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Cai, Xiaoshu. "DEVELOPMENT OF COMPUTATIONAL APPROACH FOR DRUG DISCOVERY." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1465403528.
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Gage, Zoe O. "Interferon, viruses and drug discovery." Thesis, University of St Andrews, 2017. http://hdl.handle.net/10023/10127.
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The interferon (IFN) response is a crucial component of cellular innate immunity, vital for controlling virus infections. Dysregulation of the IFN response however can lead to serious medical conditions including autoimmune disorders. Modulators of IFN induction and signalling could be used to treat these diseases and as tools to further understand the IFN response and viral infections. We have developed cell-based assays to identify modulators of IFN induction and signalling, based on A549 cell lines where a GFP gene is under the control of the IFN-β promoter (A549/pr(IFN-β).GFP) and the ISRE containing MxA promoter (A549/pr(ISRE).GFP) respectively. The assays were optimized, miniaturized and validated as suitable for HTS by achieving Z' Factor scores >0.6. A diversity screen of 15,667 compounds using the IFN induction reporter assay identified 2 hit compounds (StA-IFN-1 and StA-IFN-4) that were validated as specifically inhibiting IFNβ induction. Characterisation of these molecules demonstrated that StA-IFN-4 potently acts at, or upstream, of IRF3 phosphorylation. We successfully expanded this HTS platform to target viral interferon antagonists acting upon IFN-signalling. An additional assay was developed where the A549/pr(ISRE).GFP.RBV-P reporter cell line constitutively expresses the Rabies virus phosphoprotein. A compound inhibiting viral protein function will restore GFP expression. The assay was successfully optimized for HTS and used in an in-house screen. We further expanded this assay by placing the expression of RBV-P under the control of an inducible promoter. This demonstrates a convenient approach for assay development and potentiates the targeting of a variety of viral IFN antagonists for the identification of compounds with the potential to develop a novel class of antiviral drugs.
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Cereto, Massagué Adrià. "Development of tools for in silico drug discovery." Doctoral thesis, Universitat Rovira i Virgili, 2017. http://hdl.handle.net/10803/454678.
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El cribratge virtual és un mètode quimioinformàtic que consisteix en cribrar molècules bioactives de grans bases de dades de molècules petites. Això permet als investigadors d’estalviar-se el cost de provar experimentalment cents o milers de compostos candidats, reduïnt-ne el nombre fins a quantitats manejables. Per a la validació dels mètodes de cribratge virtual calen biblioteques de molècules cimbell. El programari DecoyFinder fou desenvolupat com a aplicació gràfica de fàcil ús per a la construcció de biblioteques de molècules cimbell, i fou posteriorment ampliat amb les troballes de recerca posterior sobre la construcció i rendiment de biblioteques de molècules cimbell.
El Protein Data Bank (PDB) és molt útil perquè proporciona estructures tridimensionals per a complexos proteïna-lligand, i per tant, informació sobre com interactuen. Pels mètodes de cribratge virtual que en depenen, n’és extremadament important la seva fiabilitat. El VHELIBS fou desenvolupat com a eina per a inspeccionar i identificar, fàcilment i intuitiva, les estructures fiables del PDB, basant-se en com de bo n’és l’encaix amb els seus corresponents mapes de densitat electrònica.
Mentre que el cribratge virtual prova de trobar noves molècules bioactives per determinades dianes, l’enfoc invers també s’empra: arran d’una molècula, cercar-ne dianes amb activitat biològica no documentada. Aquest cribratge invers és conegut en anglès com a “in silico target fishing”, o pesca de dianes “in silico”, i és especialment útil a l’àmbit de la reutilització de fàrmacs En començar aquesta tesi, no hi havia cap plataforma de “target fishing” de lliure accés, i tot i que durant els anys se n’han desenvolupat algunes, en tots els casos la seva predicció de bioactivitat és qualitativa. Per això es desenvolupà una plataforma pròpia de “target fishing” de lliure accés, amb la implementació d’un nou mètode que proporciona la primera predicció quantitativa de bioactivitat per aquest tipus de plataforma.El cribado virtual es un método quimioinformático que consiste en la criba de moléculas bioactivas de grandes bases de datos de moléculas pequeñas. Esto permite a los investigadores ahorrarse el coste de probar experimentalmente cientos o miles de compuestos candidatos, reduciéndolos hasta cantidades manejables. Para la validación de los métodos de cribado virtual hacen falta bibliotecas de moléculas señuelo. El software DecoyFinder fue desarrollado como aplicación gráfica de fácil uso para la construcción de bibliotecas de moléculas señuelo, y fue posteriormente ampliado con los hallazgos de investigación posterior sobre la construcción i rendimiento de bibliotecas de moléculas señuelo. El Protein Data Bank (PDB) es muy útil porque proporciona estructuras tridimensionales para complejos proteina-ligando, y por tanto, información sobre como interactúan. Para los métodos de cribado virtual que dependen de ellas, es extremadamente importante su fiabilidad. VHELIBS fue desarrollado como herramienta para inspeccionar e identificar, fácil e intuitivamente, las estructuras fiables del PDB, basándose en como de bueno es su encaje con sus correspondientes mapas de densidad electrónica. Mientras que el cribado virtual intenta encontrar nuevas moléculas bioactivas para determinadas dianas, el enfoque inverso también se utiliza: a partir de una molécula, buscar dianas donde presente actividad biológica no documentada. Este cribado inverso es conocido en inglés como “in silico target fishing”, o pesca de dianas “in silico”, y es especialmente útil en el ámbito de la reutilización de fármacos. Al comenzar esta tesis, no había ninguna plataforma de “target fishing” de libre acceso, y aunque durante los años se han desarrollado algunas, en todos los casos su predicción de bioactividad es cualitativa. Por eso se desarrolló una plataforma propia de “target fishing” de libre acceso, con la implementación de un nuevo método que proporciona la primera predicción cuantitativa de bioactividad para este tipo de plataforma.
Virtual screening is a cheminformatics method that consists of screening large small-molecule databases for bioactive molecules. This enables the researcher to avoid the cost of experimentally testing hundreds or thousands of compounds by reducing the number of candidate molecules to be tested to manageable numbers. For their validation, virtual screening approaches need decoy molecule libraries. DecoyFinder was developed as an easy to use graphical application for decoy library building, and later updated after some research into decoy library building and their performance when used for 2D similarity approaches. The Protein Data Bank (PDB) is very useful because it provides 3D structures for protein-ligand complexes and, therefore, information on how certain ligands bind and interact with their targets. For virtual screening apporaches relying on these structures, it is of the utmost importance that the data available on the PDB for the ligand and its binding site are reliable. VHELIBS was developed as a tool to easily and intuitively inspect and identify reliable PDB structures based on the goodness of fitting between ligands and binding sites and their corresponding electron density map. While virtual screening aims to find new bioactive molecules for certain targets, the opposite approach is also used: starting from a given molecule, to search for a biological target for which it presents previously undocumented bioactivity. This reverse screening is known as in silico or computational target fishing or reverse pharmacognosy, and it is specially useful for drug repurposing or repositioning. When this thesis was started, there were no freely available target fishing platforms, but some have been developed during the years. However, they are qualitative in the nature of their activity prediction, and thus we set out to develop a freely accessible target fishing web service implementing a novel method which provides the first quantitative activity prediction: Anglerfish.
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Acoca, Stephane. "In silico methods in drug discovery and development." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=110376.
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Computational drug design methods have become increasingly invaluable in the drug discovery and development process. Throughout this thesis will be described the development and application of methods that are used at every stage of the drug discovery and development pipeline. In Chapter 2 will take a look at the use computational methods towards the understanding and development of two novel Bcl-2 inhibitors, Obatoclax and ABT-737, being developed for the treatment of Cancer. The study proposes certain mechanisms through which ABT-737 displays selectivity towards certain targets within the Bcl-2 family. Additionally, we propose a binding mode for Obatoclax which is in accordance with experimental data. The following Chapter addresses the use of virtual screening for the identification of novel lead compounds. Trypanosoma brucei RNA Editing Ligase 1 was chosen as the target for the development of treatments against Trypanosoma infections and C35, a potent novel inhibitor of the enzyme, was identified. Furthermore, our research shows that the action of C35 extends to inhibition of several critical enzyme activities required for the RNA editing process as well as compromising the integrity of the multiprotein complex which carries it out. The following Chapter takes a look at the use of mass spectrometry data in order to expedite discovery of bioactive compounds in natural products. We developed an algorithm which analyses MS/MS data in order to derive the Molecular Formula of the compound. The novel algorithm obtained a 95% success rate on a test set of 91 compounds. The last Chapter of the thesis explores the use of molecular dynamics to generate a conformational ensemble of targets for virtual screening. Conformational ensembles were generated for a target test set taken from the Directory for Useful Decoys. The results showed that molecular dynamics-based conformational ensembles provided remarkable improvements on 2 of the targets tested due to the enhanced capacity to properly dock compounds in otherwise restricted structures. The last Chapter of the thesis is a general discussion on the work of the thesis and a proposal on how all can be integrated within the drug discovery and development pipeline.Les méthodes the modélisation sont devenues un outil inestimable dans le processus de découverte et de développement de nouveaux médicaments. Au cours de cette thèse va être décrit le développement et l'application de méthodes utilisés à chaque stage de la découverte et du développement de produits pharmaceutiques. Le Chapitre 2 est un aperçu sur l'utilisation de méthodes computationnelles vers le développement de deux nouveaux inhibiteurs des protéines Bcl-2, Obatoclax et ABT-737, en développement pour le traitement du Cancer. L'étude propose certains mécanismes d'ABT-737 qui expliquent ca sélectivité envers les membres de la famille Bcl-2. De plus, nous proposons un mécanisme d'attachement pour Obatoclax qui conforme aux données expérimentales. Le Chapitre suivant adresse l'utilisation du dépistage virtuel pour l'identification de nouvelles molécules mère. La Ligase de l'Edition d'ARN du Trypanosoma brucei a été choisie comme cible pour le développement de traitements contre des infections dû au Trypanosome et C35 a été identifié comme nouvel inhibiteur de l'enzyme. En outre, notre recherche démontre que l'action de C35 s'étends a l'inhibition de plusieurs enzymes nécessaires pour le mécanisme d'édition de l'ARN en plus de compromettre l'intégrité du complexe multi-protéinique qui l'effectue. Le Chapitre suivant prends regard a l'utilisation de donnes dérivant de la spectrométrie de masse pour but d'accélérer la découverte de molécules bioactives venant de sources naturelles. Nous avons développé un algorithme qui analyse les données MS/MS pour but de dériver la formule moléculaire du composé. Le nouvel algorithme a obtenu un taux de succès s'élevant à 95% sur un ensemble test de 91 molécules. Le dernier Chapitre de la thèse explore l'utilisation de simulations de dynamique moléculaire pour générer en ensemble conformationel de protéines cible pour son utilisation dans le dépistage virtuel. Les ensembles conformationel ont étés généré pour une série test obtenu d'un répertoire attitré 'Directory for Useful Decoys'. Les résultats démontrent que les ensembles conformationel dérivés de la dynamique moléculaire ont apporté des améliorations remarquables sur deux des cibles testées dû à une capacité accrue de placement approprié des molécules dans un site qui est autrement très restreint. Le dernier Chapitre de cette thèse est une discussion générale sur le travail accomplie et une proposition sur la manière dont tous les éléments sont intégrer dans un protocole de découverte et de développement de produits pharmaceutiques.
