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  • 1
    In: The Lancet HIV, Elsevier BV, Vol. 5, No. 3 ( 2018-03), p. e116-e125
    Type of Medium: Online Resource
    ISSN: 2352-3018
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
    detail.hit.zdb_id: 2802805-3
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  • 2
    In: Clinical Infectious Diseases, Oxford University Press (OUP), ( 2024-05-31)
    Abstract: Analytical treatment interruption (ATI) is the gold standard in HIV research for assessing the capability of new therapeutic strategies to control viremia without antiretroviral treatment (ART). The viral setpoint is commonly used as endpoint to evaluate their efficacy. However, in line with recommendations from a consensus meeting, to minimize the risk of increased viremia without ART, trials often implement short ATI phases and stringent virological ART restart criteria. This approach can limit the accurate observation of the setpoint. Methods We analyzed viral dynamics in 235 people with HIV from 3 trials, examining virological criteria during ATI phases. Time-related (eg time to rebound, peak, and setpoint) and viral load magnitude–related criteria (peak, setpoint, and time-averaged AUC [nAUC]) were described. Spearman correlations were analyzed to identify (1) surrogate endpoints for setpoint and (2) optimal virological ART restart criteria mitigating the risks of ART interruption and the evaluation of viral control. Results Comparison of virological criteria between trials showed strong dependencies on ATI design. Similar correlations were found across trials, with nAUC the most strongly correlated with the setpoint, with correlations & gt;0.70. A threshold & gt;100 000 copies/mL for 2 consecutive measures is requested as a virological ART restart criterion. Conclusions Our results are in line with recommendations and emphasize the benefits of an ATI phase & gt;12 weeks, with regular monitoring, and a virological ART restart criterion of 10 000 copies/mL to limit the risk for patients while capturing enough information to keep nAUC as an optimal proxy to the setpoint.
    Type of Medium: Online Resource
    ISSN: 1058-4838 , 1537-6591
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2024
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    detail.hit.zdb_id: 2002229-3
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  • 3
    Online Resource
    Online Resource
    Cellule MathDoc/Centre Mersenne ; 2022
    In:  MathematicS In Action Vol. 11, No. 1 ( 2022-09-27), p. 213-242
    In: MathematicS In Action, Cellule MathDoc/Centre Mersenne, Vol. 11, No. 1 ( 2022-09-27), p. 213-242
    Type of Medium: Online Resource
    ISSN: 2102-5754
    Language: English
    Publisher: Cellule MathDoc/Centre Mersenne
    Publication Date: 2022
    detail.hit.zdb_id: 3191846-3
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  • 4
    In: eLife, eLife Sciences Publications, Ltd, Vol. 11 ( 2022-07-08)
    Abstract: The definition of correlates of protection is critical for the development of next-generation SARS-CoV-2 vaccine platforms. Here, we propose a model-based approach for identifying mechanistic correlates of protection based on mathematical modelling of viral dynamics and data mining of immunological markers. The application to three different studies in non-human primates evaluating SARS-CoV-2 vaccines based on CD40-targeting, two-component spike nanoparticle and mRNA 1273 identifies and quantifies two main mechanisms that are a decrease of rate of cell infection and an increase in clearance of infected cells. Inhibition of RBD binding to ACE2 appears to be a robust mechanistic correlate of protection across the three vaccine platforms although not capturing the whole biological vaccine effect. The model shows that RBD/ACE2 binding inhibition represents a strong mechanism of protection which required significant reduction in blocking potency to effectively compromise the control of viral replication.
    Type of Medium: Online Resource
    ISSN: 2050-084X
    Language: English
    Publisher: eLife Sciences Publications, Ltd
    Publication Date: 2022
    detail.hit.zdb_id: 2687154-3
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  • 5
    In: npj Vaccines, Springer Science and Business Media LLC, Vol. 8, No. 1 ( 2023-11-08)
    Abstract: The persistence of the long-term immune response induced by the heterologous Ad26.ZEBOV, MVA-BN-Filo two-dose vaccination regimen against Ebola has been investigated in several clinical trials. Longitudinal data on IgG-binding antibody concentrations were analyzed from 487 participants enrolled in six Phase I and Phase II clinical trials conducted by the EBOVAC1 and EBOVAC2 consortia. A model based on ordinary differential equations describing the dynamics of antibodies and short- and long-lived antibody-secreting cells (ASCs) was used to model the humoral response from 7 days after the second vaccination to a follow-up period of 2 years. Using a population-based approach, we first assessed the robustness of the model, which was originally estimated based on Phase I data, against all data. Then we assessed the longevity of the humoral response and identified factors that influence these dynamics. We estimated a half-life of the long-lived ASC of at least 15 years and found an influence of geographic region, sex, and age on the humoral response dynamics, with longer antibody persistence in Europeans and women and higher production of antibodies in younger participants.
