Model-based Angiogenic Inhibition of Tumor Growth using Modern Robust Control Method

Cancer is one of the most destructive and lethal illnesses of the modern civilization. In the last decades, clinical cancer research shifted towards molecular targeted therapies which have limited side e�ects in comparison to conventional chemotherapy and radiation therapy. Anti-angiogenic therapy is one of the most promising cancer treatment methods. The dynamical model for tumor growth under angiogenic stimulator/inhibitor control was posed by Hahnfeldt et al. (1999), and it was investigated and partly modi�ed many times. In this paper, a modi�ed version of the originally published model is used in order to describe a continuous infusion therapy. To generalize individualized therapies a robust control method is proposed using H 1 methodology. Uncertainty weighting functions are determined based on the real pathophysiological case and simulations are performed on di�erent tumor volumes to demonstrate the robustness of the proposed method

Parameter optimization of H∞ controller designed for tumor growth in the light of physiological aspects

According to the fact that cancer diseases are leading causes of death all around the world, development of cancer fighting therapies is necessary. Beside the medical knowledge, there is an extra need for engineering approach to solve this complex problem. The aim of this paper is to design controller for tumor growth under angiogenic inhibition, which on the one hand minimizes the input signal as far as possible (in order to have less side effects and greater cost-effectiveness) and on the other hand results in appropriately low tumor volume. Since the model contains uncertainties and measurement noise, the controller was designed using modern robust control methodology. Choosing of the ideal system and the weighting functions were done in the light of physiological aspects

Potential Benefits of Discrete-Time Controllerbased Treatments over Protocol-based Cancer Therapies

In medical practice, the effectiveness of fighting cancer is not only determined by the composition of the used drug, but determ ined by the administration method as well. As a result, having drugs with a suitable action profile is just a promising beginning, but without appropriate delivery method s , the therapy still can be ineffective. Finding the optimal biologic dose is an empir ical process in medical practice; however, using controllers, an automated optimal administration can be determined . In this paper , we evaluate the effectiveness of different drug delivery protocols; using in silico simulations (like bolus dose s, low - dose metron omic regimen and continuous infusion therapy ). In addition, we compare these results with discrete - time controller - based treatments containing state feedback, setpoint control, actual state observer and load estimation

Study of Modern Control Methodologies Applied to Tumor Growth under Angiogenic Inhibition

Cancer treatment is one of the most important research fields of modern medicine. In the last decades, targeted molecular therapies showed pr osperous results. These treatments achieve tumor regression with limited side-effects. Mathem atical models were posed which describe the dynamics of tumor regression under the applied control. The current paper investigates antiangiogenic therapy, which inhibits the t umor to grow its own endothelial capillaries and thus inhibits tumor to grow over a certain si ze. Many different control approaches were elaborated and published since the model formulation w as posed. The aim of this paper is to give an overview of these methods and results, and to rev iew the work carried out by the authors

Model-based Angiogenic Inhibition of Tumor Growth using Adaptive Fuzzy Techniques

Fighting tumors is one of the most important problems of medical research. In this paper, antiangiogenic cancer therapy is investigated through its mathematical model.This tumor treatment method targets the endothelium of a growing tumor and belongs to the targeted molecular therapies.The aim of the therapy is not to eliminate the entire tumor,but to decrease the tumor to a minimal volume. An advantage of applying antiangiogenic treatment is that tumor cells show lower tendency of becoming resistant to the applied drugs.Adaptive fuzzy control is implemented for a simplified model to elaborate a control technique which is able to handle the effects of parameter perturbations and uncertainties while keeping the daily and total inhibitor inlet under a given limit

Comparison of Path Tracking Flat Control and Working Point Linearization Based Set Point Control of Tumor Growth with Angiogenic Inhibition

Targeted molecular therapies (TMT) represent new perspectives in cancer treatment, fighting against the specific characteristic of the investigated tumor. Antiangiogenic therapy represents a specific TMT and its role is to stop the angiogenesis of the tumor, the process of forming new blood vessels; hence, to stop tumor growth. Proper control algorithms for tumor growth control with angiogenic inhibition are analyzed in the current article in order to find optimal therapeutic protocols. Two slightly different approaches are compared: nonlinear control by exact linearization with path tracking control, and linear control by working point linearization with set point control. The control strategies are compared in terms of the characteristics of the input signal (the inhibitor, drug intake) that is crucial if the therapy will be put into practice