Modelado en Dinámica de Sistemas de la respuesta inmune ante la infección del VIH-1

Se ha construido un modelo en Dinámica de Sistemas compuesto por cuatro vistas. Cada una de ellas representa una parte fundamental de la infección: Los linfocitos T CD4+ (células helper), linfocitos B y anticuerpos, linfocitos T CD8+ (citotóxicos) y carga viral.Para explicar la dinámica del VIH/SIDA en interacción con el sistema inmunológico se han realizado numerosos estudios clínico de monitorización de los enfermos a lo largo del tiempo. Sin embargo, dichos estudios no han conseguido captar los numerosos aspectos dinámicos implicados dada la complejidad existente entre sistema inmune y el VIH-1. El conjunto de datos clínicos y matemáticos reunidos pone de manifiesto que el VIH, además de replicarse masivamente en los pacientes infectados, sufre reiteradas mutaciones, generándose así una enorme diversidad de poblaciones víricas; estos mutantes capaces de evadir, en cierta medida, el ataque inmunitario hacen su aparición predominando hasta que el sistema inmunitario recababa la energía para sofocarlos, aunque mientras tanto nuevos "mutantes elusivos" (que eluden el sistema inmune) inician su multiplicación. La ventaja en la lucha pasa así repetidamente del virus al sistema inmunitario, y de éste a aquél. Sin embargo, estos vaivenes no se mantienen de manera indefinida ya que la densidad de poblaciones víricas que escapan del sistema inmunitario crece paulatinamente inclinando gradualmente la balanza en favor del virus.El modelo que aquí se describe consigue representar la complejidad de dichas interacciones a nivel celular y viral proponiéndose con una herramienta útil en el conocimiento biológico y clínico del VIH-1

Nicotine and cotinine exposure from electronic cigarettes: a population approach

BACKGROUND AND OBJECTIVES: Electronic cigarettes (e-cigarettes) are a recent technology that has gained rapid acceptance. Still, little is known about them in terms of safety and effectiveness. A basic question is how effectively they deliver nicotine; however, the literature is surprisingly unclear on this point. Here, a population pharmacokinetic model was developed for nicotine and its major metabolite cotinine with the aim to provide a reliable framework for the simulation of nicotine and cotinine concentrations over time, based solely on inhalation airflow recordings and individual covariates [i.e., weight and breath carbon monoxide (CO) levels]. METHODS: This study included ten adults self-identified as heavy smokers (at least one pack of cigarettes per day). Plasma nicotine and cotinine concentrations were measured at regular 10-min intervals for 90 min while human subjects inhaled nicotine vapor from a modified e-cigarette. Airflow measurements were recorded every 200 ms throughout the session. A population pharmacokinetic model for nicotine and cotinine was developed based on previously published pharmacokinetic parameters and the airflow recordings. All of the analyses were performed with the non-linear mixed-effect modeling software NONMEM(®) version 7.2. RESULTS: The results show that e-cigarettes deliver nicotine effectively, although the pharmacokinetic profiles are lower than those achieved with regular cigarettes. Our pharmacokinetic model effectively predicts plasma nicotine and cotinine concentrations from the inhalation volume, and initial breath CO. CONCLUSION: E-cigarettes are effective at delivering nicotine. This new pharmacokinetic model of e-cigarette usage might be used for pharmacodynamic analysis where the pharmacokinetic profiles are not available

A computational analysis of protein-protein interaction networks in neurodegenerative diseases

