Dipartimento di Ingegneria Chimica, Chimica Industriale e Scienza dei Materiali, University of Pisa, Pisa, Italy.
IEEE Trans Biomed Eng. 2010 May;57(5):1040-50. doi: 10.1109/TBME.2009.2039571. Epub 2010 Feb 17.
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. Such promising results are obtained with realistic assumptions of infrequent (possibly noisy) measurements of a subset of model state variables. Furthermore, the control objectives are achieved even in the presence of mismatch between the dynamics of true patients and that of the MPC model.
本文将模型预测控制（MPC）策略应用于人类免疫缺陷病毒感染的控制，最终目标是实现最佳的结构化治疗中断方案。本文提出的 MPC 算法使用了最近开发的动态模型，以便模拟瞬态响应和最终行为，并相应描述常用药物在高效抗逆转录病毒治疗（HAART）中的不同作用。仿真研究表明，所提出的方法在尊重对 CD4+细胞和游离病毒浓度的期望约束的同时，达到了减少药物消耗（从而最小化 HAART 药物严重副作用）的目的。在对模型状态变量子集进行不频繁（可能存在噪声）测量的现实假设下，获得了有希望的结果。此外，即使在真实患者动力学与 MPC 模型动力学之间存在不匹配的情况下，控制目标也能实现。
