Controlling IL-7 Injections in HIV-Infected Patients

Abstract

Immune interventions consisting in repeated injections are broadly used as they are thought to improve the quantity and the quality of the immune response. However, they also raise several questions that remain unanswered, in particular the number of injections to make or the delay to respect between different injections to achieve 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 interleukin-7 (IL-7) injections in HIV-infected patients under antiretroviral treatment, but still unable to restore normal levels of [Formula: see text] T lymphocytes. Clinical trials have already shown that repeated cycles of injections of IL-7 could help maintaining [Formula: see text] T lymphocytes levels over the limit of 500 cells/[Formula: see text]L, by affecting proliferation and survival of [Formula: see text] T cells. We then aim at answering the question: how to maintain a patients level of [Formula: see text] T lymphocytes by using a minimum number of injections (i.e., optimizing the strategy of injections)? Based on mechanistic models that were previously developed for the dynamics of [Formula: see text] 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 define an optimal strategy for a given patient. This method could be applied to optimize injections schedules in clinical trials.

Modeling the immune response to HIV infection

“…Finally, cytokines play key role in regulating functional activities of immune effector cells; cytokines including IL-2, IL-7, IL-15 and IL-21 have shown some promise against HIV infection [118]. While there is mathematical modeling of IL-2 and IL-7 administration aimed at optimizing their use in boosting CD4+ responses [119–121], this also remains an under-studied area.…”

EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models

“…Machine learning approaches have become widely used in medicine, but remain of limited use in population health (Flaxman & Vos, 2018). In such context, the optimal control of epidemiological models mainly involved Pontryagin's maximum principle or dynamic programming approaches (Pasin et al, 2018). The first approach has recently been used in the context of the COVID-19 pandemics (Lemecha Obsu & Feyissa Balcha, 2020;Perkins & Espana, 2020).…”

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

“…First, the parameters are not considered to be known and the adaptation of the IL7 injections is done while the estimations of the individual parameters and the predictions are improved with new observations. This dynamic approach is also referred as dynamic drug monitoring in the work of Murphy et al Second, the statistical approach of treatment optimization that we propose is less computationally demanding because we are not looking for an optimal strategy over the space of all potential strategies . Rather, we are optimizing the strategy according to that patient's characteristics, by learning the random effects values as information increases.…”

“…This dynamic approach is also referred as dynamic drug monitoring in the work of Murphy et al 25 Second, the statistical approach of treatment optimization that we propose is less computationally demanding because we are not looking for an optimal strategy over the space of all potential strategies. 45 Rather, we are optimizing the strategy according to that patient's characteristics, by learning the random effects values as information increases. In our application, this was very relevant because we could take into account the diversity of response of the patients.…”