<p>Abstract</p> <p>Background</p> <p>Recent developments have meant that network theory is making an important contribution to the topological study of biological networks, such as protein-protein interaction (PPI) networks. The identification of differentially expressed genes in DNA array experiments is a source of information regarding the molecular pathways involved in disease. Thus, considering PPI analysis and gene expression studies together may provide a better understanding of multifactorial neurodegenerative diseases such as Multiple Sclerosis (MS) and Alzheimer disease (AD). The aim of this study was to assess whether the parameters of degree and betweenness, two fundamental measures in network theory, are properties that differentiate between implicated (seed-proteins) and non-implicated nodes (neighbors) in MS and AD. We used experimentally validated PPI information to obtain the neighbors for each seed group and we studied these parameters in four networks: MS-blood network; MS-brain network; AD-blood network; and AD-brain network.</p> <p>Results</p> <p>Specific features of seed-proteins were revealed, whereby they displayed a lower average degree in both diseases and tissues, and a higher betweenness in AD-brain and MS-blood networks. Additionally, the heterogeneity of the processes involved indicate that these findings are not pathway specific but rather that they are spread over different pathways.</p> <p>Conclusion</p> <p>Our findings show differential centrality properties of proteins whose gene expression is impaired in neurodegenerative diseases.</p

Modeling Sitagliptin Effect on Dipeptidyl Peptidase 4 (DPP4) Activity in Adults with Hematological Malignancies After Umbilical Cord Blood (UCB) Hematopoietic Cell Transplant (HCT)

Background and Objectives— Dipeptidyl peptidase-4 (DPP4) inhibition is a potential strategy to increase the engraftment rate of hematopoietic stem/progenitor cells. A recent clinical trial using sitagliptin, a DPP4 inhibitor approved for type 2 diabetes mellitus, has shown to be a promising approach in adults with hematological malignancies after umbilical cord blood (UCB) hematopoietic cell transplant (HCT). Based on data from this clinical trial, a semi-mechanistic model was developed to simultaneously describe DPP4 activity after multiple doses of sitagliptin in subjects with hematological malignancies after a single-unit UCB HCT. Methods— The clinical study included 24 patients that received myeloablative conditioning followed by 4 oral sitagliptin 600mg with single-unit UCB HCT. Using a nonlinear mixed effects approach, a semi-mechanistic pharmacokinetic/pharmacodynamic model was developed to describe DPP4 activity from this trial data using NONMEM 7.2. The model was used to drive Monte-Carlo simulations to probe various dosage schedules and the attendant DPP4 response. Results— The disposition of sitagliptin in plasma was best described by a 2-compartment model. The relationship between sitagliptin concentration and DPP4 activity was best described by an indirect response model with a negative feedback loop. Simulations showed that twice a day or three times a day dosage schedules were superior to once daily schedule for maximal DPP4 inhibition at the lowest sitagliptin exposure. Conclusion— This study provides the first pharmacokinetic/pharmacodynamic model of sitagliptin in the context of HCT, and provides a valuable tool for exploration of optimal dosing regimens, critical for improving time to engraftment in patients after UCB HCT

Dynamic cross-regulation of antigen-specific effector and regulatory T cell subpopulations and microglia in brain autoimmunity

Background: Multiple Sclerosis (MS) is considered a T-cell-mediated autoimmune disease with a prototypical oscillatory behavior, as evidenced by the presence of clinical relapses. Understanding the dynamics of immune cells governing the course of MS, therefore, has many implications for immunotherapy. Here, we used flow cytometry to analyze the time-dependent behavior of antigen-specific effector (Teff) and regulatory (Treg) T cells and microglia in mice model of MS, Experimental Autoimmune Encephalomyelitis (EAE), and compared the observations with a mathematical cross-regulation model of T-cell dynamics in autoimmune disease. Results: We found that Teff and Treg cells specific to myelin olygodendrocyte glycoprotein (MOG) developed coupled oscillatory dynamics with a 4- to 5-day period and decreasing amplitude that was always higher for the Teff populations, in agreement with the mathematical model. Microglia activation followed the oscillations of MOG-specific Teff cells in the secondary lymphoid organs, but they were activated before MOG-specific T-cell peaks in the CNS. Finally, we assessed the role of B-cell depletion induced by anti-CD20 therapy in the dynamics of T cells in an EAE model with more severe disease after therapy. We observed that B-cell depletion decreases Teff expansion, although its oscillatory behavior persists. However, the effect of B cell depletion was more significant in the Treg population within the CNS, which matched with activation of microglia and worsening of the disease. Mathematical modeling of T-cell cross-regulation after anti-CD20 therapy suggests that B-cell depletion may influence the dynamics of T cells by fine-tuning their activation. Conclusions: The oscillatory dynamics of T-cells have an intrinsic origin in the physiological regulation of the adaptive immune response, which influences both disease phenotype and response to immunotherapy.This work was supported in part by the Spanish network of excellence in MS of the Instituto de Salud Carlos III, Spain to PV and JGO (RD07/0060), by grant FIS-PI12/01823, the European Commission 7FP (CombiMS, contract grant: 305397), and by an unrestricted grant from Roche to PV; a grant of the Ministerio de Ciencia e Innovación (Spain, project FIS2009-13360) and by the ICREA Academia program to JG

