This thesis deals with a concept of system for automatic passenger counting in different  modes of transport. Counting units are placed in top of the door area in the vehicle. Passengers are detected at the disparity map counted from the stereo-camera images. Object tracking is achieved with Global nearest neighbor and Multiple hypothesis tracking algorithm. This system is used for public transportation surveys.

La maladie d’Alzheimer (MA) est une maladie neurodégénérative complexe caractérisée par une perte progressive de la mémoire et de la cognition. C’est la première cause de démence chez le sujet âgé et affecte environs 4.6 millions de personnes par an, selon un rapport de l’association « Alzheimer’s disease International », le nombre de patients pourrait s’élever à 135.5 millions en 2050. Du fait de sa complexité, la MA reste incurable et seuls 4 médicaments aux vertus palliatives dont 3 visant à inhiber l’acétylcholinestérase (AChE) ont reçu une autorisation de mise sur le marché à ce jour. L’approche multi-cible parait particulièrement adaptée du fait du grand nombre de cibles potentielles de la pathologie, et du caractère multifactoriel de la maladie. Cette approche consiste à associer sur une seule molécule, plusieurs pharmacophores afin qu’ils puissent agir simultanément sur différentes cibles impliquées dans le processus neurodégénératif. Dans ce contexte, en parallèle de la resynthèse d’un ligand multi-cible conjugué alliant un inhibiteur d’AChE (IAChE) et un antioxydant, deux nouvelles familles de ligands multi-cibles conjuguées, combinant un IAChE et un agoniste des récepteurs nicotiniques α7 (α7 nAChR) ont été conçues et leur synthèse abordée. Dans le cas de la première famille, le fragment ivastigmine a été choisi pour sa capacité à inhiber de manière pseudo-irréversible l’AChE et a été conjugué à un motif quinuclidine, un puissant agoniste des α7 nAChRs impliqués dans la MA. En combinant ces deux fragments, il a été observé que les propriétés biologiques in vitro de chaque pharmacophore étaient améliorées. La structure de la seconde famille est basée sur le Donepezil, un IAChE réversible de plus forte affinité, combiné au même motif quinuclidine que dans la série précédente. Si des intermédiaires avancés ont été obtenus, un ou deux étapes restent à finaliser pour finaliser la synthèse de cette troisième famille de MTDL. / Alzheimer’s disease (AD) is a complex neurodegenerative disease characterised by a progressive loss of memory and cognition. Nowadays, 4.6 million new patients are identified every year and according to the “Alzheimer’s diseases International” association, the number of patients could reach 135.5 million in 2050. Due to its complexity, AD remains uncurable and only 4 palliative drugs, of which 3 are acetylcholinesterase (AChE) inhibitors (AChEI), have been approved by FDA to date. AD being a multifactorial illness, with many potential targets involved in the pathology, the MTDL approach seems promising. This strategy associates in one single molecule, different pharmacophores (at least) acting on different targets involved in this CNS-related disorder. In this context, in parallel with the upscaled synthesis of a conjugated MTDL combining an AChEI inhibitor and an antioxidant, two new families of conjugated MTDLs associating an AChEI and a α7 nicotinic receptor (α7 nAChR) agonist have been investigated. The structure of the first family was based on a Rivastigmine scaffold, known to be a pseudo-irreversible AChE inhibitor, and a quinuclidine fragment, a potent α7 nAChR agonist. By combining these two fragments, it was brought to light that the in vitro biological properties were improved on both targets. The second family was based on a donepezil fragment, a more potent AChEI, and the same quinuclidine fragment than in the first family. Advanced intermediates have been obtained, and two last steps remain to be achieved for the completion of this third MTDL series.

