Periodic Reporting for period 2 - SimInSitu (In-silico Development- and Clinical-Trial-Platform for Testing in-situ Tissue Engineered Heart Valves)
Okres sprawozdawczy: 2022-07-01 do 2023-12-31
SimInSitu is aiming to develop a sophisticated in-silico method to predict the short- and long-term behaviour of in-situ Tissue Engineered Heart Valves (TEHV) by combing advanced tissue remodelling algorithms with a personalized virtual heart modelling approach. The method will be specifically developed to predict the complex transformation process of biodegradable heart valves from the initially synthetic scaffold into a fully remodelled & functional valve. Though, significant progress was made during the past years in developing TEHV based devices, it remains challenging, costly, time-consuming, and rich with obstacles. New knowledge can only be generated through a tedious trial & error process (requiring preclinical and clinical studies), since the restorative process cannot be replicated in an in-vitro environment.
Advanced Computer Modelling & Simulation technologies have the potential to overcome this limitation by allowing to test new designs, modified scaffold compositions, or other applications in a virtual patient-specific environment – in-silico. The availability of this computer model could contribute significantly to an acceleration of especially the TEHV device development and accelerate their translation into the clinic and market.
SimInSitu’s main goals can be summarized as follows:
•To develop the first in-silico model able to predict the short and long-term behaviour of in-situ Tissue Engineered Heart Valves in a patient-specific environment while combining state-of-the-art Fluid-Structure-Interaction with Growth-Remodelling-Algorithms
•To generate virtual patient libraries that can be used to test virtually aortic medical devices.
•To implement thoroughly established Verification, Validation, and Uncertainty Quantification (VVUQ) standards / guidance in the computational modelling but also in the experimental assessments while following a hierarchical system approach
•To share and disseminate actively academic and practical findings & learnings with relevant stakeholders and the in-silico community.
Advanced Computer Modelling & Simulation technologies have the potential to overcome this limitation by allowing to test new designs, modified scaffold compositions, or other applications in a virtual patient-specific environment – in-silico. The availability of this computer model could contribute significantly to an acceleration of especially the TEHV device development and accelerate their translation into the clinic and market.
SimInSitu’s main goals can be summarized as follows:
•To develop the first in-silico model able to predict the short and long-term behaviour of in-situ Tissue Engineered Heart Valves in a patient-specific environment while combining state-of-the-art Fluid-Structure-Interaction with Growth-Remodelling-Algorithms
•To generate virtual patient libraries that can be used to test virtually aortic medical devices.
•To implement thoroughly established Verification, Validation, and Uncertainty Quantification (VVUQ) standards / guidance in the computational modelling but also in the experimental assessments while following a hierarchical system approach
•To share and disseminate actively academic and practical findings & learnings with relevant stakeholders and the in-silico community.
The SimInSitu project has completed its third year and has completed a significant portion of the defined tasks & work-packages and has achieved many milestones. All scaffold materials were fully characterized using a multitude of test modalities. Additionally, the epistemics uncertainty origination from the test methods and process implementation was evaluated via Round Robin test, while the aleatory uncertainty was evaluated by using a statistically adequate number of test samples. Furthermore, crack-growth was assessed experimentally. Relying on these experimental characterization data, complex anisotropic, hyperelastic material models were developed, implemented in commercial Finite Element Method (FEM) software, verified and validated. Using specifically developed Bayesian-statistic based calibration process, all needed material parameters and their distribution functions were evaluated. A separate validation, sensitivity analysis, and uncertainty quantification completed this work. Within the Device-Modelling work-package, two out of three heart-valve devices are completely developed in the Finite Element Method (FEM), Computation Fluid Dynamics (CFD), and Fluid Structure Interaction (FSI) domains. For the FEM-models, Verification, in-vitro Validation, and Uncertainty Quantification were completed successfully. For the CFD domain, one model has completed the verification, in-vitro validation, and QU phase, while the second model is currently in the in-vitro validation, using advanced test methodologies, such as laser-based Particle image velocimetry (PIV). The third device-model is currently in the FEM development phase. In parallel, patient-specific model-libraries were developed for the Transcatheter Aortic Valve Implantation (TAVI) and Right Ventricle Outflow Tract (RVOT) models. 130 patient-specific left-heart models were developed, while the device & FSI implementation and associated verification phase was completed. Currently the clinical validation is ongoing focussing on device deployment (structural) and haemodynamic performance (FSI). For the RVOT side, the patient-specific models are all developed (>25), and a semi-automatic device implementation simulating the surgical placement is completed as well. FSI integration is also completed. After its verification phase, these models will be used for a clinical validation as well. Additionally, the Endogenous Tissue Restauration (ETR) process has been developed, verified, and calibrated in terms of two competing constitutive growth-and-remodelling material models. Several animal trials, which were not always fully successful, were conducted to generate data that was used to calibrate the ETR models. Additionally, historical data from a large collection of animal trial data is provided by a consortium partner, which supports the calibration process. Currently, an advanced sequential modelling process (ETR-FSI) is being developed, which accounts for the impact of a growing tissue on the haemodynamic situation. The in-silico platform work-package has not fully started yet, except for some preparation activities to define an in-silico trial process. Dissemination and Exploitation is picking up momentum with continuous stream of social-media / web-based contributions, a second combined EU workshop (innovaHeart 2024), a collaboration with other related consortia in the EU funded Horizon Results Booster, and additional publications.
At the completion of the project, it is expected that two in-silico models / platforms are developed, verified, and validated as much as possible given the available time, resources, and encountered complexity: a TAVI (left heart) patient-specific model and a RVOT (right heart) model. It is further expected that the models were fully assessed at the individual complexity levels and the overall uncertainty is evaluated using a propagation approach. SimInSitu has potential impact on multiple levels. At the technical modelling and simulation level, many advances for device modelling, FSI modelling, patient-specific, and ETR modelling can be expected. For the uncertainty quantification and propagation, many advanced with respect to implementation in complex models and usage of emulator can be expected. With regards to credibility assessment, the hierarchical modelling and VVUQ approach has a strong potential to make significant impact on the regulatory framework for in-silico modelling. At the socio-economic level, SimInSitu, like other in-silico models will have a positive impact on modern healthcare, by reducing the time-to market for innovative high-risk medical devices and by reducing their development costs. Also, it will reduce the burden in the patient during clinical studies by improving the risk-benefit-ratio and will support the development of safer medical devices.