A phenotype-structured mathematical model for the influence of hypoxia on oncolytic virotherapy

IF 2.6 4区 数学 Q2 BIOLOGY
Mathematical Biosciences Pub Date : 2026-01-01 Epub Date: 2025-11-19 DOI:10.1016/j.mbs.2025.109570
David Morselli , Giulia Chiari , Federico Frascoli , Marcello Edoardo Delitala
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引用次数: 0

Abstract

The effectiveness of oncolytic virotherapy is significantly affected by several elements of the tumour microenvironment, which reduce the ability of the virus to infect cancer cells. In this work, we focus on the influence of hypoxia on this therapy and develop a novel continuous mathematical model that considers both the spatial and epigenetic heterogeneity of the tumour. We investigate how oxygen gradients within tumours affect the spatial distribution and replication of both the tumour and oncolytic viruses, focusing on regions of severe hypoxia versus normoxic areas. Additionally, we analyse the evolutionary dynamics of tumour cells under hypoxic conditions and their influence on susceptibility to viral infection. Our findings show that the reduced metabolic activity of hypoxic cells may significantly impact the virotherapy effectiveness; the knowledge of the tumour’s oxygenation could, therefore, suggest the most suitable type of virus to optimise the outcome. The combination of numerical simulations and theoretical results for the model equilibrium values allows us to elucidate the complex interplay between viruses, tumour evolution and oxygen dynamics, ultimately contributing to developing more effective and personalised cancer treatments.
缺氧对溶瘤病毒治疗影响的表型结构数学模型。
溶瘤病毒治疗的有效性受到肿瘤微环境的几个因素的显著影响,这些因素降低了病毒感染癌细胞的能力。在这项工作中,我们关注缺氧对这种治疗的影响,并开发了一种新的连续数学模型,该模型考虑了肿瘤的空间和表观遗传异质性。我们研究了肿瘤内的氧梯度如何影响肿瘤和溶瘤病毒的空间分布和复制,重点研究了严重缺氧区与常氧区。此外,我们分析了肿瘤细胞在缺氧条件下的进化动力学及其对病毒感染易感性的影响。我们的研究结果表明,低氧细胞的代谢活性降低可能显著影响病毒治疗的效果;因此,对肿瘤氧合作用的了解可以建议最合适的病毒类型来优化结果。数值模拟和模型平衡值的理论结果相结合,使我们能够阐明病毒、肿瘤进化和氧动力学之间复杂的相互作用,最终有助于开发更有效和个性化的癌症治疗方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Mathematical Biosciences
Mathematical Biosciences 生物-生物学
CiteScore
7.50
自引率
2.30%
发文量
67
审稿时长
18 days
期刊介绍: Mathematical Biosciences publishes work providing new concepts or new understanding of biological systems using mathematical models, or methodological articles likely to find application to multiple biological systems. Papers are expected to present a major research finding of broad significance for the biological sciences, or mathematical biology. Mathematical Biosciences welcomes original research articles, letters, reviews and perspectives.
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