Detection of multiple-lane markings (lane-line) on road surfaces is an essential aspect of autonomous vehicles. Although several approaches have been proposed to detect lanes, detecting multiple lane-lines consistently, particularly across a stream of frames and under varying lighting conditions is still a challenging problem. Since the road's markings are designed to be smooth and parallel, lane-line sampled features tend to be spatially and temporally correlated inside and between frames. In this thesis, we develop novel methods to model these spatial and temporal dependencies in the form of the target tracking problem. In fact, instead of resorting to the conventional method of processing each frame to detect lanes only in the space domain, we treat the overall problem as a Multiple Extended Target Tracking (METT) problem. In the first step, we modelled lane-lines as multiple "independent" extended targets and developed a spline mathematical model for the shape of the targets. We showed that expanding the estimations across the time domain could improve the result of estimation. We identify a set of control points for each spline, which will track over time. To overcome the clutter problem, we developed an integrated probabilistic data association fi lter (IPDAF) as our basis, and formulated a METT algorithm to track multiple splines corresponding to each lane-line.In the second part of our work, we investigated the coupling between multiple extended targets. We considered the non-parametric case and modeled target dependency using the Multi-Output Gaussian Process. We showed that considering dependency between extended targets could improve shape estimation results. We exploit the dependency between extended targets by proposing a novel recursive approach called the Multi-Output Spatio-Temporal Gaussian Process Kalman Filter (MO-STGP-KF). We used MO-STGP-KF to estimate and track multiple dependent lane markings that are possibly degraded or obscured by traffic. Our method tested for tracking multiple lane-lines but can be employed to track multiple dependent rigid-shape targets by using the measurement model in the radial space In the third section, we developed a Spatio-Temporal Joint Probabilistic Data Association Filter (ST-JPDAF). In multiple extended target tracking problems with clutter, sometimes extended targets share measurements: for example, in lane-line detection, when two-lane markings pass or merge together. In single-point target tracking, this problem can be solved using the famous Joint Probabilistic Data Association (JPDA) filter. In the single-point case, even when measurements are dependent, we can stack them in the coupled form of JPDA. In this last chapter, we expanded JPDA for tracking multiple dependent extended targets using an approach called ST-JPDAF. We managed dependency of measurements in space (inside a frame) and time (between frames) using different kernel functions, which can be learned using the trained data. This extension can be used to track the shape and dynamic of dependent extended targets within clutter when targets share measurements. The performance of the proposed methods in all three chapters are quanti ed on real data scenarios and their results are compared against well-known model-based, semi-supervised, and fully-supervised methods. The proposed methods offer very promising results. / Thesis / Doctor of Philosophy (PhD)

In the medtech industry models of the cardiiovascular systems and simulations are valuable tools for the development of new products ad therapies. The simulator Aplysia has been developed over several decade and is able to replicate a wide range of phenomena involved in the physiology and pathophysiology of breathing and circulation. Aplysia is also able to simulate the hemodynamics phenomena starting from a set of patient model parameters enhancing the idea of a "digital twin", i.e. a patient-specific representative simulation. Having a good starting estimate of the patient model parameters is a crucial aspect to start the simulation. A first estimate can be given by looking at patient monitoring data but medical expertise is required. The goal of this thesis is to address the parameter estimation task by developing machine learning and deep learning model to give an estimate of the patient model parameter starting from a set of time-varying data that we will refers as state variables. Those state variables are descriptive of a specific patient and for our project we will generate them through Aplysia starting from the simulation presets already available in the framework. Those presets simulates different physiologies, from healthy cases to different cardiovascular diseases. The thesis propose a comparison between a machine learning pipeline and more complex deep learning architecture to simultaneously predicting all the model parameters. This task is referred as Multi Target Regression (MTR) so the performances will be assessed in terms of MTR performance metrics. The results shows that a gradient boosting regressor with a regressor-stacking approach achieve overall good performances, still it shows some lack of performances on some target model parameters. The deep learning architectures did not produced any valuable results because of the amount of our data: to deploy deep architectures such as ResNet or more complex Convolutional Neural Network (CNN) we need more simulations then the one that were done for this thesis work. / Simulatorn Aplysia har under flera decennier utvecklats för forskning och FoU inom området kardiovaskulära systemmodeller och simuleringar och kan idag replikera ett brett spektrum av fenomen involverade i andningens och cirkulationens fysiologi och patofysiologi. Aplysia kan också simulera hemodynamiska fenomen med utgångspunkt från en uppsättning patientmodellparametrar och detta förstärker idén om en digital tvilling", det vill säga en patientspecifik representativ simulering. Att ha en bra startuppskattning av patientmodellens parametrar är en avgörande aspekt för att starta simuleringen. En första uppskattning kan ges genom att titta på patientövervakningsdata men medicinsk expertis krävs för tolkningen av sådana data. Målet med denna mastersuppsats är att addressera parameteruppskattningsuppgiften genom att utveckla maskininlärnings-och djupinlärningsmodeller för att erhålla en uppskattning av patientmodellparametrar utgående från en uppsättning tidsvarierande data som vi kommer att referera till som tillståndsvariabler. Dessa tillståndsvariabler är beskrivande för en specifik patient och för vårt projekt kommer vi att generera dem med hjälp av Aplysia med utgångspunkt från de modellförinställningar som redan finns tillgängliga i ramverket. Dessa förinställningar simulerar olika fysiologier, från friska fall till olika hjärt-kärlsjukdomar. Uppsatsen presenterar en jämförelse mellan en maskininlärningspipeline och en mer komplex djupinlärningsarkitektur för att samtidigt förutsäga alla modellparametrar. Denna uppgift bygger på MTR så resulterande prestanda kommer att bedömas i termer av MTR prestationsmått. Resultaten visar att en gradientförstärkande regressor med en regressor-stacking-metod uppnår överlag goda resultat, ändå visar den en viss brist på prestanda på vissa målmodellparametrar. Deep learning-arkitekturerna gav inga värdefulla resultat på grund av den begränsade mängden av data vi kunde generera. För att träna djupa arkitekturer som ResNet eller mer komplexa CNN behöver vi fler simuleringar än den som gjordes för detta examensarbete.

