Predicting the effectiveness of chemotherapy using stochastic ODE models of tumor growth

Sharpe, Samara; Dobrovolny, Hana M.

doi:10.1016/j.cnsns.2021.105883

Cited by 8 publications

(2 citation statements)

References 58 publications

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“…Recently, mathematical and dynamic modeling of the interaction between healthy and cancerous cells based on Ordinary Differential Equation (ODE) in different types of cancers has attracted much attention from researchers globally. These models can forecast the outcome of complex biological systems, including the growth behavior of tumors and their impact on the body (Guzev et al, 2022;Sharpe & Dobrovolny, 2021). Moreover, control algorithms have provided qualitative and quantitative information for a better understanding of biological systems (Phan et al, 2020;Swan, 1990;Swierniak, Kimmel, & Smieja, 2009;Talkington, Dantoin, & Durrett, 2018).…”

Section: Introductionmentioning

confidence: 99%

Optimized Finite-Time Integral Fast Terminal Sliding Mode Control for Leukemia Cancer Treatment

Hazareh,

Ghadiri,

Ranjbar

et al. 2023

Preprint

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Leukemia is a type of blood cancer that affects the bone marrow and lymphatic system. Chemotherapy as a drug treatment method is one of the popular ways of treating this disease to destroy fast-growing cells. In this paper, the finite-time adaptive integral fast terminal sliding mode control (AIFTSMC) as a robust strategic method for treating leukemia cancer based on the chemotherapy process has been introduced. Two different treatment modes called uniform and non-uniform have been investigated in detail. Our goal in this trial is to reduce the number of cancer cells during treatment while minimizing damage to healthy cells. Moreover, the controller's coefficients in the sliding surface have been optimized using the water cycle algorithm, a novel type of metaheuristic algorithm. The simulation results show that AIFTSMC effectively targets cancer cells while minimizing damage to healthy cells. The results promise a novel and practical way to treat leukemia in clinical applications.

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Section: Introductionmentioning

confidence: 99%

Optimized Finite-Time Integral Fast Terminal Sliding Mode Control for Leukemia Cancer Treatment

Hazareh,

Ghadiri,

Ranjbar

et al. 2023

Preprint

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“…Recent advances in mathematical modeling have led to the development of ordinary differential equation-based (ODE) models capable of predicting the outcome of complex biological systems, including tumor growth [ 1 , 2 ]. These models have led to interesting applications regarding tumor growth biology [ 3 , 4 ], and predictions regarding the selection of effective therapeutic protocols aimed at reducing the toxic side effects of chemotherapy [ 5 , 6 , 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Validation of a Mathematical Model Describing the Dynamics of Chemotherapy for Chronic Lymphocytic Leukemia In Vivo

Guzev

Jadhav

Hezkiy

et al. 2022

Cells

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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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Stochastic Modeling of Bacterial Population Growth with Antimicrobial Resistance

Mansour

2023

J Stat Phys

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In this paper we consider a stochastic model of bacterial population growth with antimicrobial resistance under the influence of random fluctuations. We analyze the model for the problem of persistence and extinction of bacterial cells. This analysis shows asymptotic extinction and conditional persistence for growing population. Moreover, we perform computer simulations in order to illustrate the model behavior. The model results have important implications for the eradication of bacterial cells and the emergence of resistance.

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Predicting the effectiveness of chemotherapy using stochastic ODE models of tumor growth

Cited by 8 publications

References 58 publications

Optimized Finite-Time Integral Fast Terminal Sliding Mode Control for Leukemia Cancer Treatment

Optimized Finite-Time Integral Fast Terminal Sliding Mode Control for Leukemia Cancer Treatment

Validation of a Mathematical Model Describing the Dynamics of Chemotherapy for Chronic Lymphocytic Leukemia In Vivo

Stochastic Modeling of Bacterial Population Growth with Antimicrobial Resistance

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