Controlling IL-7 injections in HIV-infected patients

TL;DR

This paper develops a mathematical framework to optimize IL-7 injection schedules in HIV patients, aiming to maintain CD4+ T cell levels efficiently while minimizing injections, using a piecewise deterministic Markov model and impulse control theory.

Contribution

It introduces a novel application of impulse control theory to optimize injection strategies based on mechanistic CD4+ T cell models in HIV treatment.

Findings

The method determines optimal injection timing for individual patients.

Proof of concept demonstrated on a reduced model.

Potential to improve clinical trial protocols.

Abstract

Immune interventions consisting in repeated injection are broadly used as they are thought to improve the quantity and the quality of the immune response. However, they also raised several questions that remains unanswered, in particular the number of injections to make or the delay to respect between different injections to acheive this goal. Practical and financial considerations add constraints to these questions, especially in the framework of human studies. We specifically focus here on the use of interleukine-7 (IL-7) injections in HIV-infected patients under antiretroviral treatment, but still unable to restore normal levels of CD4+ T lymphocytes. Clinical trials have already shown that repeated cycles of injections of IL-7 could help maintaining CD4+ T lymphocytes levels over the limit of 500 cells per microL, by affecting proliferation and survival of CD4+ T cells. We then aim at answering the question : how to maintain a patient's level of CD4+ T lymphocytes by using a minimum number of injections (ie optimizing the strategy of injections) ? Based on mechanistic models that were previously developed for the dynamics of CD4+ T lymphocytes in this context, we model the process by a piecewise deterministic Markov model. We then address the question by using some recently established theory on impulse control problem in order to develop a numerical tool determining the optimal strategy. Results are obtained on a reduced model, as a proof of concept : the method allows to defined an optimal strategy for a given patient. This method could applied to optimize injections schedules in clinical trials.