COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography.

Tractography is a class of algorithms aiming at in vivo mapping the major neuronal pathways in the white matter from diffusion magnetic resonance imaging (MRI) data. These techniques offer a powerful tool to noninvasively investigate at the macroscopic scale the architecture of the neuronal connections of the brain. However, unfortunately, the reconstructions recovered with existing tractography algorithms are not really quantitative even though diffusion MRI is a quantitative modality by nature. As a matter of fact, several techniques have been proposed in recent years to estimate, at the voxel level, intrinsic microstructural features of the tissue, such as axonal density and diameter, by using multicompartment models. In this paper, we present a novel framework to reestablish the link between tractography and tissue microstructure. Starting from an input set of candidate fiber-tracts, which are estimated from the data using standard fiber-tracking techniques, we model the diffusion MRI signal in each voxel of the image as a linear combination of the restricted and hindered contributions generated in every location of the brain by these candidate tracts. Then, we seek for the global weight of each of them, i.e., the effective contribution or volume, such that they globally fit the measured signal at best. We demonstrate that these weights can be easily recovered by solving a global convex optimization problem and using efficient algorithms. The effectiveness of our approach has been evaluated both on a realistic phantom with known ground-truth and in vivo brain data. Results clearly demonstrate the benefits of the proposed formulation, opening new perspectives for a more quantitative and biologically plausible assessment of the structural connectivity of the brain.

A convex optimization framework for global tractography

In this article we present a novel approach for diffusion MRI global tractography. Our formulation models the signal in each voxel as a linear combination of fiber-tract basis func- tions, which consist of a comprehensive set of plausible fiber tracts that are locally compatible with the measured MR signal. This large dictionary of candidate fibers is directly estimated from the data and, subsequently, efficient convex optimization techniques are used for recovering the smallest subset globally best fitting the measured signal. Experimen- tal results conducted on a realistic phantom demonstrate that our approach significantly reduces the computational cost of global tractography while still attaining a reconstruction quality at least as good as the state-of-the-art global methods.

Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data.

Microstructure imaging from diffusion magnetic resonance (MR) data represents an invaluable tool to study non-invasively the morphology of tissues and to provide a biological insight into their microstructural organization. In recent years, a variety of biophysical models have been proposed to associate particular patterns observed in the measured signal with specific microstructural properties of the neuronal tissue, such as axon diameter and fiber density. Despite very appealing results showing that the estimated microstructure indices agree very well with histological examinations, existing techniques require computationally very expensive non-linear procedures to fit the models to the data which, in practice, demand the use of powerful computer clusters for large-scale applications. In this work, we present a general framework for Accelerated Microstructure Imaging via Convex Optimization (AMICO) and show how to re-formulate this class of techniques as convenient linear systems which, then, can be efficiently solved using very fast algorithms. We demonstrate this linearization of the fitting problem for two specific models, i.e. ActiveAx and NODDI, providing a very attractive alternative for parameter estimation in those techniques; however, the AMICO framework is general and flexible enough to work also for the wider space of microstructure imaging methods. Results demonstrate that AMICO represents an effective means to accelerate the fit of existing techniques drastically (up to four orders of magnitude faster) while preserving accuracy and precision in the estimated model parameters (correlation above 0.9). We believe that the availability of such ultrafast algorithms will help to accelerate the spread of microstructure imaging to larger cohorts of patients and to study a wider spectrum of neurological disorders.

Virtual screening and computational optimization for the discovery of covalent prolyl oligopeptidase inhibitors with activity in human cells.

Our docking program, Fitted, implemented in our computational platform, Forecaster, has been modified to carry out automated virtual screening of covalent inhibitors. With this modified version of the program, virtual screening and further docking-based optimization of a selected hit led to the identification of potential covalent reversible inhibitors of prolyl oligopeptidase activity. After visual inspection, a virtual hit molecule together with four analogues were selected for synthesis and made in one-five chemical steps. Biological evaluations on recombinant POP and FAPα enzymes, cell extracts, and living cells demonstrated high potency and selectivity for POP over FAPα and DPPIV. Three compounds even exhibited high nanomolar inhibitory activities in intact living human cells and acceptable metabolic stability. This small set of molecules also demonstrated that covalent binding and/or geometrical constraints to the ligand/protein complex may lead to an increase in bioactivity.