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Hoffman, Benjamin. "The Genetics of Cancer in Pharmacological Drug Development." Diss., Temple University Libraries, 2011. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/212455.
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Molecular and Cellular PhysiologyPh.D.
The field of cancer therapeutic development has been dominated by two research and discovery paradigms, the cytotoxicity-based or phenotype driven strategy and the target-based rational approach. This thesis describes the standardization of novel assays used in both approaches and the discoveries made using these processes. Rational drug design or the target-based approach to discovering novel anti-cancer agents requires a basic understanding of the oncogenic signals that induce uncontrolled cellular proliferation. c-MET is a proto-oncogene, linked to a number of different cancers, that encodes a receptor tyrosine kinase. As an oncogene, c-MET has been shown to transform cells in the laboratory setting and is dysregulated in number of malignancies. Thus, we sought to discover a small molecule inhibitor of c-MET kinase activity by screening a novel library of small molecules. In the second part of this dissertation, we describe the standardization of a high-throughput assay to identify putative c-MET inhibitors and the results of our screening attempt. Cytotoxicity-based screening is another validated approach that is used to discover anti-cancer agents. As a parallel program to our c-MET discovery effort, we designed a high-throughput cytotoxicity assay to identify a novel small molecule with high cytotoxic activity towards tumor cells. The result of this screen was the identification of ON015640, a novel anti-cancer therapeutic with tubulin-depolymerizing activity. Throughout the course of this project, we tried to discern the advantages and disadvantages of the two predominant paradigms in cancer therapeutic research. Both strategies require careful assay design and an acute understanding of the molecular and genetic underpinnings of cancer. While it is clear that structure-based rational drug design has its merits and its success stories, it has become increasingly clear that seeking out a desired biological effect may serve as a more effective staring point when dealing with cancers for which no clear oncogene addiction phenotype has been observed.
Temple University--Theses
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Schreiber, Kimberly C. M. "Assay development for use in drug discovery against Bovine Trichomoniasis." Scholarly Commons, 2007. https://scholarlycommons.pacific.edu/uop_etds/650.
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Bovine trichomoniasis is a venereal disease that affects cattle. The causative agent of this disease is Tritrichomonas foetus, a flagellated protozoan. There is no current FDA approved treatment for this disease. The purpose of this study was to develop new compound screening assays that will make the discovery of new compounds faster and more accurate. The CellTiter-Glo Luminescent Cell Viability Assay, a high throughput screening (HTS) assay from Promega, was found to be as affective at measuring cytotoxicity as traditional assaying techniques. For the first time. preen florescent protein. a reporter gene used in cell viability assavs was successfully transformed into T. foetus for use in HTS systems. This study also identified new compounds that can potentially be used as new treatments for this disease.
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Miller, Lisa Margaret. "The development of small molecule inhibitors for fibrosis drug discovery." Thesis, University of Strathclyde, 2016. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=27922.
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Fibrotic diseases can be attributed to approximately 45% of deaths within western developed countries. This category of disease can affect nearly every tissue in the body, predominantly liver, kidney, and lung. The severity of fibrotic diseases is widely recognised but currently there is no accepted effective disease modifying treatment. There have been a number of potential drug targets identified in recent years, including the enzyme autotaxin (ATX) and the RGD integrins, which are known to play a key role in the pathogenesis. In collaboration with GlaxoSmithKline, the projects detailed in this report were aimed to develop small molecule inhibitors with drug like physicochemical properties for fibrosis drug discovery. Chapter 1 focusses on the secreted enzyme ATX, which is responsible for the hydrolysis of lysophosphatidylcholine (LPC) to the bioactive lysophosphatidic acid (LPA) and choline. The ATX-LPA signalling pathway is implicated in cell survival, migration, and proliferation; thus, the inhibition of ATX is a recognised therapeutic target for a number of diseases including fibrotic diseases, cancer, and inflammation, amongst others. Many of the developed synthetic inhibitors for ATX have resembled the lipid chemotype of the native ligand; however, a small number of inhibitors have been described that deviate from this common scaffold. Herein, Chapter 1 details the structure-activity relationship (SAR) exploration of a previously reported small molecule ATX inhibitor through the design, synthesis, and biological evaluation of aseries of analogues. Furthermore, using enzyme kinetics studies it is shown that analogues of this chemotype are noncompetitive inhibitors, and using a crystal structure with ATX the discrete binding mode was confirmed. This work has provided valuable insight into the binding of this chemotype, which could aid the design of novel ATX inhibitors with non-lipid-like scaffolds. Chapter 2 describes a lead-optimisation project targeting the RGD subfamily of the integrin receptors. The RGD integrins are recognised therapeutic targets for thrombosis, fibrosis, and cancer, amongst others. Current inhibitors are designed to mimic the tripeptide sequence of the natural ligands (arginine-glycine-aspartic acid); however, the RGD-mimetic antagonists for one particular RGD integrin (αIIbβ3) have been shown to cause partial agonism, leading to the opposite pharmacological effect. The challenge of obtaining oral activity and synthetic tractability with RGD-mimetic molecules, along with the issues relating to pharmacology, has left integrintherapeutics in need of a new strategy. Recently, a new generation of inhibitor has emerged that lacks the RGD-mimetic. The work detailed herein aimed to build on this emerging area, with the design, synthesis, and biological evaluation of novel small molecule inhibitors targeting the αvβ3 integrin. These compounds are shown to be accessed via synthetically divergent routes, allowing for the quick exploration of adiverse set of potential lead compounds. Initial efforts led to the identification offour promising lead-like inhibitors with pIC50 values ranging from 4.1-5.5 for the target integrin αvβ3. Unfortunately, the initial hit compound, that the subsequent compound design stemmed from, was later determined to be a false positive, and as a result work on the project ceased. Thus, Chapter 2 details a project that was misled due to false positive assay results.
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Hatherley, Rowan. "Structural bioinformatics studies and tool development related to drug discovery." Thesis, Rhodes University, 2016. http://hdl.handle.net/10962/d1020021.
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This thesis is divided into two distinct sections which can be combined under the broad umbrella of structural bioinformatics studies related to drug discovery. The first section involves the establishment of an online South African natural products database. Natural products (NPs) are chemical entities synthesised in nature and are unrivalled in their structural complexity, chemical diversity, and biological specificity, which has long made them crucial to the drug discovery process. South Africa is rich in both plant and marine biodiversity and a great deal of research has gone into isolating compounds from organisms found in this country. However, there is no official database containing this information, making it difficult to access for research purposes. This information was extracted manually from literature to create a database of South African natural products. In order to make the information accessible to the general research community, a website, named “SANCDB”, was built to enable compounds to be quickly and easily searched for and downloaded in a number of different chemical formats. The content of the database was assessed and compared to other established natural product databases. Currently, SANCDB is the only database of natural products in Africa with an online interface. The second section of the thesis was aimed at performing structural characterisation of proteins with the potential to be targeted for antimalarial drug therapy. This looked specifically at 1) The interactions between an exported heat shock protein (Hsp) from Plasmodium falciparum (P. falciparum), PfHsp70-x and various host and exported parasite J proteins, as well as 2) The interface between PfHsp90 and the heat shock organising protein (PfHop). The PfHsp70-x:J protein study provided additional insight into how these two proteins potentially interact. Analysis of the PfHsp90:PfHop also provided a structural insight into the interaction interface between these two proteins and identified residues that could be targeted due to their contribution to the stability of the Hsp90:Hop binding complex and differences between parasite and human proteins. These studies inspired the development of a homology modelling tool, which can be used to assist researchers with homology modelling, while providing them with step-by-step control over the entire process. This thesis presents the establishment of a South African NP database and the development of a homology modelling tool, inspired by protein structural studies. When combined, these two applications have the potential to contribute greatly towards in silico drug discovery research.
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Yamaura, Kei. "Novel methods for drug discovery and development using ligand-directed chemistry." 京都大学 (Kyoto University), 2016. http://hdl.handle.net/2433/217177.
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Pilger, Jens. "Development and application of NMR methods for challenges in drug discovery." Doctoral thesis, Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2013. http://hdl.handle.net/11858/00-1735-0000-0015-C6E8-4.
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Aldhumani, Ali Hamed. "Pharmacophore Model Development: Targeting Noncoding RNA for Antibacterial/Antiviral Drug Discovery." Ohio University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1610705872573225.
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Grace, Christopher Philip. "Detection and exploitation of expression QTL in drug discovery and development." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:7b174e64-d17f-4e2c-b366-065684bfd813.
