Cytokine Release Syndrome

Cytokine release syndrome (CRS) is an inflammatory immune reaction that clinically manifests as fever, headache, joint, and muscle pain in mild cases to hypotension, vascular leakage, and organ failure in severe cases (Shimabukuro-Vornhagen et al., 2018). CRS is associated with a tremendous surge of cytokines, chemokines, and soluble mediators which are primarily proinflammatory (e.g., IL-6, IL-1, MCP-1, IFNɣ, TNFɑ, and others) and cause hyperactivation of immune cells (Teachey et al., 2016; Hay et al., 2017). The time course of CRS is broadly divided into initiation, peak and resolution stages, with each stage connected to different cytokine signatures. Of note, cytokines such as IL-10 which are typically considered anti-inflammatory also increase in the early stages (Panoskaltsis, 2021). However, it is thought that either the anti-inflammatory cytokines do not increase as much as the proinflammatory cytokines or the combined effect of many proinflammatory cytokines overwhelms the homeostatic mechanism and causes the adverse effects of CRS.

Clinical Challenges in Addressing CRS

There are many known causes of CRS, including graft-versus-host disease, infections, and therapeutic agents such chimeric antigen receptor-T (CAR-T) cells, T cell engagers (TCE), and immune checkpoint inhibitor antibodies (Shimabukuro-Vornhagen et al., 2018). Cases of high-grade clinical CRS have been documented frequently in patients treated with cancer immunotherapeutics and have become a major concern since it limits the efficacy of potent life-saving treatments. However, cytokine levels and clinical symptoms vary from patient to patient, and not all patients develop CRS even if they have elevated cytokines compared to untreated patients. The incidence and severity of CRS are decided based on clinical symptoms as there are no specific diagnostic tests thus far; there are also no conclusive links between the clinical CRS grades/symptoms and underlying biomarker signatures. However, tumor burden, peak concentration of IL-6 and C-reactive protein, lymphodepletion, and CAR-T construct and dosing have been identified as potential risk factors (Yan et al., 2021).

Limitations of Experimental Models in Predicting CRS

In vitro and preclinical models have not always successfully predicted CRS risks for patients. As non-human primate models do not have a tumor, they are somewhat less impactful in capturing the intensity of CRS. Humanized mouse models bearing tumors can be useful for assessing immune infiltration, activation, and toxicity for immunotherapies, but the inherent differences in mouse and human immune systems compel us to be cautious of any findings from these models. One notable failure of preclinical testing in the past was the lack of CRS prediction for the superagonist anti-CD28 monoclonal antibody TGN1412, which led to the hospitalization and extensive treatment of six healthy young volunteers (Suntharalingam et al., 2006). In vitro co-culture models with PBMC or whole blood can provide mechanistic insights into drug binding and cytokine induction; but the dynamics and the complex feedback loops between the cytokines themselves and the cells that they activate, which are critical elements of CRS propagation, can be overlooked with these models (Shah et al., 2023).

How QSP Modeling Improves Prediction and Management of CRS

定量系统药理学（QSP）建模是回答与特定药物的作用机制和诱发 CRS 的可能性相关的一些基本问题的一种方法。可以根据特定组织中已知的免疫细胞频率、贩运和增殖模式建立 QSP 模型，以预测药物依赖性细胞因子的释放以及驱动 CRS 的次生细胞因子依赖性细胞动力学。QSP 模型可以整合体外细胞因子和体内毒理学数据，以合理的确定性预测细胞因子释放在群体水平上的剂量反应。由于缺乏明确的临床指南，根据细胞因子释放情况预测 CRS 变得更加复杂，但一些重要研究表明，临床 CRS 等级越高，细胞因子的峰值水平越高（Teachey 等人 2016; Hay 等人 2017; Diorio 等人 2022）。因此，在模型中，CRS 的严重程度可与促炎细胞因子的强度或与基线相比的折叠变化相关联。

在模型中区分 CRS 与非 CRS 受试者（或严重与轻度 CRS）的其他潜在指标可能是促炎细胞因子与抗炎细胞因子的比例，或药物在早期诱导特定细胞因子（如 TNFɑ 或 IFNɣ）的情况。在一些 TCE 药物（如抗 CD19 疗法）中观察到了这些特征，但这些特征是否广泛适用于免疫疗法引起的 CRS 仍有待观察。从群体水平的预测出发，定制的 QSP 模型或许能够考虑到通常会导致 CRS 风险较高的患者特征（如肿瘤负荷、疾病类型、淋巴消耗等），并更准确地预测个体反应。

在临床上，患者会接受 IL-6 阻断疗法，如抗 IL6 受体 Tocilizumab，以减轻 CRS（Si 等人2020）。虽然 Tocilizumab 已被证明具有疗效，而且还没有证据表明会影响抗癌免疫疗法的疗效，但也有患者对其不产生反应的情况，其原因尚不清楚（Si 等人 2020）。此外，使用 Tocilizumab 的适当时机也不尽相同，必须根据患者的临床状况进行调整。QSP 模型可帮助规划缓解策略，包括预测细胞因子阻断剂的疗效、确定起始剂量（初始低剂量，然后是高维持剂量）以及剂量分次或分次给药。迄今为止，已对起始剂量的选择进行了经验性测试，由于剂量水平和时间点众多，临床优化剂量可能耗时且昂贵。此外，其中一些策略可能会影响免疫疗法的疗效，因此需要根据具体情况进行评估。

值得注意的是，在 CRS 领域仍存在重大的知识空白，需要仔细考虑。细胞因子的测量通常是在血液而非组织中进行的，即使是血癌，骨髓分析也因其侵入性而受到限制。因此，这些地方的可溶性生物标志物和细胞水平还没有得到很好的描述。此外，细胞因子的水平极不稳定，在数小时至数天内会发生变化（Yan 等人 2021）因此取样时间至关重要。许多研究都是定期取样，但这并不能充分反映 CRS 背后事件的复杂性（Panoskaltsis, 2021）。同样，许多研究只关注细胞因子，但跟踪细胞因子释放和炎症最终消退的关键细胞类型的活化、增殖和可能衰竭的重要性怎么强调都不为过。将 CRS 类别的临床特征与细胞和细胞因子特征及动态变化联系起来，将大大有助于开发出减少 CRS 的预测方法。

QSP modeling can quantitatively link drug dose to cell activation and expansion, which in turn can be related to cytokine accumulation and finally to symptoms such as fever, pain, etc, as long as there are distinguishable trends in the different CRS groups. Applying QSP modeling in combination with large-scale clinical data gathering and analysis of available immune-activating drugs, along with the development of robust in vitro and in vivo experimental models of CRS, can improve our understanding of the concurrence of therapeutic and patient characteristics that increase the risk of CRS. 最终，它将开发出一个预测框架，用于安全地筛选免疫肿瘤药物和选择有效的治疗策略。

参考文献

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