Accelerated Microstructure Imaging via Convex Optimization (AMICO) in crossing fibers

Mapping the microstructure properties of the local tissues in the brain is crucial to understand any pathological condition from a biological perspective. Most of the existing techniques to estimate the microstructure of the white matter assume a single axon orientation whereas numerous regions of the brain actually present a fiber-crossing configuration. The purpose of the present study is to extend a recent convex optimization framework to recover microstructure parameters in regions with multiple fibers.

l0-deconvolution for compressive diffusion MRI

Diffusion MRI is a well established imaging modality providing a powerful way to non-invasively probe the structure of the white matter. Despite the potential of the technique, the intrinsic long scan times of these sequences have hampered their use in clinical practice. For this reason, a wide variety of methods have been proposed to shorten acquisition times. [...] We here review a recent work where we propose to further exploit the versatility of compressed sensing and convex optimization with the aim to characterize the fiber orientation distribution sparsity more optimally. We re-formulate the spherical deconvolution problem as a constrained l0 minimization.

On evaluating the accuracy and biological plausibility of diffusion MRI tractograms

In diffusion MRI, traditional tractography algorithms do not recover truly quantitative tractograms and the structural connectivity has to be estimated indirectly by counting the number of fiber tracts or averaging scalar maps along them. Recently, global and efficient methods have emerged to estimate more quantitative tractograms by combining tractography with local models for the diffusion signal, like the Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT) framework. In this abstract, we show the importance of using both (i) proper multi-compartment diffusion models and (ii) adequate multi-shell acquisitions, in order to evaluate the accuracy and the biological plausibility of the tractograms.

Recrystallisation rims in zircon (Valle d'Arbedo, Switzerland): an integrated cathodoluminescence, LA-ICP-MS, SHRIMP, and TEM study

Recrystallization rims are a common feature of zircon crystals that underwent metamorphism. We present a microstructural and microchemical study of partially recrystallized zircon grains collected in polymetamorphic migmatites (Valle d'Arbedo, Ticino, Switzerland). The rims are bright in cathodo-luminescence (CL), with sharp and convex contacts characterized by inward-penetrating embayments transgressing igneous zircon cores. Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) data and transmission electron microscopy (TEM) imaging indicate that the rims are chemically and microstructurally different from the cores. The rims are strongly depleted in REE, with concentrations up to two orders of magnitude lower than in the cores, indicating a significant loss of REE during zircon recrystallization. Enrichment in non-formula elements, such as Ca, has not been observed in the rims. The microstructure of zircon cores shows a dappled intensity at and below the 100 nm scale, possibly due to radiation damage. Other defects such as pores and dislocations are absent in the core except at healed cracks. Zircon rims are mostly dapple-free, but contain nanoscale pores and strain centers, interpreted as fluid inclusions and chemical residues, respectively. Sensitive high-resolution ion microprobe (SHRIMP) U-Pb ages show that the recrystallization of the rims took place >200 Ma ago when the parent igneous zircon was not metamict. The chemical composition and the low-Ti content of the rims indicate that they form at sub-solidus temperatures (550-650 degrees C). Recrystallization rims in Valle d'Arbedo zircon are interpreted as the result of the migration of chemical reaction fronts in which fluid triggered in situ and contemporaneous interface-coupled dissolution-reprecipitation mechanisms. This study indicates that strong lattice strain resulting from the incorporation of a large amount of impurities and structural defects is not a necessary condition for zircon to recrystallize. Our observations suggest that the early formation of recrystallization rims played a major role in preserving zircon from the more recent Alpine metamorphic overprint.

Efficient total variation algorithm for fetal brain MRI reconstruction.

Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy, Total Variation (TV)- based energies and more recently non-local means. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm or fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n2) and O(1/√ε), while existing techniques are in O(1/n2) and O(1/√ε). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.

