Modeling CD4+ T cells dynamics in HIV-infected patients receiving repeated cycles of exogenous Interleukin 7

Combination Antiretroviral Therapy (cART) succeeds to control viral replication in most HIV infected patients. This is normally followed by a reconstitution of the CD4$^+$ T cells pool; however, this does not happen for a substantial proportion of patients. For these patients, an immunotherapy based on injections of Interleukin 7 (IL-7) has been recently proposed as a co-adjutant treatment in the hope of obtaining long-term reconstitution of the T cells pool. Several questions arise as to the long-term efficiency of this treatment and the best protocol to apply. We develop a model based on a system of ordinary differential equations and a statistical model of variability and measurement. We can estimate key parameters of this model using the data from INSPIRE, INSPIRE 2 $\&$ INSPIRE 3 trials. In all three studies, cycles of three injections have been administered; in the last two studies, for the first time, repeated cycles of exogenous IL-7 have been administered. Our aim was to estimate the possible different effects of successive injections in a cycle, to estimate the effect of repeated cycles and to assess different protocols. The use of dynamical models together with our complex statistical approach allow us to analyze major biological questions. We found a strong effect of IL-7 injections on the proliferation rate; however, the effect of the third injection of the cycle appears to be much weaker than the first ones. Also, despite a slightly weaker effect of repeated cycles with respect to the initial one, our simulations show the ability of this treatment of maintaining adequate CD4$^+$ T cells count for years. We were also able to compare different protocols, showing that cycles of two injections should be sufficient in most cases. %Finally, we also explore the possibility of adaptive protocols

Mixed models for longitudinal left-censored repeated measures

Longitudinal studies could be complicated by left-censored repeated measures. For example, in Human Immunodeficiency Virus infection, there is a detection limit of the assay used to quantify the plasma viral load. Simple imputation of the limit of the detection or of half of this limit for left-censored measures biases estimations and their standard errors. In this paper, we review two likelihood-based methods proposed to handle left-censoring of the outcome in linear mixed model. We show how to fit these models using SAS Proc NLMIXED and we compare this tool with other programs. Indications and limitations of the programs are discussed and an example in the field of HIV infection is shown

Quantification of the Relative Importance of CTL, B Cell, NK Cell, and Target Cell Limitation in the Control of Primary SIV-Infection

CD8+ cytotoxic T lymphocytes (CTLs), natural killer (NK) cells, B cells and target cell limitation have all been suggested to play a role in the control of SIV and HIV-1 infection. However, previous research typically studied each population in isolation leaving the magnitude, relative importance and in vivo relevance of each effect unclear. Here we quantify the relative importance of CTLs, NK cells, B cells and target cell limitation in controlling acute SIV infection in rhesus macaques. Using three different methods, we find that the availability of target cells and CD8+ T cells are important predictors of viral load dynamics. If CTL are assumed to mediate this anti-viral effect via a lytic mechanism then we estimate that CTL killing is responsible for approximately 40% of productively infected cell death, the remaining cell death being attributable to intrinsic, immune (CD8+ T cell, NK cell, B cell) -independent mechanisms. Furthermore, we find that NK cells have little impact on the death rate of infected CD4+ cells and that their net impact is to increase viral load. We hypothesize that NK cells play a detrimental role in SIV infection, possibly by increasing T cell activation

