Utilising an in silico model to predict outcomes in senescence-driven acute liver injury
Utilising an in silico model to predict outcomes in senescence-driven acute liver injury
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利用计算机模型预测衰老驱动的急性肝损伤的结果
DOI：
10.1101/2023.10.11.561528
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Ashmore-Harris C
中科院分区：
文献类型：
--
作者：
Ashmore-Harris C
目前肝移植是肝病的唯一治疗选择，但器官可用性无法满足患者需求。替代再生疗法，包括细胞移植，旨在调节损伤的微环境，使其从炎症和瘢痕形成走向再生。肝损伤反应的复杂性使得仅依靠实验方法来确定合适的治疗靶点具有挑战性。因此，我们采用了一种结合体内-计算机模拟的方法，并开发了一种能够预测宿主对损伤和潜在干预措施的反应的急性肝病常微分方程模型。衰老驱动的肝损伤的Mdm 2fl/fl小鼠模型用于产生肝损伤中涉及的关键细胞参与者（巨噬细胞、内皮细胞、肌成纤维细胞）和细胞外基质的定量动态表征。这是由数学模型定性捕获的。然后使用该数学模型来预测响应于较轻和较严重水平的衰老诱导的肝损伤的损伤结果，并使用体内实验数据进行验证。然后使用经验证的模型进行计算机模拟实验，以询问增强再生的潜在方法。这些预测增加巨噬细胞表型转换的速率或增加系统中促再生巨噬细胞的数量将加速衰老细胞清除和消退的速率。这些结果展示了机械数学建模的潜在好处，用于捕获复杂生物系统的动态，并确定治疗干预措施，可以提高我们对损伤修复机制的理解，减少翻译瓶颈。
Currently liver transplantation is the only treatment option for liver disease, but organ availability cannot meet patient demand. Alternative regenerative therapies, including cell transplantation, aim to modulate the injured microenvironment from inflammation and scarring towards regeneration. The complexity of the liver injury response makes it challenging to identify suitable therapeutic targets when relying on experimental approaches alone. Therefore, we adopted a combined in vivo-in silico approach and developed an ordinary differential equation model of acute liver disease able to predict the host response to injury and potential interventions. The Mdm2fl/flmouse model of senescence-driven liver injury was used to generate a quantitative dynamic characterisation of the key cellular players (macrophages, endothelial cells, myofibroblasts) and extra cellular matrix involved in liver injury. This was qualitatively captured by the mathematical model. The mathematical model was then used to predict injury outcomes in response to milder and more severe levels of senescence-induced liver injury and validated with experimental in vivo data. In silico experiments using the validated model were then performed to interrogate potential approaches to enhance regeneration. These predicted that increasing the rate of macrophage phenotypic switch or increasing the number of pro-regenerative macrophages in the system will accelerate the rate of senescent cell clearance and resolution. These results showcase the potential benefits of mechanistic mathematical modelling for capturing the dynamics of complex biological systems and identifying therapeutic interventions that may enhance our understanding of injury-repair mechanisms and reduce translational bottlenecks.
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