肿瘤对联合αPDL1与αCTLA4的放射免疫治疗响应的生物数学模型
A biomathematical model of tumor response to radioimmunotherapy with $α$PDL1 and $α$CTLA4
AI总结：
本文提出纳入辐射诱导免疫效应的肿瘤放射免疫治疗生物数学模型，可拟合αPDL1/αCTLA4联合放疗的临床前数据，并用于优化治疗方案。
摘要：
有证据表明放射治疗与免疫治疗之间存在协同作用。放射治疗可增加肿瘤抗原的释放，从而引起抗肿瘤T细胞的激活，而这一效应可通过免疫治疗得到增强。因此，放射免疫治疗有望提高肿瘤控制率。建立针对放射免疫治疗响应的生物数学模型，有助于理解影响响应的机制，并协助临床医生设计最佳治疗策略。在本研究中，我们提出了一个肿瘤对放射免疫治疗响应的生物数学模型。该模型采用肿瘤细胞对辐射的线性二次响应（或其变体），并在以往研究进展的基础上纳入辐射诱导的免疫效应。我们将研究重点放在放射治疗与αPDL1/αCTLA4治疗的联合效应上。该模型能够拟合在不同剂量分割方案以及αPDL1/αCTLA4条件下获得的肿瘤体积动态和控制情况的临床前数据。一项关于最佳联合策略的生物数学研究表明，充分理解所涉及的生物学延迟、免疫治疗药物的生物动力学以及二者之间的相互作用，对于设计最佳放射免疫治疗方案可能至关重要。诸如此类的生物数学模型能够帮助解释关于放射治疗与免疫治疗协同作用的实验数据，并协助设计更有效的治疗方案。
英文摘要：
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 $α$PDL1/$α$CTLA4 therapies. The model can fit preclinical data of volume dynamics and control obtained with different dose fractionations and $α$PDL1/$α$CTLA4. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
