L'équité d'accès aux ressources en eau : fin ou moyen d'amélioration de l'efficacité de l'irrigation ? : la gestion des ressources en eau dans l'état du Gujarat (Inde) dans la perpective de la political ecology

La thèse présentée ici est le résultat d'une étroite collaboration avec une ONG indienne, AKRSP(I), intervenant dans le développement de l'irrigation au Gujarat depuis plus de 25 ans. Un SIG prototype a été mis en oeuvre et nous permet de proposer ime analyse spatiale et quantitative de l'action de cette ONG ainsi qu'une réflexion plus générale sur les leviers de mise en valeur et de gestion des ressources en eau à des fins agricoles. On peut souligner trois principaux enseignements: Les perspectives d'application des SIG au sein des ONG sont manifestes. Les exigences des bailleurs de fonds peuvent néanmoins faire obstacle à leur développement car, indi-rectement, ils favorisent la mise en oeuvre de SI voués à la justification plutôt qu'à la planification et au suivi des programmes d'actions. Ce résultat soulève la question de la pertinence de l'encadrement, des critères d'évaluation et de la conditionnalité de l'aide publique au développement. Les ONG ont un fort potentiel pour participer à la mise en valeur des ressources en eau en Inde et aider à relever le défi agro-démographique indien, en particulier dans les zones marginales où les services étatiques sont en retrait. Les stratégies d'action basées principalement sur l'application des instruments économiques et techniques doivent cependant être modifiées. Nous montrons qu'elles favorisent une inégalité d'accès aux ressources qui débouche sur une efficacité limitée des pratiques d'irrigation, sur un plan agro-technique. Ces résultats soulignent la nécessité de poursuivre une réflexion critique des discours et solutions dominants en matière de gestion des ressources en eau. Deux pistes d'amélioration sont avancées: 1. considérer l'équité d'accès comme un moyen d'optimiser la gestion de la ressource (limiter le volume d'eau par agriculteur pour encourager les choix de cultures irriguées peu consommatrices et l'adoption des technologies d'économie d'eau), 2. prêter attention à l'ordre dans lequel les différents instruments de gestion disponibles sont employés afin de les articuler dans un séquençage temporel pertinent. La Political Ecology apparait comme un cadre conceptuel très pertinent pour engager cette réflexion critique. Elle permet d'intégrer différentes échelles d'asymétries de pouvoirs à la compréhension des situations et des blocages observables localement : inégalités de capabilités et forces socio-politiques à l'échelle locale, politiques agro-industrielles (coton) et jeux d'alliances politiques des castes à l'échelle nationale, discours et conflits idéologiques ou orientations stratégiques des bailleurs de fonds à l'échelle internationale... Notre recherche empirique contribue modestement au développement de cette Political Ecology de la mise en valeur et de la gestion des ressources en eau. - The present research is based on a close collaboration with an indian NGO, AKRSP(I), which is active in the development of irrigation facilities in Gujarat for the past 25 years. We built a GIS prototype providing quantitative and spatial datas to analyse the NGO intervention and propose a general reflection about water resources development and management issues. Three main findings may be emphasized : The potential of GIS within the workings of an NGO is obvious, as an information ma-nagement tool as much as for developing analytical capacity. However, financial backers expectations may not favour a relevant development of this technology. Indirectly, they promote Information Systems built to justify rather than to plan or monitor action pro¬grammes. This raises the question of stricter framework, conditionality criters and stan¬dardised assessment indicators surrounding official development assistance. There is strong potential that NGOs can assist with the improvement of water resources in India. They can help in overcoming Indian demographic-related agricultural challenges, especially in marginal rural areas neglected by state services. However, intervention strategies mainly based on technical and economic management tools has to be adapted. We found that they lead to inequitable access and distribution of water resources what induces a low efficiency of irrigation practices from an agro-technical point of view. These results underline the need to go further in criticizing dominant ideas and guidelines regarding water resources management. We suggest two other options : 1. to consider equitable access has a tool to improve the effective use of water for agricul¬tural purposes (limiting the volume of water available per farmer would encourage them to adopt low water consumption crops and water saving technics), 2. to consider more carefully the order of use of the various management tools available and to structure them in a relevant sequence. Here, Political Ecology seems to be a relevant conceptual framework to enter into such a critical reflection, integrating different levels and scales of political asymmetries at the core of environmental issues. Indeed, the understanding of regional water situations and social stumbling blocks needs not only to consider local capabilities and socio-political inequities, but also agro-industrial policy (e.i. cotton) and caste political alliances at a national scale, as well as ideological and narrative struggles or strategical orientations of financial backers at an international level. Our empirical research modestly contributes to the development of such a Political Ecology of water resources development and management.

