Fixed-dose combinations as initial therapy for hypertension : a review of approved agents and a guide to patient selection.

Recent guidelines recommend initiation of antihypertensive therapy with fixed-dose combinations in high-risk patients because such patients usually need two or more blood pressure (BP)-lowering agents in order to normalize their BP. Agents that block the renin-angiotensin system (ACE inhibitors or angiotensin II receptor antagonists [angiotensin receptor blockers; ARBs]) are preferred for the management of hypertension in most patients exhibiting subclinical target organ damage, or established cardiovascular or renal diseases. Unless contraindicated they should be one of the components of fixed-dose combinations, whereas the other component may be either a calcium channel antagonist or a thiazide diuretic. Fixed-dose combinations containing an ACE inhibitor or ARB plus a calcium channel antagonist appear particularly effective in preventing complications of coronary heart disease.

A LC-tandem MS assay for the simultaneous measurement of new antiretroviral agents: Raltegravir, maraviroc, darunavir, and etravirine.

Raltegravir (RAL), maraviroc (MVC), darunavir (DRV), and etravirine (ETV) are new antiretroviral agents with significant potential for drug interactions. This work describes a sensitive and accurate liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the determination of plasma drug levels. Single-step extraction of RAL, MVC, DRV, ETV and RTV from plasma (100 microl) is performed by protein precipitation using 600 microl of acetonitrile, after the addition of 100 microl darunavir-d(9) (DRV-d(9)) at 1000 ng/ml in MeOH/H(2)O 50/50 as internal standard (I.S.). The mixture is vortexed, sonicated for 10 min, vortex-mixed again and centrifuged. An aliquot of supernatant (150 microl) is diluted 1:1 with a mixture of 20 mM ammonium acetate/MeOH 40/60 and 10 microl is injected onto a 2.1 x 50 mm Waters Atlantis-dC18 3 microm analytical column. Chromatographic separations are performed using a gradient program with 2 mM ammonium acetate containing 0.1% formic acid and acetonitrile with 0.1% formic acid. Analytes quantification is performed by electrospray ionisation-triple quadrupole mass spectrometry using the selected reaction monitoring detection in the positive mode. The method has been validated over the clinically relevant concentrations ranging from 12.5 to 5000 ng/ml, 2.5 to 1000 ng/ml, 25 to 10,000 ng/ml, 10 to 4000 ng/ml, and 5 to 2000 ng/ml for RAL, MRV, DRV, ETV and RTV, respectively. The extraction recovery for all antiretroviral drugs is always above 91%. The method is precise, with mean inter-day CV% within 5.1-9.8%, and accurate (range of inter-day deviation from nominal values -3.3 to +5.1%). In addition our method enables the simultaneous assessment of raltegravir-glucuronide. This is the first analytical method allowing the simultaneous assay of antiretroviral agents targeted to four different steps of HIV replication. The proposed method is suitable for the Therapeutic Drug Monitoring Service of these new regimen combinations administered as salvage therapy to patients having experienced treatment failure, and for whom exposure, tolerance and adherence assessments are critical.

Indications and limitations of chemotherapy and targeted agents in non-small cell lung cancer brain metastases.

Lung cancer is characterized by the highest incidence of solid tumor-related brain metastases, which are reported with a growing incidence during the last decade. Prognostic assessment may help to identify subgroups of patients that could benefit from more aggressive therapy of metastatic disease, in particular when central nervous system is involved. The recent sub-classification of non-small cell lung cancer (NSCLC) into molecularly-defined "oncogene-addicted" tumors, the emergence of effective targeted treatments in molecularly defined patient subsets, global improvement of advanced NSCLC survival as well as the availability of refined new radiotherapy techniques are likely to impact on outcomes of patients with brain dissemination. The present review focuses on key evidence and research strategies for systemic treatment of patients with central nervous system involvement in non-small cell lung cancer.

Fast analysis of doping agents in urine by ultra-high-pressure liquid chromatography-quadrupole time-of-flight mass spectrometry I. Screening analysis.

