Benchmarking therapeutic drug monitoring software: A systematic evaluation of available computer tools

Objectives: Therapeutic drug monitoring (TDM) aims at optimizing treatment by individualizing dosage regimen based on blood concentrations measurement. Maintaining concentrations within a target range requires pharmacokinetic (PK) and clinical capabilities. Bayesian calculation represents a gold standard in TDM approach but requires computing assistance. The aim of this benchmarking was to assess and compare computer tools designed to support TDM clinical activities.¦Methods: Literature and Internet were searched to identify software. Each program was scored against a standardized grid covering pharmacokinetic relevance, user-friendliness, computing aspects, interfacing, and storage. A weighting factor was applied to each criterion of the grid to consider its relative importance. To assess the robustness of the software, six representative clinical vignettes were also processed through all of them.¦Results: 12 software tools were identified, tested and ranked. It represents a comprehensive review of the available software characteristics. Numbers of drugs handled vary from 2 to more than 180, and integration of different population types is available for some programs. Nevertheless, 8 programs offer the ability to add new drug models based on population PK data. 10 computer tools incorporate Bayesian computation to predict dosage regimen (individual parameters are calculated based on population PK models). All of them are able to compute Bayesian a posteriori dosage adaptation based on a blood concentration while 9 are also able to suggest a priori dosage regimen, only based on individual patient covariates. Among those applying Bayesian analysis, MM-USC*PACK uses a non-parametric approach. The top 2 programs emerging from this benchmark are MwPharm and TCIWorks. Others programs evaluated have also a good potential but are less sophisticated or less user-friendly.¦Conclusions: Whereas 2 software packages are ranked at the top of the list, such complex tools would possibly not fit all institutions, and each program must be regarded with respect to individual needs of hospitals or clinicians. Programs should be easy and fast for routine activities, including for non-experienced users. Although interest in TDM tools is growing and efforts were put into it in the last years, there is still room for improvement, especially in terms of institutional information system interfacing, user-friendliness, capability of data storage and automated report generation.

EADock: docking of small molecules into protein active sites with a multiobjective evolutionary optimization.

In recent years, protein-ligand docking has become a powerful tool for drug development. Although several approaches suitable for high throughput screening are available, there is a need for methods able to identify binding modes with high accuracy. This accuracy is essential to reliably compute the binding free energy of the ligand. Such methods are needed when the binding mode of lead compounds is not determined experimentally but is needed for structure-based lead optimization. We present here a new docking software, called EADock, that aims at this goal. It uses an 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 A around the center of mass of the ligand position in the crystal structure, and on the contrary to other benchmarks, our algorithm was fed with optimized ligand positions up to 10 A root mean square deviation (RMSD) from the crystal structure, excluding the latter. This validation illustrates the efficiency of our sampling strategy, 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 could be explained by the presence of crystal contacts in the experimental structure. Finally, the ability of EADock to accurately predict binding modes on a real application was illustrated by the successful docking of the RGD cyclic pentapeptide on the alphaVbeta3 integrin, starting far away from the binding pocket.

EORTC topics in neurooncology: The long path from a focus on neurological complications of cancer towards molecularly defined trials and therapies in neurooncology

Over the past decade a series of trials of the EORTC Brain Tumor Group (BTG) has substantially influenced and shaped the standard-of-care of primary brain tumors. All these trials were coupled with biological research that has allowed for better understanding of the biology of these tumors. In glioblastoma, EORTC trial 26981/22981 conducted jointly with the National Cancer Institute of Canada Clinical Trials Group showed superiority of concomitant radiochemotherapy with temozolomide over radiotherapy alone. It also identified the first predictive marker for benefit from alkylating agent chemotherapy in glioblastoma, the methylation of the O6-methyl-guanyl-methly-transferase (MGMT) gene promoter. In another large randomized trial, EORTC 26951, adjuvant chemotherapy in anaplastic oligodendroglial tumors was investigated. Despite an improvement in progression-free survival this did not translate into a survival benefit. The third example of a landmark trial is the EORTC 22845 trial. This trial led by the EORTC Radiation Oncology Group forms the basis for an expectative approach to patients with low-grade glioma, as early radiotherapy indeed prolongs time to tumor progression but with no benefit in overall survival. This trial is the key reference in deciding at what time in their disease adult patients with low-grade glioma should be irradiated. Future initiatives will continue to focus on the conduct of controlled trials, rational academic drug development as well as systematic evaluation of tumor tissue including biomarker development for personalized therapy. Important lessons learned in neurooncology are to dare to ask real questions rather than merely rapidly testing new compounds, and the value of well designed trials, including the presence of controls, central pathology review, strict radiology protocols and biobanking. Structurally, the EORTC BTG has evolved into a multidisciplinary group with strong transatlantic alliances. It has contributed to the maturation of neurooncology within the oncological sciences.

