Trotz weltweiter Bemühungen, die Tropenkrankheit Malaria zurückzudrängen, erkranken jährlich bis zu einer halben Milliarde Menschen an Malaria mit der Folge von über einer Million Todesopfern. Da zur Zeit eine wirksame Impfung nicht in Sicht ist und sich Resistenzen gegen gängige Medikamente ausbreiten, werden dringend neue Antimalariamittel benötigt. Um die Suche nach neuen Angriffsorten für Medikamente zu unterstützen, untersucht die vorliegende Arbeit mit einem rechnergestützten Ansatz den Stoffwechsel von Plasmodium falciparum, dem tödlichsten Malaria-Erreger. Basierend auf einem aus dem aktuellen Forschungsstand rekonstruierten metabolischen Netzwerk des Parasiten werden metabolische Flüsse für die einzelnen Stadien des Lebenszyklus von P. falciparum berechnet. Dabei wird ein im Rahmen dieser Arbeit entwickelter Fluss-Bilanz-Analyse-Ansatz verwendet, der ausgehend von in den jeweiligen Entwicklungsstadien gemessenen Genexpressionsprofilen entsprechende Flussverteilungen ableitet. Für das so ermittelte stadienspezifische Flussgeschehen ergibt sich eine gute Übereinstimmung mit bekannten Austauschprozessen von Stoffen zwischen Parasit und infiziertem Erythrozyt. Knockout Simulationen, die mit Hilfe einer ähnlichen Vorhersagemethode durchgeführte werden, decken essentielle metabolische Reaktionen im Netzwerk auf. Fast 90% eines Sets von experimentell bestimmten essentiellen Enzymen wird wiedergefunden, wenn die Annahme getroffen wird, dass Nährstoffe nur begrenzt aus der Wirtszelle aufgenommen werden können. Die als essentiell vorhergesagten Enzyme stellen mögliche Angriffsorte für Medikamente dar. Anhand der Flussverteilungen, die für die einzelnen Entwicklungsstadien berechnet wurden, können diese potenziellen Targets nach Relevanz für Malaria Prophylaxe und Therapie sortiert werden, je nachdem, in welchem Stadium die Enzyme als aktiv vorhergesagt wurden. Dies bietet einen vielversprechenden Startpunkt für die Entwicklung von neuen Antimalariamitteln. / Despite enormous efforts to combat malaria, the disease still afflicts up to half a billion people each year, of which more than one million die. Currently no effective vaccine is within sight, and resistances to antimalarial drugs are wide-spread. Thus, new medicines against malaria are urgently needed. In order to aid the process of drug target detection, the present work carries out a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. A comprehensive compartmentalized metabolic network is assembled, which is able to reproduce metabolic processes known from the literature to occur in the parasite. On the basis of this network metabolic fluxes are predicted for the individual life cycle stages of P. falciparum. In this context, a flux balance approach is developed to obtain metabolic flux distributions that are consistent with gene expression profiles observed during the respective stages. The predictions are found to be in good accordance with experimentally determined metabolite exchanges between parasite and infected erythrocyte. Knockout simulations, which are conducted with a similar approach, reveal indispensable metabolic reactions within the parasite. These putative drug targets cover almost 90% of a set of experimentally confirmed essential enzymes if the assumption is made that nutrient uptake from the host cell is limited. A comparison demonstrates that the applied flux balance approach yields target predictions with higher specificity than the topology based choke-point analysis. The previously predicted stage-specific flux distributions allow to filter the obtained set of drug target candidates with respect to malaria prophylaxis, therapy or both, providing a promising starting point for further drug development.

