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.
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Publication date
2013-01-23Supervisor
Hossain, M. AlamgirMajumder, Md A.A.
Keyword
Multi-drug chemotherapyCancer
Cancer treatment
Side effects
Mathematical models
Tumour growth, control
Optimal control
Rights
The University of Bradford theses are licenced under a Creative Commons Licence.
Institution
University of BradfordDepartment
School of Computing, Informatics and MediaAwarded
2012Metadata
Show full item recordAbstract
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.Type
ThesisQualification name
PhDCollections
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