Opioid analgesics have been a major contribution to pain therapy with opioids being used as an effective treatment for various recalcitrant pain conditions. The drug class has come under increased scrutiny due to the raising concerns about the public health crisis of opioid misuse and addiction, thereby increasing the need for alternative and safer analgesics. The exploration of alternative pharmacotherapy for pain management has led to an increasing paradigm shift towards the development of a single-drug-multiple-target approach that takes inspiration from numerous naturally occurring drugs. The mu-opioid receptor has been the primary target for the management of pain; however, the voltage-gated sodium channel Nav1.7 is gaining attention as a putative antinociceptive target based on human genetic evidence. The proposed research aims to develop multi-target directed ligands (MTDL) that modulates two key targets for pain perception, the MOR, and Nav1.7 to generate analgesics with reduced side effects and enhanced analgesia. This will be achieved by exploiting polypharmacology to develop hybrid analgesia in two ways: (i) performing structure-activity relationship (SAR) studies to design a single drug with two pharmacophores that specifically interacts with both the targets (ii) exploiting in silico techniques by performing structure-based virtual ligand screening (VLS) of a chemical library. In our work, we report that through SAR studies and molecular docking studies that the designed compounds having in combination the pharmacophore of PZM21 and aryl sulfonamide demonstrate significant interactions between the active compounds and both the MOR and Nav1.7 proteins. This study also reports the first ever bifunctional virtual ligand screening where a library consisting of over a million compounds was screened for bifunctional activity at the MOR and the Nav1.7 ion channel. We also report the development of a novel mechanism-specific membrane potential assay to that can be used to screen for subtype selective Nav1.7 inhibitors. The research performed in this thesis will serve as a platform to explore the possibility of MTDL as potential therapeutic solutions to diseases of complex etiologies such as chronic pain. It will also serve as a starting point to exploring bifunctional VLS as a way to screen large chemical libraries for MTDLs.

Indoor localization remains an open problem in geolocation research, and once this is solved the localization enables counting and tracking of building occupants. This information is vital in an emergency, enables occupancy-optimized heating or cooling, and assists smart buildings in tailoring services for occupants. Unfortunately, two prevalent technologies---GPS and cellular-based positioning---perform poorly indoors due to attenuation and multipath from the building. To address this issue, the research community devised many alternatives for indoor localization (e.g., beacons, RFID tags, Wi-Fi fingerprinting, and UWB to cite just a few examples). A drawback with most is the requirement for those being located to carry a properly-configured device at all times. An alternative based on computer vision techniques poses significant privacy concerns due to cameras recording building occupants. By contrast, ILoViT research makes novel use of accelerometers already present in some buildings. These sensors were originally intended to monitor structural health or to study structural dynamics. The key idea is that when a person's footstep-generated floor vibrations can be detected and located then it becomes possible to locate persons moving within a building. Vibration propagation in buildings has complexities not encountered by acoustic or radio wave propagation in air; thus, conventional localization algorithms are inadequate. ILoVIT algorithms account for these conditions and have been demonstrated in a public building to provide sub-meter accuracy. Localization provides the foundation for counting and tracking, but providing these additional capabilities confronts new challenges. In particular, how does one determine the correct association of footsteps to the person making them? The ILoViT research created two methods for solving the data association problem. One method only provides occupancy