MPC based optimization applied to treatment of HCV infections. (October 2021) - Polisano, Fabio; Ryals, Andrea Dan; Pannocchia, Gabriele; Landi, Alberto
Highlights: Hepatitis C Virus optimized therapy taking into account pharmacological cost. Non-Linear Model Predictive Control to optimize drug consumption. Analysis of different disease scenarios: PVR and Triphasic Decline. Therapy discretization of continuous drug dosage. Robustness of therapy to different patient scenarios. Abstract: 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. Different closed-loop simulations are presented both in nominal and in mismatch conditions. In addition, a more easily implementable treatment is proposed, which is based on a discrete dosage approach, where days on/off therapy are considered instead of continuous therapy modulation. Results : Results show that therapy modulation allows one to achieve the same infection evolution as with full therapy, with a reduction of drug consumption between 10% and 40%. The alternative discrete dosage approach shows similar results achieved with therapy modulation, both in terms of therapy effectiveness and drug consumption reduction. Conclusions : The proposed model predictive control therapy optimization strategies appear to be effective, implementable and robust to model errors. It therefore represents a potentially useful approach to alleviate the burden of HCV therapy cost on national health systems.