Trypanosoma brucei, the causative agent of African trypanosomiasis, and its relatives Trypanosoma cruzi and several Leishmania species belong to a class of protozoa called kinetoplastids that cause a significant health burden in tropical and semitropical countries across the world. While an improved therapy was recently approved for African trypanosomiasis, the therapies available to treat infections caused by T. cruzi and Leishmania spp. remain relatively poor. Improving our understanding of T. brucei metabolism can inform on metabolism of its relatives. The purpose of the research presented in this dissertation was to develop novel tools and methods to study metabolism in T. brucei with the ultimate aim to improve treatments of all kinetoplastid diseases. We developed a novel tool to study glycosomal pH in the bloodstream form of T. brucei. Using this tool, we discovered that this life stage regulates glycosomal pH differently than the procyclic form, or insect-dwelling stage, and only uses sodium/proton transporters to regulate glycosomal pH. I pioneered a thermal proteome profiling method in this parasite to discover drug targets and their effects on cell pathways. Using this method, I found that other proteins may be involved in glycosomal pH regulation, including PEX11 and a vacuolar ATPase. This method also illuminated several important pathways influenced by glycosomal pH regulation, including glycosome proliferation, vesicle trafficking, protein glycosylation, and amino acid transport. Metabolic studies in kinetoplastid parasites are currently hampered by the lack of available chemical probes. We developed a novel flow cytometry-based high-throughput drug screening assay to discover chemical probes of T. brucei glycolysis. This method combines the advantages of phenotypic (or cell-based) screens with the advantage of targeted (purified protein) screens by multiplexing biosensors that measure multiple glycolytic metabolites simultaneously, such as glucose, ATP, and glycosomal pH. The complementary information gained is then used to distinguish the part of glycolysis identified inhibitors target. We validated the method using the well characterized glycolytic and alternative oxidase inhibitors 2-deoxyglucose and salicylhydroxamic acid respectively. We demonstrated the screening assay with a pilot screen of 14,976 compounds with decent hit rates for each sensor (0.2-0.4%). About 64% of rescreened hits repeated activity in at least one sensor. We demonstrated one compound with micromolar activity against two biosensors. In summary, we developed and demonstrated a novel screening method that can discover glycolytic chemical probes to better study metabolism in this and related parasites. There are few methods to study enzyme kinetics in the live-cell environment. I developed a kinetic flow cytometry assay that can measure enzyme and transporter activity using fluorescent biosensors. I demonstrated this by measuring glucose transport kinetics and alternative oxidase inhibition kinetics, with the measured kinetic parameters similar to those previously reported. We plan to expand on this method to measure transport kinetics in the glycosome and other organelles which has not been done before. We previously performed a drug screen to identify inhibitors that decrease intracellular glucose in T. brucei. I have performed preliminary work identifying the glucose transporter THT1 as one of the targets of optimized glucose inhibitors using the previously mentioned thermal proteome profiling method. We expect this finding will improve our ability to move these compounds from hit to lead in the drug discovery pipeline. Together, I have developed several flow cytometry and proteomics methods to better study metabolism in T. brucei. These tools are beginning to be used in related parasites. We expect the discoveries made using these tools will improve our ability to treat these neglected tropical diseases.

The rapid pace of scientific advances is enabling a greater understanding of diseases at the molecular level. In turn, the process for researching and developing new medicines is growing in difficulty, costs, and length as a result of the scientific, technical, and regulatory challenges related to the development process. In light of these challenges, drug repositioning, the utilization of known drugs for a new medical indication, has emerged as an increasingly important strategy for the new drug discovery. Availability of prior knowledge regarding safety, efficacy and the appropriate administration route significantly reduces the development costs and cuts down the development time resulting in less effort to successfully bring a repositioned drug to market. In another aspect, a protein’s shape is closely linked with its function; thereby, the ability to predict this structure unlocks a greater understanding of what it does and how it works. Nowadays, more than 10,000 biologically relevant protein structures are yearly released and available to the scientific community. A number suspected to triple over the following years due to the recent breakthroughs in structure prediction techniques. This work introduces a novel structure-based drug repositioning approach, exploiting the similarities of drugs’ binding mode via identification and virtual screening of interaction patterns. Such patterns are uncovered with the use of PLIP, an automated tool for the in silico detection of non-covalent interactions defining the binding mode between drugs and their protein targets. Besides, the approach has been applied to a series of case studies with tangible results: the uncovering of an antimalarial drug as potential chemoresistance treatment, the explained binding mode of ibrutinib to the target VEGR2 as potential B-cells deactivator in autoimmune diseases, and three over the counter drugs with a proved anti-trypanocidal activity as treatments for Chagas disease. Overall the structure-based approach with interaction patterns proved to be a suitable framework for identifying novel repositioning candidates. The uncovered candidates were structurally unrelated to the currently available treatments, and experimental assays successfully demonstrated their inhibitory activity on the protein targets of interest. Furthermore, the approach represents a promising option for the 'in high demand' diseases and all rare and neglected diseases for which no reliable treatment has yet been found and for which the pharmaceutical industry makes only a little investment.

