Evidence map›Paper›PMID 31991193›Full record

ArticleMethods (San Diego, Calif.)2021

In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products.

Marco Viceconti, Francesco Pappalardo, Blanca Rodriguez, Marc Horner, Jeff Bischoff, Flora Musuamba Tshinanu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Methods (San Diego, Calif.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 150 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
150citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

150 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  14. Causal graph neural networks for healthcare.Nature biomedical engineering · 2026
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90 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Marco VicecontiDepartment of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Italy; Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy. Electronic address: marco.viceconti@unibo.it.
Francesco PappalardoDipartimento di Scienze del Farmaco, University of Catania, Italy.
Blanca RodriguezDepartment of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, UK.
Marc HornerANSYS, Inc., Evanston, IL, USA.
Jeff BischoffCorporate Research Department, Zimmer Biomet, Warsaw, IN, USA.
Flora Musuamba TshinanuFederal Agency for Medicines and Health Products, Brussels, Belgium.

Funding

National Centre for the Replacement, Refinement and Reduction of Animals in Research NC/P001076/1Wellcome TrustWellcome Trust 214290/Z/18/Z
6 · The paper itself

Abstract

Historically, the evidences of safety and efficacy that companies provide to regulatory agencies as support to the request for marketing authorization of a new medical product have been produced experimentally, either in vitro or in vivo. More recently, regulatory agencies started receiving and accepting evidences obtained in silico, i.e. through modelling and simulation. However, before any method (experimental or computational) can be acceptable for regulatory submission, the method itself must be considered "qualified" by the regulatory agency. This involves the assessment of the overall "credibility" that such a method has in providing specific evidence for a given regulatory procedure. In this paper, we describe a methodological framework for the credibility assessment of computational models built using mechanistic knowledge of physical and chemical phenomena, in addition to available biological and physiological knowledge; these are sometimes referred to as "biophysical" models. Using guiding examples, we explore the definition of the context of use, the risk analysis for the definition of the acceptability thresholds, and the various steps of a comprehensive verification, validation and uncertainty quantification process, to conclude with considerations on the credibility of a prediction for a specific context of use. While this paper does not provide a guideline for the formal qualification process, which only the regulatory agencies can provide, we expect it to help researchers to better appreciate the extent of scrutiny required, which should be considered early on in the development/use of any (new) in silico evidence.

Indexed as

Computer SimulationModels, TheoreticalMachine LearningUncertaintyIn silico trialsModel credibilityRegulatory affairsValidationVerification

Identifiers

PMID31991193
PMCPMC7883933

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.