A measurement-based control design approach for efficient cancer chemotherapy
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2015 Elsevier Inc. This paper presents a new measurement-based control design method for cancer chemotherapy. Cancer chemotherapy aims basically at simultaneously eradicating or significantly reducing the number of cancer cells and maintaining tolerable levels of drug concentration and toxicity. To achieve such aim, drugs are often injected into the patient's body according to a drug schedule specifying the drug dose and delivery time. Several strategies for planning cancer chemotherapy have been developed in the literature, where evolutionary algorithms have been applied to find optimal drug schedules of cancer treatment under constraints on some key treatment parameters such as drug concentration and toxic side effects. In such methods, the amount of drug doses, delivered in the body at each time during the treatment, does not depend on the current drug concentration, toxicity level, and/or number of cancer cells. Successful design of chemotherapy drug scheduling requires the availability of an accurate mathematical model that perfectly predicts the number of cancerous cells and describes effects of treatment. Several models with either complex or simple structures are available in the literature. Complex-structure models are proposed to deeply understand interactions between cancer and normal cells that affect the performance of the cancer chemotherapy. Nevertheless, such complex models are based on a high-order set of differential equations which can be difficult to solve. Simple-structure models, which are often obtained on the basis of some simplifying assumptions, can be viewed only as an approximation of the cancer system. Hence, designing chemotherapy drug schedules on the basis of simplified models may result in unsuccessful cancer treatment. Unlike conventional control strategies for cancer chemotherapy, our attempt in this paper is to address the problem of designing a control system for cancer treatment using a set of frequency-domain data. Hence, a two-degree-of-freedom PID (proportional-integral-derivative) control scheme is proposed to control cancer growth. These PID controllers are designed to simultaneously provide the optimal amount of drug doses to be delivered into the patient's body according to the current drug concentration and toxicity level, and maintain the drug concentration and toxicity levels within their pre-specified ranges. The proposed cancer control technique is validated through a first simulation example. Another example to control biological systems is also presented to show the feasibility of the proposed method. Simulation results obtained have demonstrated the capability of the proposed control scheme to address cancer chemotherapy problems.