En étudiant les variations génétiques au sein d'une population, il est possible d'identifier des polymorphismes génétiques qui confèrent une protection naturelle contre la maladie. Si l'on parvient à comprendre le mécanisme moléculaire qui sous-tend cette protection, par exemple en reliant la variation génétique à la perturbation d'une protéine bien précise, il pourrait être possible de développer des thérapies pharmacologiques qui agissent sur la même cible biologique. Cette relation entre les médicaments et les variations génétiques est une des prémisses centrales de la validation génétique de cibles pharmacologiques qui est un facteur de réussite dans le développement de médicaments. Dans cette thèse, nous utiliserons un modèle génétique pour prédire les effets bénéfiques et indésirables de l'ivabradine, un médicament utilisé afin de réduire la fréquence cardiaque. L'ivabradine est un inhibiteur du canal ionique potassium/sodium hyperpolarization-activated cyclic nucleotide-gated channel 4, encodé par le gène HCN4, dont les bénéfices sont hétérogènes chez différentes populations de patients. Ce médicament est efficace pour le traitement de l'angine et de l'insuffisance cardiaque, mais s'est avéré inefficace en prévention secondaire chez des patients coronariens stables sans dysfonction systolique. La caractérisation des effets de l'ivabradine s'est échelonnée sur une période de 6 ans et trois grands essais de phase III ont été menées. Nous étudierons la possibilité d'avoir prédit ou accéléré ce processus à l'aide de modèles génétiques et nous contrasterons les effets spécifiques à l'ivabradine des effets généraux de la réduction de la fréquence cardiaque par une approche de randomisation mendélienne. Deuxièmement, une approche génétique sera utilisée pour évaluer l'effet de l'inhibition de la cholesteryl ester tranfer protein (CETP), une enzyme responsable du transfert des cholestérols estérifiés et des triglycérides entre différentes lipoprotéines ainsi qu'une cible pharmacologique largement étudiée pour le traitement de la maladie coronarienne. Les études génétiques prédisent un bénéfice à l'inhibition de CETP, mais les essais randomisés ont eu des résultats hétérogènes et décevants. Nous utiliserons un modèle génétique d'inhibition de la CETP pour identifier des variables qui peuvent moduler l'effet de l'inhibition de la CETP sur des biomarqueurs et la maladie ischémique. Les biomarqueurs pris en compte comprennent les taux de cholestérol à lipoprotéines de basse et haute densité, mais aussi la capacité du plasma à absorber le cholestérol, une mesure fonctionnelle importante et sous-étudiée. Le sexe et l'indice de masse corporelle se sont avérés être deux variables qui modifient fortement les effets d'une réduction génétiquement prédite de la concentration de CETP sur les paramètres étudiés. Notre modèle prédit un bénéfice plus important de l'inhibition de la CETP pour les femmes et les individus ayant un indice de masse corporelle normal sur le profil lipidique, mais nous n'avons pas pu démontrer une modulation de l'effet sur la maladie ischémique. Cette étude reste importante sur le plan méthodologique, car elle soulève la possibilité d'utiliser des modèles génétiques de cibles pharmacologiques pour prédire l'hétérogénéité dans la réponse au médicament, une lacune des essais randomisés classiques. Enfin, nous avons adopté une approche centrée sur les gènes pour caractériser l'effet de 19 114 protéines humaines sur 1 210 phénotypes de la UK Biobank. Les résultats de cette étude sont accessibles au public (https://exphewas.statgen.org/) et constituent une ressource précieuse pour cerner rapidement les conséquences phénotypiques associées à un locus. Dans le contexte de validation de cibles pharmacologiques, cette plate-forme web peut aider à rapidement identifier les problèmes de sécurité potentiels ou à découvrir des possibilités de repositionnement du médicament. Un exemple d'utilisation de cette plate-forme est présenté où nous identifions le gène de la myotiline comme un nouvel acteur potentiel dans la pathogénèse de la fibrillation auriculaire. / Using population-level data, it is possible to identify genetic polymorphisms that confer natural protection against disease. If the molecular mechanism underlying this protection can be understood, for example by linking variants to the disruption of a particular protein, it may be possible to develop drugs that act on the same biological target. This link between drugs and variants is a central premise of genetic drug target validation. In this work, a genetic model is used to predict the beneficial and adverse effects of ivabradine, a drug used to lower heart rate. Ivabradine is an inhibitor of the ion channel potassium/sodium hyperpolarization-activated cyclic nucleotide-gated channel 4, encoded by the HCN4 gene, with heterogeneous benefits in different patient populations. This drug is effective in the treatment of angina and heart failure but it is ineffective in patients with stable coronary artery disease without systolic dysfunction. Characterization of the effect of ivabradine has occurred over a 