数学模型表明,在高预处理SIV病毒载量的情况下,CD8+T细胞毒性的调节性抑制可以限制IL-15免疫疗法的疗效。

IF 4.3 2区 生物学
PLoS Computational Biology Pub Date : 2023-08-24 eCollection Date: 2023-08-01 DOI:10.1371/journal.pcbi.1011425
Jonathan W Cody, Amy L Ellis-Connell, Shelby L O'Connor, Elsje Pienaar
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引用次数: 0

摘要

免疫治疗性细胞因子可以激活免疫细胞对抗癌症和慢性感染。N-803是一种IL-15超级拮抗剂,可扩增CD8+T细胞并增加其细胞毒性。N-803还暂时降低了感染猴免疫缺陷病毒(SIV)的非人类灵长类动物的病毒载量,这是一种HIV模型。然而,尚未在所有SIV队列中观察到病毒抑制,这可能取决于治疗前的病毒载量和对CD8+T细胞的相应影响。从现有的SIV免疫治疗N-803的机制数学模型开始,我们开发了一个模型,该模型包括通过抗原、炎症和N-803激活SIV特异性和非SIV特异性CD8+T细胞。还包括抑制CD8+T细胞增殖和功能的调节性反反应,代表免疫检查点分子和免疫抑制细胞的作用。我们同时将模型校准为两个独立的SIV队列。第一个队列在治疗前病毒载量较低(≈3-4 log病毒RNA拷贝当量(CEQ)/mL),N-803治疗暂时抑制了病毒载量。第二种具有更高的预处理病毒载量(≈5-7 log CEQ/mL),并且与N-803没有一致的病毒抑制。该数学模型可以基于不同的预处理病毒载量和由于这些病毒载量对CD8+T细胞的不同调节抑制水平(即模型的初始条件)来复制两个队列的病毒和CD8+T淋巴细胞动力学。我们的预测得到了来自这些和其他SIV队列的额外数据的验证。虽然两个队列在模拟中都有大量活化的SIV特异性CD8+T细胞,但在高病毒载量队列中,由于细胞毒性抑制作用增强,病毒抑制被排除。因此,我们从数学上证明了治疗前病毒载量如何影响免疫治疗效果,强调了体内条件和联合治疗可以最大限度地提高疗效并改善治疗结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Mathematical modeling indicates that regulatory inhibition of CD8+ T cell cytotoxicity can limit efficacy of IL-15 immunotherapy in cases of high pre-treatment SIV viral load.

Mathematical modeling indicates that regulatory inhibition of CD8+ T cell cytotoxicity can limit efficacy of IL-15 immunotherapy in cases of high pre-treatment SIV viral load.

Mathematical modeling indicates that regulatory inhibition of CD8+ T cell cytotoxicity can limit efficacy of IL-15 immunotherapy in cases of high pre-treatment SIV viral load.

Mathematical modeling indicates that regulatory inhibition of CD8+ T cell cytotoxicity can limit efficacy of IL-15 immunotherapy in cases of high pre-treatment SIV viral load.

Immunotherapeutic cytokines can activate immune cells against cancers and chronic infections. N-803 is an IL-15 superagonist that expands CD8+ T cells and increases their cytotoxicity. N-803 also temporarily reduced viral load in a limited subset of non-human primates infected with simian immunodeficiency virus (SIV), a model of HIV. However, viral suppression has not been observed in all SIV cohorts and may depend on pre-treatment viral load and the corresponding effects on CD8+ T cells. Starting from an existing mechanistic mathematical model of N-803 immunotherapy of SIV, we develop a model that includes activation of SIV-specific and non-SIV-specific CD8+ T cells by antigen, inflammation, and N-803. Also included is a regulatory counter-response that inhibits CD8+ T cell proliferation and function, representing the effects of immune checkpoint molecules and immunosuppressive cells. We simultaneously calibrate the model to two separate SIV cohorts. The first cohort had low viral loads prior to treatment (≈3-4 log viral RNA copy equivalents (CEQ)/mL), and N-803 treatment transiently suppressed viral load. The second had higher pre-treatment viral loads (≈5-7 log CEQ/mL) and saw no consistent virus suppression with N-803. The mathematical model can replicate the viral and CD8+ T cell dynamics of both cohorts based on different pre-treatment viral loads and different levels of regulatory inhibition of CD8+ T cells due to those viral loads (i.e. initial conditions of model). Our predictions are validated by additional data from these and other SIV cohorts. While both cohorts had high numbers of activated SIV-specific CD8+ T cells in simulations, viral suppression was precluded in the high viral load cohort due to elevated inhibition of cytotoxicity. Thus, we mathematically demonstrate how the pre-treatment viral load can influence immunotherapeutic efficacy, highlighting the in vivo conditions and combination therapies that could maximize efficacy and improve treatment outcomes.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology 生物-生化研究方法
CiteScore
7.10
自引率
4.70%
发文量
820
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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