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

Investigating the effects of animal venoms in ovarian cancer

Objectives: Ovarian Cancer is considered the most lethal gynaecological disease with over 9 million women dying annually on a global scale. Current standards of care which consists of debulking surgery and adjuvant chemotherapy are proving to be inadequate due to chemoresistance whilst targeted-therapies available are limited. This prompts research into identifying the therapeutic potential of animal venoms in relation to ovarian cancer. Materials and Methods: Using a 50% threshold, the ability of crude venom from Parabuthus transvaalicus (Transvaalicus thick-tailed scorpion), Heterometrus madraspatensis (Madras forest scorpion) and Heterometrus mysorensis to inhibit SK-OV-3 cell metabolism was analysed using dose response assays. Furthermore, fractioned venoms Naja nigricollis_r11 (Black-necked spitting cobra) and Pandinus cavimanus_r28 (Tanzanian red clawed scorpion) were also investigated for their inhibitory effects on the cell line. Results: Crude Parabuthus transvaalicus venom at a concentration of 200µg/ml inhibited 44.55% of SK-OV-3 cell metabolism. Heterometrus madraspatensis and Heterometrus mysorensis venom at a concentration of 500µg/ml inhibited 0.78% and 2.35% of cell metabolism respectively. Fractioned venom Pandinus cavimanus_r28 at a concentration of 15.63µg/ml inhibited 2.54% of cell metabolism whilst Naja nigricollis_r11 venom fraction produced an LD50 of 37.23µg/ml

Brain tumor quantification equation: modeled on complete step response algorithm

In Image Guided neuro-Surgery (IGnS) protocol relating to tumor, the planning stage is the bottleneck where most times are spent reconstructing the slices in order to; quantify the tumor, get the tumor shape and location relative to adjacent cells, and determine best incursion route among others. This time consuming assignment is handled by a surgeon using any of the standardized IGnS software. It has been observed that the approach taken to quantify tumor in those software are simply replicating the surgeons’ experience-based brain tumor quantification technique fashionable in the pre-imaging era. The result is a quantification method that is time consuming, and at bests an approximation. What is presented here is a novel brain tumor quantification method based on step response algorithm utilizing a model which itself was based on step response model resulting in smart and rapid quantification of brain tumor volume

Controller tuning by means of evolutionary multiobjective optimization: current trends and applications

Control engineering problems are generally multi-objective problems; meaning that there are several specifications and requirements that must be fulfilled. A traditional approach for calculating a solution with the desired trade-off is to define an optimisation statement. Multi-objective optimisation techniques deal with this problem from a particular perspective and search for a set of potentially preferable solutions; the designer may then analyse the trade-offs among them, and select the best solution according to his/her preferences. In this paper, this design procedure based on evolutionary multiobjective optimisation (EMO) is presented and significant applications on controller tuning are discussed. Throughout this paper it is noticeable that EMO research has been developing towards different optimisation statements, but these statements are not commonly used in controller tuning. Gaps between EMO research and EMO applications on controller tuning are therefore detected and suggested as potential trends for research.The first author is grateful for the hospitality and availability of the UTC at the University of Sheffield during his academic research stay at 2011; especially to Dr. P.J. Fleming for his valuable comments and insights in the development of this paper. This work was partially supported by Grant FPI-2010/19 and project PAID-2011/2732 from the Universitat Politecnica de Valencia and projects TIN2011-28082 and ENE2011-25900 from the Spanish Ministry of Economy and Competitiveness.Reynoso Meza, G.; Blasco Ferragud, FX.; Sanchís Saez, J.; Martínez Iranzo, MA. (2014). Controller tuning by means of evolutionary multiobjective optimization: current trends and applications. Control Engineering Practice. 28:58-73. https://doi.org/10.1016/j.conengprac.2014.03.003S58732

Improved Biological Activity and Stability of enzyme L-Asparaginase in Solid Lipid Nanoparticles Formulation

