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用于研究炎症性肠病及其治疗选择的细胞水平系统生物学模型。

Cell-level systems biology model to study inflammatory bowel diseases and their treatment options.

机构信息

Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.

Graduate Research Training Program PharMetrX: Pharmacometrics & Computational Disease Modelling, Freie Universität Berlin & University of Potsdam, Berlin, Germany.

出版信息

CPT Pharmacometrics Syst Pharmacol. 2023 May;12(5):690-705. doi: 10.1002/psp4.12932. Epub 2023 Feb 24.

Abstract

To help understand the complex and therapeutically challenging inflammatory bowel diseases (IBDs), we developed a systems biology model of the intestinal immune system that is able to describe main aspects of IBD and different treatment modalities thereof. The model, including key cell types and processes of the mucosal immune response, compiles a large amount of isolated experimental findings from literature into a larger context and allows for simulations of different inflammation scenarios based on the underlying data and assumptions. In the context of a large and diverse virtual IBD population, we characterized the patients based on their phenotype (in contrast to healthy individuals, they developed persistent inflammation after a trigger event) rather than on a priori assumptions on parameter differences to a healthy individual. This allowed to reproduce the enormous diversity of predispositions known to lead to IBD. Analyzing different treatment effects, the model provides insight into characteristics of individual drug therapy. We illustrate for anti-TNF-α therapy, how the model can be used (i) to decide for alternative treatments with best prospects in the case of nonresponse, and (ii) to identify promising combination therapies with other available treatment options.

摘要

为了帮助理解复杂且具有治疗挑战性的炎症性肠病(IBD),我们开发了一个肠道免疫系统的系统生物学模型,该模型能够描述 IBD 的主要方面及其不同的治疗方式。该模型包括粘膜免疫反应的关键细胞类型和过程,将大量来自文献的孤立实验结果编译到一个更大的背景中,并根据基础数据和假设模拟不同的炎症情况。在一个大型和多样化的虚拟 IBD 人群中,我们根据患者的表型(与健康个体不同,他们在触发事件后会出现持续的炎症)而不是基于对健康个体参数差异的先验假设来对患者进行特征描述。这使得能够重现导致 IBD 的巨大多样性的易感性。分析不同的治疗效果,该模型提供了对个体药物治疗特征的深入了解。我们以抗 TNF-α 治疗为例,说明了该模型如何(i)在无应答的情况下选择前景最好的替代治疗方法,以及(ii)确定具有其他可用治疗选择的有前途的联合治疗方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd2e/10196411/b3cb8a202dee/PSP4-12-690-g007.jpg

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