Clinical growth rate of acoustic schwannomas: correlation with the growth fraction as defined by the monoclonal antibody ki-67.

The growth rate of acoustic tumors, although slow, varies widely. There may be a continuous spectrum or distinct groups of tumor growth rates. Clinical, audiologic, and conventional histologic tests have failed to shed any light on this problem. Modern immunohistochemical methods may stand a better chance. The Ki-67 monoclonal antibody stains proliferating cells and is used in this study to investigate the growth fraction of 13 skull base schwannomas. The acoustic tumors can be divided into two different growth groups, one with a rate five times the other. The literature is reviewed to see if this differentiation is borne out by the radiologic studies. Distinct growth rates have been reported: one very slow, taking 50 years to reach 1 cm in diameter, a second rate with a diameter increase of 0.2 cm/year, and a third rate five times the second, with a 1.0 cm increase in diameter per year. A fourth group growing at 2.5 cm/year is postulated, but these tumors cannot be followed for long radiologically, since symptoms demand surgical intervention. The clinical implications of these separate growth rates are discussed.

A novel IFN-gamma regulated human melanoma associated antigen gp33-38 defined by monoclonal antibody Me14/D12. I. Identification and immunochemical characterization.

A novel melanoma-associated differentiation Ag whose surface expression can be enhanced or induced by IFN-gamma was identified by mAb Me14/D12. Testing of numerous tumor cell lines and tumor tissue sections showed that Me14/D12-defined Ag was present not only on melanoma but also on other tumor lines of neuroectodermal origin such as gliomas and neuroblastomas and on some lymphoblastic B cell lines, on monocytes and macrophages. Immunoprecipitation by mAb Me14/D12 of lysates from [35S]methionine-labeled melanoma cells analyzed by SDS-PAGE revealed two polypeptide chains of 33 and 38 KDa, both under reducing and nonreducing conditions. Cross-linking experiments indicated that the two chains were present at the cell surface as a dimeric structure. Two-dimensional gel electrophoresis showed that the two chains of 33 and 38 KDa had isoelectric points of 6.2 and 5.7, respectively. Treatment of the melanoma cells with tunicamycin, an inhibitor of N-linked glycosylation, resulted in a reduction of the Mr from 33 to 24 KDa and from 38 to 26 KDa. Peptide maps obtained after Staphylococcus aureus V8 protease digestion showed no shared peptides between the two chains. Although biochemical data indicate that Me14/D12 molecules do not correspond to any known MHC class II Ag, their dimeric structure, tissue distribution, and regulation of IFN-gamma suggest that they could represent a new member of the MHC class II family.

ConnectomeViewer - Multi-Modal Multi-Level Network Visualization and Analysis.

