Multiscale modeling of tumor response to vascular endothelial growth factor (VEGF) inhibitor.

Q2 Medicine In Silico Biology Pub Date : 2021-01-01 DOI:10.3233/ISB-210235
Melisa Hendrata, Janti Sudiono
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Abstract

Vascular endothelial growth factor (VEGF) has been known as a key mediator of angiogenesis in cancer. Bevacizumab is anti-VEGF monoclonal antibody that has been approved by the FDA as a first-line treatment in many types of cancer. In this paper, we extend a previously validated multiscale tumor model to comprehensively include the multiple roles of VEGF during the course of angiogenesis and its binding mechanism with bevacizumab. We use the model to simulate tumor system response under various bevacizumab concentrations, both in stand-alone treatment and in combination with chemotherapy. Our simulation indicates that periodic administration of bevacizumab with lower concentration can achieve greater efficacy than a single treatment with higher concentration. The simulation of the combined therapy also shows that the continuous administration of bevacizumab during the maintenance phase can lead to antitumor activity which further suppresses its growth. Agreement with experimental results indicates the potential of the model in predicting the efficacy of anti-VEGF therapies and could therefore contribute to developing prospective clinical trials.

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肿瘤对血管内皮生长因子(VEGF)抑制剂反应的多尺度模型。
血管内皮生长因子(VEGF)被认为是肿瘤血管生成的关键介质。贝伐单抗是一种抗vegf单克隆抗体,已被FDA批准作为多种癌症的一线治疗药物。在本文中,我们扩展了先前验证的多尺度肿瘤模型,以全面包括VEGF在血管生成过程中的多重作用及其与贝伐单抗的结合机制。我们使用该模型来模拟不同贝伐单抗浓度下的肿瘤系统反应,包括单独治疗和联合化疗。我们的模拟表明,低浓度的贝伐单抗周期性给药比高浓度的单次治疗更有效。联合治疗的模拟还表明,在维持阶段持续给予贝伐单抗可以导致抗肿瘤活性,从而进一步抑制其生长。与实验结果的一致表明,该模型在预测抗vegf治疗的疗效方面具有潜力,因此可能有助于开展前瞻性临床试验。
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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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