"Grupo de Sistemas No Lineales", Institutode Desarrollo Tecnológico para la Industria Química, Facultad de Ingeniería Qímica (Universidad Nacional del Litoral, Consejo Nacional de Investigaciones Científicas y Técnicas), 3000 Santa Fe, Argentina.
IEEE Trans Biomed Eng. 2010 Sep;57(9):2079-89. doi: 10.1109/TBME.2010.2049845. Epub 2010 May 24.
A control-theoretic approach to the problem of designing "low-side-effects" therapies for HIV patients based on highly active drugs is substantiated here. The evolution of side effects during treatment is modeled by an extra differential equation coupled to the dynamics of virions, healthy T-cells, and infected ones. The new equation reflects the dependence of collateral damages on the amount of each dose administered to the patient and on the evolution of the viral load detected by periodical blood analysis. The cost objective accounts for recommended bounds on healthy cells and virions, and also penalizes the appearance of collateral morbidities caused by the medication. The optimization problem is solved by a hybrid dynamic programming scheme that adhere to discrete-time observation and control actions, but by maintaining the continuous-time setup for predicting states and side effects. The resulting optimal strategies employ less drugs than those prescribed by previous optimization studies, but maintaining high doses at the beginning and the end of each period of six months. If an inverse discount rate is applied to favor early actions, and under a mild penalization of the final viral load, then the optimal doses are found to be high at the beginning and decrease afterward, thus causing an apparent stabilization of the main variables. But in this case, the final viral load turns higher than acceptable.
基于高效抗逆转录病毒药物，提出了一种控制理论方法来设计针对 HIV 患者的“低副作用”疗法。通过将一个额外的微分方程与病毒、健康 T 细胞和受感染 T 细胞的动力学联系起来，对治疗过程中副作用的演变进行建模。新方程反映了副作用与每个剂量的大小以及定期血液分析检测到的病毒载量变化的关系。成本目标考虑了对健康细胞和病毒的建议限制，还对药物引起的副作用进行了惩罚。通过混合动态规划方案解决了优化问题，该方案遵守离散时间观察和控制动作，但保持了连续时间预测状态和副作用的设置。所得到的最优策略比以前的优化研究中规定的药物用量少，但在每 6 个月的周期的开始和结束时保持高剂量。如果应用逆折扣率来支持早期行动，并对最终病毒载量进行轻微惩罚，那么最优剂量在开始时较高，随后降低，从而导致主要变量明显稳定。但是在这种情况下，最终的病毒载量会变得更高，超出可接受的范围。
