MPC based optimization applied to treatment of HCV infections

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 pro-posed, 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 effec-tive, 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. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study proposes a strategy to optimize hepatitis C treatment using model predictive control, which successfully reduces treatment costs while maintaining good pharmacological control. The results demonstrate the feasibility and effectiveness of this approach.