARHellberg, Sven January 1986 (has links)
Quantitative structure-activity relationships (OSAR) constitute empirical analogy models connecting chemical structure and biological activity. The analogy approach to QSAR assume that the factors important in the biological system also are contained in chemical model systems. The development of a QSAR can be divided into subproblems: 1. to quantify chemical structure in terms of latent variables expressing analogy, 2. to design test series of compounds, 3. to measure biological activity and 4. to construct a mathematical model connecting chemical structure and biological activity. In this thesis it is proposed that many possibly relevant descriptors should be considered simultaneously in order to efficiently capture the unknown factors inherent in the descriptors. The importance of multivariately and multipositionally varied test series is discussed. Multivariate projection methods such as PCA and PLS are shown to be appropriate far QSAR and to closely correspond to the analogy assumption. The multivariate analogy approach is applied to a beta- adrenergic agents, b haloalkanes, c halogenated ethyl methyl ethers and d four different families of peptides. / <p>Diss. (sammanfattning) Umeå : Umeå universitet, 1986, härtill 8 uppsatser</p> / digitalisering@umu
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Relations structure-activité pour le métabolisme et la toxicitéMuller, Christophe 24 January 2013 (has links) (PDF)
Prédire à l'avance quels composés seront toxiques chez l'homme ou non représente un réel challenge dans le monde pharmaceutique. En effet, les mécanismes à l'origine de la toxicité ne sont pas toujours bien connus, et à cela s'ajoute le fait qu'un composé peut devenir néfaste seulement après qu'il ait été métabolisé. Nous proposons ici une approche originale utilisant les graphes condensés de réactions afin de modéliser les réactions métaboliques et prédire le devenir des xénobiotiques dans l'organisme humain. Différentes formes de toxicité sont aussi prédites : la mutagénicité et l'hépatotoxicité. Pour cette seconde toxicité, l'approche utilisée est la première à notre connaissance à prédire avec succès les molécules toxiques décrites par des données autres que résultant d'observations in vivo.
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Computer-aided design of novel antithrombotic agents / Conception des nouveaux agents anti-thrombiques assistée par ordinateurKhristova, Tetiana 15 November 2013 (has links)
La thrombose est le plus important processus pathologique sous-jacent à de nombreuses maladies cardiovasculaires, qui sont responsables d’une mortalité élevée dans le monde entier. Dans cette thèse, la conception assistée par ordinateur de nouveaux agents antithrombotiques capables d’inhiber deux types de récepteurs situés à la surface des plaquettes a été appliquée. Le premier - αIIbβ3 - est responsable de l’interaction des plaquettes activées avec le fibrinogène pour former des caillots, tandis que le second – le thromboxane A2 – est responsable de l’activation des plaquettes par l’un des agonistes excrétés par les plaquettes activées. Afin d’atteindre cet objectif, différents types de modèles ont été développés en utilisant les informations expérimentales disponibles et la structure des complexes protéine-ligand, comprenant des modèles QSAR, des pharmacophores 3D basés sur la structure de la protéine ou du ligand, des pharmacophores 2D, des modèles basés sur la forme et sur le champ moléculaire. L’ensemble des modèles développés ont été utilisés en criblage virtuel. Cette étude a abouti sur la suggestion de nouveaux antagonistes potentiels des récepteurs αIIbβ3 et thromboxane A2. Les antagonistes de αIIbβ3 suggérés pouvant se lier soit à la forme ouverte soit à la forme fermée du récepteur ont été synthétisés et testés expérimentalement. L’expérience montre qu’ils font preuve d’une forte activité; de plus, certains des composés conçus théoriquement sont plus efficaces que le Tirofiban, qui est un médicament commercialisé. Les antagonistes recommandés du récepteur thromboxane A2 ont déjà été synthétisés mais les tests biologiques n’ont pas encore été complétés. / Thrombosis is the most important pathological process underlying many cardiovascular diseases, which are responsible for high mortality worldwide. In this theses the computer-aided design of new anti-thrombotic agents able to inhibit two types of receptors located on the surface of the platelets has been applied. The first one - αIIbβ3 - is responsible for the interaction of activated platelets with fibrinogen to form clots, whereas the second one - thromboxane A2 - is responsible for platelet activation by one of agonists excreted by activated platelets. To achieve this, different types of models have been developed using experimentally available information and structure of protein-ligand complexes. This concerns: QSAR models, structure-based and ligand-based 3D pharmacophore models, 2D pharmacophore models, shape-based and molecular field-based models. The ensemble of the developed models were used in virtual screening. This study resulted in suggestion of new potential antagonists of αIIbβ3 and thromboxane A2 receptors. Suggested antagonists of αIIbβ3 able to bind either open or closed form of the receptor have been synthesized and tested experimentally. Experiments show that they display high activity; moreover some of theoretically designed compounds are more efficient than Tirofiban – the commercialized drug molecule. The recommended antagonists of thromboxane A2 receptor have been already synthesized but biological tests have not been completed yet.
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