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The concept of bioreductive prodrug chemotherapy is introduced in chapter 1. Tumour cell hypoxia is a significant factor in limiting tumour growth control with conventional radiotherapy and some chemotherapeutic agents. Following therapy these cells can repopulate and cause a relapse of the cancer. On the other hand, hypoxia is unique to tumours, and is therefore potentially exploitable. Bioreductive prodrugs are compounds in which a oxygen inhibited redox-based bioactivation step triggers a reaction leading to a lethal intermediate. The concept of bioreductive DNA alkylators and DNA topoisomerase 11 inhibitors is discussed. The synthesis of model thiazolylindole compounds based on the natural product BE \0988 are discussed in chapter 2. Two strategies were employed for the construction of the thiazolylindoles: the Hantzsch reaction; and nucleophilic substitution on 2-bromothiazole by an indolyl anion. The synthesis of thiazolylindolequinone compounds are discussed in chapter 3. The quinone C(5) position of the thiazolylindolequinone analogues was elaborated to provide a series of cyclic and acyclic C(5)-amino derivatives. Synthetic strategies towards the synthesis of indole-2-carboxylates are discussed in chapter 4. The Moody-Rees and Cadogan-Sundberg reactions were employed to provide a synthesis of the useful highly substituted indole [154]. The Brederek imidazole reaction (i.e., the reaction of a amidine and a-halo ketone) is discussed in chapter 5. Application of the Brederek reaction was employed towards the construction of the bisindole imidazole natural compounds, the nortopsentins. The biological properties of the compounds of the compounds synthesised are discussed in chapter 6. The compounds were tested for DNA topoisomerase 11 inhibitory activity and cytotoxicity under a hypoxic environment.

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.

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 Education