counting but has modest, polynomial time complexity. The other method draws inspiration from prior work in the radar community on the multi-target tracking problem, specifically drawing from the multiple hypothesis tracking strategy. This dissertation research makes new enhancements to this tracking strategy to account for human gait and characteristics of footstep-derived multilateration. The Virginia Polytechnic Institute and State University's College of Engineering recognized this dissertation research with the Paul E. Torgersen Graduate Student Research Excellence Award. / Ph. D. / Indoor localization remains an open problem in geolocation research, and once this is solved the localization enables counting and tracking of building occupants. This information is vital in an emergency, enables occupancy-optimized heating or cooling and assists smart buildings in tailoring services for occupants. Unfortunately, two prevalent technologies—GPS and cellular-based positioning—are ill-suited here due to the way a building’s weakens and distorts wireless signals. To address this issue the research community devised many alternatives for indoor localization. A drawback with most is the requirement for those being located to carry a properly-configured device at all times. An alternative based on computer vision techniques poses significant privacy concerns due to cameras recording building occupants. By contrast, ILoViT research makes novel use of a mature sensor technology already present in some buildings. These sensors were originally intended to monitor structural health or to study structural dynamics. The key idea behind this unconventional role for building sensors is that when a person’s footstep-generated floor vibrations can be detected and located then it is possible to locate persons moving within a building. Vibration propagation in buildings has complexities not encountered by acoustic or radio wave propagation in air; thus, conventional localization algorithms designed for those applications are inadequate. ILoVIT algorithms account for these conditions and have been demonstrated in a public building to provide sub-meter accuracy. Localization provides the foundation for counting and tracking, but providing these additional capabilities confronts new challenges. In particular, how does one determine the correct association of footsteps to the person making them? The ILoViT research created two methods for solving the data association problem. One method only provides area occupancy counting but has modest complexity. The other method draws inspiration from prior work in the radar community on the multi-target tracking problem, and the dissertation research makes new enhancements to account for human gait and footstep-based localization. The Virginia Polytechnic Institute and State University’s College of Engineering recognized this dissertation research with the Paul E. Torgersen Graduate Student Research Excellence Award.

In preclinical research on Alzheimer's disease pharmacotherapy, attention is paid to multipotent compounds, enabling intensification of the effect by targeting multiple pathophysiological mechanisms. The aim of the thesis was to assess the effect of multipotent compounds and combination therapy in models of cognitive deficit in the rat. The mechanism of action of the tested compounds was modulation of neurotransmitter systems. The aim of the first part of the study was to compare the effect of experimental monotherapy and combination therapy with an N-methyl-D-aspartate (NMDA) receptor antagonist and a γ-aminobutyric acid type A (GABAA) receptor positive modulator in the trimethyltin-induced model. Superiority of the combination therapy was proven by histological analysis of hippocampal neurodegeneration; however, it did not reach statistical significance in the cognitive test. The other part of the thesis focused on multipotent tacrine derivatives. We demonstrated a positive effect of 6- chlorotacrine-6-nitrobenzothiazole hybrid, as well as 6-chlorotacrine-L-tryptophan hybrid, acting as acetylcholinesterase inhibitors, in the scopolamine-induced model of cognitive deficit. Besides, we demonstrated a low risk of serious side effects of other tacrine derivatives acting as NMDA receptor antagonists....

