Abstract
In this paper, we have used an agent-based stochastic tumor growth model and presented a mathematical and theoretical perspective to cancer therapy. This perspective can be used to theoretical study of precision medicine and combination therapy in individuals. We have conducted a series of in silico combination therapy experiments. Based on cancer drugs and new findings of cancer biology, we hypothesize relationships between model parameters which in some cases represent individual genome characteristics and cancer drugs, i.e., in our approach, therapy players are delegated by biologically reasonable parameters. In silico experiments showed that combined therapies are more effective when players affect tumor via different mechanisms and have different physical dimensions. This research presents for the first time an algorithm as a theoretical viewpoint for the prediction of effectiveness and classification of therapy sets.
Highlights
IntroductionDiagnosis and appropriate therapies can be a significant help to the improvement of cancer survivals
Study of cancer as the second leading cause of human mortality is essential
We assume that the tumor growth system is the same as the one given in Figure 14
Summary
Diagnosis and appropriate therapies can be a significant help to the improvement of cancer survivals. Surgery in the case of solid tumors, antitumor drugs, radiation, and immunotherapy have been the treatment of choice in some instances, but ineffectiveness of treatments, drug resistance, side effects of therapies, and tailoring treatment to the individual characteristics of each patient are still major clinical problems. A high percent of oncology drugs and therapies fails in clinical trials [1]. This imposes extra expenses to patients and causes the loss of time in cancer therapies. Mathematical models, in silico experiments, and simulations can be a great help for evaluation of different therapies and examining diverse strategies of drug therapies
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