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Expression quantitative trait loci (eQTLs) are genetic markers associated with transcription of Ribonucleic Acid (RNA). eQTLs are detected using association analysis to detect correlations between RNA expression data (microarray or RNA-SEQ) and the genotypes of individuals within a study. Trans-ethnic meta-analysis can increase power to detect genetic variants for eQTLs and improve fine-mapping resolution because of differential patterns of linkage disequilibrium (LD) between diverse populations. Lymphoblastoid cell lines (LCLs) from samples in the Phase II and III HapMap populations have been used to detect cis eQTLs using association analysis followed by meta-analysis. Phase III HapMap samples have also been imputed using the 1000 Genomes March 2012 "all ancestries" panel. The goals of this thesis are to perform meta-analysis on multi-ethnic association summary statistics in order to: Increase the power to detect eQTLs, leverage differences in LD between ancestry groups to fine map eQTL variants and investigate and characterize heterogeneity in allelic effect sizes on expression between diverse populations. In addition to this, eQTLs identified are used to perform integration with signals from genome-wide association studies (GWAS) of complex human traits. A pipeline has been developed where eSNPs from the eQTL datasets are integrated with disease SNPs (dSNPs) from the NHGRI GWAS catalog using reciprocal conditional analysis to determine whether eSNP and dSNP tag or are the same causal variant. Also, eQTLs which are also "absorption, distribution, metabolism, and excretion" (ADME) genes are studied in more detail, specifically looking for heterogeneity and enrichment in this dataset. The analysis shows that combining association analysis summary statistics using meta-analysis leads to an increase in power to detect eQTLs. Differences in LD between ancestry groups can be used to improve fine mapping resolution, as measured by "credible sets" of variants most likely to drive the eQTL signal, when all ancestry groups are combined. Considerable heterogeneity between ancestry groups has been detected, much of which is due to differing LD between tag SNP and causal variants across ancestry groups. Furthermore, the GWAS integration has led to the identification of several dSNP â eSNP pairs for disease such as Ulcerative Colitis, Inflammatory Bowel Disease, Bechet's Disease, Sarcoidosis, Crohn's Disease, Graveâs Disease and Primary Biliary Cirrhosis, and have provided potential novel insights of genes through which these disease association signals are mediated. Several eQTLs for genes within the ADME dataset have also been identified some of which have significant heterogeneity.
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Pevzner, Yuri. "Development and application of web-based open source drug discovery platforms." Scholar Commons, 2015. https://scholarcommons.usf.edu/etd/5550.
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Computational modeling approaches have lately been earning their place as viable tools in drug discovery. Research efforts more often include computational component and the usage of the scientific software is commonplace at more stages of the drug discovery pipeline. However, as software takes on more responsibility and the computational methods grow more involved, the gap grows between research entities that have the means to maintain the necessary computational infrastructure and those that lack the technical expertise or financial means to obtain and include computational component in their scientific efforts. To fill this gap and to meet the need of many, mainly academic, labs numerous community contributions collectively known as open source projects play an increasingly important role. This work describes design, implementation and application of a set of drug discovery workflows based on the CHARMMing (CHARMM interface and graphics) web-server. The protocols described herein include docking, virtual target screening, de novo drug design, SAR/QSAR modeling as well as chemical education. The performance of the newly developed workflows is evaluated by applying them to a number of scientific problems that include reproducibility of crystal poses of small molecules in protein-ligand systems, identification of potential targets of a library of natural compounds as well as elucidating molecular targets of a vitamin. The results of these inquiries show that protocols developed as part of this effort perform comparably to commercial products, are able to produce results consistent with the experimental data and can substantially enrich the research efforts of labs with otherwise little or no computational component.
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Stelzhammer, Viktoria. "Major depressive disorder : molecular profiling to aid drug target discovery." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.607830.
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Álvarez, García Daniel. "Protein solvation preferences: applications to drug discovery." Doctoral thesis, Universitat de Barcelona, 2014. http://hdl.handle.net/10803/285451.
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Computer-aided drug design is a key player in current drug discovery projects. Structure-based computational approaches use the target structural information to suggest potentially active and safe drugs. However, the process is far from trivial and novel methodologies are continuously sought to address two main factors usually simplified and overlooked: Target flexibility and the effect and structure of water molecules at the binding site.
As demonstrated by different NMR and crystallography experiments, small organic solvents (e.g. ethanol, isopropanol, acetonitrile) are able to identify binding sites and provide clues for rational drug design. MDmix is a simulation-based method that exploits this natural behavior in silico. By using small organic molecules and water mixtures, each one with a distinct chemical nature, key interaction spots are identified on the protein surface allowing the identification and characterization of binding sites for hit discovery and lead optimization.
The work presented in this thesis is divided in two main publications: In the first one, the effect of target flexibility was investigated to establish some guidelines on how to treat this important factor during the simulations. We found that flexibility is essential for correctly identifying induced binding sites but might lead to uninterpretable results when large conformational changes occur. Soft restraints applied during the simulation are suggested as a way to obtain reproducible results and still characterize high affinity interaction sites (hot spots) with mild errors on the energy estimates.
In the second publication, the use of solvent mixtures for the identification of experimentally known pharmacophores was evaluated in two test systems for which many inhibitors are known (e.g. heat shock protein 90 and HIV protease 1). The explicit treatment of water molecules provides interaction maps which identify the most favorable interactions in the binding site with unprecedented accuracy when compared to classical molecular interaction potentials. Moreover, we demonstrate how the interaction maps obtained for the water molecules accompanying the small organic solvents are useful to identify non-displaceable waters. Both the solvent interaction maps and the water interaction maps are extremely useful information for the identification of novel active molecules and for the optimization of potency for already active ones.
Finally, a software package is presented that aims at facilitating the use of the methodology and at helping in adopting it to everyday drug design projects. A final chapter treats ongoing and future research where method improvements and practical uses in real examples are discussed.
MDmix being a simulation-based method, the target flexibility and the explicit treatment of the solvent provide significant advantages over traditional approaches for binding site finding and characterization. This novel approach, which is applicable to previously unmet targets and binding sites, offers a new alternative in the challenging process of drug design.El diseño de fármacos asistido por ordenador es actualmente un actor fundamental en el proceso de descubrimiento de nuevos fármacos. Las aproximaciones basadas en estructura usan la información estructural de la Diana terapéutica para proponer moléculas activas y seguras. Sin embargo, el proceso dista de ser sencillo y nuevas metodologías están continuamente siendo investigadas para solventar las limitaciones actuales, siendo la flexibilidad de la diana y el tratamiento y la estructura del agua en la cavidad, dos factores usualmente obviados o simplificados. Como ha sido demostrado por varios experimentos de NMR y cristalografía, moléculas pequeñas de solventes orgánicos (p.e. etanol, acetamida o acetonitrilo), son capaces de identificar sitios de unión y proporcionan pistas para el diseño racional de nuevas moléculas bioactivas. MDmix es un método basado en simulación molecular que explota dicho fenómeno in silico. Usando mezclas de moléculas orgánicas pequeñas y agua, cada una con propiedades químicas diferentes, se identifican mapas energéticos de interacción sobre la superficie de la diana. Esta información nos permite identificar sitios de unión para ligandos y caracterizar dicha interacción para guiar el proceso de identificación de hits y la optimización de cabezas de serie. El trabajo presentado en esta tesis se puede dividir en dos publicaciones principales: En la primera, el efecto de la flexibilidad de la diana es estudiado para establecer unas guías de actuación a la hora de simular el sistema. Encontramos que la flexibilidad es fundamental a la hora de identificar cavidades inducidas o con alto grado de flexibilidad pero, a la vez, la interpretación de los resultados es mucho más compleja cuando hay cambios conformacionales. Por otra banda, aplicando restricciones suaves a la movilidad de los átomos, se gana reproducibilidad en los resultados y los errores en la estimación energética son mínimos. En la segunda publicación, se estudió el uso de diferentes mezclas de solventes para la identificación de farmacóforos experimentales en dos sistemas test (heat shock protein 90 y HIV proteasa 1). El tratamiento explícito del agua proporciona mapas energéticos capaces de identificar correctamente los puntos de interacción más favorables con una precisión sin precedentes cuando se compara con otros métodos. Además, demostramos como los mapas energéticos obtenidos para las moléculas de agua son capaces de discernir entre aguas desplazables y no desplazables por un potencial ligando. La información extraída de dichos mapas puede ser de alta utilidad para guiar la identificación de nuevas moléculas activas y para la optimización de la potencia de ligandos ya identificados. Finalmente, se presenta un programa de código abierto escrito en python cuyo objetivo es facilitar el uso de la metodología así como su adopción en cualquier proyecto de diseño de fármacos. En el capítulo final se discuten posibles mejoras y aplicaciones prácticas del método en proyectos actualmente en investigación y direcciones futuras a seguir. MDmix, siendo un método basado en simulación molecular, permite incorporar la flexibilidad de la diana y tratar explícitamente el efecto del solvente. Por ello, ofrece ventajas significativas sobre aproximaciones tradicionales en la identificación de sitios de unión y su caracterización. Siendo aplicable sobre cualquier diana, aún sin conocimiento previo, ofrece una nueva alternativa en el siempre desafiante proceso del diseño de fármacos.
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Mohiddin, Syed Basha. "Development of novel unsupervised and supervised informatics methods for drug discovery applications." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1138385657.
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Hara, Takuji. "Innovation in the pharmaceutical industry : the process of drug discovery and development /." Cheltenham [u.a.] : Elgar, 2003. http://www.gbv.de/dms/bs/toc/357041208.pdf.
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Balfer, Jenny [Verfasser]. "Development and Interpretation of Machine Learning Models for Drug Discovery / Jenny Balfer." Bonn : Universitäts- und Landesbibliothek Bonn, 2015. http://d-nb.info/1080561374/34.
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Mohiddin, Syed B. "Development of novel unsupervised and supervised informatics methods for drug discovery applications." The Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=osu1138385657.
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Schreyer, Adrian Michael. "Characterisation of protein-ligand interactions and their application to drug discovery." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609324.
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Gómez, Tamayo Jose Carlos. "Development and application of computational techniques to drug discovery and structure-function relationships." Doctoral thesis, Universitat Autònoma de Barcelona, 2016. http://hdl.handle.net/10803/400278.
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Esta tesis doctoral presenta los resultados de la aplicación de varias técnicas computacionales al complejo campo del desarrollo de fármacos, particularmente en GPCRs, una de las familias más importantes de proteínas de membrana.
La tesis está organizada en las siguientes secciones: introducción, métodos, objetivos, resultados y conclusiones.
Introducción: incluye los fundamentos de biología molecular de los receptores de membrana acoplados a proteínas G.; clasificación, estructura, farmacología, señalización y activación.
Métodos: describe las técnicas computaciones usadas en la tesis.
Objetivos: describe los objetivos propuestos en el desarrollo de la tesis.
Resultados: En esta sección se presentan los resultados obtenidos. En primer lugar se contextualice todo el trabajo hecho, se listan las contribuciones y se exponen resumidamente los diferentes resultados obtenidos. La siguientes subsecciones describen el trabajo realizado en los diferentes proyectos de manera detallada.
Proyectos:
LigandFinder
Esta aplicación web proporciona una interfaz de fácil uso para realizar “virtual screening” rápido para encontrar nuevos compuestos similares a un set de compuestos de estructura conocida. Explora el espacio químico de una base de datos con más de 20 millones de compuestos. Hasta donde llega nuestro conocimiento, LigandFinder es la primera aplicación web de “virtual screening” capaz de usar varios ligandos como input.
