Modeling of Tumor Growth and Optimization of Therapeutic Protocol Design

Abstract

Cancer is a leading fatal disease with millions of people falling victim to it every year. Indeed, the figures are alarming and increasing significantly with each passing year. Cancer is a complex disease characterized by uncontrolled and unregulated growth of cells in the body. Cancer growth can be broadly classified into three stages namely, avascular, angiogenesis and metastasis based on their location and extent of spread in the body. Mechanisms of cancer growth have been poorly understood thus far and considerable resources have been committed to elucidate these mechanisms and arrive at effective therapeutic strategies that have minimal side effects. Mathematical modeling can help in the modeling of cancer mechanisms, to propose and validate hypothesis and to develop therapeutic protocols. This research intends to contribute to this important area of cancer modeling and treatment.

Among these stages, study of avascular stage is quite relevant to the present trend of technology development. Many mathematical models have been developed to comprehend the avascular tumor growth, but the availability of a compendious model is still elusive. This thesis proposes a simple mechanistic model to explain the phenomenon of tumor growth observed from the multicellular tumor spheroid experiments. The main processes incorporated in the mechanistic model for the avascular tumor growth are diffusion of nutrients through the tumor from the microenvironment, consumption rate of the nutrients by the cells in the tumor and cell death by apoptosis and necrosis.

Chemotherapy and immunotherapy are the main focus of this thesis - tumor growth models are integrated with the pharmacokinetic and pharmacodynamic models of therapeutic drugs. The integrated model is used to optimize the therapeutic interventions in order to kill the tumor cells and avert the catastrophic side effects by effectively leveraging multi-objective optimization and control methods. Furthermore, scaling and sensitivity analysis are applied on the tumor-immune models to screen the dominating mechanisms affecting the tumor growth. Then, the dominant mechanisms are used to test out the aspects of intrapatient and interpatient variability. Application of reactive scheduling approach is addressed to nullify the effects of intrapatient variability on the therapeutic outcome. Similarly, population-based simulation studies are carried out to design diagnostic and therapeutic protocols and to find the parametric combinations that determine the treatment outcome.

Overall, this thesis showcases the utility and ability of process systems engineering approaches in improving the cancer diagnosis and treatment.