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Lori, F., Lewis, M.G., Xu, J., Varga, G., Zinn, D.E., Crabbs, C., Wagner, W., Greenhouse, J., Silvera, P., Yalley-Ogunro, J., Tinelli, C. and Lisziewicz, J. (2000) Control of SIV Rebound through Structured Treatment Interruptions during Early Infection. Science, 290, 1591-1593.
https://doi.org/10.1126/science.290.5496.1591

has been cited by the following article:

• TITLE: An Optimal Control Approach to Structured Treatment Interruptions for HIV Patients: A Personalized Medicine Perspective

  AUTHORS: Adam Attarian, Hien Tran

  KEYWORDS: Personalized Medicine, HIV Model, Sensitivity Analysis, Subset Selection, Parameter Estimation, Structured Treatment Interruptions, Receding Horizon Control

  JOURNAL NAME: Applied Mathematics, Vol.8 No.7, July 14, 2017

  ABSTRACT: Highly Active Antiretroviral Therapy (HAART) has changed the course of human immunodeficiency virus (HIV) treatments since its introduction. However, for many patients, long term continuous HAART is expensive and can include problems with drug toxicity and side effects, as well as increased drug resistance. Because of these reasons, some HIV infected patients will voluntarily terminate HAART. Some of these patients will also interrupt the continuous prescribed therapies for short or long periods. After discontinuing HAART, patients will usually experience a rapid increase in viral load coupled with an immediate decline in CD4+ counts. The canonical example of a patient undergoing unsupervised breaks in HAART is that of the “Berlin patient”. In this case, the patient was able to control viral load in the absence of treatment by cycling HAART on and off due to non-related infections. Due to this patient, interest in the use of structured treatment interruptions (STI) as a mechanism to regulate an HIV infection piqued. This paper describes an optimal control approach to determine STI regimen for HIV patients. The optimal STI was implemented in the context of the receding horizon control (RHC) using a mathematical model for the in-vivo dynamics of an HIV type 1 infection. Using available clinical data, we calibrate the model by estimating on a patient specific basis, a best estimable set of parameters using sensitivity analysis and subset selection. We demonstrate how customized STI protocols can be designed through the variation of control parameters on a patient specific basis.