4.5 Article

Investigation of cancer response to chemotherapy: a hybrid multi-scale mathematical and computational model of the tumor microenvironment

期刊

BIOMECHANICS AND MODELING IN MECHANOBIOLOGY
卷 21, 期 4, 页码 1233-1249

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s10237-022-01587-0

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Tumor growth; Angiogenesis; MLD; MTD; Combination chemotherapy

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Tumor microenvironment (TME) is a complex biological environment that plays a crucial role in tumor dynamics. In this study, a hybrid multi-scale mathematical model of TME was developed to simulate tumor progression and response to different chemotherapy approaches. The results showed that combination therapy of anti-angiogenic drug and anti-cancer drug improved treatment performance and reduced side effects. The model also suggested that lower dose chemotherapy over a long period could effectively treat cancer with fewer side effects.
Tumor microenvironment (TME) is a multi-scale biological environment that can control tumor dynamics with many biomechanical and biochemical factors. Investigating the physiology of TME with a heterogeneous structure and abnormal functions not only can achieve a deeper understanding of tumor behavior but also can help develop more efficient anti-cancer strategies. In this work, we develop a hybrid multi-scale mathematical model of TME to simulate the progression of a three-dimensional tumor and elucidate its response to different chemotherapy approaches. The chemotherapy approaches include multiple low dose (MLD) of anti-cancer drug, maximum tolerated dose (MTD) of anti-cancer drug, combination therapy of MLD and anti-angiogenic drug, and combination therapy of MTD and anti-angiogenic drug. The results show that combining anti-angiogenic agent with anti-cancer drug increases the performance of cancer treatment and decreases side effects for normal tissue. Indeed, the vascular normalization caused by anti-angiogenic therapy improves anti-cancer drug delivery for both MLD and MTD approaches. The results show that anti-cancer drug administered in a lower dose than the maximum tolerated dose repetitively over a long period treats cancer with a considerable performance and fewer side effects. We also show that tumor morphology and distribution of cancer cell phenotypes can be considered as the characteristics to distinguish different chemotherapy approaches. This robust model can be applied to predict the behavior of any type of cancer and quantify cancer response to different chemotherapy approaches. The computational results of cancer response to chemotherapy are in good agreement with experimental measurements.

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