Estudio de los aminoácidos con azufre
En este trabajo se estudian las interacciones de los aminoácidos con azufre en las proteínas de membrana. Las interacciones de estos aminoácidos entre ellos y con otros aminoácidos se extraen de una base de datos con estructuras refinadas, se agrupan, y se calcula la energía de interacción de las estructuras representativas usando cálculos químico cuánticos de alto nivel.
GPCR-SAS
Esta aplicación web se aprovecha de la similaridad estructural de las regiones transmembrana de la familia de las GPCRs para realizar cálculos estadísticos de las posiciones de secuencia o los motivos dentro de las hélices transmembrana. Es posible realizar estos cálculos en las clases A, B, C y F. Los cálculos estadísticos disponibles incluyen el cálculo de la conservación de una posición/posiciones/rango de posiciones, entropía, coevolución (covarianza) y correlación. Adicionalmente, hay la posibilidad de obtener una representación bidimensional de la conservación (snakeplot).
La entrada extracelular otorga selectividad a los antogonistas de 5-HT7 con comportamiento antidepresivo en vivo.
Este trabajo resalta la importancia de la poco conservada parte extracelular de los receptores de serotonina en la selectividad de sus ligandos. Se describe el modo de unión de un antagonista selectivo de 5-HT7 con comportamiento antidepresivo.This thesis present the results of various computational techniques applied to the complex world of drug development with a particular focus to its application to one of the most important membrane protein family, the GPCRs. The thesis is organized in the following sections: introduction, methods, objectives, results and conclusion. Introduction: covers the fundamentals of G-protein coupled receptors molecular biology; classification, structure, pharmacology, signaling and activation. Methods: describes the computational techniques used in the thesis. Results: In this section, the results obtained are presented. A first paragraph contextualizes all the work done, describing its objectives, stating the contribution of the author; moreover a brief introduction of each part of the results paragraphs. The following subsections contain a detailed explanation of all projects done in this thesis. Projects: LigandFinder This web application provides a user-friendly way to perform fast virtual screening to find new compounds similar to a set of compounds of known structure. It explores the chemical space of a database with more than 20M compounds. To our knowledge, LigandFinder is the first virtual screening web application able to use several ligands as input. Sulfur-containing amino acids study In this work the interactions of sulfur-containing amino acids (cysteine and methionine) in membrane proteins are studied. A refined crystal structures database is screened in order to find interactions of sulfur-containing residues with themselves and other residues. These interactions are clustered, and the interaction energy of representative structures is calculated through high-level quantum chemical calculations. GPCR-SAS This web application takes advantage of the structural similarity among GPCRs’ transmembrane regions to perform statistical analysis of sequence positions or motifs within the transmembrane helices of GPCR A, B, C, and F classes. GPCRSAS provides different types of analysis such as position/positions/range of positions conservation, entropy, co-evolutionary (co-variance) and correlation analysis. Additionally, there is the possibility of drawing a snakeplot representation with conservation information. The extracellular entrance provides selectivity to 5-HT7 receptor antagonists with antidepressant –like behavior in vivo This work highlights the importance of the low conserved extracellular part of the serotonin receptors in ligand selectivity. The binding mode of a selective 5HT7 receptor antagonist with antidepressant activity is described, opening the way for a new generation of antidepressant drugs.
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Foguet, Coll Carles. "Development of model-driven approaches for metabolic flux analysis and anticancer drug discovery." Doctoral thesis, Universitat de Barcelona, 2019. http://hdl.handle.net/10803/668644.
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Metabolism is a hallmark of life and underlies most biological processes in both health and disease. For instance, dysregulation of liver metabolism underlies multifactorial disorders such as diabetes or obesity. Similarly, cancer progression involves a reprogramming of metabolism to support unchecked proliferation, metastatic spread and other facets of the cancer phenotype. Hence, the study of metabolism is of great biomedical interest.
The metabolic phenotype emerges from the complex interactions of metabolites, enzymes, and the signaling cascades regulating their expression and thus must be studied following a holistic approach. With this aim, Systems Biology formulates the interactions between the molecular components of metabolism as a set of mathematical expressions, termed metabolic models, and uses them as a framework to integrate multiple layers of data (e.g., transcriptomics, proteomics and metabolomics) and simulate the emergent metabolic phenotype. The Systems Biology toolbox for the analysis of metabolism consists of several complementary model-based approaches, each with its strengths and limitations. For instance, constraint-based modeling can predict flux distributions at a genome-scale, whereas kinetic modeling and 13C metabolic flux analysis (13C MFA) can more accurately model central carbon metabolism.
As part of this Ph.D. thesis, we have expanded this toolbox through the development of new model-based approaches for computing both detailed metabolic maps of central carbon metabolism and genome-scale flux maps. With this aim, we developed HepatoDyn, a highly detailed kinetic model of hepatocyte metabolism capable of dynamic 13C MFA and used it to characterize the negative effects of fructose in hepatic metabolic function. Similarly, we also developed Iso2Flux, a novel steady-state 13C MFA software, and parsimonious 13C MFA, a new 13C MFA algorithm that can integrate transcriptomics to trace flux through large metabolic networks. Even more, we developed r2MTA a constraint-based modeling algorithm to robustly identify the optimal interventions to induce a transition towards a therapeutically desirable metabolic state. Finally, we also developed a workflow for integrating transcriptomics, metabolomics, gene dependencies, and 13C MFA to predict genome-scale flux maps.
Furthermore, we apply the systems biology toolbox, using both newly developed and existing tools, to the genome-scale analysis of the molecular drivers underlying cancer stem cells and metastasis in prostate and colorectal cancer, respectively. We identify putative therapeutic interventions against both phenotypes paving the way for a new generation of anticancer drugs.
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Mezzanotte, Laura <1982>. "Bioanalytical applications of multicolour bioluminescence imaging: new tools for drug discovery and development." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2011. http://amsdottorato.unibo.it/3536/.
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The subject of this thesis is multicolour bioluminescence analysis and how it can provide new tools for drug discovery and development.The mechanism of color tuning in bioluminescent reactions is not fully understood yet but it is object of intense research and several hypothesis have been generated. In the past decade key residues of the active site of the enzyme or in the surface surrounding the active site have been identified as responsible of different color emission. Anyway since bioluminescence reaction is strictly dependent from the interaction between the enzyme and its substrate D-luciferin, modification of the substrate can lead to a different emission spectrum too. In the recent years firefly luciferase and other luciferases underwent mutagenesis in order to obtain mutants with different emission characteristics. Thanks to these new discoveries in the bioluminescence field multicolour luciferases can be nowadays employed in bioanalysis for assay developments and imaging purposes. The use of multicolor bioluminescent enzymes expanded the potential of a range of application in vitro and in vivo. Multiple analysis and more information can be obtained from the same analytical session saving cost and time. This thesis focuses on several application of multicolour bioluminescence for high-throughput screening and in vivo imaging. Multicolor luciferases can be employed as new tools for drug discovery and developments and some examples are provided in the different chapters. New red codon optimized luciferase have been demonstrated to be improved tools for bioluminescence imaging in small animal and the possibility to combine red and green luciferases for BLI has been achieved even if some aspects of the methodology remain challenging and need further improvement. In vivo Bioluminescence imaging has known a rapid progress since its first application no more than 15 years ago. It is becoming an indispensable tool in pharmacological research. At the same time the development of more sensitive and implemented microscopes and low-light imager for a better visualization and quantification of multicolor signals would boost the research and the discoveries in life sciences in general and in drug discovery and development in particular.
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Chen, Jonathan Jun Feng. "Data Mining/Machine Learning Techniques for Drug Discovery: Computational and Experimental Pipeline Development." University of Akron / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=akron1524661027035591.
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Hendry, Adam. "Xenopus laevis as a chemical genetic screening tool for drug discovery and development." Thesis, University of East Anglia, 2014. https://ueaeprints.uea.ac.uk/49595/.
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In this thesis we explore the applicability of the X.laevis chemical genetic screening model towards drug discovery and drug development. The NCI diversity set II compound library was screened to identify abnormal pigmentation generating phenotypes that may have therapeutic application towards the treatment of melanoma cancer. 13 hit compounds identified were shown to have significantly lower IC50’s in the A375 melanoma cell line when compared to two control cell lines. Using the structural data of compounds screened (combined with the phenotypic data generated by the X.laevis screen), a report in which targets were predicted for each phenotypic category is described. Of the 10 targets predicted to generate an abnormal melanophore migration phenotype, six presented abnormal pigmentation phenotypes by compound antagonists. Two of these targets had no known previous link towards melanoma cancer. Many of the identified targets were also predicted to be targeted by nine out of 13 of the identified NCI compounds in the library screen. Thus, through a combination of forward chemical genetic screening, appropriate cell based assays and chemoinformatical analysis we have developed an efficient and effective screening strategy for the rapid identification of hit compounds that are likely to be acting through either well known or novel targets that may have possible implications towards the treatment of melanoma cancer. To assess the applicability of the X.laevis model towards drug development, in collaboration with AstraZeneca we designed a renal function toxicity assay. Renal toxicity is a serious concern in the pharmaceutical industry, being responsible for 7% of preclinical compound dropouts. I developed a biochemical assay in which renal function would be monitored by quantfying the concentration of ammonia excreted by embryos into media. A decrease in ammonia detected in the presence of nephrotoxic compounds was hypothesised to iii represent a decrease in renal function, and therefore indicate toxicity. Despite promising preliminary experiments, the original salicylic acid ammonia assay detection method was inhibited by the presence of the compound solvant DMSO. A second assay (the glutamate dehydrogenase assay (GDH)) was trialled which could not detect a change in renal function in response to nephrotoxic compounds when compared to the vehicle control. In its current form, the X.laevis renal function assay is not capable of identifying nephrotoxic compounds and so further work is required.
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Myers, Samuel Harry. "Development of novel receptor tyrosine kinase inhibitors by a chemocentric approach." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28769.
