Credibility of In Silico Trial Technologies—A Theoretical Framing

Abstract

Different research communities have developed various approaches to assess the credibility of predictive models. Each approach usually works well for a specific type of model, and under some epistemic conditions that are normally satisfied within that specific research domain. Some regulatory agencies recently started to consider evidences of safety and efficacy on new medical products obtained using computer modelling and simulation (which is referred to as In Silico Trials); this has raised the attention in the computational medicine research community on the regulatory science aspects of this emerging discipline. But this poses a foundational problem: in the domain of biomedical research the use of computer modelling is relatively recent, without a widely accepted epistemic framing for model credibility. Also, because of the inherent complexity of living organisms, biomedical modellers tend to use a variety of modelling methods, sometimes mixing them in the solution of a single problem. In such context merely adopting credibility approaches developed within other research communities might not be appropriate. In this paper we propose a theoretical framing for assessing the credibility of a predictive models for In Silico Trials, which accounts for the epistemic specificity of this research field and is general enough to be used for different type of models.

In-silico trial of intracranial flow diverters confirms and expands insights from conventional clinical trials

“…Viceconti et al [50] defined three principles that every in-silico trial should follow to be credibly comparable to a conventional clinical trial: (i) every virtual patient has to be plausible when compared to patients in a clinical trial cohort, (ii) the accuracy of the virtual endpoint should represent the accuracy of the endpoint in the clinical trial cohort, and (iii) for sufficiently large physical and virtual cohorts, the conclusions made from observed effects should be identical.…”

How can we accelerate COVID-19 vaccine discovery?

“…In Silico Trials (IST) is a new class of computational methods for the development and assessment of new medical products. A typical IST model involves four elements: a physiology model, a disease model, and a treatment model, which predicts how close to normality such behavior can protect/cure individuals from the disease, when they are treated with the preventive or therapeutic vaccine under investigation, as compared to the placebo (or the comparator treatment) [ 13 ].…”

Machine learning methods to support personalized neuromusculoskeletal modelling

“…We have summarised model personalization into five main categories (Table 1): mathematical definitions (e.g., functions prescribing complex joint kinematics), model parameters (e.g., muscle tendon parameters), anatomy (i.e., external and internal structure), tissue material properties, and (neuro) physiology. All features influence model performance and their incorporation should be guided by the research question and the credibility the authors require of their results (Viceconti et al 2020a). If model results will have high levels of influence (e.g., decisively inform a therapy or design) and carry significant consequences (e.g., if wrong, people are harmed or worse), the model is high risk and requires extensive validation and verification (Viceconti et al 2020b).…”