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Cited by 67Animal testing and alternativesPhilosophy and History of ScienceComputational Drug Discovery Methods

Credibility of In Silico Trial Technologies—A Theoretical Framing

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Viceconti, Marco, Juarez, Miguel A., Curreli, Cristina, Pennisi, Marzio, Russo, Giulia, Pappalardo, Francesco·2020·IEEE Journal of Biomedical and Health Informatics
DOI 10.1109/jbhi.2019.2949888
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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.

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  • ASME developed the V&V 40 standard in 2012 as a means of describing verification and validation activities in the modeling and simulation of medical devices.

    “The ASME developed the V &V 40 standard in 2012 as a means of describing verification and validation activities in the modeling and simulation of medical devices [64].”

    From Adapting modeling and simulation credibility standards to computational systems biology (2023) · cites this paper as [64]

    The Technical Standards section identifies ASME V&V 40 as concerning verification and validation in computational modeling of medical devices. The paper does not say ASME developed the standard in 2012; its References instead cite “V&V 40 -2018” (no page numbers provided). (LLM, conf=0.88)

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  • This paper complements and builds upon related work, including a theoretical framing for credibility of in silico studies, a discussion of limitations of ASME V&V40 2018 for ISCTs, a framework for executing an ISCT using VVUQ concepts, and a proposed alteration to ASME V&V40 2018 for ISCTs.

    “This paper complements and builds upon related work, including a theoretical framing for credibility of in silico studies [10], a discussion of limitations of ASME V&V40 2018 for ISCTs [11], a framework for executing an ISCT using VVUQ concepts [12], and a proposed alteration to ASME V&V40 2018 for ISCTs, including modification of the risk assessment stage which is outside the scope of the present”

    From Credibility assessment of in silico clinical trials for medical devices (2024) · cites this paper as [10]

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Research works printed but not linked: 3

  1. ANSYS, Inc., "ANSYS user's guide release 18.0," J. Geographic Inf. Syst., 2017. [Online]. Avaialble: https://doi.org/10.4236/jgis.2013.51006 · DOI
  2. O. Bousquet, U. von Luxburg, and G. Rätsch, Eds., Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures. Berlin, Germany: Springer, 2004.
  3. D. Bzdok, N. Altman, and M. Krzywinski, "Points of significance: Statis- tics versus machine learning," Nature Methods, vol. 15, pp. 233-234, Apr. 2018.

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Authors
Viceconti, M., Juarez, M. A., Curreli, C., Pennisi, M., Russo, G., & Pappalardo, F.
Affiliations
  • Istituto Ortopedico Rizzoli (IT)
  • University of Bologna (IT)
  • University of Catania (IT)
ORCID
Venue
IEEE Journal of Biomedical and Health Informatics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020-01
Volume
24
Issue
1
Pages
4-13
ISSN
  • 2168-2194 (ISSN-L)
  • 2168-2208
DOI
10.1109/jbhi.2019.2949888
OpenAlex
W2982618177
Cited by
67
References
33
Citations by year
2026: 10 · 2025: 6 · 2024: 8 · 2023: 11 · 2022: 9 · 2021: 17 · 2020: 6
FWCI
10.58
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Journal of Biomedical and Health Informatics (publishedVersion)
  • PubMed (publishedVersion)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
Funders
  • STriTuVaD ((SC1-PM-16-2017-777123))
  • MOBILISE-D ((IMI2-2017-13-7-820820))
  • PRIMAGE ((SC1-DTH-07-2018-826494))
  • Engineering and Physical Sciences Research Council (EP/K03877X/1)
Topics
  • Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords
in silico clinical trials, model credibility, prediction model, regulatory science, computational medicine, model evaluation
MeSH terms
Computer Simulation, Humans, Reproducibility of Results, Biomedical Research

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