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In recent years, there has been a major movement in the pharmaceutical industry towards the development of molecules that selectivity inhibit a previously-validated specific target. This is referred to as target-based drug discovery. It was hoped that adopting this approach would usher in a new golden age of drug discovery. However, this has not been the case, with issues arising such as the target’s mechanism of action being poorly understood, with it not playing the expected role in the disease progression, or feedback resistance mechanisms causing the target to lose its role in the disease. In contrast to this, in the past 20 years it has been argued that developing drugs in a target-agnostic way and screening them against an expressed phenotype i.e. phenotypic drug discovery, has been more successful, despite fewer programs being run in the manner. The AXL kinase is a receptor tyrosine kinase (RTK) and a member of the TAM family, along with MER and TYRO3. AXL has long been associated with numerous types of cancer. Having been first discovered in 1991 in acute myeloid leukaemia (AML), it has gone on to be more associated with advanced solid tumours such as brain, breast, and lung, with the trend being that increased AXL correlates with a poorer prognosis for the patient. Upon the activation of AXL by the vitamin K ligand GAS6, a series of downstream pathways are activated that go on to encourage cell survival, proliferation, and migration. In addition to this, AXL has been shown to be involved in crosstalk with other kinase pathways, resulting in AXL expression being associated with chemoresistance and survival mechanisms. Despite the promising outlook for AXL inhibitors, to date only one selective AXL inhibitor, BGB324 (formally R428) has entered clinical trials, with selective AXL inhibitors being difficult to develop due to a lack of a crystal structure or a reliable homology model. To address the aforementioned issues that target-based approaches can suffer from, and due to AXL lacking a crystal structure, the work in this thesis utilised a pragmatic drug design method that started from ligands/existing scaffolds known to inhibit the target from the literature (publications, clinical trials and patents). A series of small libraries were prepared and then tested against a selected phenotype e.g. cell viability, in at least two cell types: one that expressed the target (e.g. AXL) and one that did not. Hits were optimised for potency against the desired phenotype. The compounds then went through target deconvolution (kinase screening) to confirm the target of the inhibitors. Employing this approach, we initially synthesised two small libraries of potential AXL inhibitors. The potency of these compounds was tested using cell-based phenotypic assays, by evaluating cell viability in both native and chemo-resistant breast cancer cells. These libraries were optimised through focused combinatorial synthesis and phenotypic screening, to yield a small collection of antiproliferative hits. These hits were then profiled against a panel of twelve select kinases. The first library, while giving some important structural information, did not inhibit the kinases screened in a meaningful manner. However, the second library gave several potent compounds, inhibiting AXL, FLT3, and RET, with one compound being selective for AXL. The leads from this series were optimised further, through SAR studies, gaining important structural information in order to improve potency and selectivity of the compounds. The flexibility of the phenotypic cell-based approach allowed the pursuit of FLT3 inhibitors, resulting in the synthesis of one of the most potent FLT3 inhibitors synthesised to date.
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Chee, Xavier. "Rational development of new inhibitors of lipoteichoic acid synthase." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/269766.
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Staphyloccocus aureus is an opportunisitic pathogen that causes soft skin and tissue infections (SSTI) such as endocarditis, osteomyelitis and meningitis. In recent years, the re-emergence of antibiotic-resistant S. aureus such as MRSA presents a formidable challenge for infection management worldwide. Amidst this global epidemic of antimicrobial resistance, several research efforts have turned their focus towards exploiting the cell-wall biosynthesis pathway for novel anti-bacterial targets. Recently, the lipoteichoic acid (LTA) biosynthesis pathway has emerged as a potential anti-bacterial target. LTA is an anionic polymer found on the cell envelope of Gram-positive bacteria. It comprises of repeating units of glycerol-phosphate (GroP) and is important for bacterial cell physiology and virulence. For example, it is critically involved in regulating ion homeostasis, cell division, host colonization and immune system invasion. Several reports showed that bacteria lacking LTA are unable to grow. At the same time, they suffer from severe cell division defects and also exhibit aberrant cell morphologies. The key protein involved in the LTA biosynthesis pathway is the Lipoteichoic acid synthase (LtaS). LtaS is located on the cell membrane of Gram-positive bacteria and can be divided into two parts: a transmembrane domain and an extra-cellular domain responsible for its enzymatic activity (annotated eLtaS). Given that LtaS is important for bacterial survival and there are no known eLtaS homologues in eukaryotic cells, this protein is an attractive antibacterial target. In 2013, a small molecule eLtaS inhibitor (termed 1771) was discovered. Although 1771 was able to deplete LTA production, the binding mechanism of 1771 to eLtaS remains unknown. Additionally, 1771 could only prolong the survival of infected mice temporarily because of its in vivo instability. Therefore, the need for finding more potent and metabolically stable inhibitors of eLtaS still remains. Computational-aided drug design (CADD) is a cost-effective and useful approach that has been widely integrated into the drug discovery process. The protein eLtaS lends itself to be a good target for CADD since its crystal structure and a known inhibitor (with limited structure-activity data) is available. In this work, I have targeted eLtaS using CADD methodology followed by prospective validation using various biophysical, biochemical and microbiological assays. My project can broadly be sub-divided into three phases: (a) identification of small molecule binding “hot spots”, (b) optimization of existing inhibitor and (c) discovery of new hits. Through a systematic use of different computational approaches, I modelled a plausible 1771-bound eLtaS complex and used the structural insights to generate new inhibitors against eLtaS. To this end, I discovered EN-19, which is a more potent inhibitor of eLtaS. Additionally, by targeting transient cryptic pockets predicted by Molecular Dynamic simulations, I have discovered a new inhibitor chemotype that seems to exhibit a different mode of action against eLtaS. Taken together, my work presents a computational platform for future drug design against eLtaS and reinforces the notion that targeting eLtaS is a viable strategy to combat Gram-positive infections.
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Joshi, Priyanka. "Targeting intrinsically disordered proteins associated with neurodegenerative diseases : a strategy towards drug discovery." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709234.
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Sudwarts, Ari. "Zebra fish as a model for translational neurobiology : implications for drug discovery and development." Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/25979.
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Diseases which affect the central nervous system present a huge burden to sufferers and caregivers. In tandem with longevity, prevalences of age-related neurodegenerative diseases are increasing. However, despite the evident necessity for pharmaceutical interventions, there has been a distinct lack of drug development to combat these disorders. This is largely attributed to high financial costs of using rodent models. Thus the validation of a more cost-effective in vivo system would facilitate pharmaceutical screening. The work presented in this thesis addresses this issue by assessing the utility of zebra fish in two costly areas of translational neurobiology { lead identi cation and safety pharmacology. An aversive classical conditioning assay was developed and automated as a behavioural screening method. This robust assay allows fast assessment of cognition and cognitive decline. The effect of neurotoxin treatment on aversive learning was then assessed using this assay, demonstrating its efficacy as a screening tool for neurodegeneration research. Subsequently, a transgenic zebra fish line - expressing a mutated form of the Alzheimer's-associated human amyloid precursor protein - was assessed, demonstrating an age-related cognitive impairment. Additionally new genetic zebra fish lines were generated, which over-express genes (both endogenous and transgenic) related to Alzheimer's-like pathologies. Whilst these were not assessed within this thesis, they present promising tools for possible future investigations. Regarding safety pharmacology, regulatory bodies require all CNS-penetrant drugs be assessed for abuse potential. Zebra fish display reward responses to several common drugs of abuse (e.g. amphetamine, cocaine, morphine). Thus, the latter sections of this thesis evaluated the utility of zebra fish for assessing human abuse potential. A CPP paradigm was utilised to test a range of drugs, with the sensitivity and specificity of zebra fish compared to previous reports using rodent. Additionally, the development of a zebra fish drug discrimination assay was attempted. However the paradigms utilised failed to develop an efficacious assay.
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Chen, Yang. "DEVELOPMENT OF COMPUTATIONAL APPROACHES FOR MEDICAL IMAGE RETRIEVAL, DISEASE GENE PREDICTION, AND DRUG DISCOVERY." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1435601642.
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O'Rourke, Catriona. "Development of novel, robust 3D CNS tissue models for neurobiological studies and drug discovery." Thesis, Open University, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.700133.
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Recreating the 3D spatial environment of the eNS allows neural cells in vitro to behave more like their counterparts in vivo, providing robust and controllable model systems that mimic key aspects of the cell biology of the nervous system. A simple, consistent and physiologically relevant model system, which uses a multi-well plate format and can potentially be used at a scale suitable for commercial R&D, has been developed. The model uses an engineered neural tissue which is prepared by a process of initial glial cell self-alignment within a tethered 3D collagen hydrogel and subsequent stabilisation of the gel. Stabilisation is achieved using RAFT technology which entails partial removal of interstitial fluid thereby increasing matrix and cell density. To establish viable production technology for the manufacture of eNS tissue models, the parameters that govern glial cell self-alignment were optimised via development of an assay system that requires a small number of cells. A CNS eo-culture system suitable for widespread adoption will require various combinations of cells to suit specific neuroscience research requirements. Both primary neuronal and glial cell types, relevant cell lines and stem cells were incorporated within engineered neural tissues and then assessed using a range of measures including neural cell survival, morphology, differentiation and sensitivity to stabilisation. In a bid to determine the limitations of these 3D eNS model, neuron-glial interactions, markers for myelination, electrophysiological responses and glial cell behaviour in response to injury and insult were also investigated. Initial studies reveal the new model system can be scaled down to facilitate increased throughput, be assembled quickly and reliably using various neural cell sources, and the eo-cultures exhibit characteristic behaviours that mimic in vivo scenarios.
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Hsin, Kun-Yi. "Development and use of databases for ligand-protein interaction studies." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/3974.
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This project applies structure-activity relationship (SAR), structure-based and database mining approaches to study ligand-protein interactions. To support these studies, we have developed a relational database system called EDinburgh University Ligand Selection System (EDULISS 2.0) which stores the structure-data files of +5.5 million commercially available small molecules (+4.0 million are recognised as unique) and over 1,500 various calculated molecular properties (descriptors) for each compound. A user-friendly web-based interface for EDULISS 2.0 has been established and is available at http://eduliss.bch.ed.ac.uk/. We have utilised PubChem bioassay data from an NMR based screen assay for a human FKBP12 protein (PubChem AID: 608). A prediction model using a Logistic Regression approach was constructed to relate the assay result with a series of molecular descriptors. The model reveals 38 descriptors which are found to be good predictors. These are mainly 3D-based descriptors, however, the presence of some predictive functional groups is also found to give a positive contribution to the binding interaction. The application of a neural network technique called Self Organising Maps (SOMs) succeeded in visualising the similarity of the PubChem compounds based on the 38 descriptors and clustering the 36 % of active compounds (16 out of 44) in a cluster and discriminating them from 95 % of inactive compounds. We have developed a molecular descriptor called the Atomic Characteristic Distance (ACD) to profile the distribution of specified atom types in a compound. ACD has been implemented as a pharmacophore searching tool within EDULISS 2.0. A structure-based screen succeeded in finding inhibitors for pyruvate kinase and the ligand-protein complexes have been successfully crystallised. This study also discusses the interaction of metal-binding sites in metalloproteins. We developed a database system and web-based interface to store and apply geometrical information of these metal sites. The programme is called MEtal Sites in Proteins at Edinburgh UniverSity (MESPEUS; http://eduliss.bch.ed.ac.uk/MESPEUS/). MESPEUS is an exceptionally versatile tool for the collation and abstraction of data on a wide range of structural questions. As an example we carried out a survey using this database indicating that the most common protein types which contain Mg-OATP-phosphate site are transferases and the most common pattern is linkage through the β- and γ-phosphate groups.
