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.

Ocular and systemic bio-distribution of rhodamine-conjugated liposomes loaded with VIP injected into the vitreous of Lewis rats.

PURPOSE: Local delivery of therapeutic molecules encapsulated within liposomes is a promising method to treat ocular inflammation. The purpose of the present study was to define the biodistribution of rhodamine-conjugated liposomes loaded with vasoactive intestinal peptide (VIP), an immunosuppressive neuropeptide, following their intravitreal (IVT) injection in normal rats. METHODS: Healthy seven- to eight-week-old Lewis male rats were injected into the vitreous with empty rhodamine-conjugated liposomes (Rh-Lip) or with VIP-loaded Rh-Lip (VIP-Rh-Lip; 50 mM of lipids with an encapsulation efficiency of 3.0+/-0.4 mmol VIP/mol lipids). Twenty-four h after IVT injection, the eyes, the cervical, mesenteric, and inguinal lymph nodes (LN), and spleen were collected. The phenotype and distribution of cells internalizing Rh-Lip and VIP-Rh-Lip were studied. Determination of VIP expression in ocular tissues and lymphoid organs and interactions with T cells in cervical LN was performed on whole mounted tissues and frozen tissue sections by immunofluorescence and confocal microscopy. RESULTS: In the eye, 24 h following IVT injection, fluorescent liposomes (Rh-Lip and VIP-Rh-Lip) were detected mainly in the posterior segment of the eye (vitreous, inner layer of the retina) and to a lesser extent at the level of the iris root and ciliary body. Liposomes were internalized by activated retinal Müller glial cells, ocular tissue resident macrophages, and rare infiltrating activated macrophages. In addition, fluorescent liposomes were found in the episclera and conjunctiva where free VIP expression was also detected. In lymphoid organs, Rh-Lip and VIP-Rh-Lip were distributed almost exclusively in the cervical lymph nodes (LN) with only a few Rh-Lip-positive cells detected in the spleen and mesenteric LN and none in the inguinal LN. In the cervical LN, Rh-Lip were internalized by resident ED3-positive macrophages adjacent to CD4 and CD8-positive T lymphocytes. Some of these T lymphocytes in close contact with macrophages containing VIP-Rh-Lip expressed VIP. CONCLUSIONS: Liposomes are specifically internalized by retinal Müller glial cells and resident macrophages in the eye. A limited passage of fluorescent liposomes from the vitreous to the spleen via the conventional outflow pathway and the venous circulation was detected. The majority of fluorescent liposomes deposited in the conjunctiva following IVT injection reached the subcapsular sinus of the cervical LN via conjuntival lymphatics. In the cervical LN, Rh-Lip were internalized by resident subcapsular sinus macrophages adjacent to T lymphocytes. Detection of VIP in both macrophages and T cells in cervical LN suggests that IVT injection of VIP-Rh-Lip may increase ocular immune privilege by modulating the loco-regional immune environment. In conclusion, our observations suggest that IVT injection of VIP-loaded liposomes is a promising therapeutic strategy to dampen ocular inflammation by modulating macrophage and T cell activation mainly in the loco-regional immune system.

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.

Late migration of percutaneous bio-absorbable devices--a word of caution.

BACKGROUND: Closures of atrial septal defects or a patent foramen ovale (PFO) are increasingly performed percutaneously. The experience of late migration of a new bio-absorbable device is presented here, followed by conceptual discussion. METHODS: Six months post PFO closure with a BioSTAR® device a patient presented with chest pain. Echocardiography showed a hyperechogenic structure perforating the aortic wall. RESULTS: Surgical exploration showed a perforation of the ascending aorta by one metallic, non absorbable arm. This is the second case of late (>6 months) dislocation of the residual framework of the occluder. CONCLUSIONS: The overall incidence of perforation of cardiac structures due to secondary dislocation is low. However this complication exists and should kept in mind in symptomatic patients with new onset of chest pain, after percutaneous procedures. The concept of biodegradation, with residual, non absorbable metal braiding, should be reviewed, analyzing in particular long term results and incidence of secondary dislocation.

New Trends in Public Facility Location Modeling

The past four decades have witnessed an explosive growth in the field of networkbased facility location modeling. This is not at all surprising since location policy is one of the most profitable areas of applied systems analysis in regional science and ample theoretical and applied challenges are offered. Location-allocation models seek the location of facilities and/or services (e.g., schools, hospitals, and warehouses) so as to optimize one or several objectives generally related to the efficiency of the system or to the allocation of resources. This paper concerns the location of facilities or services in discrete space or networks, that are related to the public sector, such as emergency services (ambulances, fire stations, and police units), school systems and postal facilities. The paper is structured as follows: first, we will focus on public facility location models that use some type of coverage criterion, with special emphasis in emergency services. The second section will examine models based on the P-Median problem and some of the issues faced by planners when implementing this formulation in real world locational decisions. Finally, the last section will examine new trends in public sector facility location modeling.

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.

Modeling of the TCR-MHC-peptide complex.