La détection et le pistage de cibles de surface, maritimes ou terrestres, constituent l’un des champs d’application de la surveillance par radar aéroporté. Dans ce contexte spécifique, il s’agit d’estimer les trajectoires d’un ou de plusieurs objets mobiles au cours du temps à partir de mesures radar bruitées. Cependant, plusieurs contraintes s’additionnent au problème d’estimation des trajectoires :1. le nombre d’objets présents dans la région d’intérêt est inconnu et peut évoluer au cours du temps,2. les mesures fournies par le radar ne correspondent pas toutes à des objets mobiles car certaines sont dues à l’environnement ; il s’agit de fausses alarmes,3. une mesure n’est pas toujours disponible pour chaque objet à chaque instant ; il s’agit de non-détections,4. les cibles de surface peuvent être très diverses en termes de capacité de manoeuvre.Pour tenir compte des trois premières exigences, les modèles d’ensembles finis aléatoires peuvent être envisagés pour procéder aux estimations simultanées du nombre d’objets et de leur trajectoire dans un formalisme bayésien. Pour répondre à la quatrième contrainte, une classification des objets à pister peut s’avérer utile. Aussi, dans le cadre de cette thèse, nous nous intéressons à deux traitements adaptatifs qui intègrent ces deux principes.Tout d’abord, nous proposons une approche conjointe de pistage et de classification dédiée au cas d’un objet évoluant en présence de fausses alarmes. Notre contribution réside dans le développement d’un algorithme incorporant un filtre fondé sur un ensemble fini aléatoire de Bernoulli. L’algorithme résultant combine robustesse aux fausses alarmes et capacité à classer l’objet. Cette classification peut être renforcée grâce à l’estimation d’un paramètre discriminant comme la longueur, qui est déduite d’une mesure d’étalement distance.Le second traitement adaptatif présenté dans cette thèse est une technique de pistage de groupes de cibles dont les mouvements sont coordonnés. Chaque groupe est caractérisé par un paramètre commun définissant la coordination des mouvements de ses cibles. Cependant, ces dernières conservent une capacité de manoeuvre propre par rapport à la dynamique de groupe. S’appuyant sur le formalisme des ensembles finis aléatoires, la solution proposée modélise hiérarchiquement la configuration multi-groupes multi-cibles. Au niveau supérieur, la situation globale est représentée par un ensemble fini aléatoire dont les éléments correspondent aux groupes de cibles. Ils sont constitués du paramètredu groupe et d’un ensemble fini aléatoire multi-cibles. Ce dernier contient les vecteurs d’état des cibles du groupe dont le nombre peut évoluer au cours du temps. L’algorithme d’estimation développé est lui-aussi organisé de manière hiérarchique. Un filtre multi-Bernoulli labélisé (LMB) permet d’estimer le nombre de groupes, puis pour chacun d’entre eux, leur probabilité d’existence ainsi que leur paramètre commun. Pour ce faire, le filtre LMB interagit avec un banc de filtres multi-cibles qui opèrent conditionnellement à une hypothèse de groupe. Chaque filtre multi-cibles estime le nombre et les vecteurs d’état des objets du groupe. Cette approche permet de fournir à l’opérationnel des informations sur la situation tactique. / Detecting and tracking maritime or ground targets is one of the application fields for surveillance by airborne radar systems. In this specific context, the goal is to estimate the trajectories of one or more moving objects over time by using noisy radar measurements. However, several constraints have to be considered in addition to the problem of estimating trajectories:1. the number of objects inside the region of interest is unknown and may change over time,2. the measurements provided by the radar can arise from the environment and do not necessarily correspond to a mobile object; the phenomenon is called false detection,3. a measurement is not always available for each object; the phenomenon is called non-detection,4. the maneuverability depends on the surface targets.Concerning the three first points, random finite set models can be considered to simultaneously estimate the number of objects and their trajectories in a Bayesian formalism. To deal with the fourth constraint, a classification of the objects to be tracked can be useful. During this PhD thesis, we developped two adaptive approaches that take into account both principles.First of all, we propose a joint target tracking and classification method dedicated to an object with the presence of false detections. Our contribution is to incorporate a filter based on a Bernoulli random finite set. The resulting algorithm combines robustness to the false detections and the ability to classify the object. This classification can exploit the estimation of a discriminating parameter such as the target length that can be deduced from a target length extent measurement.The second adaptive approach presented in this PhD dissertation aims at tracking target groups whose movements are coordinated. Each group is characterized by a common parameter defining the coordination of the movements of its targets. However, the targets keep their own capabilities of maneuvering relatively to the group dynamics. Based on the random finite sets formalism, the proposed solution represents the multi-target multi-group configuration hierarchically. At the top level, the overall situation is modeled by a random finite set whose elements correspond to the target groups. They consist of the common parameter of the group and a multi-target random finite set. The latter contains the state vectors of the targets of the group whose number may change over time. The estimation algorithm developed is also organized hierarchically. A labeled multi-Bernoulli filter (LMB) makes it possible to estimate the number of groups, and for each of them, to obtain their probability of existence as well as their common parameter. For this purpose, the LMB filter interacts with a bank of multi-target filters working conditionally to a group hypothesis. Each multi-target filter estimates the number and state vectors of the objects in the group. This approach provides operational information on the tactical situation.