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Tiwana, Gaganpreet Singh. "Discovery and investigation of novel radiosensitising genes." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:ee44297c-9b01-4c31-a4f8-6be3585c3557.
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Radiotherapy is second only to surgery in the curative management of patients with cancer, and yet the molecular mechanisms that determine the sensitivity of tumours to radiation remain largely unclear. A high-throughput radiosensitivity screening method based on clonogenicity was developed and a siRNA library against kinase targets was screened. The gold standard colony formation endpoint was chosen for determining reproductive cell death after radiation treatment, since effects on proliferation often do not reflect survival. Thiamine pyrophosphokinase-1 (TPK1), a key component of Vitamin B1/thiamine metabolism, was identified as a target for radiosensitisation. TPK1 knockdown caused significant radiosensitisation in cancer but not normal tissue cell lines. Other means of blocking this pathway such as knockdown of thiamine transporter-1 (THTR1) or treatment with the thiamine analogue pyrithiamine hydrobromide (PyrH) caused significant tumour specific radiosensitisation. There was persistent DNA damage in cells irradiated after TPK1 and THTR1 knockdown or PyrH treatment. Thus this screen allowed the identification of thiamine metabolism as a novel radiosensitisation target that affects DNA repair. Short-term modulation of thiamine metabolism could be a clinically exploitable strategy to achieve tumour specific radiosensitisation. Three additional genes, signal recognition particle-72 kDa (SRP72), glycogen synthase 3-beta (GSK3β) and MAP/Microtubule Affinity-Regulating Kinase 2 (MARK2) were also investigated. Knockdown of these genes radiosensitised both tumour and normal tissue cell lines and expression of two of them, GSK3β and SRP72 were found to be associated with poor recurrence-free survival in early breast cancer patients.
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Mehta, Kalpita Deepak. "Commercialization of Transiently Transfected Cell Lines for High Throughput Drug Screening and Profiling Applications." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1269628794.
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Zhang, Xudong. "3-D cell-based high-throughput screening for drug discovery and cell culture process development." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1204701561.
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Santiago, Daniel Navarrete. "Use and Development of Computational Tools in Drug Discovery: From Small Molecules to Cyclic Peptides." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4398.
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The scope of this work focuses on computationally modeling compounds with protein structures. While the impetus of drug discovery is the innovation of new therapeutic molecules, it also involves distinguishing molecules that would not be an effective drug. This can be achieved by inventing new tools or by refining old tools. Virtual screening (VS, also called docking), the computational modeling of a molecule in a receptor structure, is a staple in predicting a molecule's affinity for an intended target. In our Virtual Target Screening system (also called inverse-docking), VS is used to find high-affinity targets, which can potentially explain absorption, distribution, metabolism, and excretion (ADME) of a molecule of interest in the human body. The next project, low-mode docking (LD), attempts to improve VS by incorporating protein flexibility into traditional docking where a static receptor structure has potential to produce poor results due to incorrectly predicted ligand poses. Finally, VS, performed mostly on small molecules, is scaled up to cyclic peptides by employing Monte Carlo simulations and molecular dynamics to mimic the steps of small molecule VS.
The first project discussed is Virtual Target Screening (also called inverse-docking) where a small molecule is virtually screened against a library of protein structures. Predicting receptors to which a synthesized compound may bind would give insights to drug repurposing, metabolism, toxicity, and lead optimization. Our protocol calibrates each protein entry with a diverse set of small molecule structures, the NCI Diversity Set I. Our test set, 20 kinase inhibitors, was predicted to have a high percentage of kinase "hits" among approximately 1500 protein structures. Further, approved drugs within the test set generally had better rates of kinase hits.
Next, normal mode analysis (NMA), which can computationally describe the fundamental motions of a receptor structure, is utilized to approach the rigid body bias problem in traditional docking techniques. Traditional docking involves the selection of a static receptor structure for VS; however, protein structures are dynamic. Simulation of the induced fit effect in protein-ligand binding events is modeled by full articulation of the approximated large-scale low-frequency normal modes of vibration, or "low-modes," coupled with the docking of a ligand structure. Low-mode dockings of 40 cyclin dependent 2 (CDK2) inhibitors into 54 low-modes of CDK2 yielded minimum root-mean-square deviation (RMSD) values of 1.82 – 1.20 Å when compared to known coordinate data. The choice of pose is currently limited to docking score, however, with ligand pose RMSD values of 3.87 – 2.07 Å. When compared to corresponding traditional dockings with RMSD values of 5.89 – 2.33 Å, low-mode docking was more accurate.
The last discussion involves the rational docking of a cyclic peptide to the murine double minute 2 (MDM2) oncoprotein. The affinity for a cyclic peptide (synthesized by Priyesh Jain, McLaughin Lab, University of South Florida), PJ-8-73, in MDM2 was found to be within an order of magnitude of a cyclic peptide from the Robinson Lab at the University of Zurich in Switzerland. Both are Β-hairpin cyclic peptides with IC50 values of 650 nm and 140 nm, respectively. Using the co-crystalized structure of the Robinson peptide (PDB 2AXI), we modeled the McLaughlin peptide based on an important interaction of the 6-chloro-tryptophan residue of the Robinson peptide occupying the same pocket in MDM2 as the tryptophan residue by the native p53 transactivation helical domain. By preserving this interaction in initial cyclic peptide poses, the resulting pose of PJ-8-73 structure in MDM2 possessed comparable active site residue contacts and surface area.
These protocols will aid medical research by using computer technology to reduce cost and time. VTS utilizes a unique structural and statistical calibration to virtually assay thousands of protein structures to predict high affinity binding. Determining unintended protein targets aids in creating more effective drugs. In low-mode docking, the accuracy of virtual screening was increased by including the fundamental motions of proteins. This newfound accuracy can decrease false negative results common in virtual screening. Lastly, docking techniques, usually for small molecules, were applied to larger peptide molecules. These modifications allow for the prediction of peptide therapeutics in protein-protein interaction modulation, a growing interest in medicine. Impactful in their own ways, these procedures contribute to the discovery of drugs, whether they are small molecules or cyclic peptides.
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Patel, Hitesh [Verfasser], and Irmgard [Akademischer Betreuer] Merfort. "Use and development of chem-bioinformatics tools and methods for drug discovery and target identification." Freiburg : Universität, 2015. http://d-nb.info/1115495917/34.
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Caldwell, Colby G. "Chemical investigations of South American plants: Applications to drug discovery, biodiversity conservation and economic development." Diss., The University of Arizona, 2000. http://hdl.handle.net/10150/279829.
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This dissertation describes chemical investigations involving 11 Argentinean plant species and a sample of Chilean propolis. In total, 18 known and four novel compounds were isolated and identified. The compounds were tested in various antimicrobial assays. Three novel triterpenes, 3,4- seco-olean-12-en-3,28-dioic acid (4), 3alpha,-hydroxyolean-11-en-28,13 beta-olide (5), and 3alpha-hydroxyolean-11:13(18)-dien-28-oic acid ( 6) were isolated from the aerial parts of the Argentinean shrub, Junellia tridens (Lag.) Mold. (Verbenaceae). Another five compounds, oleanolic acid (1), oleanonic acid (2) and epioleanolic acid (3), all biosynthetically related to the three new oleananes, as well as epibetulinic acid (7) and sitosterol (8), were also isolated. LC-MS data are provided on the occurrence of these triterpenes in six other species of Junellia. We report the minimum inhibitory concentrations (MICs) of compounds 1--8 against Mycobacterium tuberculosis, and conclude that they are responsible for the antitubercular activity originally observed in the crude plant extract. Four other plants showing preliminary antitubercular activity were also investigated. The EtOAc extracts of Acantholippia seriphioides and Adesmia ameghinoi contained oleanolic acid (1) as their main constituent. The organic soluble portions of Chiliotrichium diffusum and Lathyrus magellanicus contained large amounts of ursolic acid (12) and sitosterol (8), respectively. Bioassay of the predominant compounds in these plants indicated that triterpenes were responsible for the antitubercular activity observed in the crude extracts. Fractionation of propolis (a product of honey beehives) from Colliguay in Central Chile led to the isolation, identification and bioassay of a novel gamma-lactone (14), five flavonoids (15--19), two diarylheptanoids (20--21), and a prenylated coumarin (22). All structures were elucidated primarily by 1D and 2D NMR and mass spectrometry. Based on the traditional use of propolis as an antimicrobial agent, the bioactivity of the purified compounds was determined against Staphylococcus aureus, Escherichia coli, Enterococcus faecium, and Candida albicans . Microscopic analysis of pollen present in the propolis provided clues to its botanical origins.
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Tian, Honglei. "A high throughput screening method for anti-cancer drug leads discovery from the herbal medicine /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?BIEN%202006%20TIAN.
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Beaudry, Francis. "Development of analytical methods based on liquid chromatography and mass spectrometry techniques to support drug discovery and development programmes." Thesis, Anglia Ruskin University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436459.
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Kashif, Muhammad. "Integrated Computational and Experimental Approaches for Accelerated Drug Combination Discovery and Development : Applications in Cancer Pharmacology." Doctoral thesis, Uppsala universitet, Cancerfarmakologi och beräkningsmedicin, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-245573.