Dynamic cross-regulation of antigen-specific effector and regulatory T cell subpopulations and microglia in brain autoimmunity

Background: Multiple Sclerosis (MS) is considered a T-cell-mediated autoimmune disease with a prototypical oscillatory behavior, as evidenced by the presence of clinical relapses. Understanding the dynamics of immune cells governing the course of MS, therefore, has many implications for immunotherapy. Here, we used flow cytometry to analyze the time-dependent behavior of antigen-specific effector (Teff) and regulatory (Treg) T cells and microglia in mice model of MS, Experimental Autoimmune Encephalomyelitis (EAE), and compared the observations with a mathematical cross-regulation model of T-cell dynamics in autoimmune disease. Results: We found that Teff and Treg cells specific to myelin olygodendrocyte glycoprotein (MOG) developed coupled oscillatory dynamics with a 4- to 5-day period and decreasing amplitude that was always higher for the Teff populations, in agreement with the mathematical model. Microglia activation followed the oscillations of MOG-specific Teff cells in the secondary lymphoid organs, but they were activated before MOG-specific T-cell peaks in the CNS. Finally, we assessed the role of B-cell depletion induced by anti-CD20 therapy in the dynamics of T cells in an EAE model with more severe disease after therapy. We observed that B-cell depletion decreases Teff expansion, although its oscillatory behavior persists. However, the effect of B cell depletion was more significant in the Treg population within the CNS, which matched with activation of microglia and worsening of the disease. Mathematical modeling of T-cell cross-regulation after anti-CD20 therapy suggests that B-cell depletion may influence the dynamics of T cells by fine-tuning their activation. Conclusions: The oscillatory dynamics of T-cells have an intrinsic origin in the physiological regulation of the adaptive immune response, which influences both disease phenotype and response to immunotherapy.This work was supported in part by the Spanish network of excellence in MS of the Instituto de Salud Carlos III, Spain to PV and JGO (RD07/0060), by grant FIS-PI12/01823, the European Commission 7FP (CombiMS, contract grant: 305397), and by an unrestricted grant from Roche to PV; a grant of the Ministerio de Ciencia e Innovación (Spain, project FIS2009-13360) and by the ICREA Academia program to JG

Modeling the effector - regulatory T cell cross-regulation reveals the intrinsic character of relapses in multiple sclerosis