Dans les systèmes de perception multicapteurs, un point central concerne le suivi d'objets multiples. Dans mes travaux de thèse, le capteur principal est un télémètre laser à balayage qui perçoit des cibles étendues. Le problème desuivi multi-objets se décompose généralement en plusieurs étapes (détection, association et suivi) réalisées de manière séquentielle ou conjointe. Mes travaux ont permis de proposer des alternatives à ces méthodes en adoptant une approche "track-before-detect" sur cibles distribuées qui permet d'éviter la succession des traitements en proposant un cadre global de résolution de ce problème d'estimation. Dans une première partie, nous proposons une méthode de détection travaillant directement en coordonnées naturelles (polaires) qui exploite les propriétés d'invariance géométrique des objets suivis. Cette solution est ensuite intégrée dans le cadre des approches JPDA et PHD de suivi multicibles résolues grâce aux méthodes de Monte-Carlo séquentielles. La seconde partie du manuscrit vise à s'affranchir du détecteur pour proposer une méthode dans laquelle le modèle d'objet est directement intégré au processus de suivi. C'est sur ce point clé que les avancées ont été les plus significatives permettant d'aboutir à une méthode conjointe de détection et de suivi. Un processus d'agrégation a été développé afin de permettre une formalisation des données qui évite tout prétraitement sous-optimal. Nous avons finalement proposé un formalisme général pour les systèmes multicapteurs (multilidar, centrale inertielle, GPS). D'un point de vue applicatif, ces travaux ont été validés dans le domaine du suivi de véhicules pour les systèmes d'aide à la conduite. / In multi-sensor perception systems, an active topic concerns the multiple object tracking methodes. In this work, the main sensor is a scanning laser rangefinder perceiving extended targets. Tracking methods are generally composed of a three-step scheme (detection, association and tracking) which is jointly or sequentially implemented. This work proposes alternative solutions by considering a track-before-detect approach on extended targets. It avoids the classic procedures by proposing a global framework to solve this estimation problem. Firstly, we propose a detection method dealing with measurements in natural coordinates (polar) which is founded on geometrical invariance properties of the tracked objects. This solution is then integrated in the JPDA and PHD multi-target tracking frameworks solved with the sequential Monte-Carlo methods. The second part of this thesis aims at avoiding the detection step to propose an approach where the object model is directly embedded in the tracking process. This lets to build a novel joint detection and tracking approach. An aggregation process was developed to construct a measurement modeling avoiding any suboptimal preprocessing. We finally proposed a general framework for multi-sensor systems ( multiple lidar, inertial sensor, GPS). Theses methods were applied in the area of multiple vehicle tracking for the Advanced Driver Assistance Systems.

La formation de nouveaux vaisseaux sanguins ou angiogenèse soutient la croissance tumorale en fournissant l'oxygène et les nutriments qui lui sont nécessaires. Le rôle clé du facteur de croissance de l'endothélium vasculaire VEGF dans ce processus a suscité le développement de stratégies anti-angiogéniques pour le traitement du cancer. Cependant, des travaux précliniques et des données cliniques suggèrent l'émergence de résistances aux anti-angiogéniques, en raison notamment de la redondance des facteurs de croissance pro-angiogéniques. Il est donc nécessaire de développer des stratégies alternatives plus efficaces. En 2010, notre laboratoire a apporté la preuve de concept d'une thérapie anti-tumorale et anti-angiogénique innovante basée la dégradation des ARNm à demi-vie courte par la protéine à doigts de zinc TIS11b. Néanmoins, l'instabilité de la protéine thérapeutique a entravé la caractérisation plus détaillée de cette stratégie. Dans ce contexte, l'objectif majeur de ma thèse était l'optimisation de la stabilité et de l'activité de TIS11b et l'évaluation de son efficacité thérapeutique. Pour cela, nous avons généré une nouvelle protéine TIS11b génétiquement modifiée sur la base d'études biochimiques et moléculaires. Notamment, nous avons observé que la phosphorylation de la sérine 334 située dans le domaine C-terminal de TIS11b augmente de façon très significative la stabilité de la protéine et potentialise son activité déstabilisatrice de l'ARNm du VEGF. De plus, la délétion du domaine N-terminal augmente également la stabilité de TIS11b sans