    Type of Medium: Online Resource
    ISSN: 2059-0105
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
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  • 6
    Online Resource
    Online Resource
    American Society of Clinical Oncology (ASCO) ; 2020
    In:  JCO Clinical Cancer Informatics , No. 4 ( 2020-11), p. 259-274
    In: JCO Clinical Cancer Informatics, American Society of Clinical Oncology (ASCO), , No. 4 ( 2020-11), p. 259-274
    Abstract: For patients with early-stage breast cancer, predicting the risk of metastatic relapse is of crucial importance. Existing predictive models rely on agnostic survival analysis statistical tools (eg, Cox regression). Here we define and evaluate the predictive ability of a mechanistic model for time to distant metastatic relapse. METHODS The data we used for our model consisted of 642 patients with 21 clinicopathologic variables. A mechanistic model was developed on the basis of two intrinsic mechanisms of metastatic progression: growth (parameter α) and dissemination (parameter μ). Population statistical distributions of the parameters were inferred using mixed-effects modeling. A random survival forest analysis was used to select a minimal set of five covariates with the best predictive power. These were further considered to individually predict the model parameters by using a backward selection approach. Predictive performances were compared with classic Cox regression and machine learning algorithms. RESULTS The mechanistic model was able to accurately fit the data. Covariate analysis revealed statistically significant association of Ki67 expression with α ( P = .001) and EGFR expression with μ ( P = .009). The model achieved a c-index of 0.65 (95% CI, 0.60 to 0.71) in cross-validation and had predictive performance similar to that of random survival forest (95% CI, 0.66 to 0.69) and Cox regression (95% CI, 0.62 to 0.67) as well as machine learning classification algorithms. CONCLUSION By providing informative estimates of the invisible metastatic burden at the time of diagnosis and forward simulations of metastatic growth, the proposed model could be used as a personalized prediction tool for routine management of patients with breast cancer.
    Type of Medium: Online Resource
    ISSN: 2473-4276
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
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  • 7
    Online Resource
    Online Resource
    AI Access Foundation ; 2021
    In:  Journal of Artificial Intelligence Research Vol. 71 ( 2021-07-23), p. 479-519
    In: Journal of Artificial Intelligence Research, AI Access Foundation, Vol. 71 ( 2021-07-23), p. 479-519
    Abstract: Modeling the dynamics of epidemics helps to propose control strategies based on pharmaceuticaland non-pharmaceutical interventions (contact limitation, lockdown, vaccination,etc). Hand-designing such strategies is not trivial because of the number of possibleinterventions and the difficulty to predict long-term effects. This task can be cast as an optimization problem where state-of-the-art machine learning methods such as deep reinforcement learning might bring significant value. However, the specificity of each domain|epidemic modeling or solving optimization problems|requires strong collaborationsbetween researchers from different fields of expertise. This is why we introduce EpidemiOptim, a Python toolbox that facilitates collaborations between researchers inepidemiology and optimization. EpidemiOptim turns epidemiological models and cost functions into optimization problems via a standard interface commonly used by optimization practitioners (OpenAI Gym). Reinforcement learning algorithms based on QLearning with deep neural networks (DQN) and evolutionary algorithms (NSGA-II) are already implemented. We illustrate the use of EpidemiOptim to find optimal policies fordynamical on-o  lockdown control under the optimization of the death toll and economic recess using a Susceptible-Exposed-Infectious-Removed (SEIR) model for COVID-19. Using EpidemiOptim and its interactive visualization platform in Jupyter notebooks, epidemiologists, optimization practitioners and others (e.g. economists) can easily compare epidemiological models, costs functions and optimization algorithms to address important choicesto be made by health decision-makers. Trained models can be explored by experts and non-experts via a web interface. This article is part of the special track on AI and COVID-19.