Background: The relapsing-remitting dynamics is a hallmark of autoimmune diseases such as Multiple Sclerosis (MS). Although current understanding of both cellular and molecular mechanisms involved in the pathogenesis of autoimmune diseases is significant, how their activity generates this prototypical dynamics is not understood yet. In order to gain insight about the mechanisms that drive these relapsing-remitting dynamics, we developed a computational model using such biological knowledge. We hypothesized that the relapsing dynamics in autoimmunity can arise through the failure in the mechanisms controlling cross-regulation between regulatory and effector T cells with the interplay of stochastic events (e.g. failure in central tolerance, activation by pathogens) that are able to trigger the immune system. Results: The model represents five concepts: central tolerance (T-cell generation by the thymus), T-cell activation, T-cell memory, cross-regulation (negative feedback) between regulatory and effector T-cells and tissue damage. We enriched the model with reversible and irreversible tissue damage, which aims to provide a comprehensible link between autoimmune activity and clinical relapses and active lesions in the magnetic resonances studies in patients with Multiple Sclerosis. Our analysis shows that the weakness in this negative feedback between effector and regulatory T-cells, allows the immune system to generate the characteristic relapsing-remitting dynamics of autoimmune diseases, without the need of additional environmental triggers. The simulations show that the timing at which relapses appear is highly unpredictable. We also introduced targeted perturbations into the model that mimicked immunotherapies that modulate effector and regulatory populations. The effects of such therapies happened to be highly dependent on the timing and/or dose, and on the underlying dynamic of the immune system. Conclusion: The relapsing dynamic in MS derives from the emergent properties of the immune system operating in a pathological state, a fact that has implications for predicting disease course and developing new therapies for MS.This work was supported by the European Commission (NEST-Pathfinder program: "ComplexDis" grant, contract number: 043241 and ITNMC program: "UEPHA*MS" grant, contract number: 212877) and the Spanish Ministry of Health (PI060117 and RD07/0060/0000-01) to PV and JGO. NV is a fellow of the Programa de Formación de Investigadores del Departamento de Educación, Universidades e Investigación, Gobierno Vasco (BFI05.9 AE). JB acknowledges the support of the MEC through the grant: FIS2005-06912-C02, DINCARD. JG is a fellow of Government of Navarra. Dr Bagnato's contribution to this work was supported by the Intramural Research Program of the NINDS-NI

Modeling the effector - regulatory T cell cross-regulation reveals the intrinsic character of relapses in multiple sclerosis

Background: The relapsing-remitting dynamics is a hallmark of autoimmune diseases such as Multiple Sclerosis (MS). Although current understanding of both cellular and molecular mechanisms involved in the pathogenesis of autoimmune diseases is significant, how their activity generates this prototypical dynamics is not understood yet. In order to gain insight about the mechanisms that drive these relapsing-remitting dynamics, we developed a computational model using such biological knowledge. We hypothesized that the relapsing dynamics in autoimmunity can arise through the failure in the mechanisms controlling cross-regulation between regulatory and effector T cells with the interplay of stochastic events (e.g. failure in central tolerance, activation by pathogens) that are able to trigger the immune system. Results: The model represents five concepts: central tolerance (T-cell generation by the thymus), T-cell activation, T-cell memory, cross-regulation (negative feedback) between regulatory and effector T-cells and tissue damage. We enriched the model with reversible and irreversible tissue damage, which aims to provide a comprehensible link between autoimmune activity and clinical relapses and active lesions in the magnetic resonances studies in patients with Multiple Sclerosis. Our analysis shows that the weakness in this negative feedback between effector and regulatory T-cells, allows the immune system to generate the characteristic relapsing-remitting dynamics of autoimmune diseases, without the need of additional environmental triggers. The simulations show that the timing at which relapses appear is highly unpredictable. We also introduced targeted perturbations into the model that mimicked immunotherapies that modulate effector and regulatory populations. The effects of such therapies happened to be highly dependent on the timing and/or dose, and on the underlying dynamic of the immune system. Conclusion: The relapsing dynamic in MS derives from the emergent properties of the immune system operating in a pathological state, a fact that has implications for predicting disease course and developing new therapies for MS.This work was supported by the European Commission (NEST-Pathfinder program: "ComplexDis" grant, contract number: 043241 and ITNMC program: "UEPHA*MS" grant, contract number: 212877) and the Spanish Ministry of Health (PI060117 and RD07/0060/0000-01) to PV and JGO. NV is a fellow of the Programa de Formación de Investigadores del Departamento de Educación, Universidades e Investigación, Gobierno Vasco (BFI05.9 AE). JB acknowledges the support of the MEC through the grant: FIS2005-06912-C02, DINCARD. JG is a fellow of Government of Navarra. Dr Bagnato's contribution to this work was supported by the Intramural Research Program of the NINDS-NI