Natural genetic variation in Arabidopsis: tools, traits and prospects for evolutionary ecology.

BACKGROUND: The model plant Arabidopsis thaliana (Arabidopsis) shows a wide range of genetic and trait variation among wild accessions. Because of its unparalleled biological and genomic resources, the potential of Arabidopsis for molecular genetic analysis of this natural variation has increased dramatically in recent years. SCOPE: Advanced genomics has accelerated molecular phylogenetic analysis and gene identification by quantitative trait loci (QTL) mapping and/or association mapping in Arabidopsis. In particular, QTL mapping utilizing natural accessions is now becoming a major strategy of gene isolation, offering an alternative to artificial mutant lines. Furthermore, the genomic information is used by researchers to uncover the signature of natural selection acting on the genes that contribute to phenotypic variation. The evolutionary significance of such genes has been evaluated in traits such as disease resistance and flowering time. However, although molecular hallmarks of selection have been found for the genes in question, a corresponding ecological scenario of adaptive evolution has been difficult to prove. Ecological strategies, including reciprocal transplant experiments and competition experiments, and utilizing near-isogenic lines of alleles of interest will be a powerful tool to measure the relative fitness of phenotypic and/or allelic variants. CONCLUSIONS: As the plant model organism, Arabidopsis provides a wealth of molecular background information for evolutionary genetics. Because genetic diversity between and within Arabidopsis populations is much higher than anticipated, combining this background information with ecological approaches might well establish Arabidopsis as a model organism for plant evolutionary ecology.

Genomic islands: tools of bacterial horizontal gene transfer and evolution.

Abstract Bacterial genomes evolve through mutations, rearrangements or horizontal gene transfer. Besides the core genes encoding essential metabolic functions, bacterial genomes also harbour a number of accessory genes acquired by horizontal gene transfer that might be beneficial under certain environmental conditions. The horizontal gene transfer contributes to the diversification and adaptation of microorganisms, thus having an impact on the genome plasticity. A significant part of the horizontal gene transfer is or has been facilitated by genomic islands (GEIs). GEIs are discrete DNA segments, some of which are mobile and others which are not, or are no longer mobile, which differ among closely related strains. A number of GEIs are capable of integration into the chromosome of the host, excision, and transfer to a new host by transformation, conjugation or transduction. GEIs play a crucial role in the evolution of a broad spectrum of bacteria as they are involved in the dissemination of variable genes, including antibiotic resistance and virulence genes leading to generation of hospital 'superbugs', as well as catabolic genes leading to formation of new metabolic pathways. Depending on the composition of gene modules, the same type of GEIs can promote survival of pathogenic as well as environmental bacteria.

Biomarkers of angiogenesis for the development of antiangiogenic therapies in oncology: tools or decorations?