EADock : design of a new molecular docking algorithm and some of its applications

3 Summary 3. 1 English The pharmaceutical industry has been facing several challenges during the last years, and the optimization of their drug discovery pipeline is believed to be the only viable solution. High-throughput techniques do participate actively to this optimization, especially when complemented by computational approaches aiming at rationalizing the enormous amount of information that they can produce. In siiico techniques, such as virtual screening or rational drug design, are now routinely used to guide drug discovery. Both heavily rely on the prediction of the molecular interaction (docking) occurring between drug-like molecules and a therapeutically relevant target. Several softwares are available to this end, but despite the very promising picture drawn in most benchmarks, they still hold several hidden weaknesses. As pointed out in several recent reviews, the docking problem is far from being solved, and there is now a need for methods able to identify binding modes with a high accuracy, which is essential to reliably compute the binding free energy of the ligand. This quantity is directly linked to its affinity and can be related to its biological activity. Accurate docking algorithms are thus critical for both the discovery and the rational optimization of new drugs. In this thesis, a new docking software aiming at this goal is presented, EADock. It uses a hybrid evolutionary algorithm with two fitness functions, in combination with a sophisticated management of the diversity. EADock is interfaced with .the CHARMM package for energy calculations and coordinate handling. A validation was carried out on 37 crystallized protein-ligand complexes featuring 11 different proteins. The search space was defined as a sphere of 15 R around the center of mass of the ligand position in the crystal structure, and conversely to other benchmarks, our algorithms was fed with optimized ligand positions up to 10 A root mean square deviation 2MSD) from the crystal structure. This validation illustrates the efficiency of our sampling heuristic, as correct binding modes, defined by a RMSD to the crystal structure lower than 2 A, were identified and ranked first for 68% of the complexes. The success rate increases to 78% when considering the five best-ranked clusters, and 92% when all clusters present in the last generation are taken into account. Most failures in this benchmark could be explained by the presence of crystal contacts in the experimental structure. EADock has been used to understand molecular interactions involved in the regulation of the Na,K ATPase, and in the activation of the nuclear hormone peroxisome proliferatoractivated receptors a (PPARa). It also helped to understand the action of common pollutants (phthalates) on PPARy, and the impact of biotransformations of the anticancer drug Imatinib (Gleevec®) on its binding mode to the Bcr-Abl tyrosine kinase. Finally, a fragment-based rational drug design approach using EADock was developed, and led to the successful design of new peptidic ligands for the a5ß1 integrin, and for the human PPARa. In both cases, the designed peptides presented activities comparable to that of well-established ligands such as the anticancer drug Cilengitide and Wy14,643, respectively. 3.2 French Les récentes difficultés de l'industrie pharmaceutique ne semblent pouvoir se résoudre que par l'optimisation de leur processus de développement de médicaments. Cette dernière implique de plus en plus. de techniques dites "haut-débit", particulièrement efficaces lorsqu'elles sont couplées aux outils informatiques permettant de gérer la masse de données produite. Désormais, les approches in silico telles que le criblage virtuel ou la conception rationnelle de nouvelles molécules sont utilisées couramment. Toutes deux reposent sur la capacité à prédire les détails de l'interaction moléculaire entre une molécule ressemblant à un principe actif (PA) et une protéine cible ayant un intérêt thérapeutique. Les comparatifs de logiciels s'attaquant à cette prédiction sont flatteurs, mais plusieurs problèmes subsistent. La littérature récente tend à remettre en cause leur fiabilité, affirmant l'émergence .d'un besoin pour des approches plus précises du mode d'interaction. Cette précision est essentielle au calcul de l'énergie libre de liaison, qui est directement liée à l'affinité du PA potentiel pour la protéine cible, et indirectement liée à son activité biologique. Une prédiction précise est d'une importance toute particulière pour la découverte et l'optimisation de nouvelles molécules actives. Cette thèse présente un nouveau logiciel, EADock, mettant en avant une telle précision. Cet algorithme évolutionnaire hybride utilise deux pressions de sélections, combinées à une gestion de la diversité sophistiquée. EADock repose sur CHARMM pour les calculs d'énergie et la gestion des coordonnées atomiques. Sa validation a été effectuée sur 37 complexes protéine-ligand cristallisés, incluant 11 protéines différentes. L'espace de recherche a été étendu à une sphère de 151 de rayon autour du centre de masse du ligand cristallisé, et contrairement aux comparatifs habituels, l'algorithme est parti de solutions optimisées présentant un RMSD jusqu'à 10 R par rapport à la structure cristalline. Cette validation a permis de mettre en évidence l'efficacité de notre heuristique de recherche car des modes d'interactions présentant un RMSD inférieur à 2 R par rapport à la structure cristalline ont été classés premier pour 68% des complexes. Lorsque les cinq meilleures solutions sont prises en compte, le taux de succès grimpe à 78%, et 92% lorsque la totalité de la dernière génération est prise en compte. La plupart des erreurs de prédiction sont imputables à la présence de contacts cristallins. Depuis, EADock a été utilisé pour comprendre les mécanismes moléculaires impliqués dans la régulation de la Na,K ATPase et dans l'activation du peroxisome proliferatoractivated receptor a (PPARa). Il a également permis de décrire l'interaction de polluants couramment rencontrés sur PPARy, ainsi que l'influence de la métabolisation de l'Imatinib (PA anticancéreux) sur la fixation à la kinase Bcr-Abl. Une approche basée sur la prédiction des interactions de fragments moléculaires avec protéine cible est également proposée. Elle a permis la découverte de nouveaux ligands peptidiques de PPARa et de l'intégrine a5ß1. Dans les deux cas, l'activité de ces nouveaux peptides est comparable à celles de ligands bien établis, comme le Wy14,643 pour le premier, et le Cilengitide (PA anticancéreux) pour la seconde.

