Optimal Multi-Drug Chemotherapy Control Scheme for Cancer Treatment. Design and development of a multi-drug feedback control scheme for optimal chemotherapy treatment for cancer. Evolutionary multi-objective optimisation algorithms were used to achieve the optimal parameters of the controller for effective treatment of cancer with minimum side effects.

Cancer is a generic term for a large group of diseases where cells of the body lose their normal mechanisms for growth so that they grow in an uncontrolled way. One of the most common treatments of cancer is chemotherapy that aims to kill abnormal proliferating cells; however normal cells and other organs of the patients are also adversely affected. In practice, it¿s often difficult to maintain optimum chemotherapy doses that can maximise the abnormal cell killing as well as reducing side effects. The most chemotherapy drugs used in cancer treatment are toxic agents and usually have narrow therapeutic indices, dose levels in which these drugs significantly kill the cancerous cells are close to the levels which sometime cause harmful toxic side effects. To make the chemotherapeutic treatment effective, optimum drug scheduling is required to balance between the beneficial and toxic side effects of the cancer drugs. Conventional clinical methods very often fail to find drug doses that balance between these two due to their inherent conflicting nature. In this investigation, mathematical models for cancer chemotherapy are used to predict the number of tumour cells and control the tumour growth during treatment. A feedback control method is used so as to maintain certain level of drug concentrations at the tumour sites. Multi-objective Genetic Algorithm (MOGA) is then employed to find suitable solutions where drug resistances and drug concentrations are incorporated with cancer cell killing and toxic effects as design objectives. Several constraints and specific goal values were set for different design objectives in the optimisation process and a wide range of acceptable solutions were obtained trading off among different conflicting objectives. Abstract v In order to develop a multi-objective optimal control model, this study used proportional, integral and derivative (PID) and I-PD (modified PID with Integrator used as series) controllers based on Martin¿s growth model for optimum drug concentration to treat cancer. To the best of our knowledge, this is the first PID/I-PD based optimal chemotherapy control model used to investigate the cancer treatment. It has been observed that some solutions can reduce the cancer cells up to nearly 100% with much lower side effects and drug resistance during the whole period of treatment. The proposed strategy has been extended for more drugs and more design constraints and objectives.Libyan Ministry of Higher Educatio

Feedback control of chemotherapy drug scheduling for phase specific cancer treatment

This paper presents a novel method of chemotherapy drug scheduling for cancer treatment using feedback control and genetic algorithm (GA). The main aim of chemotherapy treatment is to eradicate the tumour, if possible, or to reduce the tumour size to a minimum level with minimum toxic side effects. A feedback control method is developed in order to maintain a predefined level of drug concentration at tumour sites. The reference to the controller is chosen in such a way as to limit the drug concentration in the plasma which in turn limits the toxic side effects. A variant of Proportional-Integral-Derivative (PID) control, namely I-PD is used to control the drug to be infused to the patient's body. A phase specific cancer tumour model is developed and used for this work. The model, initially proposed by Martin (1), describes the effects of drug on different cell populations, plasma drug concentration and toxic side effects. The output of the I-PD control, which is chemotherapy drug dose, is applied to the model to observe its effects. Moreover, GA is used to optimise the parameters of the controller that in turn improves the drug scheduling as well as the effectiveness of the proposed approach. Results show that our method can reduce the tumour size significantly at the end of the treatment. Furthermore, the toxic side effects are always remained very low throughout the whole period. A comparative assessment is also provided to highlight the novelty of the proposed technique. It is noted that the proposed model offers best performance as compared to any reported models