Since 2004, four antiangiogenic drugs have been approved for clinical use in patients with advanced solid cancers, on the basis of their capacity to improve survival in phase III clinical studies. These achievements validated the concept introduced by Judah Folkman that the inhibition of tumor angiogenesis could control tumor growth. It has been suggested that biomarkers of angiogenesis would greatly facilitate the clinical development of antiangiogenic therapies. For these four drugs, the pharmacodynamic effects observed in early clinical studies were important to corroborate activities, but were not essential for the continuation of clinical development and approval. Furthermore, no validated biomarkers of angiogenesis or antiangiogenesis are available for routine clinical use. Thus, the quest for biomarkers of angiogenesis and their successful use in the development of antiangiogenic therapies are challenges in clinical oncology and translational cancer research. We review critical points resulting from the successful clinical trials, review current biomarkers, and discuss their potential impact on improving the clinical use of available antiangiogenic drugs and the development of new ones.

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.

Revisiting sodium and water reabsorption with functional genomics tools.

PURPOSE OF REVIEW: The kidney plays an essential role in maintaining sodium and water balance, thereby controlling the volume and osmolarity of the extracellular body fluids, the blood volume and the blood pressure. The final adjustment of sodium and water reabsorption in the kidney takes place in cells of the distal part of the nephron in which a set of apical and basolateral transporters participate in vectorial sodium and water transport from the tubular lumen to the interstitium and, finally, to the general circulation. According to a current model, the activity and/or cell-surface expression of these transporters is/are under the control of a gene network composed of the hormonally regulated, as well as constitutively expressed, genes. It is proposed that this gene network may include new candidate genes for salt- and water-losing syndromes and for salt-sensitive hypertension. A new generation of functional genomics techniques have recently been applied to the characterization of this gene network. The purpose of this review is to summarize these studies and to discuss the potential of the different techniques for characterization of the renal transcriptome. RECENT FINDINGS: Recently, DNA microarrays and serial analysis of gene expression have been applied to characterize the kidney transcriptome in different in-vivo and in-vitro models. In these studies, a set of new interesting genes potentially involved in the regulation of sodium and water reabsorption by the kidney have been identified and are currently under detailed investigation. SUMMARY: Characterization of the kidney transcriptome is greatly expanding our knowledge of the gene networks involved in multiple kidney functions, including the maintenance of sodium and water homeostasis.

Design of new tools to improve the efficacy of DNA-damaging drugs used in cancer therapy

