Design of potent inhibitors of human RAD51 recombinase based on BRC motifs of BRCA2 protein: modeling and experimental validation of a chimera peptide.

We have previously shown that a 28-amino acid peptide derived from the BRC4 motif of BRCA2 tumor suppressor inhibits selectively human RAD51 recombinase (HsRad51). With the aim of designing better inhibitors for cancer treatment, we combined an in silico docking approach with in vitro biochemical testing to construct a highly efficient chimera peptide from eight existing human BRC motifs. We built a molecular model of all BRC motifs complexed with HsRad51 based on the crystal structure of the BRC4 motif-HsRad51 complex, computed the interaction energy of each residue in each BRC motif, and selected the best amino acid residue at each binding position. This analysis enabled us to propose four amino acid substitutions in the BRC4 motif. Three of these increased the inhibitory effect in vitro, and this effect was found to be additive. We thus obtained a peptide that is about 10 times more efficient in inhibiting HsRad51-ssDNA complex formation than the original peptide.

A New Imaging Software To Evaluate The Centration Of Capsulorhexis And Intraocular Lens (IOL) After Cataract Surgery

Purpose: IOL centration and stability after cataract surgery is of high interest for cataract surgeons and IOL-producing companies. We present a new imaging software to evaluate the centration of the rhexis and the centration of the IOL after cataract surgery.Methods: We developed, in collaboration with the Biomedical Imaging Group (BIG), EPFL, Lausanne, a new working tool in order to assess precisely outcomes after IOL-implantation, such as ideal capsulorhexis and IOL-centration. The software is a plug-in of ImageJ, a general-purpose image processing and image-analysis package. The specifications of this software are: evaluation of the rhexis-centration and evaluation the position of the IOL in the posterior chamber. The end points are to analyze the quality of the centration of a rhexis after cataract surgery, the deformation of the rhexis with capsular bag retraction and the centration of the IOL after implantation.Results: This software delivers tools to interactively measure the distances between limbus, IOL and capsulorhexis and its changes over time. The user is invited to adjust nodes of three radial curves for the limbus, rhexis and the optic of the IOL. The radial distances of the curves are computed to evaluate the IOL implantation. The user is also able to define patterns for ideal capsulorhexis and optimal IOL-centration. We are going to present examples of calculations after cataract surgery.Conclusions: Evaluation of the centration of the rhexis and of the IOL after cataract surgery is an important end point for optimal IOL implantation after cataract surgery. Especially multifocal or accommodative lenses need a precise position in the bag with a good stability over time. This software is able to evaluate these parameters just after the surgery but also its changes over time. The results of these evaluations can lead to an optimizing of surgical procedures and materials.

Plasma and cerebrospinal fluid population pharmacokinetics of temozolomide in malignant glioma patients