altérer son activité. Nous avons alors généré deux nouvelles protéines thérapeutiques, la protéine ZnC et la protéine ZnC334D pour laquelle la troncation du domaine N-terminal et la substitution de la sérine S334 par un aspartate mimant une phosphorylation ont été combinées. Les nouvelles protéines ont été fusionnées à une étiquette polyarginine R9 leur permettant de traverser les membranes cellulaires (R9-ZnC et R9-ZnCS334D). Nous avons montré que R9-ZnC et R9-ZnCS334D inhibent l'expression de VEGF in vitro dans la lignée de cancer du sein murin 4T1. De plus, R9-ZnCS334D exerce une activité anti-proliférative, anti-migratoire et anti-invasive dans ces cellules. In vivo, l'injection intra-tumorale de R9-ZnCS334D dans des tumeurs 4T1 préétablies inhibe significativement l'expression du VEGF, la croissance et la vascularisation tumorales. De façon remarquable, l'analyse des extraits tumoraux indique que le traitement diminue l'expression de chimiokines clés dans les processus d'angiogenèse, d'inflammation et d'invasion (Fractalkine, MCP-1, NOV, SDF-1, Pentraxin…). Enfin, R9-ZnC et R9-ZnCS334D inhibent l'expression de marqueurs de la transition épithélio-mésenchymateuse, un processus impliqué dans la dissémination métastatique. L'ensemble de ces travaux indique que R9-ZnC et R9-ZnCS334D sont des molécules anti-tumorales multi-cibles, qui inhibent plusieurs étapes clés de la progression tumorale. Cette étude confirme que le ciblage de la stabilité des ARNm est une stratégie prometteuse et novatrice pour le développement de nouvelles thérapies anti-cancéreuses. / One of the innovative aspects of anti-cancer therapies is the possibility of preventing tumor growth by blocking blood supply. Cancer cells induce the formation of their own blood vessels from pre-existing vasculature, a process called angiogenesis. One of the most important proangiogenic factors is vascular endothelial growth factor (VEGF). The success of bevacizumab (a humanized anti-VEGF monoclonal antibody) combined to chemotherapy for the treatment of human metastatic cancers has validated VEGF as an efficient target. However, despite the initial enthusiasm, resistance to these anti-angiogenic treatments resulting from compensatory mechanisms occurs upon time. For this reason, there is a real need for new anti-angiogenic drugs that will target the angiogenic process through distinct mechanisms. In 2010, our laboratory has successfully developed an anti-angiogenic and anti-tumoral therapy based on destabilization of short-lived mRNAs by the zinc finger protein TIS11b. However, the therapeutic protein was highly unstable, thus making it difficult to further characterize the experimental therapy. In this context, the main task of my thesis was the optimization of TIS11b stability and activity followed by the evaluation of the multi-target action of our novel protein on tumor development. In a first part of this work, biochemical and molecular approaches allowed us to demonstrate that phosphorylation of the C-terminal serine S334 in TIS11b protein markedly increases its stability. In addition, deletion of the N-terminal domain of TIS11b highly increases its protein stability without affecting its activity. Therefore, we integrated N-terminal truncation (ZnC) and C-terminal substitution of S334 by an aspartate to mimic a permanent phosphorylation at S334 (ZnCS334D) as a novel TIS11b engineering strategy. Both proteins were fused subsequently to a cell-penetrating peptide polyarginine (R9). In vitro studies revealed that R9-ZnC and R9-ZnCS334D inhibit VEGF expression in the murine breast cancer cells 4T1. In addition, R9-ZnCS334D impaired proliferation, migration, invasion and anchorage-independent growth of 4T1 cells. In vivo, intra-tumoral injection of either protein significantly reduced VEGF expression and tumor vascularization. Strikingly, antibody array analyses of tumor extracts demonstrated a reduced expression of several chemokines such as Fractalkine, MCP-1, NOV, SDF-1 and Pentraxin upon R9-ZnC or R9-ZnCS334D treatment. These factors, which are produced by several cell types within tumor tissue, are key drivers of tumor angiogenesis, tumor-promoting inflammation and invasion. Furthermore, the expression of markers of the epithelial-to-mesenchymal transition was also significantly reduced, suggesting an anti-metastatic effect of R9-ZnC and R9-ZnCS334D. Thus, we provide R9-ZnC and R9-ZnCS334D as potential novel multi-target agents which inhibit key hallmarks of cancer progression. This work supports the emerging link between mRNA stability and cancer and proposes novel concepts for the development of innovative anti-cancer therapies.