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脓毒症靶点验证用于再利用和联合补体及免疫检查点抑制治疗。

Sepsis target validation for repurposing and combining complement and immune checkpoint inhibition therapeutics.

机构信息

School of Medicine, Systems Immunity Research Institute, Cardiff University, Cardiff, UK.

Department of Mathematics, Swansea University, Swansea, UK.

出版信息

Expert Opin Drug Discov. 2021 May;16(5):537-551. doi: 10.1080/17460441.2021.1851186. Epub 2020 Dec 14.

Abstract

: Sepsis is a disease that occurs due to an adverse immune response to infection by bacteria, viruses and fungi and is the leading pathway to death by infection. The hallmarks for maladapted immune reactions in severe sepsis, which contribute to multiple organ failure and death, are bookended by the exacerbated activation of the complement system to protracted T-cell dysfunction states orchestrated by immune checkpoint control. Despite major advances in our understanding of the condition, there remains to be either a definitive test or an effective therapeutic intervention.: The authors consider a combinational drug therapy approach using new biologics, and mathematical modeling for predicting patient responses, in targeting innate and adaptive immune mediators underlying sepsis. Special consideration is given for emerging and inhibitors that may be repurposed for sepsis treatment.: In order to overcome the challenges inherent to finding new therapies for the complex dysregulated host response to infection that drives sepsis, it is necessary to move away from monotherapy and promote precision for personalized combinatory therapies. Notably, combinatory therapy should be guided by predictive systems models of the immune-metabolic characteristics of an individual's disease progression.

摘要

脓毒症是一种由细菌、病毒和真菌感染引起的免疫反应失调疾病,是感染导致死亡的主要途径。严重脓毒症中适应性免疫反应失调的标志是补体系统的过度激活,以及由免疫检查点控制的、持续的 T 细胞功能障碍状态所导致的多器官衰竭和死亡。尽管我们对这种疾病的认识有了重大进展,但仍然缺乏明确的诊断测试或有效的治疗干预措施。

作者考虑使用新的生物制剂和数学建模来预测患者的反应,针对脓毒症的固有和适应性免疫介质,采用联合药物治疗方法。特别考虑了新兴的和抑制剂,它们可能被重新用于脓毒症的治疗。

为了克服寻找治疗复杂失调宿主对感染反应的新疗法所固有的挑战,有必要从单一疗法转向促进个性化联合疗法的精准治疗。值得注意的是,联合疗法应该由个体疾病进展的免疫代谢特征的预测系统模型来指导。

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