Journal
CELLS
Volume 11, Issue 15, Pages -Publisher
MDPI
DOI: 10.3390/cells11152325
Keywords
A20 cells; cytotoxicity rate; in vivo experiments; logistic cancer growth rate; mathematical model; personalized chemotherapy; stability analysis; tumor dynamic
Categories
Funding
- Ariel University Research and Development [RA19000179]
- Ariel University School of Graduate Studies
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Mathematical models have become an important tool in cancer research, combining quantitative analysis and natural processes. This study focuses on Chronic Lymphocytic Leukemia (CLL) and uses a mathematical model to predict drug efficacy, which could potentially lead to personalized chemotherapy prediction software.
In recent years, mathematical models have developed into an important tool for cancer research, combining quantitative analysis and natural processes. We have focused on Chronic Lymphocytic Leukemia (CLL), since it is one of the most common adult leukemias, which remains incurable. As the first step toward the mathematical prediction of in vivo drug efficacy, we first found that logistic growth best described the proliferation of fluorescently labeled murine A20 leukemic cells injected in immunocompetent Balb/c mice. Then, we tested the cytotoxic efficacy of Ibrutinib (Ibr) and Cytarabine (Cyt) in A20-bearing mice. The results afforded calculation of the killing rate of the A20 cells as a function of therapy. The experimental data were compared with the simulation model to validate the latter's applicability. On the basis of these results, we developed a new ordinary differential equations (ODEs) model and provided its sensitivity and stability analysis. There was excellent accordance between numerical simulations of the model and results from in vivo experiments. We found that simulations of our model could predict that the combination of Cyt and Ibr would lead to approximately 95% killing of A20 cells. In its current format, the model can be used as a tool for mathematical prediction of in vivo drug efficacy, and could form the basis of software for prediction of personalized chemotherapy.
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