Publications by authors named "Gabriele Pannocchia"

Background And Objective: The recent introduction of antivirals for the treatment of the hepatitis C virus opens new frontiers but also poses a significant burden on public health systems. This paper presents a simulation study in which model predictive control (MPC) is proposed for optimizing the therapy aiming to obtain a reduction of the costs of therapy, while maintaining the best pharmacological control of the infection.

Methods: A dynamic model describing the evolution of hepatitis C is deployed as internal model for MPC implementation, using nominal values of parameters.

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This paper describes a simulation study which aims at optimizing the therapy for the control of Chronic Myeloid Leukemia according to the following objectives: the reduction of the administered drug and vaccine amounts, the establishment of a auto-immune response and the long-term control of disease without reducing the effective of therapy with respect to the full treatment. A therapy optimization method is developed defining and solving a Model Predictive Control algorithm, preceded by an accurate Initial Guess search based on Monte-Carlo like approach. Simulation results show that the suggested procedure achieves the proposed goals.

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In this paper, model predictive control (MPC) strategies are applied to the control of human immunodeficiency virus infection, with the final goal of implementing an optimal structured treatment interruptions protocol. The MPC algorithms proposed in this paper use a dynamic model recently developed in order to mimic both transient responses and ultimate behavior, and to describe accordingly the different effect of commonly used drugs in highly active antiretroviral therapy (HAART). Simulation studies show that the proposed methods achieve the goal of reducing the drug consumption (thus minimizing the severe side effects of HAART drugs) while respecting the desired constraints on CD4+ cells and free virions concentration.

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