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Today the norm in modern cancer treatment is to use different forms of drug combinations. Recently anti-cancer treatment using drug combinations has gained increased attention due to the outstanding pharmacotherapeutic opportunities provided by combination therapies. However, the potential of this field is largely unexplored, partly due to the complexities associated with the astronomical number of possible combinations and partly due to the lack of means for quantifying clinically relevant adverse side effects in the early stages of the combination discovery and development process. This has resulted in relatively limited progress in this area. Motivated by this unfortunate state-of-affairs, the research reported in this thesis was aimed at developing and implementing computational and experimental methods to facilitate and accelerate the discovery and development of anti-cancer therapies. In paper I, the largely overlooked concept of therapeutic synergy is re-introduced and demonstrated to be useful already at the level of combination discovery by taking both curative and adverse effects into account. In paper II, a semiautomatic combination discovery platform was developed based on a tailored programming of a pipetting robot system and application of a new in-house developed combination search algorithm, the therapeutic algorithmic combinatorial screen (TACS) algorithm. TACS seems to be the first algorithm of its kind that takes experimental variability into account during the iterative search process. The semiautomatic hardware platform along with TACS can perform de novo or knowledge based combination drug discovery and development without brute force comprehensive search efforts. One promising discovery made using this platform is a combination of the drugs 17-AAG, afungin and trichostatin a for treatment of colorectal cancer carcinoma (CRC). In paper III, an algorithm is developed and applied in order to use single drug induced systemic gene expression profiles for rational drug combination design by assuming additive combination effects. The resulting algorithm, combo-CMap, is applied and validated using a slightly extended version of the freely available Connectivity Map (CMap) database which is currently containing 6190 chemically induced mRNA gene expression signatures. In paper IV, a software (R package) was developed and applied to perform improved synergy/antagonism analysis, in particular joint Loewe and Bliss analyses while taking associated experimental variability into account using non-parametric statistics including bootstrap intervals. Applying this software to the synergy analysis of interaction effects among clinically used and/or relevant drugs in CRC cell lines revealed complex patterns of synergy and antagonism. In conclusion, the work presented here offers important contributions and findings that may accelerate and/or improve different parts of the field of drug combination discovery and development.
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Mitchell, Sophie Lousie. "A fragment-based drug discovery approach for the development of selective inhibitors of protein kinase CK2." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/278650.
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Over the last twenty years, fragment-based drug discovery (FBDD) has emerged as a highly successful way to provide lead compounds for subsequent optimisation into drug candidates. Initial hits usually exhibit lower potency than those identified by more traditional techniques, such as High-Throughput Screening (HTS), but the optimisation phase of FBDD is highly efficient, thus providing superior lead-like compounds. The recent application of FBDD in a variety of protein kinase campaigns has successfully led to the identification of novel binding sites and highly efficient chemical ligands. This demonstrates the utility of the FBDD strategy against well-established kinase targets, where selectivity is otherwise challenging due to significant conservation of the ATP-binding site. Protein kinase CK2 is a ubiquitously expressed and constitutively active regulator of cell growth, proliferation and apoptosis. Elevated levels of CK2 protein and activity have historically been involved in human cancer, including lung, cervical and head and neck cancer types, and its overexpression is associated with worse prognosis. A number of CK2 inhibitors are currently available displaying activity against multiple cancers in vitro and in the clinic, however the majority of these candidates target the ATP-binding site and thus display poor selectivity in kinase panel assays. Here we explore the application of FBDD towards the development of potent and selective inhibitors of the catalytic α-subunit of CK2. This project exploits a novel, conserved binding site, named the αD pocket, for the generation of allosteric inhibitor molecules. Following structure-based optimisation of a potent inhibitor series, and characterisation of a previously unreported binding mode, a fragment linking strategy between the lead αD-site fragment and a low-affinity pseudosubstrate peptide is investigated. This work validates the utility of FBDD towards the discovery of new binding modes, presents a first in class CK2α allosteric inhibitor series and provides the first X-ray crystal structure of protein kinase CK2 in complex with a ligand binding in the substrate-binding channel.
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Kontijevskis, Aleksejs. "Modeling the Interaction Space of Biological Macromolecules: A Proteochemometric Approach : Applications for Drug Discovery and Development." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8916.
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Ghiasi, Zahra. "Development of a Computational Mechanism to Generate Molecules with Drug-likeCharacteristics." Ohio University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou162861276157897.
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Ortiz, València Héctor. "Contributions to the development and validation of fMRI-based biomarkers for drug discovery in social anxiety disorder." Doctoral thesis, Universitat Politècnica de Catalunya, 2016. http://hdl.handle.net/10803/440530.
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This document presents the theoretical background and experimental work made to develop and validate a set of experiments based on functional magnetic resonance imaging (fMRI). These experiments are aimed to demonstrate that fMRI can be a valuable tool to objectivize drug treatment response in Social Anxiety Disorder (SAD) patients.
Functional MRI is a non-invasive imaging technique which provides localized indicators of brain activity. The analysis of fMRI data has recently facilitated neuroscience to make a leap forward in the understanding of the human brain.
Psychiatric clinical research, however, hasn’t fully embraced yet the potential of fMRI. In parallel, the societal costs of new psychiatric drug discovery are reaching unbearable limits. It has been hypothesized that the addition of fMRI in clinical trials of pharmacologic treatments of SAD can provide new biomarkers of treatment response which, in the future, shall reduce the temporal and economic burden of drug discovery. Five studies are presented in this dissertation in an evolving path towards the validation of the hypothesis.
In study 1, a widely validated state-of-the-art emotional face processing paradigm was piloted in a non-clinical sample. Task related activations were in line with the findings previously reported in the literature. However, the results of experiment did not show a correlation with symptom severity. An additional exploratory psychophysiological interaction analysis revealed that the relationship between two emotion-processing areas had a significant correlation with SAD symptom severity. This emphasized the potential value of studies based on functional connectivity for our objectives.
Study 2 explored the reproducibility of connectivity analysis of fMRI data. To do so, a brain network was selected and explored with Independent Component Analysis (ICA) on data collected from three categorically differentiated paradigms: A resting task, a moral dilemma task and in a cognitive-challenging Stroop task. The selected network was systematically identified in the three cases, exemplifying the robustness of the technique in three extreme cases.
Study 3 explores the sensitivity of ICA by analyzing resting-state data acquired before and after an experimental induction of sad mood. Multiple regions reflected changes in their intranetwork connectivity after sad mood induction. Results were validated using a split-half re-analysis and confirmatory seed-based functional connectivity measurements. These results support the idea that spatial ICA of fMRI is not only reliable, but also a sensitive paradigm.
Study 4 presents a novel SAD symptom-provoking paradigm that was validated on SAD patients and controls. The analysis of this pilot revealed a striking non-linear relationship between task activation and social anxiety scores. This non-linear relationship is compatible with some of the divergences found in literature regarding the alteration of emotional regulation brain areas in SAD.
Study 5 presents the results of a small placebo-controlled clinical trial using a common treatment for SAD (paroxetine) in SAD patients. Subjects underwent the emotional face processing, the symptom-provoking and resting state tasks that were administered and analyzed following the experience obtained in studies 1 to 4. The selected fMRI paradigms and analysis methods showed significant sensitivity to the effects of paroxetine treatment on SAD. Treatment effects were identified in areas related to the processing of fear stress and anxiety, which are known to be altered in SAD. Remarkably, ICA revealed sensitivity to pharmacologically-induced clinical improvement in the same areas and direction than the symptom-provocation task.
Along with the evidences reported in the literature review, the methods and results obtained throughout this dissertation provide a proof of concept on the usage of fMRI as a biomarker for SAD pharmacologic research.Aquest document presenta el marc teòric I el treball experimental fet per a desenvolupar i validar un conjunt d’experiments basats en ressonància magnètica funcional (RMf). Aquests experiments estan orientats a demostrar que la RMf pot contribuir a la quantificació de la resposta al tractament de pacients amb fòbia social (FS). La RMf és una tècnica d’imatge no invasiva que proporciona indicadors d’activitat cerebral localitzats espacialment. L’anàlisi de RMf ha contribuït al recent salt en la comprensió del cervell humà. No obstant, la recerca clínica en psiquiatria encara no aprofita tot el potencial de la RMf. Al mateix temps, els costos d’avaluació de fàrmacs estan arribant a nivells inassumibles. Es fa la hipòtesi que la incorporació de RMf als assajos clínics de tractaments per a fòbia social pot proporcionar biomarcadors de resposta al tractament. Aquests biomarcadors podrien, en un futur, reduir els costos associats al desenvolupament farmacològic. En aquest document es presenten cinc estudis que formen un camí evolutiu cap a la validació de la hipòtesis. L’estudi 1 mostra un estudi pilot amb un paradigma validat a la literatura en una mostra no clínica. Les activacions identificades amb relació al paradigma estan alineades amb la literatura. No obstant, els resultats no mostren una correlació amb la severitat dels símptomes. Un anàlisi exploratori revela que la interacció de la activitat de dues àrees associades al processament emocional sí que tenen una correlació significativa amb els símptomes de fòbia social. Aquests resultats varen emfatitzar la potencial contribució als nostres objectius dels estudis basats en connectivitat funcional . L’estudi 2 explora la reproductibilitat d’anàlisis de connectivitat de RMf. Per a fer-ho, una xarxa cerebral ha estat seleccionada i explorada amb Anàlisi de Components Independents (ICA). Això s’ha realitzat amb dades capturades en tres paradigmes substancialment diferents: En repòs, en estat de dilema moral i en una tasca de Stroop d’alta demanda cognitiva. La xarxa seleccionada es va identificar sistemàticament en els tres casos, exemplificant la robustesa de la tècnica. L’estudi 3 explora la sensitivitat de ICA analitzant dades on el subjecte està en repòs abans i després d’una inducció experimental de tristesa. Múltiples regions varen mostrar canvis en la seva connectivitat intra-xarxa després de la inducció de tristesa. Els resultats es varen validar fent servir un re-anàlisi de dues meitats de la mostra i amb anàlisi de connectivitat funcional basada en un punt font (seed). Els resultats donen suport a la idea que l’anàlisi espacial de ICA no només és robust, sinó també sensible. L’estudi 4 presenta un nou paradigma de provocació de símptomes en FS que es valida en una mostra de pacients i controls. L’anàlisi d’aquesta prova pilot mostra un reveladora relació no lineal entre l’activació produïda per la tasca i l’índex de simptomatologia de fòbia social. Aquesta relació no lineal és compatible amb algunes de les inconsistències identificades a la literatura en relació a l’alteració en FS d’àrees del cervell vinculades a la regulació emocional. L’estudi 5 presenta els resultats d’un petit assaig clínic controlat per placebo que avalua, en pacients, la resposta d’un tractament d’ús habitual en FS (paroxetina). Emprant la experiència dels estudis 1 a 4, els subjectes es varen explorar executant la tasca de processament de cares emotives, la tasca de provocació de símptomes i en repòs. S’han identificat efectes del tractament en àrees vinculades al processament de por, estrès i angoixa, que s’han reportat prèviament com a alterades amb FS. És de destacar que ICA va revelar sensitivitat a millores clíniques induïdes farmacològicament en les mateixes àrees que la tasca de provocació de símptomes. En conjunció amb la recerca bibliogràfica, els mètodes i resultats obtinguts al llarg d’aquesta test proporcionen una prova de concepte en l’ús de la RMf per a la recerca clínica en FS.