The general strategy to perform anti-doping analyses of urine samples starts with the screening for a wide range of compounds. This step should be fast, generic and able to detect any sample that may contain a prohibited substance while avoiding false negatives and reducing false positive results. The experiments presented in this work were based on ultra-high-pressure liquid chromatography coupled to hybrid quadrupole time-of-flight mass spectrometry. Thanks to the high sensitivity of the method, urine samples could be diluted 2-fold prior to injection. One hundred and three forbidden substances from various classes (such as stimulants, diuretics, narcotics, anti-estrogens) were analysed on a C(18) reversed-phase column in two gradients of 9min (including two 3min equilibration periods) for positive and negative electrospray ionisation and detected in the MS full scan mode. The automatic identification of analytes was based on retention time and mass accuracy, with an automated tool for peak picking. The method was validated according to the International Standard for Laboratories described in the World Anti-Doping Code and was selective enough to comply with the World Anti-Doping Agency recommendations. In addition, the matrix effect on MS response was measured on all investigated analytes spiked in urine samples. The limits of detection ranged from 1 to 500ng/mL, allowing the identification of all tested compounds in urine. When a sample was reported positive during the screening, a fast additional pre-confirmatory step was performed to reduce the number of confirmatory analyses.

Effects of antiresorptive agents on bone micro-architecture assessed by Trabecular Bone Score in women age 50 and older. The Manitoba Prospective Study

Antiresorptive agents such as bisphosphonates induce a rapid increase of BMD during the 1st year of treatment and a partial maintenance of bone architecture. Trabecular Bone Score (TBS), a new grey-level texture measurement that can be extracted from the DXA image, correlates with 3D parameters of bone micro-architecture. Aim: To evaluate the longitudinal effect of antiresorptive agents on spine BMD and on site-matched spine microarchitecture as assessed by TBS. Methods: From the BMD database for Province of Manitoba, Canada, we selected women age >50 with paired baseline and follow up spine DXA examinations who had not received any prior HRT or other antiresorptive drug.Women were divided in two subgroups: (1) those not receiving any HRT or antiresorptive drug during follow up (=non-users) and (2) those receiving non-HRT antiresorptive drug during follow up (=users) with high adherence (medication possession ratio >75%) from a provincial pharmacy database system. Lumbar spine TBS was derived by the Bone Disease Unit, University of Lausanne, for each spine DXA examination using anonymized files (blinded from clinical parameters and outcomes). Effects of antiresorptive treatment for users and non-users on TBS and BMD at baseline and during mean 3.7 years follow-up were compared. Results were expressed % change per year. Results: 1150 non-users and 534 users met the inclusion criteria. At baseline, users and non-users had a mean age and BMI of [62.2±7.9 vs 66.1±8.0 years] and [26.3±4.7 vs 24.7±4.0 kg/m²] respectively. Antiresorptive drugs received by users were bisphosphonates (86%), raloxifene (10%) and calcitonin (4%). Significant differences in BMD change and TBS change were seen between users and nonusers during follow-up (p<0.0001). Significant decreases in mean BMD and TBS (−0.36± 0.05% per year; −0.31±0.06% per year) were seen for non-users compared with baseline (p<0.001). A significant increase in mean BMD was seen for users compared with baseline (+1.86±0.0% per year, p<0.0018). TBS of users also increased compared with baseline (+0.20±0.08% per year, p<0.001), but more slowly than BMD. Conclusion: We observed a significant increase in spine BMD and a positive maintenance of bone micro-architecture from TBS with antiresorptive treatment, whereas the treatment naïve group lost both density and micro-architecture. TBS seems to be responsive to treatment and could be suitable for monitoring micro-architecture. This article is part of a Special Issue entitled ECTS 2011. Disclosure of interest: M.-A. Krieg: None declared, A. Goertzen: None declared, W. Leslie: None declared, D. Hans Consulting fees from Medimaps.

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.

