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Cited by 361Animal testing and alternativesComputational Drug Discovery MethodsStatistical Methods in Clinical Trials

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

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Viceconti, Marco, Pappalardo, Francesco, Rodriguez, Blanca, Horner, Marc, Bischoff, Jeff, Musuamba Tshinanu, Flora·2021·Methods
DOI 10.1016/j.ymeth.2020.01.011PMC PMC7883933
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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.

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  • The European Medicines Agency issued its Guideline on the Reporting of Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation in 2018.

    “Of particular relevance for the field of systems biology is The European Medicines Agency’s (EMA) Guideline on the Reporting of Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation issued in 2018 [9, 65].”

    From Adapting modeling and simulation credibility standards to computational systems biology (2023) · cites this paper as [65]

    The Introduction (page unavailable) identifies an EMA guideline on reporting PBPK modelling and simulations, and the pharmaceutical-products Overview says EMA released a PBPK guideline. Neither passage dates the EMA guideline to 2018; the Introduction explicitly assigns 2018 to the separate ASME standard. (LLM, conf=0.85)

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Research works printed but not linked: 1

  1. [46]A.R. MacLeod, H.S. Gill, Generating preclinical evidence for MHRA - an in silico clinical trial examining the safety of a novel device for knee arthritis treatment,” presented at the Bath Biomechanics Symposium 2019, Bath, 2019.

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Authors
Viceconti, M., Pappalardo, F., Rodriguez, B., Horner, M., Bischoff, J., & Musuamba Tshinanu, F.
Affiliations
  • Istituto Ortopedico Rizzoli (IT)
  • University of Catania (IT)
Corresponding author
Viceconti, Marco
ORCID
Venue
Methods
Publisher
Elsevier BV
Published
2021-01
Volume
185
Pages
120-127
ISSN
1046-2023 (ISSN-L)
DOI
10.1016/j.ymeth.2020.01.011
PMC
PMC7883933
OpenAlex
W3001465925
Cited by
361
References
48
Citations by year
2026: 85 · 2025: 71 · 2024: 72 · 2023: 48 · 2022: 33 · 2021: 44 · 2020: 8
FWCI
31.72
Type
journal article
Language
en
Open access
Licence
Locations
  • Methods (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by-nc-nd)
  • INFM-OAR (INFN Catania) (submittedVersion, cc-by)
Funders
Wellcome Trust
Topics
  • Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords
verification and validation, uncertainty quantification, biophysical model, regulatory evaluation, drug approval, context of use, risk analysis, acceptability threshold, prediction model, regulatory qualification
MeSH terms
Machine Learning, Computer Simulation, Models, Theoretical, Uncertainty

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