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Weigt, David [Verfasser], and Britta [Akademischer Betreuer] Brügger. "Development of cellular metabolite-based MALDI mass spectrometry assays for drug discovery / David Weigt ; Betreuer: Britta Brügger." Heidelberg : Universitätsbibliothek Heidelberg, 2019. http://d-nb.info/1198484314/34.
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Abshire, James R. (James Robbins). "Development of novel chemical biology tools to probe malaria parasite physiology and aid in antimalarial drug discovery." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98921.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references.
Malaria remains a major burden to global public health. Antimalarial drugs are a mainstay in efforts to control and eventually eradicate this disease. However, increasing drug resistance threatens to reverse recent gains in malaria control, making the discovery of new antimalarials critical. Antimalarial discovery is especially challenging due to the unique biology of malaria parasites, the scarcity of tools for identifying new drug targets, and the poorly understood mechanisms of action of existing antimalarials. Therefore, this work describes the development of two chemical biology tools to address unmet needs in antimalarial drug discovery. A particular challenge in antimalarial development is a shortage of validated parasite drug targets. Potent antimalarials with demonstrated clinical efficacy, like the aminoquinolines and artemisinins, represent a promising basis for rational drug development. Unfortunately, the molecular targets of these drugs have not been identified. While both are thought to interact with parasite heme, linking in vitro heme binding with drug potency remains challenging because labile heme is difficult to quantify in live cells. This work presents a novel genetically-encoded heme biosensor and describes its application to quantify labile heme in live malaria parasites and test mechanisms of antimalarial action. Another challenge is posed by the widespread malaria parasite Plasmodium vivax, which, unlike P. falciparum, cannot be propagated in vitro, hindering research into parasite biology and drug target identification. P. vivax preferentially invades reticulocytes, which are impractical to obtain in continuous supply. The basis for this invasion tropism remains incompletely understood, mainly because current tools cannot directly link molecular binding events to invasion outcomes. This work presents novel methods for immobilizing synthetic receptors on the red blood cell surface. These receptors are used in proof-of-concept experiments to investigate requirements for efficient invasion via a well-characterized P. falciparum invasion pathway, suggesting this method can be used to elucidate molecular mechanisms underlying parasite invasion tropisms. Future receptor designs could promote the invasion of P. vivax into mature red blood cells and potentially facilitate practical in vitro culture. Taken together, these tools present new opportunities for drug discovery to aid efforts in malaria control and eventual eradication.
by James R. Abshire.
Ph. D.
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Fraser, Craig. "Design and development of novel mTOR and SRC family kinase inhibitors via a phenotypic drug discovery approach." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/21689.
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Traditionally, drug discovery programs have focused on prioritising compounds by their affinity to a specific target in isolation, which was hypothesised to be the cause of a particular disease. Through chemical inhibition, the disease could, thus, be prevented or at the very least, controlled. These hypotheses require significant validation before drug screening can begin which relates to lengthy and expensive programs. Furthermore, drug screening against a single target in isolation is not a realistic model of cellular behaviour and is not appropriately tailored to more complex diseases such as cancer. Phenotypic drug discovery, on the other hand, bypasses any involvement of known targets, instead focusing on the desired outcome – the phenotype. In this way, drugs are biased by their potency on the phenotype and not against any particular targets. The molecular mechanism of action need not be known at all, however, it can be useful to later reveal the target(s) involved by various deconvolution methods. This thesis describes a cooperative ligand based phenotypic drug discovery approach, undertaken in order to develop more suitable small molecule drugs for cancer treatment. For this purpose, the promiscuous pyrazolopyrimidine inhibitor PP1 was chosen as a starting model compound. Modification of PP1 on the N1 position allowed a series of water solubilising groups to be incorporated into the pyrazolopyrimidine scaffold which created an initial 12-membered library. Testing against MCF7 breast cancer cells and looking at phenotypic end points such as cell proliferation, cell mobility and cell cycle, generated early target-agnostic structure/anti-proliferative activity relationships. These early results, along with compounds published in recent literature, were used to generate further libraries. Profiling lead compounds against a selection of 18 kinases known to be targeted by PP1, showed the compounds were inhibiting either SRC family or mTOR kinases which enabled the creation of two, structure specific, groups of inhibitors. Further lead optimisation led to the rapid discovery of preclinical candidates with excellent drug-like properties and potencies in both cellular assays and against their respective targets. Compounds also showed improved selectivity profiles compared to PP1 and commonly known inhibitors of SRC and mTOR kinases. Reported, herein, is the discovery of the first sub-nanomolar SRC inhibitor which does not inhibit the kinase ABL and shows excellent properties suitable for further preclinical development.
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Fienberg, Stephen. "Development of N-domain selective Angiotensin-I Converting Enzyme (ACE) inhibitors using Computer Aided Drug Discovery (CADD)." Doctoral thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/25656.
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Angiotensin-I (Ang-I) converting enzyme (ACE) is a zinc metalloprotease that plays a vital role in the Renin Angiotensin Aldosterone System (RAAS) and is a key antihypertensive drug target. In addition to Ang-I, ACE cleaves many other physiological substrates, thus extending its function beyond the regulation of blood pressure. Somatic ACE (sACE) consists of two structurally homologous yet distinct catalytic sites termed the N- and C-domains. The two catalytic domains of ACE have distinct substrate affinities and play different regulatory roles. The antifibrotic tetrapeptide Ac-SDKP is hydrolysed solely by the N-domain and thus is a potential target for interactions between the ligand and unique residues within the active site of the N- and C-domains, which need to be exploited to effect either N- or Cdomain selectivity. N-domain selective ACE inhibition has been demonstrated with peptides while crystallographic studies have shown that the N-domain to C-domain substitution of Arg381 with Glu403 within the S₂ subsite is integral to N-domain selective ACE inhibition. Three computer aided drug discovery (CADD) approaches were pursued to design N-domain selective drug-like ACE inhibitors (ACEi) with an acidic P₂ functional group that would confer N-domain selectivity via an interaction with Arg381 in the S₂ subsite. Firstly, a fragment-based screening protocol was performed by running a set of chemical filters on 16 000 drug fragment compounds (MW < 350), all of which contained a metal chelating group. 60 Ligands capable of binding to both the zinc metal and Arg381 in the S₂ subsite of the N-domain were tested for ACE inhibition against the two domains of ACE. Two of the fragments identified in this screen showed a modest ACE inhibition (IC₅₀ +/- 200 μM), but no domain selectivity. Secondly, a combinatorial library was created to explore the P₂ structure activity relationship (SAR) of a scaffold based on the core structure of the clinical ACEi, Enalaprilat. Over 400 variants were created to generate a combinatorial library. These compounds were docked against the two domains of ACE and a synthetic scheme was developed to synthesise compounds from this library. Using this scheme, one Enalaprilat analogue, SF07 was synthesised as a mixture of diastereomers. SF07 exhibited low micromolar N-domain inhibition with no C-domain inhibition observable below 100 μM. For the third approach, 25 000 compounds containing biological data pertaining to ACE were extracted from the GVK BIO GOSTAR database. These compounds were filtered for drug-like properties and manually inspected for promising P₂ functionality. The N-domain selectivity of these compounds was then assessed via molecular docking against the two domains of ACE. This screen identified a series of diprolyl compounds with varied groups in the P₂ position. These compounds were subsequently synthesised and tested in vitro for inhibition against both domains. The most N-domain selective compound from the series proved to be SG6, a diprolyl compound with an Asp group in the P₂ position. SG6 displayed potent inhibition (Kᵢ = 12 nM) and was 83-fold more selective towards the N-domain than the C-domain. This study has demonstrated the N-domain selective inhibition of ACE by drug-like peptidomimetics. Two promising leads on drug-like N-domain selective ACE inhibitors, SG6 and SF07, have been identified. These two compounds have the potential to pave the way for clinical N-domain selective ACEis and a novel treatment for cardiac and pulmonary fibrosis.
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Shuman, Cynthia F. "Interaction Characteristics of Viral Protease Targets and Inhibitors : Perspectives for drug discovery and development of model systems." Doctoral thesis, Uppsala universitet, Institutionen för naturvetenskaplig biokemi, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3342.
Full textAbstract:
Viral proteases are important targets for anti-viral drugs. Discovery of protease inhibitors as anti-viral drugs is aided by an understanding of the interactions between viral protease and inhibitors. This thesis addresses the characterization of protease-inhibitor interactions for application to drug discovery and model system development. The choice of a relevant target is essential to molecular interaction studies. Therefore, full-length NS3 protein of hepatitis C virus (HCV) was obtained, providing a more relevant target and a better model for the development of HCV protease inhibitors. In addition, resistance to anti-viral drugs, a serious problem in the treatment of AIDS, prompted the investigation of resistant variants of human immunodeficiency virus (HIV) protease. Drug resistance was initially explored by characterization of the interactions between a series of closely related inhibitors and resistant variants of HIV protease, using an inhibition assay to determine the inhibition dissociation constants (Ki). The relationship between structure, activity and resistance profiles was not clarified, indicating that the effect of structural changes in the inhibitors and the protease are not predictable and must be analyzed case wise. It was proposed that additional kinetic characterization of the interactions was required and a biosensor-based method allowing for determination of affinity, KD, and interaction rate constants, kon and koff, was adopted. The increased physiological relevance of this method was confirmed, and the affinity data have better correlation with cell culture data. In addition, interactions between clinical inhibitors of HIV protease and enzyme variants indicate that increased dissociation rates (koff) are associated with the development of resistance. Thermodynamic characterization of the interactions between HIV-1 protease and clinically relevant inhibitors revealed distinct energetic characteristics for inhibitors. The resolution of the energetics of association and dissociation identified an inhibitor with unique interaction characteristics and confirmed the validity of using this method for further characterization of molecular interactions. This work resulted in the development of model systems for the analysis of kinetics, resistance and thermodynamic characteristics of protein-inhibitor interactions. The results give increased understanding of the biomolecular interactions and can be applied to drug discovery.