Agents anti-infectieux et fonction réna!e: vers des posologies sur mesure [Antimicrobial agents and renal elimination: towards individual dosage adjustment?].

The efficacy and safety of anti-infective treatments are associated with the drug blood concentration profile, which is directly correlated with a dosing adjustment to the individual patient's condition. Dosing adjustments to the renal function recommended in reference books are often imprecise and infrequently applied in clinical practice. The recent generalisation of the KDOQI (Kidney Disease Outcome Quality Initiative) staging of chronically impaired renal function represents an opportunity to review and refine the dosing recommendations in patients with renal insufficiency. The literature has been reviewed and compared to a predictive model of the fraction of drug cleared by the kidney based on the Dettli's principle. Revised drug dosing recommendations integrating these predictive parameters are proposed.

Thrombin-sensitive photodynamic agents: a novel strategy for selective synovectomy in rheumatoid arthritis.

Protease-sensitive macromolecular prodrugs have attracted interest for bio-responsive drug delivery to sites with up-regulated proteolytic activities such as inflammatory or cancerous lesions. Here we report the development of a novel polymeric photosensitizer prodrug (T-PS) to target thrombin, a protease up-regulated in synovial tissues of rheumatoid arthritis (RA) patients, for minimally invasive photodynamic synovectomy. In T-PS, multiple photosensitizer units are tethered to a polymeric backbone via short, thrombin-cleavable peptide linkers. Photoactivity of the prodrug is efficiently impaired due to energy transfer between neighbouring photosensitizer units. T-PS activation by exogenous and endogenous thrombin induced an increase in fluorescence emission by a factor of 16 after in vitro digestion and a selective fluorescence enhancement in arthritic lesions in vivo, in a collagen-induced arthritis mouse model. In vitro studies on primary human synoviocytes showed a phototoxic effect only after enzymatic digestion of the prodrug and light irradiation, thus demonstrating the functionality of T-PS induced PDT. The developed photosensitizer prodrugs combine the passive targeting capacity of macromolecular drug delivery systems with site-selective photosensitizer release and activation. They illuminate lesions with pathologically enhanced proteolytic activity and induce cell death, subsequent to irradiation.

Maximising the duration of disease control in metastatic renal cell carcinoma with targeted agents: an expert agreement.

With six targeted agents approved (sorafenib, sunitinib, temsirolimus, bevacizumab [+interferon], everolimus and pazopanib), many patients with metastatic renal cell carcinoma (mRCC) will receive multiple therapies. However, the optimum sequencing approach has not been defined. A group of European experts reviewed available data and shared their clinical experience to compile an expert agreement on the sequential use of targeted agents in mRCC. To date, there are few prospective studies of sequential therapy. The mammalian target of rapamycin (mTOR) inhibitor everolimus was approved for use in patients who failed treatment with inhibitors of vascular endothelial growth factor (VEGF) and VEGF receptors (VEGFR) based on the results from a Phase III placebo-controlled study; however, until then, the only licensed agents across the spectrum of mRCC were VEGF(R) inhibitors (sorafenib, sunitinib and bevacizumab + interferon), and as such, a large body of evidence has accumulated regarding their use in sequence. Data show that sequential use of VEGF(R) inhibitors may be an effective treatment strategy to achieve prolonged clinical benefit. The optimal place of each targeted agent in the treatment sequence is still unclear, and data from large prospective studies are needed. The Phase III AXIS study of second-line sorafenib vs. axitinib (including post-VEGF(R) inhibitors) has completed, but the data are not yet published; other ongoing studies include the Phase III SWITCH study of sorafenib-sunitinib vs. sunitinib-sorafenib (NCT00732914); the Phase III 404 study of temsirolimus vs. sorafenib post-sunitinib (NCT00474786) and the Phase II RECORD 3 study of sunitinib-everolimus vs. everolimus-sunitinib (NCT00903175). Until additional data are available, consideration of patient response and tolerability to treatment may facilitate current decision-making regarding when to switch and which treatment to switch to in real-life clinical practice.

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.