Introduction: The field of Connectomic research is growing rapidly, resulting from methodological advances in structural neuroimaging on many spatial scales. Especially progress in Diffusion MRI data acquisition and processing made available macroscopic structural connectivity maps in vivo through Connectome Mapping Pipelines (Hagmann et al, 2008) into so-called Connectomes (Hagmann 2005, Sporns et al, 2005). They exhibit both spatial and topological information that constrain functional imaging studies and are relevant in their interpretation. The need for a special-purpose software tool for both clinical researchers and neuroscientists to support investigations of such connectome data has grown. Methods: We developed the ConnectomeViewer, a powerful, extensible software tool for visualization and analysis in connectomic research. It uses the novel defined container-like Connectome File Format, specifying networks (GraphML), surfaces (Gifti), volumes (Nifti), track data (TrackVis) and metadata. Usage of Python as programming language allows it to by cross-platform and have access to a multitude of scientific libraries. Results: Using a flexible plugin architecture, it is possible to enhance functionality for specific purposes easily. Following features are already implemented: * Ready usage of libraries, e.g. for complex network analysis (NetworkX) and data plotting (Matplotlib). More brain connectivity measures will be implemented in a future release (Rubinov et al, 2009). * 3D View of networks with node positioning based on corresponding ROI surface patch. Other layouts possible. * Picking functionality to select nodes, select edges, get more node information (ConnectomeWiki), toggle surface representations * Interactive thresholding and modality selection of edge properties using filters * Arbitrary metadata can be stored for networks, thereby allowing e.g. group-based analysis or meta-analysis. * Python Shell for scripting. Application data is exposed and can be modified or used for further post-processing. * Visualization pipelines using filters and modules can be composed with Mayavi (Ramachandran et al, 2008). * Interface to TrackVis to visualize track data. Selected nodes are converted to ROIs for fiber filtering The Connectome Mapping Pipeline (Hagmann et al, 2008) processed 20 healthy subjects into an average Connectome dataset. The Figures show the ConnectomeViewer user interface using this dataset. Connections are shown that occur in all 20 subjects. The dataset is freely available from the homepage (connectomeviewer.org). Conclusions: The ConnectomeViewer is a cross-platform, open-source software tool that provides extensive visualization and analysis capabilities for connectomic research. It has a modular architecture, integrates relevant datatypes and is completely scriptable. Visit www.connectomics.org to get involved as user or developer.

Infarctus myocardique aigu: importance du "networking" dans la prise en charge initiate [Acute myocardial infarction: importance of the networking in the initial management].

Early reperfusion with prompt re-establishment of coronary blood flow improves survival in patients suffering from acute ST-elevation myocardial infarction (STEMI). Leaving systemic thrombolysis for primary percutaneous coronary intervention (PCI) is justified by clinical results in favor of PCI. Nevertheless, primary PCI necessitates additional transfer time and requires an efficient territorial networking. The present article summarizes the up-to-dated management of patients with acute STEMI and/or overt cardiogenic shock.

Evaluation of errors in alpine skiing video analysis

INTRODUCTION: Video records are widely used to analyze performance in alpine skiing at professional or amateur level. Parts of these analyses require the labeling of some movements (i.e. determining when specific events occur). If differences among coaches and differences for the same coach between different dates are expected, they have never been quantified. Moreover, knowing these differences is essential to determine which parameters reliable should be used. This study aimed to quantify the precision and the repeatability for alpine skiing coaches of various levels, as it is done in other fields (Koo et al, 2005). METHODS: A software similar to commercialized products was designed to allow video analyses. 15 coaches divided into 3 groups (5 amateur coaches (G1), 5 professional instructors (G2) and 5 semi-professional coaches (G3)) were enrolled. They were asked to label 15 timing parameters (TP) according to the Swiss ski manual (Terribilini et al, 2001) for each curve. TP included phases (initiation, steering I-II), body and ski movements (e.g. rotation, weighting, extension, balance). Three video sequences sampled at 25 Hz were used and one curve per video was labeled. The first video was used to familiarize the analyzer to the software. The two other videos, corresponding to slalom and giant slalom, were considered for the analysis. G1 realized twice the analysis (A1 and A2) at different dates and TP were randomized between both analyses. Reference TP were considered as the median of G2 and G3 at A1. The precision was defined as the RMS difference between individual TP and reference TP, whereas the repeatability was calculated as the RMS difference between individual TP at A1 and at A2. RESULTS AND DISCUSSION: For G1, G2 and G3, a precision of +/-5.6 frames, +/-3.0 and +/-2.0 frames, was respectively obtained. These results showed that G2 was more precise than G1, and G3 more precise than G2, were in accordance with group levels. The repeatability for G1 was +/-3.1 frames. Furthermore, differences among TP precision were observed, considering G2 and G3, with largest differences of +/-5.9 frames for "body counter rotation movement in steering phase II", and of 0.8 frame for "ski unweighting in initiation phase". CONCLUSION: This study quantified coach ability to label video in term of precision and repeatability. The best precision was obtained for G3 and was of +/-0.08s, which corresponds to +/-6.5% of the curve cycle. Regarding the repeatability, we obtained a result of +/-0.12s for G1, corresponding to +/-12% of the curve cycle. The repeatability of G2 and G3 are expected to be lower than the precision of G1 and the corresponding repeatability will be assessed soon. In conclusion, our results indicate that the labeling of video records is reliable for some TP, whereas caution is required for others. REFERENCES Koo S, Gold MD, Andriacchi TP. (2005). Osteoarthritis, 13, 782-789. Terribilini M, et al. (2001). Swiss Ski manual, 29-46. IASS, Lucerne.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Optimal CD8 T cell responsiveness is controlled within a defined TCR affinity window

