Advanced in vitro and in silico models to predict and prevent deep venous thrombosis

深静脉血栓形成（DVT）是一种危及生命和使人衰弱的病症，其中在深静脉（例如腿部的股静脉）内形成血块。这些凝块可能变得不稳定并导致致命的病症，例如肺栓塞（PE）[Esmon，Blood Rev.，23，2009; Bovill等人，Annu.生理学修订版，73，2011]。在英国，DVT和PE合并每年导致25，000例死亡[Hunt，Br. J. Haematol.，144，2009年]。目前，人们普遍认识到需要一种预测工具。通过这个项目，我们的目标是研究和理解DVT的预测因子，该预测因子基于一种新的更接近现实的体外和计算机模拟模型，该模型通过再现动物模型的解剖结构来开发，以减少其不必要的使用。我们已经开发了计算模拟[Ariane et al.，Comput.生物医学，89，2017; Ariane等人，Comput. Fluids，166，2018]和实验微流体模型[斯科菲尔德等人，提交，2019]的静脉瓣膜周围的流动扰动。在这里，我们将开发先进的微流体体外模型，其中内皮细胞将生长以模拟血管和柔性瓣膜，而无需牺牲动物。然后，我们将研究血液流动特性对血栓形成的影响。此外，我们还将开发计算机模拟。在计算机模拟模型中，我们将能够微调静脉和瓣膜的三维几何形状，并独立量化每个参数对DVT发病的相对影响。这种新的方法将阐明病理性血流在血栓形成和传播中的作用，并确定诱发DVT的新因素。这将允许一种个性化的方法来识别和预防处于重大风险中的人。然后，我们将能够跟踪血栓形成过程，并分析增加血栓形成发病率的解剖学特征。这一点特别重要，因为目前基于体内研究的方案未能确定这一点。此外，这种方法需要使用全世界成千上万的动物，这些动物可以通过实施我们的方法来拯救。

Deep vein thrombosis (DVT) is a life-threatening and debilitating condition where blood clots form within the deep veins (e.g. the femoral vein in the leg). These clots can become unstable and cause fatal conditions such as pulmonary embolism (PE) [Esmon, Blood Rev., 23, 2009; Bovill et al., Annu. Rev. Physiol., 73, 2011]. DVT and PE combined cause 25,000 deaths annually in the UK [Hunt, Br. J. Haematol., 144, 2009]. At the moment, the need for a prediction tool is widely recognised. With this project, we aim to investigate and understand the predictors of DVT based on a novel closer-to-reality in-vitro and in silico model developed by reproducing the anatomy of animal models in order to reduce their unnecessary use.We have already developed computational simulations [Ariane et al., Comput. Biol. Med., 89, 2017; Ariane et al., Comput. Fluids, 166, 2018] and experimental microfluidic models [Schofield et al., Submitted, 2019] of flow disturbances around valves of the veins. Here, we will develop advanced microfluidic in-vitro models, in which endothelial cells will be grown to mimic blood vessels and flexible valves without the need of sacrificing animals. We will then study the influence of blood flow characteristics on thrombus development Additionally, we will develop computational simulations. In the in-silico model we will be able to finely tune the three-dimensional geometry of the vein and valves, and to independently quantify the relative influence of each parameter on the insurgence of DVT. This novel approach will clarify the role of pathological blood flow in thrombosis initiation and propagation, and identify new factors predisposing to DVT. This will allow a personalized approach to identification and prophylaxis of people at major risk. We will then be able to follow the thrombus formation process and analyse the anatomical characteristics increasing thrombosis incidence. This is particular important as current protocols based on in-vivo studies failed to identify this. Moreover, this approach requires the use of thousands of animals worldwide that can be saved by implementing our approach.