    Type of Medium: Online Resource
    ISSN: 1076-9757
    Language: Unknown
    Publisher: AI Access Foundation
    Publication Date: 2021
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  • 8
    In: Journal of Virology, American Society for Microbiology, Vol. 93, No. 18 ( 2019-09-15)
    Abstract: The Ebola vaccine based on Ad26.ZEBOV/MVA-BN-Filo prime-boost regimens is being evaluated in multiple clinical trials. The long-term immune response to the vaccine is unknown, including factors associated with the response and variability around the response. We analyzed data from three phase 1 trials performed by the EBOVAC1 Consortium in four countries: the United Kingdom, Kenya, Tanzania, and Uganda. Participants were randomized into four groups based on the interval between prime and boost immunizations (28 or 56 days) and the sequence in which Ad26.ZEBOV and MVA-BN-Filo were administered. Consecutive enzyme-linked immunosorbent assay (ELISA) measurements of the IgG binding antibody concentrations against the Kikwit glycoprotein (GP) were available for 177 participants to assess the humoral immune response up to 1 year postprime. Using a mathematical model for the dynamics of the humoral response, from 7 days after the boost immunization up to 1 year after the prime immunization, we estimated the durability of the antibody response and the influence of different factors on the dynamics of the humoral response. Ordinary differential equations (ODEs) described the dynamics of antibody response and two populations of antibody-secreting cells (ASCs), short-lived (SL) and long-lived (LL). Parameters of the ODEs were estimated using a population approach. We estimated that half of the LL ASCs could persist for at least 5 years. The vaccine regimen significantly affected the SL ASCs and the antibody peak but not the long-term response. The LL ASC compartment dynamics differed significantly by geographic regions analyzed, with a higher long-term antibody persistence in European subjects. These differences could not be explained by the observed differences in cellular immune response. IMPORTANCE With no available licensed vaccines or therapies, the West African Ebola virus disease epidemic of 2014 to 2016 caused 11,310 deaths. Following this outbreak, the development of vaccines has been accelerated. Combining different vector-based vaccines as heterologous regimens could induce a durable immune response, assessed through antibody concentrations. Based on data from phase 1 trials in East Africa and Europe, the dynamics of the humoral immune response from 7 days after the boost immunization onwards were modeled to estimate the durability of the response and understand its variability. Antibody production is maintained by a population of long-lived cells. Estimation suggests that half of these cells can persist for at least 5 years in humans. Differences in prime-boost vaccine regimens affect only the short-term immune response. Geographical differences in long-lived cell dynamics were inferred, with higher long-term antibody concentrations induced in European participants.
    Type of Medium: Online Resource
    ISSN: 0022-538X , 1098-5514
    Language: English
    Publisher: American Society for Microbiology
    Publication Date: 2019
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    detail.hit.zdb_id: 80174-4
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  • 9
    Online Resource
    Online Resource
    Wiley ; 2019
    In:  Statistics in Medicine Vol. 38, No. 2 ( 2019-01-30), p. 221-235
    In: Statistics in Medicine, Wiley, Vol. 38, No. 2 ( 2019-01-30), p. 221-235
    Abstract: In human immunodeficiency virus–infected patients, antiretroviral therapy suppresses the viral replication, which is followed in most patients by a restoration of CD4+ T cells pool. For patients who fail to do so, repeated injections of exogenous interleukin 7 (IL7) are experimented. The IL7 is a cytokine that is involved in the T cell homeostasis and the INSPIRE study has shown that injections of IL7 induced a proliferation of CD4+ T cells. Phase I/II INSPIRE 2 and 3 studies have evaluated a protocol in which a first cycle of three IL7 injections is followed by a new cycle at each visit when the patient has less than 550 CD4 cells/ μ L. Restoration of the CD4 concentration has been demonstrated, but the long‐term best adaptive protocol is yet to be determined. A mechanistic model of the evolution of CD4 after IL7 injections has been developed, which is based on a system of ordinary differential equations and includes random effects. Based on the estimation of this model, we use a Bayesian approach to forecast the dynamics of CD4 in new patients. We propose four prediction‐based adaptive protocols of injections to minimize the time spent under 500 CD4 cells/ μ L for each patient, without increasing the number of injections received too much. We show that our protocols significantly reduce the time spent under 500 CD4 over a period of two years, without increasing the number of injections. These protocols have the potential to increase the efficiency of this therapy.
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2019
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    detail.hit.zdb_id: 1491221-1
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  • 10
    Online Resource
    Online Resource
    The R Foundation ; 2017
    In:  The R Journal Vol. 9, No. 2 ( 2017), p. 105-
    In: The R Journal, The R Foundation, Vol. 9, No. 2 ( 2017), p. 105-
    Type of Medium: Online Resource
    ISSN: 2073-4859
    Language: English
    Publisher: The R Foundation
    Publication Date: 2017
    detail.hit.zdb_id: 2642918-4
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