Protective immune responses relyon TCR-mediated recognition of antigenspresented by MHC molecules. Tcells directed against tumor antigensare thought to express TCRs of loweraffinity/avidity than pathogen-specificT lymphocytes. An attractivestrategy to improve anti-tumor T cellresponses is to adoptively transferCD8+ T cells engineered with TCRsof optimized affinity. However, themechanisms that control optimal Tcell activation and responsiveness remainpoorly defined. We aim at characterizingTCR-pMHC binding parametersand downstream signalingevents that regulate T cell functionalityby using an in silico designedpanel of tumor antigen-specific TCRsof incremental affinity for pMHC(Kd100 M- 15 nM).We found that optimalT cell responses (cytokine secretionand target cell killing) occurredwithin a well-defined window ofTCR-pMHC binding affinity (5 M-1 M), while drastic functional declinewas detected in T cells expressingvery low and very high TCRaffinities,which was not caused by any increasein apoptosis. Whole-genomemicroarray analysis revealed that Tcells with optimal TCR affinitieshighly up-regulated transcription ofgenes typical of T cell activation (i.e.IFN-, NF-B and TNFR), while reducedexpression was detected in Tcells of very low or very high TCR affinity.Strikingly, hierarchical clusteringshowed that the latter two variantsclustered together with the un-stimulatedcontrol Tcells.Yet, despite commonclustering, several genes seemedto be differentially expressed, suggestingthat the mechanisms involvedin this "unresponsiveness state" maydiffer between those two variants. Finally,calcium influx assays also demonstratedattenuated responses in Tcells of very high TCR affinity. Ourresults indicate that optimal T cellfunction is tightly controlled within adefinedTCRaffinity window throughvery proximal TCR-mediated mechanisms,possibly at the TCR-pMHCbinding interface. Uncovering themechanisms regulating optimal/maximalT cell function is essential to understandand promote therapeutic designlike adoptive T cell therapy.

Tissue homing and persistence of defined antigen-specific CD8+ tumor-reactive T-cell clones in long-term melanoma survivors.

Tumor antigen-specific cytotoxic T cells (CTLs) play a major role in the adaptive immune response to cancers. This CTL response is often insufficient because of functional impairment, tumor escape mechanisms, or inhibitory tumor microenvironment. However, little is known about the fate of given tumor-specific CTL clones in cancer patients. Studies in patients with favorable outcomes may be very informative. In this longitudinal study, we tracked, quantified, and characterized functionally defined antigen-specific T-cell clones ex vivo, in peripheral blood and at tumor sites, in two long-term melanoma survivors. MAGE-A10-specific CD8+ T-cell clones with high avidity to antigenic peptide and tumor lytic capabilities persisted in peripheral blood over more than 10 years, with quantitative variations correlating with the clinical course. These clones were also found in emerging metastases, and, in one patient, circulating clonal T cells displayed a fully differentiated effector phenotype at the time of relapse. Longevity, tumor homing, differentiation phenotype, and quantitative adaptation to the disease phases suggest the contribution of the tracked tumor-reactive clones in the tumor control of these long-term metastatic survivor patients. Focusing research on patients with favorable outcomes may help to identify parameters that are crucial for an efficient antitumor response and to optimize cancer immunotherapy.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

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.

Quantification of Myocardial Perfusion Using 13N-ammonia PET: a Cross-Comparison Study on Analysis Software Packages - Carimas, PMOD and FlowQuant