To protect the biological activity of an enzyme during the development of formulations is one of the biggest challenges. The tetrameric form of L-Asparaginase is used to treat Acute Lymphocytic Leukaemia. It possesses shorter in vivo half–life. Using a modified (water/oil)/water-emulsion method followed by solvent evaporation L-Asn was successfully encapsulated at the core of Solid Lipid Nanoparticles made of lipid glyceryl monostearate. This study elucidated that the preparation of L-Asn loaded SLN develop a colloidal formulation with enhanced activity. The in-vitro release profile of the enzyme revealed first bursts has been increased. The study of the lyophilised formulation also shows that the enzyme holds its biological activity and retains its particle size distribution. Consequently, by using an apt combination of homogenisation speed, temperature and additives the storage and biological activity of L-Asn in SLN formulation can be improved. Keywords: L-Asparaginase, Solid Lipid Nanoparticles, Controlled release, Lymphocytic Leukaemi

Directed Intervention Crossover Approaches in Genetic Algorithms with Application to Optimal Control Problems

Genetic Algorithms (GAs) are a search heuristic modeled on the processes of evolution. They have been used to solve optimisation problems in a wide variety of fields. When applied to the optimisation of intervention schedules for optimal control problems, such as cancer chemotherapy treatment scheduling, GAs have been shown to require more fitness function evaluations than other search heuristics to find fit solutions. This thesis presents extensions to the GA crossover process, termed directed intervention crossover techniques, that greatly reduce the number of fitness function evaluations required to find fit solutions, thus increasing the effectiveness of GAs for problems of this type. The directed intervention crossover techniques use intervention scheduling information from parent solutions to direct the offspring produced in the GA crossover process towards more promising areas of a search space. By counting the number of interventions present in parents and adjusting the number of interventions for offspring schedules around it, this allows for highly fit solutions to be found in less fitness function evaluations. The validity of these novel approaches are illustrated through comparison with conventional GA crossover approaches for optimisation of intervention schedules of bio-control application in mushroom farming and cancer chemotherapy treatment. These involve optimally scheduling the application of a bio-control agent to combat pests in mushroom farming and optimising the timing and dosage strength of cancer chemotherapy treatments to maximise their effectiveness. This work demonstrates that significant advantages are gained in terms of both fitness function evaluations required and fitness scores found using the proposed approaches when compared with traditional GA crossover approaches for the production of optimal control schedules

RNA Interference Approaches and Assessment of New Delivery Systems to Target Heparanase in Soft-tissue Sarcomas

Soft tissue sarcomas are rare tumours of mesenchymal origin characterised by high biomolecular complexity and heterogeneity. The clinical management of advanced diseases remains a major challenge which needs new therapeutic strategies. Mounting evidence supports that the heparanase/heparan sulfate proteoglycan system plays a key role in sustaining tumour progression and dissemination and preclinical studies suggest its potential relevance as a therapeutic target. To better understand the specific role of heparanase, we performed RNA interference in rhabdoid tumour and synovial sarcoma models, and assessed the functional effects of heparanase silencing. We tested both miRNA and siRNA targeting heparanase, but only siRNAs were able to silence mRNA and down-regulate the protein following transient transfection. The reduced expression and enzymatic activity of secreted heparanase observed upon siRNA transfection were consistent with a strong inhibition of cell migration and invasion capability. The expression of pro-angiogenic factors was also reduced both in rhabdoid tumour and synovial sarcoma cell lines. In contrast, we observed that heparanase localised in specific subcellular compartments was resistant to the siRNA effect. The clinical application of siRNA-based therapeutic strategies faces significant challenges, mainly due to siRNA in vivo poor stability and inefficient delivery. In this work, we evaluated the use of different nanodelivery systems aimed at overcoming these limitations. The first approach consisted of hybrid nanoparticles with a silica core and a pH-responsive hydrogel shell. These nanoparticles showed promising features, such as remarkable loading and efficient release of siRNA, endo-lysosomal escape and in vivo accumulation at the tumour site. Nevertheless, siRNA delivered through this approach did not inhibit heparanase expression. The second nanodelivery system was leukosome, a biomimetic nanoparticle with leukocyte proteins included in a phospholipidic bilayer. Leukosomes efficiently delivered siRNA into the cells avoiding the endo-lysosomal entrapment. Moreover, these biomimetic nanoparticles mainly accumulated at the tumour primary and metastatic sites. Despite these promising features, also leukosomes were unable to deliver an active siRNA. Overall, these findings demonstrate that specific inhibition of heparanase by gene silencing is a potential therapeutic approach for soft tissue sarcoma treatment. However, the siRNA delivery systems tested in this work, although promising in terms of tumour and metastasis targeting, still need further improvement to be suitable for nucleic acid delivery

