In Silico Trials, Real Impacts!

Simpler or Complex: Rethinking Occam's Razor in Modelling Science

Is simplicity always the smartest choice in science? Or could complexity be the key to groundbreaking discoveries? This episode explores the evolving role of simplicity and complexity in scientific modelling, questioning the age-old principle of Occam's razor in light of modern advancements: Plurality should not be posited without necessity. 
Discover how digital approaches, from computational modelling to simulated clinical trials, are reshaping medical product development, making healthcare safer, more efficient, and unexpectedly precise. As we journey through recent research and breakthroughs, you'll gain insights into the tension between simple and complex scientific models, and how they impact the healthcare landscape.
Whether you're a researcher, regulator, patient representative, or just curious about the future of medicine, this episode unpacks the concept of parsimony and its implications, especially as technology progresses. Learn about the balance between simplicity and complexity and how they complement each other as tools in science.
 
Source:
Dubova M, Chandramouli S, Gigerenzer G, Grünwald P, Holmes W, Lombrozo T, Marelli M, Musslick S, Nicenboim B, Ross LN, Shiffrin R, White M, Wagenmakers EJ, Bürkner PC, Sloman SJ. Is Ockham's razor losing its edge? New perspectives on the principle of model parsimony. Proc Natl Acad Sci U S A. 2025 Feb 4;122(5):e2401230121.
 
 
 
 
 
 

Evaluating Continuous Glucose Monitoring Sensors with in Silico Trials: A Real-World Exemplar

Could computer simulations redefine diabetes care? In this episode, we focus on a real medical product and the role of in silico trials in the transition from the Medtronic Guardian Sensor 3 to the Guardian 4 sensor, central to the Medtronic 780G pump system, which aims to simplify diabetes management by eliminating the need for fingerstick calibrations. Discover how computer simulations, clinical studies, and real-world data converge to evaluate this significant shift, revealing slight shifts in glucose metrics but proving the reliability of in silico methods in predicting real outcomes.
Join us to uncover the potential of digital modeling to transform healthcare, making it more precise while retaining safety, and contemplate how these advances might accelerate personalized medicine delivery, bypassing some traditional trial limitations.
Source:
Grosman B, Parikh N, Roy A, Lintereur L, Vigersky R, Cohen O, Rhinehart A. In Silico Evaluation of the Medtronic 780G System While Using the GS3 and Its Calibration-Free Successor, the G4S Sensor. Ann Biomed Eng. 2023 Jan;51(1):211-224.
 
 
 

Unveiling Evidence-Based Medicine Plus and Mechanistic Reasoning

What is Evidence-Based Medicine Plus? This podcast episode explores the transformative power of computational modelling and simulated clinical trials in medical innovation. Discover the significance of evidence-based medicine (EBM) and evidential pluralism, emphasising the role of mechanistic evidence alongside traditional studies. Join us as we delve into compelling examples like mobile phone radiation and ACE inhibitors, highlighting how these insights enhance the external validity of treatments. Perfect for researchers and regulators alike, or anyone interested in the evolving landscape of medicine.
Sources:
Russo F, Williamson J. Epistemic causality and evidence-based medicine. Hist Philos Life Sci. 2011;33(4):563-81.Parkkinen, V.-P., Wallmann, C., Wilde, M., Clarke, B., Illari, P., Kelly, M. P., Norell, C., Russo, F., Shaw, B., & Williamson, J. (Eds.). (2023). Evaluating Evidence of Mechanisms in Medicine: Principles and Procedures. Oxford University Press.
 
 
 

What Underpins Model Credibility? Three Philosophical Perspectives

How do philosophers of science think about model evaluation? Dive deep into the crucial process of model evaluation, a fundamental step in digital science that determines the effectiveness and precision of computational models.
This episode explores three primary perspectives on model quality: the mirror view, relevant similarity, and fitness for purpose. Learn about the complexities and challenges involved in evaluating models, from data limitations to the intricate balance between accuracy and usability.
Whether you're a researcher, regulator, or a curious listener, discover how these cutting-edge methodologies are reshaping healthcare, improving safety, efficiency, and equitability, one simulated trial at a time. Join us as we delve into the philosophy behind model evaluation and unveil the real-world impact of digital innovation.
Parker WS. Model Evaluation: An Adequacy-for-Purpose View. Philos Sci. 2020;87(3):457-477.
Bokulich A, Parker W. Data models, representation and adequacy-for-purpose. Eur J Philos Sci. 2021;11(31). 
 