An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization.

Although fetal anatomy can be adequately viewed in new multi-slice MR images, many critical limitations remain for quantitative data analysis. To this end, several research groups have recently developed advanced image processing methods, often denoted by super-resolution (SR) techniques, to reconstruct from a set of clinical low-resolution (LR) images, a high-resolution (HR) motion-free volume. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has been quite attracted by Total Variation energies because of their ability in edge preserving but only standard explicit steepest gradient techniques have been applied for optimization. In a preliminary work, it has been shown that novel fast convex optimization techniques could be successfully applied to design an efficient Total Variation optimization algorithm for the super-resolution problem. In this work, two major contributions are presented. Firstly, we will briefly review the Bayesian and Variational dual formulations of current state-of-the-art methods dedicated to fetal MRI reconstruction. Secondly, we present an extensive quantitative evaluation of our SR algorithm previously introduced on both simulated fetal and real clinical data (with both normal and pathological subjects). Specifically, we study the robustness of regularization terms in front of residual registration errors and we also present a novel strategy for automatically select the weight of the regularization as regards the data fidelity term. Our results show that our TV implementation is highly robust in front of motion artifacts and that it offers the best trade-off between speed and accuracy for fetal MRI recovery as in comparison with state-of-the art methods.

Efficient total variation algorithm for fetal MRI reconstruction

Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy [1], Total Variation (TV)based energies [2,3] and more recently non-local means [4]. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm for fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n(2)) and O(1/root epsilon), while existing techniques are in O(1/n) and O(1/epsilon). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.

A cell therapy approach for erythropoietin delivery using encapsulated human primary fibroblasts