Aim. Several software packages (SWP) and models have been released for quantification of myocardial perfusion (MP). Although they all are validated against something, the question remains how well their values agree. The present analysis focused on cross-comparison of three SWP for MP quantification of 13N-ammonia PET studies. Materials & Methods. 48 rest and stress MP 13N-ammonia PET studies of hypertrophic cardiomyopathy (HCM) patients (Sciagrà et al., 2009) were analysed with three SW packages - Carimas, PMOD, and FlowQuant - by three observers blinded to the results of each other. All SWP implement the one-tissue-compartment model (1TCM, DeGrado et al. 1996), and first two - the two-tissue-compartment model (2TCM, Hutchins et al. 1990) as well. Linear mixed model for the repeated measures was fitted to the data. Where appropriate we used Bland-Altman plots as well. The reproducibility was assessed on global, regional and segmental levels. Intraclass correlation coefficients (ICC), differences between the SWPs and between models were obtained. ICC≥0.75 indicated excellent reproducibility, 0.4≤ICC<0.75 indicated fair to good reproducibility, ICC<0.4 - poor reproducibility (Rosner, 2010). Results. When 1TCM MP values were compared, the SW agreement on global and regional levels was excellent, except for Carimas vs. PMOD at RCA: ICC=0.715 and for PMOD vs. FlowQuant at LCX:ICC=0.745 which were good. In segmental analysis in five segments: 7,12,13, 16, and 17 the agreement between all SWP was excellent; in the remaining 12 segments the agreement varied between the compared SWP. Carimas showed excellent agreement with FlowQuant in 13 segments and good in four - 1, 5, 6, 11: 0.687≤ICCs≤0.73; Carimas had excellent agreement with PMOD in 11 segments, good in five_4, 9, 10, 14, 15: 0.682≤ICCs≤0.737, and poor in segment 3: ICC=0.341. PMOD had excellent agreement with FlowQuant in eight segments and substantial-to-good in nine_1, 2, 3, 5, 6,8-11: 0.585≤ICCs≤0.738. Agreement between Carimas and PMOD for 2TCM was good at a global level: ICC=0.745, excellent at LCX (0.780) and RCA (0.774), good at LAD (0.662); agreement was excellent for ten segments, fair-to-substantial for segments 2, 3, 8, 14, 15 (0.431≤ICCs≤0.681), poor for segments 4 (0.384) and 17 (0.278). Conclusions. The three SWP used by different operators to analyse 13N-ammonia PET MP studies provide results that agree well at a global level, regional levels, and mostly well even at a segmental level. Agreement is better for 1TCM. Poor agreement at segments 4 and 17 for 2TCM needs further clarification.

Baseline Results of an Acute Ischemic Stroke Registry Including Acute Multimodal Imaging (ASTRAL)

Background: The DEFUSE (n_74) and EPITHET (n_101) studies have in common that a baseline MRI was obtained prior to treatment (tPA in DEFUSE; tPA or placebo in EPITHET) in the 3-6 hour time-window. There were however important methodological differences between the studies. A standardized reanalysis of pooled data was undertaken to determine the effect of these differences on baseline characteristics and study outcomes. Methods: To standardize the studies 1) the DWI and PWI source images were reprocessed and segmented using automated image processing software (RAPID); 2) patients were categorized according to their baseline MRI profile as either Target Mismatch (PWITmax_6/DWI ratio_ 1.8 and an absolute mismatch _15mL), Malignant (DWI or PWITmax_10 lesion _ 100 mL), or No Mismatch. 3) favorable clinical response was defined as NIHSS score of 0-1 or a _8 points improvement on the NIHSSS at day 90. Results: Prior to standardization there was no difference in the proportion of Target Mismatch patients between EPITHET and DEFUSE (54% vs 49%, p_0.6), but the EPITHET study had more patients with the Malignant profile than DEFUSE (35% vs 9%, p_0.01) and fewer patients that had No Mismatch (11% vs 42%, p_0.01). These differences in baseline MRI profiles between EPITHET and DEFUSE were largely eliminated by standardized processing of PWI and DWI images with RAPID software (Target Mismatch 49% vs 48%; Malignant 15% vs 8%; No Mismatch 36% vs 25%; p_NS for all comparisons) Reperfusion was strongly associated with a favorable clinical response in mismatch patients (figure). This relationship was not affected by the standardization procedures (pooled odds ratio of 8.8 based on original data and 6.6 based on standardized data). Conclusion: Standardization of image analyses procedures in acute stroke is important as non-standardized techniques introduce significant variability in DWI and PWI imaging characteristics. Despite methodological differences, the DEFUSE and EPITHET studies show a consistent and robust association between reperfusion and favorable clinical response in Target Mismatch patients regardless of standardization. These data support an RCT of iv tPA in the 3-6 hour time-window for Target Mismatch patients identified using RAPID.