RESUME : Objectif: Le glioblastome multiforme (GBM) est la tumeur cérébrale maligne la plus agressive qui conduit au décès de la majorité des patients moins d'une année après le diagnostic. La plupart des agents chimiothérapeutiques actuellement disponibles ne traversent pas la barrière hémato¬encéphalique et ne peuvent par conséquent pas être utilisés pour ce type de tumeur. Le Temozolomide (TMZ) est un nouvel agent alkylant récemment développé pour le traitement des gliomes malins. A ce jour, très peu d'informations sont disponibles sur la pénétration intra-cérébrale de cet agent. Au cours d'une étude pilote de phase II menée auprès de 64 patients atteints de GBM, l'administration précoce de TMZ combinée à une radiothérapie standard (RT) afin d'intervenir au plus tôt dans l'évolution de la maladie, a permis de prolonger la survie de ces patients, résultat qui pu être confirmé par la suite lors de l'étude randomisée de phase III. L'objectif de cette étude a été de déterminer les paramètres pharmacocinétique du TMZ dans le plasma et le liquide céphalo-rachidien (LCR), d'évaluer l'influence de certains facteurs individuels (âge, sexe, surface corporelle, fonction rénale/hépatique, co-médications, RT concomitante) sur ces différents paramètres, et enfin d'explorer la relation existant entre l'exposition au TMZ et certains marqueurs cliniques d'efficacité et de toxicité. Matériel et Méthode: Les concentrations de TMZ ont été mesurées par chromatographie liquide à haute performance (HPLC) dans le plasma et le LCR de 35 patients atteints de GBM nouvellement diagnostiqués (étude pilote) ou de gliomes malins en récidive (étude récidive). L'analyse pharmacocinétique de population a été réalisée à l'aide du programme NONMEM. L'exposition systémique et cérébrale, définie par les AUC (Area Under the time-concentration Curve) dans le plasma et le LCR, a été estimée pour chaque patient et corrélée à la toxicité, la survie ainsi que la survie sans progression tumorale. Résultats: Un modèle à 1 compartiment avec une cinétique d'absorption et de transfert Kplasma -> LCR de ordre a été retenu afin de décrire le profil pharmacocinétique du TMZ. Les valeurs moyennes de population ont été de 10 L/h pour la clairance, de 30.3 L pour le volume de distribution, de 2.1 h pour la 1/2 vie d'élimination, de 5.78 hE-1 pour la constante d'absorption, de 7.2 10E4 hE-1 pour Kplasma->LCR et de 0.76 hE-1 pour KLCR plasma. La surface corporelle a montré une influence significative sur la clairance et le volume de distribution, alors que le sexe influence la clairance uniquement. L'AUC mesurée dans le LCR représente ~20% de celle du plasma et une augmentation de 15% de Kplasma->LCR a été observée lors du traitement concomitant de radiochimiothérapie. Conclusions: Cette étude est la première analyse pharmacocinétique effectuée chez l'homme permettant de quantifier la pénétration intra-cérébrale du TMZ. Le rapport AUC LCR/AUC Plasma a été de 20%. Le degré d'exposition systémique et cérébral au TMZ ne semble pas être un meilleur facteur prédictif de la survie ou de la tolérance au produit que ne l'est la dose cumulée seule. ABSTRACT Purpose: Scarce information is available on the brain penetration of temozolomide (TMZ), although this novel methylating agent is mainly used for the treatment of ma¬lignant brain tumors. The purpose was to assess TNIZ phar¬macokinetics in plasma and cerebrospinal fluid (CSF) along with its inter-individual variability, to characterize covari¬ates and to explore relationships between systemic or cere¬bral drug exposure and clinical outcomes. Experimental Design: TMZ levels were measured by high-performance liquid chromatography in plasma and CSF samples from 35 patients with newly diagnosed or recurrent malignant gliomas. The population pharmacoki¬netic analysis was performed with nonlinear mixed-effect modeling software. Drug exposure, defined by the area un¬der the concentration-time curve (AUC) in plasma and CSF, was estimated for each patient and correlated with toxicity, survival, and progression-free survival. Results: A three-compartment model with first-order absorption and transfer rates between plasma and CSF described the data appropriately. Oral clearance was 10 liter/h; volume of distribution (VD), 30.3 liters; absorption constant rate, 5.8 hE-1; elimination half-time, 2.1 h; transfer rate from plasma to CSF (Kplasma->CSF), 7.2 x 10E-4hE-1 and the backwards rate, 0.76hE-1. Body surface area signifi¬cantly influenced both clearance and VD, and clearance was sex dependent. The AU CSF corresponded to 20% of the AUCplasma. A trend toward an increased K plasma->CSF of 15% was observed in case of concomitant radiochemo-therapy. No significant correlations between AUC in plasma or CSF and toxicity, survival, or progression-free survival were apparent after deduction of dose-effect. Conclusions: This is the first human pharmacokinetic study on TMZ to quantify CSF penetration. The AUC CSF/ AUC plasma ratio was 20%. Systemic or cerebral exposures are not better predictors than the cumulative dose alone for both efficacy and safety.

