Cloud native EDA tools & pre-optimized hardware platforms
Posted on 23 October 2023 by Jessica James
We have recently caught up with Simpleware software users Zeike Taylor, Nishant Ravikumar, and Alex Frangi of the University of Leeds that are carrying out exciting work in the field of in silico clinical trials. In this interview, we learnt about the research and its potential impact of in silico trials for the medical device sector and long-term improvements in safety and efficacy.
Simpleware software being used for in silico device implantation
We are part of the Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) at the University of Leeds. The Centre has around 45 members, with backgrounds spanning computer science, engineering, mathematics, informatics, biological sciences, and medicine. In a nutshell, our remit is Computational Medicine, and in this context, we have developed image- and simulation-based solutions for numerous clinical problems, and across various clinical domains: cardiovascular, neurology, orthopedics, and oncology.
An overarching goal of our work is to realize the vision of in silico trials (ISTs) in the medical devices sector. ISTs, as virtual counterparts to conventional clinical trials, use computational modelling & simulation to assess the safety and efficacy of medical devices ¨C and, critically, without direct involvement of real patients. Our goal therefore is to have ISTs established as integral parts of medical device lifecycles and embraced by industry, regulators, the clinic, and patients alike.
The main work of our group is focused on creating the technologies that must underpin this vision. We address three main areas:
If fully realized, ISTs will fundamentally change the way medical devices are developed, assessed, and regulated. Clinical trials, which come at the end of normally lengthy and expensive research and development cycles, are themselves responsible for a large portion of the costs of getting new devices to market. They are also not a flawless means of evidencing device acceptability because they have practical limitations on duration, patient recruitment, and restrictive inclusion/exclusion criteria. The high-profile post-market failures of various devices in recent years are testament to this.
ISTs offer an alternative (cheaper, faster, and safer) means of generating evidence (so-called digital evidence) of performance/safety before a device ever goes near a patient. High-level impacts will include:
We have been using Simpleware tools for around 1? years. This has been motivated especially by our current efforts to virtually recreate an existing phase III trial of a transcatheter aortic valve implant (TAVI). Simpleware software with the automated Heart CT segmentation and Valve Analysis tools, allow us to generate high-fidelity virtual twins of the trial participants from their CT angiograms, which are then used in simulations of the device deployment and hemodynamic function. These twins include the aortic root and left ventricular outflow tract anatomy, valve calcification patterns, and geometric features like aortic centerlines and key anatomical landmarks. Given the numbers involved (>700 participants), the ability to automate model construction, from images to high-quality computational meshes, is critical.
Patient-specific TAVI simulation workflow using Simpleware software
As mentioned, there are some core technical areas we will remain focused on (virtual populations, interventions, etc.). There is much to be done for all of these. In parallel, we also aim to develop further exemplar studies, like the one for TAVI, that demonstrate equivalence of in silico evidence, reaching the same conclusions about device behavior as do comparable real trials. We want to show how ISTs can give us richer insights that are difficult or impossible to get in-vivo, but easy in silico, and how this can benefit the economy and the patients.
There will always be technical challenges to solve, but in a sense, these are the ¡°easy¡± ones; at least, they are the ones we engineers and scientists are trained to address. The bigger challenge is how we build trust in in silico methods across the different stakeholders: manufacturers, regulators, clinicians, and patients. For these groups to have confidence in the reliability and value of ISTs, when clinical trials and other empirical methods have for long been the indispensable gold standards, requires a rigorous and well-targeted body of evidence. This approach needs to be rigorous because it must stand up to whatever scrutiny is imposed. It also needs to be well-targeted because it must answer in an accessible manner the questions those stakeholders are actually interested in. As a technical community, we need to engage with and listen to the end users to achieve these goals.
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