6-year period and three large phase III trials have been conducted. We will investigate whether this process could have been streamlined using genetic models and contrast the ivabradine-specific effect with the general effect of heart rate reduction using a Mendelian Randomization approach. Second, a genetic approach is used to study the effect of inhibiting cholesteryl ester tranfer protein (CETP), an enzyme responsible for the transfer of cholestery esters and triglycerides between different lipoproteins and a widely studied drug target for the treatment of coronary artery disease. Genetic studies predict a benefit of CETP inhibition, but randomized trials yielded heterogeneous and disappointing results. We will use a genetic model of CETP inhibition to identify variables that may modulate the effect of CETP inhibition on biomarkers and ischemic disease. The biomarkers we considered included low- and high-density lipoprotein cholesterol levels but also the plasma cholesterol efflux capacity, an important and understudied functional measure of high density lipoproteins. Sex and body mass index strongly modulated the effect of a genetically predicted lower CETP concentration on the lipid profile. Our model predicts a greater benefit of CETP inhibition in women and individuals with normal body mass index on the lipid profile, but these observations did not translate to changes in the effect on cardiovascular outcomes. This study remains methodologically important because it demonstrates the possibility of using genetic models of drug targets to predict heterogeneity in drug response, a shortcoming of conventional randomized trials. Finally, we adopted a gene-centric approach to characterize the effect of 19,114 human protein-coding genes on 1,210 UK Biobank phenotypes. The results of this study are publicly available (https://exphewas.statgen.org/) and provide a valuable resource to rapidly screen the phenotypic consequences associated with a gene. In the context of drug target validation, this platform can help quickly identify potential safety issues or discover drug repurposing opportunities. An example of the use of this platform is presented where we identify the myotilin gene as a potential atrial fibrillation gene.

Mass spectroscopy imaging (MSI) has become a vital tool in modern research due to its ability to visualize the spatial distribution of molecules within tissue samples. The collaboration between researchers at AZ, the University of Gothenburg, and Chalmers University of Technology using the NanoSIMS instrument and MSI-SIMS technology has opened up new avenues of exploration in pharmaceutical development, particularly in examining drugs and metabolites at sub-cellular levels. This groundbreaking research has the potential to significantly improve the efficacy and safety of future pharmaceutical products. NanoSIMS possesses a unique imaging and processing technique that enables high-resolution imaging of cellular structures and subcellular compartments. This powerful tool allows for the visualization and measurement of elements and isotopes at the subcellular level. The technique involves bombarding a sample with a focused primary ion beam, which causes the emission of secondary ions. These secondary ions are then analyzed to determine the elemental and isotopic composition of the sample. NanoSIMS is particularly useful for analyzing biomolecules since traditional Mass spectrometry methods cannot provide information about how molecules behave at the cellular level. Given that many of the drugs used today have intra-cellular targets, hence understanding the drug's cellular pathways is extremely important, especially in cases where the risk for organ toxicity is high due to the high dosage of the drugs.  Our data from the image analysis indicated the presence of amiodarone inside the lysosomes; however, the lack of enrichment from the 13C portion of the dual-labeled molecule made it difficult to reach a variation below the LOD. Since our LOD is relatively high when working with 13C12C, we focused on the fact that accuracy, precision, and sensitivity would be the most crucial factors in our study. After adjusting these parameters, we obtained an image that made the measurement possible. This project aims to utilize a dual-labeled drug (13C and 127I) to bridge the absolute quantification ability of the 13C labeling scheme to the more sensitive labeling scheme. The focus of this study lies therefore on optimization and the relationship between Spatial resolution, Sensitivity, Mass Resolution, Accuracy, and Precision. This technique is extremely promising, but the limit of detection is relatively high mainly due to the high percentage of carbon in the sample. Despite this fact, we were able to present some valuable data.  Our analysis showed that the sensitivity of the 127I is much better than 13C, however, we produced an image where the ratio between the labels was above the detection limit. Using this data, a Relative sensitivity factor (RSF) value was measured, and the concentration of the drug could be estimated by applying the quantification equation.