Summary Cell therapy has emerged as a strategy for the treatment of various human diseases. Cells can be transplanted considering their morphological and functional properties to restore a tissue damage, as represented by blood transfusion, bone marrow or pancreatic islet cells transplantation. With the advent of the gene therapy, cells also were used as biological supports for the production of therapeutic molecules that can act either locally or at distance. This strategy represents the basis of ex vivo gene therapy characterized by the removal of cells from an organism, their genetic modification and their implantation into the same or another individual in a physiologically suitable location. The tissue or biological function damage dictates the type of cells chosen for implantation and the required function of the implanted cells. The general aim of this work was to develop an ex vivo gene therapy approach for the secretion of erythropoietin (Epo) in patients suffering from Epo-responsive anemia, thus extending to humans, studies previously performed with mouse cells transplanted in mice and rats. Considering the potential clinical application, allogeneic primary human cells were chosen for practical and safety reasons. In contrast to autologous cells, the use of allogeneic cells allows to characterize a cell lineage that can be further transplanted in many individuals. Furthermore allogeneic cells avoid the potential risk of zoonosis encountered with xenogeneic cells. Accordingly, the immune reaction against this allogeneic source was prevented by cell macro- encapsulation that prevents cell-to-cell contact with the host immune system and allows to easy retrieve the implanted device. The first step consisted in testing the survival of various human primary cells that were encapsulated and implanted for one month in the subcutaneous tissue of immunocompetent and naturally or therapeutically immunodepressed mice, assuming that xenogeneic applications constitute a stringent and representative screening before human transplantation. A fibroblast lineage from the foreskin of a young donor, DARC 3.1 cells, showed the highest mean survival score. We have then performed studies to optimize the manufacturing procedures of the encapsulation device for successful engraftment. The development of calcifications on the polyvinyl alcohol (PVA) matrix serving as a scaffold for enclosed cells into the hollow fiber devices was reported after one month in vivo. Various parameters, including matrix rinsing solutions, batches of PVA and cell lineages were assessed for their respective role in the development of the phenomenon. We observed that the calcifications could be totally prevented by using ultra-pure sterile water instead of phosphate buffer saline solution in the rinsing procedure of the PVA matrix. Moreover, a higher lactate dehydrogenase activity of the cells was found to decrease calcium depositions due to more acidic microenvironment, inhibiting the calcium precipitation. After the selection of the appropriate cell lineage and the optimization of encapsulation conditions, a retroviral-based approach was applied to DARC 3.1 fibroblasts for the transduction of the human Epo cDNA. Various modifications of the retroviral vector and the infection conditions were performed to obtain clinically relevant levels of human Epo. The insertion of a post-transcriptional regulatory element from the woodchuck hepatitis virus as well as of a Kozak consensus sequence led to a 7.5-fold increase in transgene expression. Human Epo production was further optimized by increasing the multiplicity of infection and by selecting high producer cells allowing to reach 200 IU hEpo/10E6 cells /day. These modified cells were encapsulated and implanted in vivo in the same conditions as previously described. All the mouse strains showed a sustained increase in their hematocrit and a high proportion of viable cells were observed after retrieval of the capsules. Finally, in the perspective of human application, a syngeneic model using encapsulated murine myoblasts transplanted in mice was realized to investigate the roles of both the host immune response and the cells metabolic requirements. Various loading densities and anti-inflammatory as well as immunosuppressive drugs were studied. The results showed that an immune process is responsible of cell death in capsules loaded at high cell density. A supporting matrix of PVA was shown to limit the cell density and to avoid early metabolic cell death, preventing therefore the immune reaction. This study has led to the development of encapsulated cells of human origin producing clinically relevant amounts of human EPO. This work resulted also to the optimization of cell encapsulation technical parameters allowing to begin a clinical application in end-stage renal failure patients. Résumé La thérapie cellulaire s'est imposée comme une stratégie de traitement potentiel pour diverses maladies. Si l'on considère leur morphologie et leur fonction, les cellules peuvent être transplantées dans le but de remplacer une perte tissulaire comme c'est le cas pour les transfusions sanguines ou les greffes de moelle osseuse ou de cellules pancréatiques. Avec le développement de la thérapie génique, les cellules sont également devenues des supports biologiques pour la production de molécules thérapeutiques. Cette stratégie représente le fondement