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

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.