A stratified approach for modeling the distribution of a threatened ant species in the Swiss National Park

We present models predicting the potential distribution of a threatened ant species, Formica exsecta Nyl., in the Swiss National Park ( SNP). Data to fit the models have been collected according to a random-stratified design with an equal number of replicates per stratum. The basic aim of such a sampling strategy is to allow the formal testing of biological hypotheses about those factors most likely to account for the distribution of the modeled species. The stratifying factors used in this study were: vegetation, slope angle and slope aspect, the latter two being used as surrogates of solar radiation, considered one of the basic requirements of F. exsecta. Results show that, although the basic stratifying predictors account for more than 50% of the deviance, the incorporation of additional non-spatially explicit predictors into the model, as measured in the field, allows for an increased model performance (up to nearly 75%). However, this was not corroborated by permutation tests. Implementation on a national scale was made for one model only, due to the difficulty of obtaining similar predictors on this scale. The resulting map on the national scale suggests that the species might once have had a broader distribution in Switzerland. Reasons for its particular abundance within the SNP might possibly be related to habitat fragmentation and vegetation transformation outside the SNP boundaries.

Modeling the biophysical impacts of global change in mountain biosphere reserves

Mountains and mountain societies provide a wide range of goods and services to humanity, but they are particularly sensitive to the effects of global environmental change. Thus, the definition of appropriate management regimes that maintain the multiple functions of mountain regions in a time of greatly changing climatic, economic, and societal drivers constitutes a significant challenge. Management decisions must be based on a sound understanding of the future dynamics of these systems. The present article reviews the elements required for an integrated effort to project the impacts of global change on mountain regions, and recommends tools that can be used at 3 scientific levels (essential, improved, and optimum). The proposed strategy is evaluated with respect to UNESCO's network of Mountain Biosphere Reserves (MBRs), with the intention of implementing it in other mountain regions as well. First, methods for generating scenarios of key drivers of global change are reviewed, including land use/land cover and climate change. This is followed by a brief review of the models available for projecting the impacts of these scenarios on (1) cryospheric systems, (2) ecosystem structure and diversity, and (3) ecosystem functions such as carbon and water relations. Finally, the cross-cutting role of remote sensing techniques is evaluated with respect to both monitoring and modeling efforts. We conclude that a broad range of techniques is available for both scenario generation and impact assessments, many of which can be implemented without much capacity building across many or even most MBRs. However, to foster implementation of the proposed strategy, further efforts are required to establish partnerships between scientists and resource managers in mountain areas.

Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.

Swain: Stata module to correct the SEM chi-square overidentification test in small sample sizes or complex models. Statistical Software Components S457617, Boston College Department of Economics.

Swain corrects the chi-square overidentification test (i.e., likelihood ratio test of fit) for structural equation models whethr with or without latent variables. The chi-square statistic is asymptotically correct; however, it does not behave as expected in small samples and/or when the model is complex (cf. Herzog, Boomsma, & Reinecke, 2007). Thus, particularly in situations where the ratio of sample size (n) to the number of parameters estimated (p) is relatively small (i.e., the p to n ratio is large), the chi-square test will tend to overreject correctly specified models. To obtain a closer approximation to the distribution of the chi-square statistic, Swain (1975) developed a correction; this scaling factor, which converges to 1 asymptotically, is multiplied with the chi-square statistic. The correction better approximates the chi-square distribution resulting in more appropriate Type 1 reject error rates (see Herzog & Boomsma, 2009; Herzog, et al., 2007).

Prediction of infant drug exposure through breastfeeding: population PK modeling and simulation of fluoxetine

BACKGROUND: Risks of significant infant drug exposurethrough breastmilk are poorly defined for many drugs, and largescalepopulation data are lacking. We used population pharmacokinetics(PK) modeling to predict fluoxetine exposure levels ofinfants via mother's milk in a simulated population of 1000 motherinfantpairs.METHODS: Using our original data on fluoxetine PK of 25breastfeeding women, a population PK model was developed withNONMEM and parameters, including milk concentrations, wereestimated. An exponential distribution model was used to account forindividual variation. Simulation random and distribution-constrainedassignment of doses, dosing time, feeding intervals and milk volumewas conducted to generate 1000 mother-infant pairs with characteristicssuch as the steady-state serum concentrations (Css) and infantdose relative to the maternal weight-adjusted dose (relative infantdose: RID). Full bioavailability and a conservative point estimate of1-month-old infant CYP2D6 activity to be 20% of the adult value(adjusted by weigth) according to a recent study, were assumed forinfant Css calculations.RESULTS: A linear 2-compartment model was selected as thebest model. Derived parameters, including milk-to-plasma ratios(mean: 0.66; SD: 0.34; range, 0 - 1.1) were consistent with the valuesreported in the literature. The estimated RID was below 10% in >95%of infants. The model predicted median infant-mother Css ratio was0.096 (range 0.035 - 0.25); literature reported mean was 0.07 (range0-0.59). Moreover, the predicted incidence of infant-mother Css ratioof >0.2 was less than 1%.CONCLUSION: Our in silico model prediction is consistent withclinical observations, suggesting that substantial systemic fluoxetineexposure in infants through human milk is rare, but further analysisshould include active metabolites. Our approach may be valid forother drugs. [supported by CIHR and Swiss National Science Foundation(SNSF)]

