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Aim: The aim of this study was to determine whether or not there is evidence for the usage of ginger as an antiemetic in patients experiencing chemotherapy related nausea, based on available scientific literature. Methods: The original articles were found by using search engines PubMed and CINAHL. The selected articles were then analyzed based on their content, quality and results. Based on the results from the content analysis the articles were placed in four different themes. Results: In the first theme two of the articles found that ginger had significant effect on chemotherapy related nausea, when used in combination with regular antiemetic treatment. Two other articles said that it does not. In the second theme four studies found that different forms of ginger lessens nausea, in contrast two other articles found that it does not have any increased effect on said nausea. In the third theme one article found that ginger has significant effect in quelling nausea whilst another article says that it does not show any difference in effect to 5HT3-receptorantagonist metoclopramide. The fourth theme contains one article that find that ginger in combination with high protein diet have antiemetic effect. Conclusions: The conclusion of this literary overview is that there is good evidence that using ginger as an antiemetic in combination with 5HT3-receptor antagonist in patients experiencing chemotherapy related nausea, mainly when given cisplatin.

Neoadjuvant chemotherapy has become standard practice in patients with high-risk early breast cancer as it improves rates of breast conservation surgery and enables prediction of recurrence and survival by using response to treatment as a surrogate. Previous studies have focused on generating molecular datasets to develop prediction models of response, though little is known on how tumours and their microenvironments are modulated by neoadjuvant chemotherapy. The thesis aims at molecularly characterising tumour changes during neoadjuvant chemotherapy in a cohort of 168 patients. Serial tumour samples at diagnosis, and, when available, midway through chemotherapy and on completion of treatment were profiled by shallow whole genome sequencing, deep exome sequencing and transcriptome sequencing, resulting in the generation of an unprecedented genomics dataset with tumours in situ while patients received chemotherapy. Molecular predictors of response to chemotherapy were inferred from the diagnostic biopsy. Several novel observations were made, including previously undescribed associations between copy number alterations, mutational genotypes, neoantigen load, HLA genotypes and intra-tumoural heterogeneity with chemosensitivity. Possible mechanisms of chemoresistance included LOH at the MHC Class I locus, decreased expression of MHC Class I and II genes and drug influx molecules, as well as increased expression of drug efflux pumps. A complex relationship between proliferation, tumour microenvironment composition (TME) and response to treatment was explored by deconvoluting bulk RNAseq data and performing digital pathology orthogonal validation. Clonal and microenvironment dynamic changes induced by/associated with chemotherapy were then modelled. Two types of genomic responses were identified, one in which the clonal composition was stable throughout treatment and another where clonal emergence and/or extinction was evident. Validation by multi-region deep sequencing confirmed the dynamics of the clonal landscape. Clonal emergence was shown to be associated with higher proliferation and decreased immune infiltrate, with an increase in genomic instability and homologous recombination deficiency during treatment. The immune TME composition and activity mirrored response to treatment, with cytolytic activity and innate and adaptive immune infiltrates linearly correlating with the degree of residual disease remaining after chemotherapy. Finally, the circulating tumour DNA (ctDNA) genomic landscape was explored by using shallow whole genome sequencing and targeted sequencing of plasma DNA. Tumour mutations detected on exome sequencing were also detected in ctDNA in plasma, supporting the use of liquid biopsies as a biomarker for monitoring response to therapy and detection of minimal residual disease.

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.

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Thesis (Ph. D.)--University of Sydney, 2009. / Title from title screen (viewed Nov. 3, 2009) Includes tables and questionnaires. Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Public Health, Faculty of Medicine. Includes bibliography. Also available in print form.

