Mathematical modeling of intratumoral immunotherapy yields strategies to improve the treatment outcomes.
Mathematical modeling of intratumoral immunotherapy yields strategies to improve the treatment outcomes.
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DOI：
10.1371/journal.pcbi.1011740
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发表时间：
2023-12
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
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作者：
免疫疗法的瘤内注射旨在最大限度地提高其在肿瘤内的活性。然而，细胞因子通过肿瘤血管清除并从肿瘤外周逃逸到宿主组织中，降低功效并引起毒性。因此，了解肿瘤的决定因素和对肿瘤内免疫治疗的免疫反应应该会导致更好的治疗结果。在这项研究中，我们开发了一个机械的数学模型，以确定肿瘤内注射的缀合细胞因子的疗效，占肿瘤微环境和缀合细胞因子的属性。该模型明确纳入了i）肿瘤血管密度和渗透性以及肿瘤水力传导率，ii）缀合的细胞因子大小和结合亲和力以及它们通过血管和周围组织的清除率，以及iii）免疫细胞-癌细胞相互作用。模型模拟显示了肿瘤和缀合的细胞因子的性质如何决定治疗结果，以及如何选择适当的参数来优化治疗。高的肿瘤组织透水性允许细胞因子均匀分布到肿瘤中，而均匀的肿瘤灌注是免疫细胞充分接近和活化所必需的。肿瘤血管的渗透性影响细胞因子的血液清除，最佳值取决于缀合物的大小。发现半径>5 nm的尺寸是最佳的，而缀合物的结合应该足够高以防止从肿瘤清除到周围组织中。总之，通过重新编程微环境沿着结合细胞因子的最佳设计来改善血管灌注和组织水力传导性的策略的开发可以增强肿瘤内免疫治疗。细胞因子是可以激活免疫反应的信号蛋白。细胞因子的肿瘤内给药已显示出改善癌症治疗功效的前景。然而，潜在的毒性仍然可能是由肿瘤释放到体循环中的细胞因子引起的。在这里，我们开发了一个细胞因子在肿瘤中的运输和随后的免疫反应的机械数学模型。该模型分别基于缀合的细胞因子和肿瘤微环境的物理和生理特征，研究了肿瘤内施用的缀合的细胞因子在肿瘤中的时空分布和治疗结果。我们的研究结果显示了肿瘤血流量、血管通透性和肿瘤组织导水率在治疗结果中的重要性。此外，我们发现缀合的细胞因子的大小和结合亲和力是治疗结果的重要决定因素。我们的模型还显示了重新编程肿瘤微环境如何改善肿瘤内注射细胞因子的治疗结果。
Intratumoral injection of immunotherapy aims to maximize its activity within the tumor. However, cytokines are cleared via tumor vessels and escape from the tumor periphery into the host-tissue, reducing efficacy and causing toxicity. Thus, understanding the determinants of the tumor and immune response to intratumoral immunotherapy should lead to better treatment outcomes. In this study, we developed a mechanistic mathematical model to determine the efficacy of intratumorally-injected conjugated-cytokines, accounting for properties of the tumor microenvironment and the conjugated-cytokines. The model explicitly incorporates i) the tumor vascular density and permeability and the tumor hydraulic conductivity, ii) conjugated-cytokines size and binding affinity as well as their clearance via the blood vessels and the surrounding tissue, and iii) immune cells—cancer cells interactions. Model simulations show how the properties of the tumor and of the conjugated-cytokines determine treatment outcomes and how selection of proper parameters can optimize therapy. A high tumor tissue hydraulic permeability allows for the uniform distribution of the cytokines into the tumor, whereas uniform tumor perfusion is required for sufficient access and activation of immune cells. The permeability of the tumor vessels affects the blood clearance of the cytokines and optimal values depend on the size of the conjugates. A size >5 nm in radius was found to be optimal, whereas the binding of conjugates should be high enough to prevent clearance from the tumor into the surrounding tissue. In conclusion, development of strategies to improve vessel perfusion and tissue hydraulic conductivity by reprogramming the microenvironment along with optimal design of conjugated-cytokines can enhance intratumoral immunotherapy. Cytokines are signaling proteins that can activate the immune response. Intratumoral administration of cytokines has shown promise in improving efficacy of cancer treatment. However, potential toxicity may still result from cytokines release from tumor into systemic circulation. Here, we developed a mechanistic mathematical model of cytokines transport in the tumor and the subsequent immune response. The model investigates the spatiotemporal distribution of intratumorally administered conjugated-cytokines in the tumor and the treatment outcomes, based on the physical and physiological characteristics of the conjugated-cytokines and the tumor microenvironment, respectively. Our results show the importance of tumor blood flow, vascular permeability and tumor tissue hydraulic conductivity in the treatment outcome. Moreover, we found that the size and binding affinity of the conjugated-cytokines are important determinants of the treatment outcome. Our model also shows how reprogramming the tumor microenvironment can improve the treatment outcome of intratumorally-injected cytokines.
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影响因子：
3.7
作者：
Lai X;Friedman A
通讯作者：
Friedman A
影响因子：
50.3
作者：
Jain RK
通讯作者：
Jain RK
影响因子：
28.1
作者：
通讯作者：
--
影响因子：
3.5
作者：
Friedman, Avner;Hao, Wenrui
通讯作者：
Hao, Wenrui
DOI：
10.1146/annurev-bioeng-071813-105259
发表时间：
2014-07-11
影响因子：
9.7
作者：
Jain RK;Martin JD;Stylianopoulos T
通讯作者：
Stylianopoulos T