Improving decision-making: Deriving patient-valued utilities from a disease-specific quality of life questionnaire for evaluating clinical trials

The aim of the work reported in this thesis was to develop a scoring algorithm that converts ratings from a validated disease-specific quality of life questionnaire called the Utility-Based Questionnaire-Cancer (UBQ-C) into a utility index that is designed for evaluating clinical trials to inform clinical decisions about cancer treatments. The UBQ-C includes a scale for global health status (1 item); and subscales for physical function (3 items), social/usual activities (4 items), self-care (1 item), and distresses due to physical and psychological symptoms (21 items). Data from three studies was used. A valuation survey consisted of patients with advanced cancer (n=204) who completed the UBQ-C and assigned time-trade-off utilities about their own health state. Clinical trials were of chemotherapy for advanced (n=325) and early (n=126) breast cancer. A scoring algorithm was derived to convert the subscales into a subset index, and combine it with the global scale into an overall quality of life index, which was converted to a utility index with a power transformation. Optimal weights were assigned to the subscales that reflected their correlations with a global scale in each study. The derived utilities were validated by comparison with other patient characteristics. Each trial was evaluated in terms of differences in utility between treatment groups. In the valuation survey, the weights (range 0 to 1) for the subset index were: physical function 0.28, social/usual activities 0.06, self-care 0.01, and distresses 0.64. Weights for the overall quality of life index were health status 0.65 and subset index 0.35. The mean of the utility index scores was similar to the mean of the time trade-off utilities (0.92 vs. 0.91, p=0.6). The weights were adjusted in each clinical trial. The utility index was substantially correlated with other measures of quality of life, discriminated between breast cancer that was advanced rather than early (means 0.88 vs 0.94, p<0.0001), and was responsive to toxic effects of chemotherapy in early breast cancer (mean change 0.07, p<0.0001). There were trends to better mean scores on the utility index for patients allocated to standard-dose versus high-dose chemotherapy in the early cancer trial (p=0.1), and oral versus intravenous chemotherapy in the advanced cancer trial (p=0.2). In conclusion, data from a simple, self-rated, disease-specific questionnaire can be converted into a utility index based on cancer patients’ preferences. The index can be optimised in different clinical contexts to reflect the relative importance of different aspects of quality of life to the patients in a trial. The index can be used to generate utility scores and quality-adjusted life-years in clinical trials. It enables the evaluation of the net effect of treatments on health-related quality of life (accounting for trade-offs between disparate aspects); the evaluation of the net benefit of treatments (accounting for trade-offs between quality of life and survival); and an alternate perspective for comparing the incremental cost-effectiveness of treatments (accounting for trade-offs between net benefit and costs). The practical significance of this work is to facilitate the integration of data about health-related quality of life with traditional trial endpoints such as survival and tumour response. This will better inform clinical decision-making, and provide an alternate viewpoint for economic decision-making. Broadly, it will help patients, clinicians and health funders make better decisions about cancer treatments, by considering potential trade-offs between effects on survival and health-related quality of life

Improving decision-making: Deriving patient-valued utilities from a disease-specific quality of life questionnaire for evaluating clinical trials

