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Treatment Monitoring of HIV-Infected Patients based on Mechanistic Models

Authors

  • Mélanie Prague,

    Corresponding author
    1. University of Bordeaux, ISPED, Centre INSERM U897-Épidémiologie-Biostatistique, F-33000 Bordeaux, France
    2. INSERM, ISPED, Centre INSERM U897-Épidémiologie-Biostatistique, F-33000 Bordeaux, France
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  • Daniel Commenges,

    1. University of Bordeaux, ISPED, Centre INSERM U897-Épidémiologie-Biostatistique, F-33000 Bordeaux, France
    2. INSERM, ISPED, Centre INSERM U897-Épidémiologie-Biostatistique, F-33000 Bordeaux, France
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  • Julia Drylewicz,

    1. Department of Immunology, University Medical Center Utrecht, The Netherlands
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  • Rodolphe Thiébaut

    1. University of Bordeaux, ISPED, Centre INSERM U897-Épidémiologie-Biostatistique, F-33000 Bordeaux, France
    2. INSERM, ISPED, Centre INSERM U897-Épidémiologie-Biostatistique, F-33000 Bordeaux, France
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email: melanie.prague@isped.u-bordeaux2.fr

Abstract

Summary For most patients, the HIV viral load can be made undetectable by highly active antiretroviral treatments highly active antiretroviral therapy: the virus, however, cannot be eradicated. Thus, the major problem is to try to reduce the side effects of the treatment that patients have to take during their life time. We tackle the problem of monitoring the treatment dose, with the aim of giving the minimum dose that yields an undetectable viral load. The approach is based on mechanistic models of the interaction between virus and the immune system. It is shown that the “activated cells model,” allows making good predictions of the effect of dose changes and, thus, could be a good basis for treatment monitoring. Then, we use the fact that in dynamical models, there is a nontrivial equilibrium point, that is with a virus load larger than zero, only if the reproductive number R0 is larger than one. For reducing side effects, we may give a dose just above the critical dose corresponding to R0 equal to 1. A prior distribution of the parameters of the model can be taken as the posterior arising from the analysis of previous clinical trials. Then the observations for a given patient can be used to dynamically tune the dose so that there is a high probability that the reproductive number is below one. The advantage of the approach is that it does not depend on a cost function, weighing side effects and efficiency of the drug. It is shown that it is possible to approach the critical dose if the model is correct. A sensitivity analysis assesses the robustness of the approach.

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