放疗和免疫疗法的免疫原性和协同效应的硅学机制探索:重要综述。

IF 2.4 4区 医学 Q3 ENGINEERING, BIOMEDICAL Physical and Engineering Sciences in Medicine Pub Date : 2024-07-17 DOI:10.1007/s13246-024-01458-1
Allison M Ng, Kelly M MacKinnon, Alistair A Cook, Rebecca A D'Alonzo, Pejman Rowshanfarzad, Anna K Nowak, Suki Gill, Martin A Ebert
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引用次数: 0

摘要

免疫疗法是一个快速发展的领域,许多模型都在试图描述免疫疗法对免疫系统的影响,尤其是与放疗结合使用时。肿瘤对这种组合疗法的反应涉及复杂的时空动态,这使得在由此产生的广泛解决方案空间内进行临床或临床前体内研究极为困难。本综述研究了放疗、免疫疗法和患者免疫系统之间相互作用的几种硅学模型。这项研究只包括用英文发表的研究放疗对免疫系统影响或免疫检查点抑制剂对免疫放疗影响的数学模型。研究结果表明,与单独使用放疗或免疫疗法相比,同时使用放疗和免疫疗法可提高疗效。然而,模型对放疗和免疫疗法的最佳时间安排和分次并不一致。这与相关的临床试验相吻合,这些临床试验报告称,联合疗法可提高疗效,但不同临床试验的最佳时间安排各不相同。模型之间的这种差异可归因于模型方法和特定癌症类型模型的差异,这使得确定最佳总体治疗方案和模型具有挑战性。需要利用类似的数据集开展进一步研究,以评估针对特定癌症类型和阶段的最佳模型和治疗方案。
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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Mechanistic in silico explorations of the immunogenic and synergistic effects of radiotherapy and immunotherapy: a critical review.

Immunotherapy is a rapidly evolving field, with many models attempting to describe its impact on the immune system, especially when paired with radiotherapy. Tumor response to this combination involves a complex spatiotemporal dynamic which makes either clinical or pre-clinical in vivo investigation across the resulting extensive solution space extremely difficult. In this review, several in silico models of the interaction between radiotherapy, immunotherapy, and the patient's immune system are examined. The study included only mathematical models published in English that investigated the effects of radiotherapy on the immune system, or the effect of immuno-radiotherapy with immune checkpoint inhibitors. The findings indicate that treatment efficacy was predicted to improve when both radiotherapy and immunotherapy were administered, compared to radiotherapy or immunotherapy alone. However, the models do not agree on the optimal schedule and fractionation of radiotherapy and immunotherapy. This corresponds to relevant clinical trials, which report an improved treatment efficacy with combination therapy, however, the optimal scheduling varies between clinical trials. This discrepancy between the models can be attributed to the variation in model approach and the specific cancer types modeled, making the determination of the optimum general treatment schedule and model challenging. Further research needs to be conducted with similar data sets to evaluate the best model and treatment schedule for a specific cancer type and stage.

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CiteScore
8.40
自引率
4.50%
发文量
110
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