The methodology for generating a homology model of the T1 TCR-PbCS-K(d) class I major histocompatibility complex (MHC) class I complex is presented. The resulting model provides a qualitative explanation of the effect of over 50 different mutations in the region of the complementarity determining region (CDR) loops of the T cell receptor (TCR), the peptide and the MHC's alpha(1)/alpha(2) helices. The peptide is modified by an azido benzoic acid photoreactive group, which is part of the epitope recognized by the TCR. The construction of the model makes use of closely related homologs (the A6 TCR-Tax-HLA A2 complex, the 2C TCR, the 14.3.d TCR Vbeta chain, the 1934.4 TCR Valpha chain, and the H-2 K(b)-ovalbumine peptide), ab initio sampling of CDR loops conformations and experimental data to select from the set of possibilities. The model shows a complex arrangement of the CDR3alpha, CDR1beta, CDR2beta and CDR3beta loops that leads to the highly specific recognition of the photoreactive group. The protocol can be applied systematically to a series of related sequences, permitting the analysis at the structural level of the large TCR repertoire specific for a given peptide-MHC complex.

Making sense of AMPA receptor trafficking by modeling molecular mechanisms of synaptic plasticity.

Synaptic plasticity involves a complex molecular machinery with various protein interactions but it is not yet clear how its components give rise to the different aspects of synaptic plasticity. Here we ask whether it is possible to mathematically model synaptic plasticity by making use of known substances only. We present a model of a multistable biochemical reaction system and use it to simulate the plasticity of synaptic transmission in long-term potentiation (LTP) or long-term depression (LTD) after repeated excitation of the synapse. According to our model, we can distinguish between two phases: first, a "viscosity" phase after the first excitation, the effects of which like the activation of NMDA receptors and CaMKII fade out in the absence of further excitations. Second, a "plasticity" phase actuated by an identical subsequent excitation that follows after a short time interval and causes the temporarily altered concentrations of AMPA subunits in the postsynaptic membrane to be stabilized. We show that positive feedback is the crucial element in the core chemical reaction, i.e. the activation of the short-tail AMPA subunit by NEM-sensitive factor, which allows generating multiple stable equilibria. Three stable equilibria are related to LTP, LTD and a third unfixed state called ACTIVE. Our mathematical approach shows that modeling synaptic multistability is possible by making use of known substances like NMDA and AMPA receptors, NEM-sensitive factor, glutamate, CaMKII and brain-derived neurotrophic factor. Furthermore, we could show that the heteromeric combination of short- and long-tail AMPA receptor subunits fulfills the function of a memory tag.

Landmark detection for fusion of fundus and MRI toward a patient-specific multimodal eye model.

Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise, e.g., Fundus photography, optical coherence tomography, computed tomography, and magnetic resonance imaging (MRI). Yet, these images are often complementary and do express the same pathologies in a different way. Some pathologies are only visible in a particular modality. Thus, it is beneficial for the ophthalmologist to have these modalities fused into a single patient-specific model. The goal of this paper is a fusion of Fundus photography with segmented MRI volumes. This adds information to MRI that was not visible before like vessels and the macula. This paper contributions include automatic detection of the optic disc, the fovea, the optic axis, and an automatic segmentation of the vitreous humor of the eye.

What kind of relationships between the evolution of pain, beliefs and bio-psycho-social complexity after a locomotor traumatism?

Introduction: Pain and beliefs have an influence on the patient's course in rehabilitation, pain causes fears and fears influence pain perception. The aim of this study is to understand pain and beliefs evolutions during rehabilitation taking into account of bio-psycho-social complexity.Patients and methods: 631 consecutive patients admitted in rehabilitation after a musculoskeletal traumatism were included and assessed at admission and at discharge. Pain was measured by VAS (Visual Analogical Scale), bio-psycho-social complexity by Intermed scale, and beliefs by judgement on Lickert scales. Four kinds of beliefs were evaluated: fear of a severe origin of pain, fear of movement, fear of pain and feeling of distress (loss of control). The association between the changes in pain and beliefs during the hospitalization was assessed by linear regressions.Results: After adjustment for gender, age, education and native language, patients with a decrease in pain during rehabilitation have higher probability of decreasing their fears. For the distress feeling, this relationship is weaker among bio-psycho-socially complex patients (odds-ratio 1.22 for each decreasing of 10mm/100 VAS) than among non-complex patients (OR 1.47). Patients with a pain decrease of 30% or more during hospitalization have higher probability of seeing their fears decrease, this relationship being stronger in complex patient for fear of a severe origin of pain.Discussion: The relationships between evolution of pain and beliefs move in the same direction. The higher a patient feels pain, the less they could be able to modify their dysfunctional beliefs. When the pain diminishes of 30% or more, the probability to challenge the beliefs is increased. The prognostic with regard to feeling of distress and fear of a severe origin of pain, is worse among bio-psycho-socially complex patients.

Modeling the forensic two-trace problem with Bayesian networks

The forensic two-trace problem is a perplexing inference problem introduced by Evett (J Forensic Sci Soc 27:375-381, 1987). Different possible ways of wording the competing pair of propositions (i.e., one proposition advanced by the prosecution and one proposition advanced by the defence) led to different quantifications of the value of the evidence (Meester and Sjerps in Biometrics 59:727-732, 2003). Here, we re-examine this scenario with the aim of clarifying the interrelationships that exist between the different solutions, and in this way, produce a global vision of the problem. We propose to investigate the different expressions for evaluating the value of the evidence by using a graphical approach, i.e. Bayesian networks, to model the rationale behind each of the proposed solutions and the assumptions made on the unknown parameters in this problem.