Towards a high-fidelity model for model based optimisation of drug delivery systems in acute myeloid leukemia
Book
Overview
Research
Identity
Additional Document Info
View All
Overview
abstract
High doses of chemotherapy drugs are required to efficiently eradicate cancer cells during treatment of Acute Myeloid Leukemia (AML). Although effective for debulking of the leukemia tumour burden, the use of these chemotherapy regimens is also highly destructive to normal cell populations, often to a life-threatening extent. Drug optimisation of treatment dose in a patient-specific and leukemia-specific regimen is therefore essential in order to balance the benefits of therapy against the risks of toxicity. This optimisation may be achieved by producing a model of the key biological parameters of the patient, the AML cells and the effects of chemotherapy on both. This model may then be used as a predictive tool of the patient response during treatment, as demonstrated in our previous work pertaining to the optimal model-based control schedule for breast cancer chemotherapy treatment (Dua et al. 2008; Dua et.al. 2005). Cancer, including AML, results from the loss of control of the cell cycle where the cells proliferate abnormally. A complicated network of reactions and cell-signalling pathways are involved in this process of leukemogenesis. The actions and targeting of chemotherapeutic treatments to interfere with this abnormal cell signalling are equally as complicated in vivo. Herein, we attempt to describe this process through the development of a dynamic model for the in vivo actions of a single chemotherapeutic drug, cytosine arabinoside (ARA-C), used routinely for the treatment of AML, where the optimum dose is calculated within tolerable levels of drug toxicity. The proposed model combines the actions on the cell cycle, which is the target of the drug, with pharmacokinetic and pharmacodynamic aspects in order to provide a comprehensive description of drug diffusion and action after administration. The model also takes into account patient factors such as age, sex, weight and height in an attempt to gain insights towards optimisation of individual treatment protocols for effective patient-specific and leukemia-specific therapy that can also minimise toxicity. 2011 Elsevier B.V.
