Press Release: Virtual In Silico Trials for Coronary Stents

Coronary artery disease remains the leading cause of mortality worldwide and accounts for over over 600,000 deaths in Europe. Coronary stents are currently the most widely used devices for treating symptomatic coronary disease. The success of this treatment depends on optimal device designs and suitable implantation procedures by the interventional cardiologist.

Since late 2017, experts from Belgium, Greece, Italy, Ireland, the Netherlands, Serbia, the United Kingdom and the US have been working jointly in the EU-funded project InSilc. The technology developed integrates the latest in silico computational models and enables the prediction of the optimal performance of drug-eluting coronary stents in the treatment of coronary artery disease interventions.

In designing a new coronary stent, there are several key aspects of performance that the device must fulfil. The stent must have sufficient structural integrity to open the diseased coronary artery, while avoiding any unnecessary damage to the surrounding tissue. Once implanted, the stent should restore natural blood flow patterns to the coronary arteries and avoid unwanted re-narrowing of the vessel due to in-stent restenosis. This presents a very challenging design problem, particularly when it is considered that these function must be fulfilled across a diverse patient population, whose coronary anatomies vary significantly.

Virtual In Silico Trials

InSIlc enables virtual trials for coronary stents by using integrated computational models to provide detailed predictions of implanted device performance. To achieve this, InSIlc has developed integrated modules that provide predictions of stent performance, functionalities and operation in the pre-operative, short-, medium- and long-term phases of the device lifetime (Figure 1).

Figure 1: The InSilc Modules

  • The Mechanical modelling module enables virtual in vitro bench testing of coronary stents. It can be used early in the design process to quickly compare the functional mechanical performance of different stent designs. It includes in silico bench testing for dimensional verification, foreshortening, dog-boning, radial force, local compression, crush resistance with parallel plates, three point bending and fatigue.
  • The 3D reconstruction and plaque characterization tool is an integrated software that accurately reconstructs parts of the arterial tree including the lumen, the outer wall, as well as plaque. This allows a wide range of patient anatomies to be considered, from straight vessels to more complex tortuous lesions.
  • The Deployment module enables the in silico reproduction of the implantation procedure for coronary stents. The deployment module predicts key acute and short-term outcomes of the implantation of coronary stents including degree of vessel re-opening, lumen gain, minimal stent area and mal-apposition to the lumen wall following stent deployment.
  • The Fluid dynamics module enables predictions of patient-specific velocity and wall shear stress patterns in human coronary arteries following implantation of a coronary stent. By also considering micro-scale interactions at the vessel wall, the module can predict whether the implanted stent would cause re-narrowing of the artery (in-stent restenosis).
  • The Drug delivery module generates simulation-based predictions of drug distribution and uptake by the vessel wall, following implantation of a drug-eluting stent. These predictions enable optimization of drug coatings on devices, which can help minimise unwanted inflammatory biological responses following implantation.
  • The Degradation module enables predictions of long-term degradation and mechanical performance of drug-eluting bioresorbable vascular scaffolds (BVS). The module predicts the reduction of load-bearing capacity of the stent over time and associated reductions in minimal stent area over time.
  • The Myocardial perfusion module allows for more realistic simulation of post-operative coronary flow in patients by describing the local response of the cardiac muscle and the coronary autoregulation system. This module can predict the degree to which the stent will enable re-perfusion of the coronary arteries following implantatoin.

InSilc Cloud Platform

The InSilc modules have been integrated into a single web-based cloud platform that enables the users to set up, monitor and analyse the results of virtual clinical trials for coronary stents. The InSilc Cloud web application interface is a visual and interactive application that enables users to design experiments based on the in-built virtual population and stent databases. The InSilc cloud platform provides high-resolution visualisation of the simulation results through the InSilc Cloud web viewer and provides a detailed analytics function, whereby the clinical endpoints from modules are quantified, enabling quick and efficient analysis of the results.

Figure 2: InSilc Cloud Platform.

Virtual Scenarios

The InSilc platform provides the unique opportunity to pose a wide range of “what if” questions on stent performance, which can facilitate useful insight in device behaviour during the design and development phase of coronary stents. This activity utilizes the InSilc Virtual population and the application of the various InSilc modules to address the performance of coronary stents in a range of virtual scenarios. For example, this capability allows users to compare the performance of different stents and/or stent designs implanted in the same artery or compare outcomes of clinical different procedures to treat the same stenotic artery. In the figure below, the same stent has been used to treat two different virtual patient anatomies and direct side-by-side comparisons of performance outcomes in Deployment, Fluid dynamic and Drug delivery modules are shown.

Figure 3: Virtual Scenario that examines the performance of vascular stent in different arterial anatomies.

Better assessment and reduced complications

By integrating the information obtained from these different in silico predictive models, the InSilc Cloud Platform enables the development, assessment and optimization of coronary stents and delivers accurate and reliable information to the Stent Biomedical Industry. The next phase for the InSilc Cloud Platform will focus on the exploitation of results and commercialisation of this innovative technology, which has the potential to streamline the design and development of new coronary stents.

EU and transatlantic cooperation

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777119 and integrates research groups, universities and companies from Belgium, Greece, Italy, Ireland, Netherlands, Serbia, the United Kingdom and the US.