de la thérapie génique ex vivo, caractérisée par le prélèvement de cellules d'un organisme, leur modification génétique et leur implantation dans le même individu ou dans un autre organisme. Le choix du type de cellule et la fonction qu'elle doit remplir pour un traitement spécifique dépend du tissu ou de la fonction biologique atteintes. Le but général de ce travail est de développer .une approche par thérapie génique ex vivo de sécrétion d'érythropoïétine (Epo) chez des patients souffrant d'anémie, prolongeant ainsi des travaux réalisés avec des cellules murines implantées chez des souris et des rats. Dans cette perpective, notre choix s'est porté sur des cellules humaines primaires allogéniques. En effet, contrairement aux cellules autologues, une caractérisation unique de cellules allogéniques peut déboucher sur de nombreuses applications. Par ailleurs, l'emploi de cellules allogéniques permet d'éviter les riques de zoonose que l'on peut rencontrer avec des cellules xénogéniques. Afin de protéger les cellules allogéniques soumises à une réaction immunitaire, leur confinement dans des macro-capsules cylindriques avant leur implantation permet d'éviter leur contact avec les cellules immunitaires de l'hôte, et de les retrouver sans difficulté en cas d'intolérance ou d'effet secondaire. Dans un premier temps, nous avons évalué la survie de différentes lignées cellulaires humaines primaires, une fois encapsulées et implantées dans le tissu sous-cutané de souris, soit immunocompétentes, soit immunodéprimées naturellement ou par l'intermédiaire d'un immunosuppresseur. Ce modèle in vivo correspond à des conditions xénogéniques et représente par conséquent un environnement de loin plus hostile pour les cellules qu'une transplantation allogénique. Une lignée fibroblastique issue du prépuce d'un jeune enfant, nommée DARC 3 .1, a montré une remarquable résistance avec un score de survie moyen le plus élevé parmi les lignées testées. Par la suite, nous nous sommes intéressés aux paramètres intervenant dans la réalisation du système d'implantation afin d'optimaliser les conditions pour une meilleure adaptation des cellules à ce nouvel environnement. En effet, en raison de l'apparition, après un mois in vivo, de calcifications au niveau de la matrice de polyvinyl alcohol (PVA) servant de support aux cellules encapsulées, différents paramètres ont été étudiés, tels que les procédures de fabrication, les lots de PVA ou encore les lignées cellulaires encapsulées, afin de mettre en évidence leur rôle respectif dans la survenue de ce processus. Nous avons montré que l'apparition des calcifications peut être totalement prévenue par l'utilisation d'eau pure au lieu de tampon phosphaté lors du rinçage des matrices de PVA. De plus, nous avons observe qu'un taux de lactate déshydrogénase cellulaire élevé était corrélé avec une diminution des dépôts de calcium au sein de la matrice en raison d'un micro-environnement plus acide inhibant la précipitation du calcium. Après sélection de la lignée cellulaire appropriée et de l'optimisation des conditions d'encapsulation, une modification génétique des fibroblastes DARC 3.1 a été réalisée par une approche rétrovirale, permettant l'insertion de l'ADN du gène de l'Epo dans le génome cellulaire. Diverses modifications, tant au niveau génétique qu'au niveau des conditions d'infection, ont été entreprises afin d'obtenir des taux de sécrétion d'Epo cliniquement appropriés. L'insertion dans la séquence d'ADN d'un élément de régulation post¬transcriptionnelle dérivé du virus de l'hépatite du rongeur (« woodchuck ») ainsi que d'une séquence consensus appelée « Kozak » ont abouti à une augmentation de sécrétion d'Epo 7.5 fois plus importante. De même, l'optimisation de la multiplicité d'infection et la sélection plus drastique des cellules hautement productrices ont permis finalement d'obtenir une sécrétion correspondant à 200 IU d'Epo/10E6 cells/jour. Ces cellules génétiquement modifiées ont été encapsulées et implantées in vivo dans les mêmes conditions que celles décrites plus haut. Toutes les souris transplantées ont montré une augmentation significative de leur hématocrite et une proportion importante de cellules présentait une survie conservée au moment de l'explantation des capsules. Finalement, dans la perspective d'une application humaine, un modèle syngénique a été proposé, basé sur l'implantation de myoblastes murins encapsulés dans des souris, afin d'investiguer les rôles respectifs de la réponse immunitaire du receveur et des besoins métaboliques cellulaires sur leur survie à long terme. Les cellules ont été encapsulées à différentes densités et les animaux transplantés se sont vus administrer des injections de molécules anti-inflammatoires ou immunosuppressives. Les résultats ont démontré qu'une réaction immunologique péri-capsulaire était à la base du rejet cellulaire dans le cas de capsules à haute densité cellulaire. Une matrice de PVA peut limiter cette densité et éviter une mort cellulaire précoce due à une insuffisance métabolique et par conséquent prévenir la réaction immunitaire. Ce travail a permis le développement de cellules encapsulées d'origine humaine sécrétant des taux d'Epo humaine adaptés à des traitements cliniques. De pair avec l'optimalisation des paramètres d'encapsulation, ces résultats ont abouti à l'initiation d'une application clinique destinée à des patients en insuffisance rénale terminale.