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一种关于肿瘤对αPDL1和αCTLA4放射免疫疗法反应的生物数学模型。

A Biomathematical Model of Tumor Response to Radioimmunotherapy With αPDL1 and αCTLA4.

作者信息

Gonzalez-Crespo Isabel, Gomez-Caamano Antonio, Pouso Oscar Lopez, Fenwick John D, Pardo-Montero Juan

出版信息

IEEE/ACM Trans Comput Biol Bioinform. 2023 Mar-Apr;20(2):808-821. doi: 10.1109/TCBB.2022.3174454. Epub 2023 Apr 3.

Abstract

There is evidence of synergy between radiotherapy and immunotherapy. Radiotherapy can increase liberation of tumor antigens, causing activation of antitumor T-cells. This effect can be boosted with immunotherapy. Radioimmunotherapy has potential to increase tumor control rates. Biomathematical models of response to radioimmunotherapy may help on understanding of the mechanisms affecting response, and assist clinicians on the design of optimal treatment strategies. In this work we present a biomathematical model of tumor response to radioimmunotherapy. The model uses the linear-quadratic response of tumor cells to radiation (or variation of it), and builds on previous developments to include the radiation-induced immune effect. We have focused this study on the combined effect of radiotherapy and αPDL1/ αCTLA4 therapies. The model can fit preclinical data of volume dynamics and control obtained with different dose fractionations and αPDL1/ αCTLA4. A biomathematical study of optimal combination strategies suggests that a good understanding of the involved biological delays, the biokinetics of the immunotherapy drug, and the interplay between them, may be of paramount importance to design optimal radioimmunotherapy schedules. Biomathematical models like the one we present can help to interpret experimental data on the synergy between radiotherapy and immunotherapy, and to assist in the design of more effective treatments.

摘要

有证据表明放射疗法和免疫疗法之间存在协同作用。放射疗法可增加肿瘤抗原的释放,从而激活抗肿瘤T细胞。免疫疗法可增强这种效果。放射免疫疗法有提高肿瘤控制率的潜力。放射免疫疗法反应的生物数学模型可能有助于理解影响反应的机制,并协助临床医生设计最佳治疗策略。在这项工作中,我们提出了一个肿瘤对放射免疫疗法反应的生物数学模型。该模型使用肿瘤细胞对辐射的线性二次反应(或其变体),并在先前的研究基础上纳入辐射诱导的免疫效应。我们将这项研究聚焦于放射疗法与αPDL1/αCTLA4疗法的联合效应。该模型能够拟合不同剂量分割和αPDL1/αCTLA4情况下获得的体积动力学和控制的临床前数据。对最佳联合策略的生物数学研究表明,深入了解相关的生物学延迟、免疫治疗药物的生物动力学以及它们之间的相互作用,对于设计最佳放射免疫治疗方案可能至关重要。像我们提出的这种生物数学模型有助于解释放射疗法和免疫疗法协同作用的实验数据,并协助设计更有效的治疗方法。

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