In Silico Regulatory Science for the Digital Era

Abstract: 

Traditional medical device product development life cycle begins with pre-clinical development. In laboratories, bench/in-vitro experiments establish plausibility for treatment efficacy. Then, in-vivo animal models with different species provide guidance on medical device efficacy/safety for humans. With success in both in-vitro/in-vivo studies, products are tested on clinical trials assessing use in humans. Testing on many people is costly, lengthy, and sometimes implausible (e.g., paediatric patients, rare diseases, and underrepresented or hard-to-reach ethnic groups). When medical devices fail at later stages, financial losses can be catastrophic. Predicting low-frequency side effects has been difficult because such side effects may not become apparent until many patients adopt the treatment. In recent years, medical devices also failed because of a lack of efficacy rather than safety. Success rates are declining, clinical trial costs are rising, innovation is stagnating, clinical trials in the US/UK are moving abroad where costs are lower, and patient profiles may differ. One reason for failure is that traditional trials aim to establish efficacy/safety for most subjects rather than individual subjects. Hence, a statistic of central tendency for the trial determines efficacy. Traditional trials do not adapt treatment to covariates of subjects. Many reports have pointed to this broken/slow innovation system and its impact on societal costs and suboptimal healthcare. However, radical changes to this innovation process are still to be developed.

In this talk, Alejandro Frangi will overview our progress in the INSILEX Programme funded by the Royal Academy of Engineering. We envision a paradigm shift in medical device innovation where quantitative sciences are exploited to carefully engineer medical device designs, explicitly optimise clinical outcomes, and thoroughly test side effects before marketing. In-silico trials (IST) are computer-based medical device trials performed on populations of virtual patients. They use computer models/simulations to conceive, develop and assess devices with the intended clinical outcome explicitly optimised from the outset (a-priori) instead of tested on humans (a-posteriori). This will include testing for potential risks to patients (side effects) and exhaustively exploring in-silico for medical device failure modes and operational uncertainties before being tested in human clinical trials. Advanced computer modelling will prove helpful to predict how a device behaves when deployed across the general population or when used in new scenarios, reaching beyond the primary prescriptions (device repurposing) and helping to help the broadest possible target patient group without unintended consequences of side effects and device interactions. INSILEX is underpinned by Computational Medicine, an emerging discipline devoted to developing quantitative approaches for understanding the mechanisms, diagnoses, and treatment of human disease through the systematic application of mathematics, engineering, and computational science. Dealing with the extraordinary multi-scale complexity and variability intrinsic to human biological systems and health data demands radically new approaches compared to methods for manufactured systems.

In-silico evidence and trials are still in a consolidation phase but are poised to transform how health and life sciences R&D and regulations are conducted in future. UK can take a leadership position in in-silico trials, which would cement its position as a global leader in health and life sciences, help drive the UK economy and provide UK citizens with early access to innovative health products. I will also summarise progress made in the UK to promote this new paradigm among regulators and policymakers and invite the audience interested in contributing to this effort to join forces. The vision, evidence and recommendations of the InSilicoUK Pro-Innovation Regulations Network have been reported in a recent landscape report [4].

Also in this talk, Marta Soares will discuss the opportunities and challenges of In Silico clinical trials for regulatory and health system decisions. ISTs are experiments that evaluate the comparative safety, accuracy and/or efficacy of an innovation but that are performed partially or entirely using individualised computer simulations. To date, most published ISTs replicate existing in vivo randomised controlled trials, RCTs, to establish validity, with reassuring findings. But ISTs have the potential to replace, or add to, the effectiveness, accuracy and safety information from in vivo studies, and serve as a source of safety and efficacy evidence in support of regulation and health system decisions about the funding and provision of medical technologies, for example, those supported by Health Technology Assessments (HTA). In this talk, I will discuss the need for a framework on the use of ISTs to support regulatory and health system decisions.

Selected References

1. Frangi AF, Taylor ZA, Gooya A. Precision Imaging: more descriptive, predictive and integrative imaging. Med Image Anal. 2016 Oct;33:27-32.

2. Sarrami-Foroushani A, Lassila T, MacRaild M, Asquith J, Roes KCB, Byrne JV, Frangi AF. In-silico trial of intracranial flow diverters replicates and expands insights from conventional clinical trials. Nat Commun. 2021 Jun 23;12(1):3861.

3. Abadi E, Segars WP, Tsui BMW, Kinahan PE, Bottenus N, Frangi AF, Maidment A, Lo J, Samei E. Virtual clinical trials in medical imaging: a review. J Med Imaging (Bellingham). 2020 Jul;7(4):042805.

4. Frangi, A., et al. Unlocking the Power of Computational Modelling and Simulation Across the Product Lifecycle in Life Sciences: A UK Landscape Report. InSilicoUK Pro-Innovation Regulations Network, 2023, doi:10.5281/zenodo.8325274.

Professor Alejandro F Frangi, FREng
Bicentennial Turing Chair in Computational Medicine
RAEng Chair in Emerging Technologies
University of Manchester and KU Leuven
https://research.manchester.ac.uk/en/persons/alejandro-frangi

Prof Frangi is the Bicentennial Turing Chair in Computational Medicine at the University of Manchester, Manchester, UK, with joint appointments at the Computer Science and Health Sciences Schools. He is a member of the Christabel Pankhurst Institute (www.pankhurst.manchester.ac.uk) on health technologies research and innovation. He is also the Royal Academy of Engineering Chair in Emerging Technologies, with a focus on Precision Computational Medicine for in silico trials of medical devices. He is an Alan Turing Institute Fellow. His research vision was recently awarded an ERC Advanced Grant from the European Research Council under the Computer Science and Informatics (PE6) panel. He also leads the InSilicoUK Pro-Innovation Regulations Network (www.insilicouk.org).

Professor Frangi's primary research interests lie at the crossroads of medical image analysis and modelling, emphasising machine learning (phenomenological models) and computational physiology (mechanistic models). He is particularly interested in statistical methods applied to population imaging and in silico clinical trials. His highly interdisciplinary work has been translated into cardiovascular, musculoskeletal and neurosciences.