Résumé Les tumeurs sont diverses et hétérogènes, mais toutes partagent la capacité de proliférer sans contrôle. Une prolifération dérégulée de cellules couplée à une insensibilité à une réponse apoptotique constitue une condition minimale pour que l'évolution d'une tumeur se produise. Un des traitements les plus utilisés pour traité le cancer à l'heure actuelle sont les chimiothérapies, qui sont fréquemment des composés chimiques qui induisent des dommages dans l'ADN. Les agents anticancéreux sont efficaces seulement quand les cellules tumorales sont plus aisément tuées que le tissu normal environnant. L'efficacité de ces agents est en partie déterminée par leur capacité à induire l'apoptose. Nous avons récemment démontré que la protéine RasGAP est un substrat non conventionnel des caspases parce elle peut induire à la fois des signaux anti et pro-apoptotiques, selon l'ampleur de son clivage par les caspases. A un faible niveau d'activité des caspases, RasGAP est clivé, générant deux fragments (le fragment N et le fragment C). Le fragment N semble être un inhibiteur général de l'apoptose en aval de l'activation des caspases. À des niveaux plus élevés d'activité des caspases, la capacité du fragment N de contrecarrer l'apoptose est supprimée quand il est clivé à nouveau par les caspases. Ce dernier clivage produit deux nouveaux fragments, N 1 et N2, qui contrairement au fragment N sensibilisent efficacement des cellules cancéreuses envers des agents chimiothérapeutiques. Dans cette étude nous avons prouvé qu'un peptide, appelé par la suite TAT-RasGAP317-326, qui est dérivé du fragment N2 de RasGAP et est rendu perméable aux cellules, sensibilise spécifiquement des cellules cancéreuses à trois génotoxines différentes utilisées couramment dans des traitements anticancéreux, et cela dans des modèles in vitro et in vivo. Il est important de noté que ce peptide semble ne pas avoir d'effet sur des cellules non cancéreuses. Nous avons également commencé à caractériser les mécanismes moléculaires expliquant les fonctions de sensibilisation de TAT-RasGAP317-326. Nous avons démontré que le facteur de transcription p53 et une protéine sous son activité transcriptionelle, nommée Puma, sont indispensables pour l'activité de TAT-RasGAP317-326. Nous avons également prouvé que TAT-RasGAP317-326 exige la présence d'une protéine appelée G3BP1, une protéine se liant a RasGAP, pour potentialisé les effets d'agents anticancéreux. Les données obtenues dans cette étude montrent qu'il pourrait être possible d'augmenter l'efficacité des chimiothérapies à l'aide d'un composé capable d'augmenter la sensibilité des tumeurs aux génotoxines et ainsi pourrait permettre de traiter de manière plus efficace des patients sous traitement chimiothérapeutiques. Summary Tumors are diverse and heterogeneous, but all share the ability to proliferate without control. Deregulated cell proliferation coupled with suppressed apoptotic sensitivity constitutes a minimal requirement upon which tumor evolution occurs. One of the most commonly used treatments is chemotherapy, which frequently uses chemical compounds that induce DNA damages. Anticancer agents are effective only when tumors cells are more readily killed than the surrounding normal tissue. The efficacy of these agents is partly determined by their ability to induce apoptosis. We have recently demonstrated that the protein RasGAP is an unconventional caspase substrate because it can induce both anti- and pro-apoptotic signals, depending on the extent of its cleavage by caspases. At low levels of caspase activity, RasGAP is cleaved, generating an N-terminal fragment (fragment N) and a C-terminal fragment (fragment C). Fragment N appears to be a general Mocker of apoptosis downstream of caspase activation. At higher levels of caspase activity, the ability of fragment N to counteract apoptosis is suppressed when it is further cleaved. This latter cleavage event generates two fragments, N1 and N2, which in contrast to fragment N potently sensitizes cancer cells toward DNA-damaging agents induced apoptosis. In the present study we show that a cell permeable peptide derived from the N2 fragment of RasGAP, thereafter called TAT-RasGAP317-326, specifically sensitizes cancer cells to three different genotoxins commonly used in chemotherapy in vitro and in vivo models. Importantly this peptide seems not to have any effect on non cancer cells. We have also started to characterize the molecular mechanisms underlying the sensitizing functions of TAT-RasGAP317-326. We have demonstrated that the p53 transcription factor and a protein under its transcriptional activity, called Puma, are required for the activity of TATRasGAP317-326. We have also showed that TAT-RasGAP317-326 requires the presence of a protein called G3BP1, which have been shown to interact with RasGAP, to increase the effect of the DNA-damaging drug cisplatin. The data obtained in this study showed that it is possible to increase the efficacy of current used chemotherapies with a compound able to increase the efficacy of genotoxins which could be beneficial for patients subjected to chemotherapy.

Accuracy profile validation of a new analytical method for butane measurement using headspace-gas chromatography-mass spectrometry.

The aim of our study was to provide an innovative HS-GC/MS method applicable to the routine determination of butane concentration in forensic toxicology laboratories. The main drawback of the GC/MS methods discussed in literature concerning butane measurement was the absence of a specific butane internal standard necessary to perform quantification. Because no stable isotope of butane is commercially available, it is essential to develop a new approach by an in situ generation of standards. To avoid the manipulation of a stable isotope-labelled gas, we have chosen to generate in situ an internal labelled standard gas (C(4)H(9)D) following the basis of the stoichiometric formation of butane by the reaction of deuterated water (D(2)O) with Grignard reagent butylmagnesium chloride (C(4)H(9)MgCl). This method allows a precise measurement of butane concentration and therefore, a full validation by accuracy profile was presented.

Anthracene-tethered ruthenium(II) arene complexes as tools to visualize the cellular localization of putative organometallic anticancer compounds.