The challenge of modeling nuclear receptor regulatory networks in mammalian cells.

Nuclear receptors are a major component of signal transduction in animals. They mediate the regulatory activities of many hormones, nutrients and metabolites on the homeostasis and physiology of cells and tissues. It is of high interest to model the corresponding regulatory networks. While molecular and cell biology studies of individual promoters have provided important mechanistic insight, a more complex picture is emerging from genome-wide studies. The regulatory circuitry of nuclear receptor regulated gene expression networks, and their response to cellular signaling, appear highly dynamic, and involve long as well as short range chromatin interactions. We review how progress in understanding the kinetics and regulation of cofactor recruitment, and the development of new genomic methods, provide opportunities but also a major challenge for modeling nuclear receptor mediated regulatory networks.

Aerodynamic drag modeling of alpine skiers performing giant slalom turns.

PURPOSE: Aerodynamic drag plays an important role in performance for athletes practicing sports that involve high-velocity motions. In giant slalom, the skier is continuously changing his/her body posture, and this affects the energy dissipated in aerodynamic drag. It is therefore important to quantify this energy to understand the dynamic behavior of the skier. The aims of this study were to model the aerodynamic drag of alpine skiers in giant slalom simulated conditions and to apply these models in a field experiment to estimate energy dissipated through aerodynamic drag. METHODS: The aerodynamic characteristics of 15 recreational male and female skiers were measured in a wind tunnel while holding nine different skiing-specific postures. The drag and the frontal area were recorded simultaneously for each posture. Four generalized and two individualized models of the drag coefficient were built, using different sets of parameters. These models were subsequently applied in a field study designed to compare the aerodynamic energy losses between a dynamic and a compact skiing technique. RESULTS: The generalized models estimated aerodynamic drag with an accuracy of between 11.00% and 14.28%, and the individualized models estimated aerodynamic drag with an accuracy between 4.52% and 5.30%. The individualized model used for the field study showed that using a dynamic technique led to 10% more aerodynamic drag energy loss than using a compact technique. DISCUSSION: The individualized models were capable of discriminating different techniques performed by advanced skiers and seemed more accurate than the generalized models. The models presented here offer a simple yet accurate method to estimate the aerodynamic drag acting upon alpine skiers while rapidly moving through the range of positions typical to turning technique.

Stoneley wave modeling in heterogeneous porous media with viscous pore fluids

We implemented Biot-type porous wave equations in a pseudo-spectral numerical modeling algorithm for the simulation of Stoneley waves in porous media. Fourier and Chebyshev methods are used to compute the spatial derivatives along the horizontal and vertical directions, respectively. To prevent from overly short time steps due to the small grid spacing at the top and bottom of the model as a consequence of the Chebyshev operator, the mesh is stretched in the vertical direction. As a large benefit, the Chebyshev operator allows for an explicit treatment of interfaces. Boundary conditions can be implemented with a characteristics approach. The characteristic variables are evaluated at zero viscosity. We use this approach to model seismic wave propagation at the interface between a fluid and a porous medium. Each medium is represented by a different mesh and the two meshes are connected through the above described characteristics domain-decomposition method. We show an experiment for sealed pore boundary conditions, where we first compare the numerical solution to an analytical solution. We then show the influence of heterogeneity and viscosity of the pore fluid on the propagation of the Stoneley wave and surface waves in general.

GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.