Controlled Drug Delivery in Cancer Immunotherapy: Stability, Optimization, and Monte Carlo Analysis

Minelli, Andrea; Topputo, Francesco; Bernelli‐Zazzera, Franco

doi:10.1137/100815190

Cited by 16 publications

(6 citation statements)

References 17 publications

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“…36). Cancer-immunity models have been constructed to investigate the effects of therapy (37)(38)(39), cancer dormancy (40), and interactions with time delay (41). Other types of modeling methods have also been applied.…”

mentioning

confidence: 99%

Modeling putative therapeutic implications of exosome exchange between tumor and immune cells

Huang

Hanash

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Development of effective strategies to mobilize the immune system as a therapeutic modality in cancer necessitates a better understanding of the contribution of the tumor microenvironment to the complex interplay between cancer cells and the immune response. Recently, effort has been directed at unraveling the functional role of exosomes and their cargo of messengers in this interplay. Exosomes are small vesicles (30-200 nm) that mediate local and long-range communication through the horizontal transfer of information, such as combinations of proteins, mRNAs and microRNAs. Here, we develop a tractable theoretical framework to study the putative role of exosome-mediated cell-cell communication in the cancer-immunity interplay. We reduce the complex interplay into a generic model whose three components are cancer cells, dendritic cells (consisting of precursor, immature, and mature types), and killer cells (consisting of cytotoxic T cells, helper T cells, effector B cells, and natural killer cells). The framework also incorporates the effects of exosome exchange on enhancement/reduction of cell maturation, proliferation, apoptosis, immune recognition, and activation/ inhibition. We reveal tristability-

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mentioning

confidence: 99%

Modeling putative therapeutic implications of exosome exchange between tumor and immune cells

Huang

Hanash

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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“…The system consists of few key immune populations; helper CD4+T cells and cytotoxic CD8+T cells. The cytotoxic T cells able to recognize by dendritic cells (the best antigen tendering cells), make a good fist of killing since cancer cell distinguish on their face [20].…”

Section: System Presented By Caputo Fractional Derivativementioning

confidence: 99%

Examining of a tumor system with Caputo derivative

Uçar

2023

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Tumor diseases have killed many people around the world for centuries. For this purpose, a lot of scientists have analyzed immune system’s cells in order to cope with tumor growths in living beings. For this reason, we examine a system reflecting the relationship between immune system and tumor growth in this study. This system is handled with the traditional Caputo fractional derivative. We give stability analysis of equilibrium points and solution properties of this system. Then, the conditions about the existence and uniqueness of the solution for this system are clear. In conclusion, we solve this fractional system benefiting from Grünwald-Letnikov scheme.

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“…In a follow-up study, Palladini et al [34] showed excellent agreement between the in silico predictions and in vivo experiments, and made additional observations, such as the importance of vaccination density in the early phase of the immune response and dependence of vaccine success on age of the mouse. GAs and evolutionary algorithms have also been used to identify potential epitopes for vaccine development [50], to develop an initial guess for a discrete optimal control problem based on the model in [42,51], and to solve a multi-objective optimization problem for a model of combination chemo-and immunotherapy [52].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling

Konstorum

Vella

Adler

et al. 2017

J. R. Soc. Interface.

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The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.

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Controlled Drug Delivery in Cancer Immunotherapy: Stability, Optimization, and Monte Carlo Analysis

Cited by 16 publications

References 17 publications

Modeling putative therapeutic implications of exosome exchange between tumor and immune cells

Modeling putative therapeutic implications of exosome exchange between tumor and immune cells

Examining of a tumor system with Caputo derivative

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling

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