Anthracene derivatives of ruthenium(II) arene compounds with 1,3,5-triaza-7-phosphatricyclo[3.3.1.1]decane (pta) or a sugar phosphite ligand, viz., 3,5,6-bicyclophosphite-1,2-O-isopropylidene-α-d-glucofuranoside, were prepared in order to evaluate their anticancer properties compared to the parent compounds and to use them as models for intracellular visualization by fluorescence microscopy. Similar IC(50) values were obtained in cell proliferation assays, and similar levels of uptake and accumulation were also established. The X-ray structure of [{Ru(η(6)-C(6)H(5)CH(2)NHCO-anthracene)Cl(2)(pta)] is also reported.

Ultra-performance liquid chromatography mass spectrometry and sensitive bioassay methods for quantification of posaconazole plasma concentrations after oral dosing.

Posaconazole (POS) is a new antifungal agent for prevention and therapy of mycoses in immunocompromised patients. Variable POS pharmacokinetics after oral dosing may influence efficacy: a trough threshold of 0.5 ?g/ml has been recently proposed. Measurement of POS plasma concentrations by complex chromatographic techniques may thus contribute to optimize prevention and management of life-threatening infections. No microbiological analytical method is available. The objective of this study was to develop and validate a new simplified ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method and a sensitive bioassay for quantification of POS over the clinical plasma concentration range. The UPLC-MS/MS equipment consisted of a triple quadrupole mass spectrometer, an electrospray ionization (ESI) source, and a C(18) analytical column. The Candida albicans POS-hypersusceptible mutant (MIC of 0.002 ?g/ml) ?cdr1 ?cdr2 ?flu ?mdr1 ?can constructed by targeted deletion of multidrug efflux transporters and calcineurin genes was used for the bioassay. POS was extracted from plasma by protein precipitation with acetonitrile-methanol (75%/25%, vol/vol). Reproducible standard curves were obtained over the range 0.014 to 12 (UPLC-MS/MS) and 0.028 to 12 ?g/ml (bioassay). Intra- and interrun accuracy levels were 106% ± 2% and 103% ± 4% for UPLC-MS/MS and 102% ± 8% and 104% ± 1% for bioassay, respectively. The intra- and interrun coefficients of variation were 7% ± 4% and 7% ± 3% for UPLC-MS/MS and 5% ± 3% and 4% ± 2% for bioassay, respectively. An excellent correlation between POS plasma concentrations measured by UPLC-MS/MS and bioassay was found (concordance, 0.96). In 26 hemato-oncological patients receiving oral POS, 27/69 (39%) trough plasma concentrations were lower than 0.5 ?g/ml. The UPLC-MS/MS method and sensitive bioassay offer alternative tools for accurate and precise quantification of the plasma concentrations in patients receiving oral posaconazole.

Analytical methods for the quantitative determination of selective serotonin reuptake inhibitors for therapeutic drug monitoring purposes in patients.

Five selective serotonin reuptake inhibitors (SSRIs) have been introduced recently: citalopram, fluoxetine, fluvoxamine, paroxetine and sertraline. Although no therapeutic window has been defined for SSRIs, in contrast to tricyclic antidepressants, analytical methods for therapeutic drug monitoring of SSRIs are useful in several instances. SSRIs differ widely in their chemical structure and in their metabolism. The fact that some of them have N-demethylated metabolites, which are also SSRIs, requires that methods be available which allow therapeutic drug monitoring of the parent compounds and of these active metabolites. most procedures are based on prepurification of the SSRIs by liquid-liquid extraction before they are submitted to separation by chromatographic procedures (high-performance liquid chromatography, gas chromatography, thin layer chromatography) and detection by various detectors (UV, fluorescence, electrochemical detector, nitrogen-phosphorus detector, mass spectrometry). This literature review shows that most methods allow quantitative determination of SSRIs in plasma, in the lower ng/ml range, and that they are, therefore, suitable for therapeutic drug monitoring purposes of this category of drugs.