The aim of the work reported in this thesis was to develop a scoring algorithm that converts ratings from a validated disease-specific quality of life questionnaire called the Utility-Based Questionnaire-Cancer (UBQ-C) into a utility index that is designed for evaluating clinical trials to inform clinical decisions about cancer treatments. The UBQ-C includes a scale for global health status (1 item); and subscales for physical function (3 items), social/usual activities (4 items), self-care (1 item), and distresses due to physical and psychological symptoms (21 items). Data from three studies was used. A valuation survey consisted of patients with advanced cancer (n=204) who completed the UBQ-C and assigned time-trade-off utilities about their own health state. Clinical trials were of chemotherapy for advanced (n=325) and early (n=126) breast cancer. A scoring algorithm was derived to convert the subscales into a subset index, and combine it with the global scale into an overall quality of life index, which was converted to a utility index with a power transformation. Optimal weights were assigned to the subscales that reflected their correlations with a global scale in each study. The derived utilities were validated by comparison with other patient characteristics. Each trial was evaluated in terms of differences in utility between treatment groups. In the valuation survey, the weights (range 0 to 1) for the subset index were: physical function 0.28, social/usual activities 0.06, self-care 0.01, and distresses 0.64. Weights for the overall quality of life index were health status 0.65 and subset index 0.35. The mean of the utility index scores was similar to the mean of the time trade-off utilities (0.92 vs. 0.91, p=0.6). The weights were adjusted in each clinical trial. The utility index was substantially correlated with other measures of quality of life, discriminated between breast cancer that was advanced rather than early (means 0.88 vs 0.94, p<0.0001), and was responsive to toxic effects of chemotherapy in early breast cancer (mean change 0.07, p<0.0001). There were trends to better mean scores on the utility index for patients allocated to standard-dose versus high-dose chemotherapy in the early cancer trial (p=0.1), and oral versus intravenous chemotherapy in the advanced cancer trial (p=0.2). In conclusion, data from a simple, self-rated, disease-specific questionnaire can be converted into a utility index based on cancer patients’ preferences. The index can be optimised in different clinical contexts to reflect the relative importance of different aspects of quality of life to the patients in a trial. The index can be used to generate utility scores and quality-adjusted life-years in clinical trials. It enables the evaluation of the net effect of treatments on health-related quality of life (accounting for trade-offs between disparate aspects); the evaluation of the net benefit of treatments (accounting for trade-offs between quality of life and survival); and an alternate perspective for comparing the incremental cost-effectiveness of treatments (accounting for trade-offs between net benefit and costs). The practical significance of this work is to facilitate the integration of data about health-related quality of life with traditional trial endpoints such as survival and tumour response. This will better inform clinical decision-making, and provide an alternate viewpoint for economic decision-making. Broadly, it will help patients, clinicians and health funders make better decisions about cancer treatments, by considering potential trade-offs between effects on survival and health-related quality of life

Evolutionary algorithms and weighting strategies for feature selection in predictive data mining

The improvements in Deoxyribonucleic Acid (DNA) microarray technology mean that thousands of genes can be profiled simultaneously in a quick and efficient manner. DNA microarrays are increasingly being used for prediction and early diagnosis in cancer treatment. Feature selection and classification play a pivotal role in this process. The correct identification of an informative subset of genes may directly lead to putative drug targets. These genes can also be used as an early diagnosis or predictive tool. However, the large number of features (many thousands) present in a typical dataset present a formidable barrier to feature selection efforts. Many approaches have been presented in literature for feature selection in such datasets. Most of them use classical statistical approaches (e.g. correlation). Classical statistical approaches, although fast, are incapable of detecting non-linear interactions between features of interest. By default, Evolutionary Algorithms (EAs) are capable of taking non-linear interactions into account. Therefore, EAs are very promising for feature selection in such datasets. It has been shown that dimensionality reduction increases the efficiency of feature selection in large and noisy datasets such as DNA microarray data. The two-phase Evolutionary Algorithm/k-Nearest Neighbours (EA/k-NN) algorithm is a promising approach that carries out initial dimensionality reduction as well as feature selection and classification. This thesis further investigates the two-phase EA/k-NN algorithm and also introduces an adaptive weights scheme for the k-Nearest Neighbours (k-NN) classifier. It also introduces a novel weighted centroid classification technique and a correlation guided mutation approach. Results show that the weighted centroid approach is capable of out-performing the EA/k-NN algorithm across five large biomedical datasets. It also identifies promising new areas of research that would complement the techniques introduced and investigated