In Silico Evaluation of HIV Short-cycle Therapies with Dynamical Models

International audienceWe aim at quantifying the effect of on/off strategies for various treatment regimens. These so-called short-cycle therapies, following the FOTO trial (Cohen et al., HIV Clin. Trials, 2007), are currently tested in phase II/III such as in 4D ANRS 162 - 4/3 days on/off (DeTruchis et al., IAS, 2016) and BREATHER - 5/2 days on/off (Breather trial group, Lancet, 2016). Mechanistic models based on Ordinary Differential Equations can model HIV and CD4+ T cells trajectories. Here, we aim at predicting the results of the current trials evaluating short-cycle therapies and suggest other strategies by using in silico trials based on mechanistic models. Using estimations from previous clinical trials such as ALBI (Prague et al., Biometrics, 2012), we show that short-cycle therapy would not be successful for old therapies based on two nucleoside analogues such as AZT+3TC or ddI+d4T. We estimated that the regimens have to be twice as potent as AZT+3TC to ensure viral load suppression using a 5/2 design. Single-round infectivity assays allow quantifying the instantaneous inhibitory potential (IIP), which is established as a measure of regimens activity. In Jilek et al., Nat. Med., 2012, efavirenz regimens are at least 2.8 times more efficient than AZT+3TC, which is enough to guarantee the success of BREATHER in most patients. We also demonstrate that 4/3 designs are likely to be more difficult to maintain in a long-term depending on patients’ characteristics at inclusion.This analysis is applied to the ANRS C03 Aquitaine observational cohort of HIV-infected patients (Prague et al., Biometrics, 2016). We focus on EFV, TDF, AZT, 3TC, ABC, FTC, LPV/r, DRV/r and ETR

Adaptive protocols based on predictions from a mechanistic model of the effect of IL7 on CD4 counts

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Immunological markers after long-term treatment interruption in chronically HIV-1 infected patients with CD4 cell count above 400 x 10(6) cells/l.

OBJECTIVE: To analyse immunological markers associated with CD4+ lymphocyte T-cell count (CD4+) evolution during 12-month follow-up after treatment discontinuation. METHOD: Prospective observational study of chronically HIV-1 infected patients with CD4+ above 400 x 10(6) cells/l. RESULTS: CD4+ changes took place in two phases: an initial rapid decrease in the first month (-142 x 10(6) cells/l on average), followed by a slow decline (-17 x 10(6) cells/l on average) The second slope of CD4+ decline was not correlated with the first and only baseline plasma HIV RNA was associated with it. The decline in CD4+ during the first month was steeper in patients with higher CD4+ and weaker plasma HIV RNA baseline levels. Moreover, the decline was less pronounced (P < 10(-4)) in patients with CD4+ nadir above 350 x 10(6) cells/l (-65 x 10(6) cells/l per month) in comparison with those below 350 x 10(6) cells/l (-200 x 10(6) cells/l per month). A high number of dendritic cells (DCs) whatever the type was associated with high CD4+ at the time of treatment interruption and its steeper decline over the first month. Moreover, the myeloid DC level was stable whereas the lymphoid DC count, which tended to decrease in association with decrease in CD4+, was negatively correlated with the HIV RNA load slope. CONCLUSIONS: The results support the use of the CD4+ nadir to predict the CD4+ dynamic after treatment interruption and consideration of the CD4+ count after 1-month of interruption merely reflects the 12-month level of CD4+. Although DCs seem to be associated with the CD4+ dynamic, the benefit of monitoring them has still to be defined

Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry Data

39 pages, 11 figuresInternational audienceFlow cytometry is a high-throughput technology used to quantify multiple surface and intracellular markers at the level of a single cell. This enables to identify cell sub-types, and to determine their relative proportions. Improvements of this technology allow to describe millions of individual cells from a blood sample using multiple markers. This results in high-dimensional datasets, whose manual analysis is highly time-consuming and poorly reproducible. While several methods have been developed to perform automatic recognition of cell populations, most of them treat and analyze each sample independently. However, in practice, individual samples are rarely independent (e.g. longitudinal studies). Here, we propose to use a Bayesian nonparametric approach with Dirichlet process mixture (DPM) of multivariate skew $t$-distributions to perform model based clustering of flow-cytometry data. DPM models directly estimate the number of cell populations from the data, avoiding model selection issues, and skew $t$-distributions provides robustness to outliers and non-elliptical shape of cell populations. To accommodate repeated measurements, we propose a sequential strategy relying on a parametric approximation of the posterior. We illustrate the good performance of our method on simulated data, on an experimental benchmark dataset, and on new longitudinal data from the DALIA-1 trial which evaluates a therapeutic vaccine against HIV. On the benchmark dataset, the sequential strategy outperforms all other methods evaluated, and similarly, leads to improved performance on the DALIA-1 data. We have made the method available for the community in the R package NPflow