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<b><u>Introduction:</b></u> Some cancer survivors are known to have an elevated risk of morbidity and mortality from cardiovascular disease. An important cause of this elevated risk is recognised to be irradiation of normal tissues during radiotherapy received as part of cancer therapy. There are substantial difficulties in studying radiation-related cardiovascular disease (RRCD). The reasons for this include the complexities of measuring radiation normal tissue doses retrospectively and the prolonged latencies of many of the cardiovascular endpoints. A variety of complimentary research methodologies can help provide additional knowledge to guide the appropriate management of patients treated in the past and of new patients in the future. <b><u>Methods:</b></u> 1) A cohort study of mortality from circulatory disease in the nationwide British Childhood Cancer Survivor Study (BCCSS). 2) A case-control study of valvular heart disease (VHD) in Dutch Hodgkin lymphoma (HL) survivors, including retrospective radiation dosimetry to estimate the radiation dose to heart valves. 3) A dosimetric study of cardiovascular radiation doses in patients entered into the UK NCRI Lymphoma Study Group RAPID trial, including predictions of 15-year cardiac mortality using innovative methods. 4) A modelling study to predict mean whole heart dose (MWHD) from involved field radiotherapy (IFRT) for HL using anatomical measures. 5) A prospective study using cardiovascular magnetic resonance (CMR) imaging to characterise the heart in women receiving radiotherapy for breast cancer. <b><u>Results:</b></u> 1) The risks of all types of circulatory mortality are elevated in survivors of childhood cancer. The absolute excess risks continue to increase 40+ years following diagnosis. The risk of death from cardiomyopathy and heart failure increased substantially with the introduction of anthracycline chemotherapy. There is no evidence of a reduction in risk of circulatory mortality in more recent eras of diagnosis. 2) There is a strong relationship between estimated radiation dose to the affected heart valve and the risk of subsequent VHD (p<0.001). This effect was modelled to allow prediction of the risk of VHD. 3) A proportion of patients treated with IFRT received a substantial cardiac radiation dose (MWHD = 8.8 Gy, SD = 5.6) but, on average, the predicted 15-year cardiac mortality following treatment is low (absolute risk 0.2%, range 0.0 to 2.7%). 4) It is possible to estimate the mean whole heart dose from IFRT prior to detailed radiotherapy planning based on pre-treatment diagnostic imaging to an accuracy of 5-6% of the prescribed dose. 5) Although women received low cardiac doses (MWHD = 1.5 Gy, SD = 0.8) and have a low predicted risk of cardiac radiation-related morbidity and mortality, there is some evidence of subclinical effects on strain and strain rate imaging of the anterior portions of the left ventricle that receive the highest radiation dose. <b><u>Conclusions:</b></u> Using a variety of methods these studies have all succeeded in adding to knowledge about the nature, magnitude and timing of RRCD. This knowledge can be used to help the future management of cancer patients. In addition, each of the studies has natural and planned extensions and will continue to contribute further knowledge into the future.

High interstitial fluid pressure (IFP) in tumours is recognized as a barrier to drug delivery resulting in reduced efficacy. High IFP impedes the normal process of convective fluid flow (CFF) from blood vessels into the interstitium. The aim of this study was to develop an in vitro model that could be used to measure CFF and to study its effects on drug delivery. The model consists of a transwell cell culture insert which supports the growth of multicell layers (MCL) on collagen coated membranes. A graduated tube is inserted into the transwell and a pressure gradient is applied across the membrane by raising the volume of medium in the tube above that of the bottom chamber. CFF is determined by measuring the weight of medium in the bottom chamber as a function of time. CFF was inversely proportional to MCL thickness and 41.1±3.6µm thick MCL has completely stopped CFF. Using a physiologically relevant hydrostatic pressure of 28mmHg, a CFF of 21µL/min was recorded using a DLD-1 MCL that was 12.21±3.2µm thick. Under these conditions, the rates of penetration of doxorubicin, imatinib and gefitinib were respectively 42, 26 and 13 folds greater than when no CFF exists. Reversing the CFF so that it opposed the drug diffusion gradient significantly impairs drug penetration. In conclusion, a novel in vitro model for assessing the impact of CFF on drug delivery has been developed. This model could be used to evaluate strategies designed to increase drug delivery to solid tumours by modifying the CFF.