 
 
 

Exploring Digital Twins: The Philosophy Behind Healthcare's Digital Revolution

Have you ever wondered what makes a digital twin truly "twin-like" and not just another computer model? This fascinating question lies at the heart of modern healthcare innovation, where virtual copies of real-world systems are revolutionising how we approach medical research and patient care. Our latest episode delves into groundbreaking research that examines the philosophical underpinnings of digital twins, exploring what sets them apart from conventional computational models.
We unpack how digital twins are transforming healthcare through their unique ability to capture complex, emergent behaviours in ways traditional models cannot. From enabling safer drug development through in silico trials to advancing personalised medicine, these sophisticated virtual representations are bridging the gap between computational simulation and real-world applications. Our discussion reveals why digital twins represent more than just technological advancement - they embody a fundamental shift in how we understand and interact with healthcare systems, promising more precise, efficient, and safer medical innovations for the future.
Reference: Wagg DJ, Burr C, Shepherd J, Xuereb Conti Z, Enzer M, Niederer S. The philosophical foundations of digital twinning. Data-Centric Engineering. 2025;6:e12. doi:10.1017/dce.2025.4
 
 
 

Cracking the Code: Unveiling the Trustworthy World of In-Silico Trials

Are computational models ready to replace traditional clinical trials? This episode delves into the fascinating world of in silico trials and their growing role in regulatory evaluation of biomedical products. We explore a methodological framework based on the ASME VV-40-2018 standard that establishes credibility through verification, validation, and uncertainty quantification. From defining contextual use to conducting thorough risk analysis, we examine how these principles apply across statistical, machine learning, Bayesian, and agent-based models. We compare regulatory approaches between different authorities and make the case for wider adoption of credibility assessment standards to ensure reliable virtual evidence. Join us as we navigate the cutting edge of computational modelling in healthcare regulation.
 
Viceconti M, Pappalardo F, Rodriguez B, Horner M, Bischoff J, Musuamba Tshinanu F. In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products. Methods. 2021 Jan;185:120-127. 

Revolutionising Healthcare: The Power of Synthetic Data

Is the healthcare sector prepared to harness synthetic data's transformative potential whilst navigating its hidden pitfalls? This thought-provoking episode delves into the murky waters of synthetic data governance in medical research, examining how generative AI is reshaping our approach to patient information. As algorithms increasingly create convincing facsimiles of real clinical data, we explore the tantalising benefits—enhanced privacy protection, expanded research capabilities, and accelerated innovation cycles—alongside the concerning governance vacuum that threatens to undermine these advances. Our discussion unpacks why the absence of standardised creation and evaluation frameworks is stalling widespread adoption, despite synthetic data's promise. We make the case for urgent development of robust data standards, ethical guidelines and cross-disciplinary collaboration to ensure this powerful tool serves healthcare's highest goals. Join us as we navigate the complex terrain where technological innovation meets patient protection, and discover why establishing clear governance frameworks today is essential for tomorrow's medical breakthroughs.
 
Boraschi D, van der Schaar M, Costa A, Milne R. Governing synthetic data in medical research: the time is now. Lancet Digit Health. 2025 Feb 20:S2589-7500(25)00011-1. 

How Can We Trust Computational Models in Healthcare? A Framework for In Silico Clinical Trials

Are you curious about how computer simulations are changing the way we evaluate medical devices? In this episode, we delve into the fascinating world of in silico clinical trials (ISCTs), a cutting-edge approach that uses computational models to assess the safety and effectiveness of medical interventions. We explore a recent paper that highlights the unique challenges of ensuring the credibility of ISCT results. The authors present a comprehensive workflow for assessing this credibility, drawing on various methodologies and frameworks, including those from the FDA. By reviewing a range of ISCT studies, we discuss innovative patient model generation techniques, the integration of clinical outcomes, and how these virtual trials can complement traditional clinical studies. Join us as we unpack the potential of ISCTs to revolutionize medical device evaluation, making it more cost-effective, time-efficient, and ethically sound.
Source: Bodner J, Kaul V. A framework for in silico clinical trials for medical devices using concepts from model verification, validation, and uncertainty quantification. J Verif Valid Uncertain Quantif. 2022;7(2):021001.