Use of dynamical models for treatment optimization in HIV infected patients : a sequential Bayesian analysis approach.

International audienceThe use of dynamic mechanistic models based on ordinary differential equations (ODE) has greatly improved the knowledge of the dynamics of HIV and of the immune system. Their flexibility for fitting data and prediction abilities make them a good tool for optimization of the design delivery and efficacy of new intervention in the HIV field. We present the problem of inference in ODE models with mixed effects on parameters. We introduce a Bayesian estimation procedure based on the maximization of the penalized likelihood and a normal approximation of posteriors, which is implemented in the NIMROD software. We investigate the impact of pooling different data by using a sequential Bayesian analysis (SBA), which uses posteriors of a previous study as new priors. We show that the normal approximation of the posteriors, which constrains the shape of new priors, leads to gains in accuracy of estimation while reducing computation times. The illustration is from two clinical trials of combination of antiretroviral therapies (cART): ALBI ANRS 070 and PUZZLE ANRS 104. This paper reproduces some unpublished work from my PhD thesis. It is an extension of my oral presentation on the same topic at the 47th Journées de Statistique organized by the French Statistical Society (SFdS) in Lille, France, May 2015, when being awarded the Marie-Jeanne Laurent-Duhamel prize

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

Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes

In a randomized study, leveraging covariates related to the outcome (e.g. disease status) may produce less variable estimates of the effect of exposure. For contagion processes operating on a contact network, transmission can only occur through ties that connect affected and unaffected individuals; the outcome of such a process is known to depend intimately on the structure of the network. In this paper, we investigate the use of contact network features as efficiency covariates in exposure effect estimation. Using augmented generalized estimating equations (GEE), we estimate how gains in efficiency depend on the network structure and spread of the contagious agent or behavior. We apply this approach to simulated randomized trials using a stochastic compartmental contagion model on a collection of model-based contact networks and compare the bias, power, and variance of the estimated exposure effects using an assortment of network covariate adjustment strategies. We also demonstrate the use of network-augmented GEEs on a clustered randomized trial evaluating the effects of wastewater monitoring on COVID-19 cases in residential buildings at the the University of California San Diego.Comment: Substantial revisio

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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Review of recent Methodological Developments in group-randomized trials: Part 2 - Analysis

International audienceIn 2004, Murray et al. published a review of methodological developments in both the design and analysis of GRTs. In the thirteen years since, there have been many developments in both areas. The goal of the current paper is to focus on developments in design with a companion paper to focus on developments in analysis. As a pair, these papers update the 2004 review. This design paper includes developments in topics included in the earlier review (e.g. clustering, matching, and individually randomized group treatment trials) and new topics including constrained randomization and a range of randomized designs that are alternatives to the standard parallel-arm GRT. These include the stepped wedge GRT, the pseudo-cluster randomized trial and the network-randomized GRT, which, like the parallel-arm GRT, require clustering to be accounted for in both their design and analysis

Accounting for interactions and complex inter-subject dependency in estimating treatment effect in cluster-randomized trials with missing outcomes

International audienceSemi-parametric methods are often used for the estimation of intervention effects oncorrelated outcomes in cluster-randomized trials (CRTs). When outcomes are missing at random(MAR), Inverse Probability Weighted (IPW) methods incorporating baseline covariates can be usedto deal with informative missingness. Also, augmented generalized estimating equations (AUG)correct for imbalance in baseline covariates but need to be extended for MAR outcomes. However,in the presence of interactions between treatment and baseline covariates, neither method aloneproduces consistent estimates for the marginal treatment effect if the model for interaction is notcorrectly specified.We propose an AUG-IPW estimator that weights by the inverse of the probabilityof being a complete case and allows different outcome models in each intervention arm. This estimatoris doubly robust (DR), it gives correct estimates whether the missing data process or the outcomemodel is correctly specified. We consider the problem of covariate interference which arises when theoutcome of an individual may depend on covariates of other individuals. When interfering covariatesare not modeled, the DR property prevents bias as long as covariate interference is not presentsimultaneously for the outcome and the missingness. An R package is developed implementing theproposed method. An extensive simulation study and an application to a CRT of HIV risk reduction-intervention in South Africa illustrate the method

Use of mathematical modeling for optimizing and adapting immunotherapy protocols in HIV-infected patients

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Population modeling of early COVID-19 epidemic dynamics in French regions and estimation of the lockdown impact on infection rate

We propose a population approach to model the beginning of the French COVID-19 epidemic at the regional level. We rely on an extended Susceptible-Exposed-Infectious-Recovered (SEIR) mechanistic model, a simplified representation of the average epidemic process. Combining several French public datasets on the early dynamics of the epidemic, we estimate region-specific key parameters conditionally on this mechanistic model through Stochastic Approximation Expectation Maximization (SAEM) optimization using Monolix software. We thus estimate basic reproductive numbers by region before isolation (between 2.4 and 3.1), the percentage of infected people over time (between 2.0 and 5.9% as of May 11 th , 2020) and the impact of nationwide lockdown on the infection rate (decreasing the transmission rate by 72% toward a R e ranging from 0.7 to 0.9). We conclude that a lifting of the lockdown should be accompanied by further interventions to avoid an epidemic rebound