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In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products
Open accessAbstract
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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“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]
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Luraghi, G., Bridio, S., Migliavacca, F., & Matas, J. F. R. (2022) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2022.103836 · Cited by 19
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Mandigers, T. J., Ramella, A., Bissacco, D., Domanin, M., van Herwaarden, J. A., Heijmen, R. H., Luraghi, G., Migliavacca, F., & Trimarchi, S. (2023) · European Journal of Vascular and Endovascular Surgery · doi:10.1016/j.ejvs.2023.06.006 · Cited by 19
An, J., & Mikhaylov, A. Y. (2024) · Journal of Open Innovation Technology Market and Complexity · doi:10.1016/j.joitmc.2024.100207 · Cited by 19
Grossi, B., Barati, S., Ramella, A., Migliavacca, F., Matas, J. F. R., Dubini, G., Chakfé, N., Heim, F., Cozzi, O., Condorelli, G., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109159 · Cited by 19
Toward widespread use of virtual trials in medical imaging innovation and regulatory science
Abadi, E., Barufaldi, B., Lago, M. A., Badal, A., Mello-Thoms, C. R., Bottenus, N. B., Wangerin, K. A., Goldburgh, M. M., Tarbox, L., Beaucage‐Gauvreau, E., et al. (2024) · Medical Physics · doi:10.1002/mp.17442 · Cited by 19
Integrating computational and experimental advances in bone multiscale mechanics
Rowe, J., Shen, S. C.-Y., de Alcântara, A. C. S., Skaf, M. S., Dini, D., Harrison, N. M., Hansen, U., Buehler, M. J., & Abel, R. L. (2025) · Progress in Materials Science · doi:10.1016/j.pmatsci.2025.101474 · Cited by 18
Pagani, S., Dede’, L., Frontera, A., Salvador, M., Limite, L. R., Manzoni, A., Lipartiti, F., Tsitsinakis, G., Hadjis, A. E., Bella, P. E. D., et al. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.673612 · Cited by 18
Phenocopying Glioblastoma: A Review
Oraiopoulou, M., Tzamali, E., Papamatheakis, J. D., & Sakkalis, V. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3111744 · Cited by 17
“data-driven and should be tested frequently in order to avoid context drift [177].”
BackgroundDerycke, L., Avril, S., & Millon, A. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12030766 · Cited by 18
“Similarly, there is a pressing need for thorough and extensive verification, validation, and uncertainty quantification of FE analysis for endovascular procedures, as was previously achieved in other domains of biomedicine [76].”
MethodResearch landscape analysis for quality in Pharma 4.0 era
Barenji, R. V., Hariry, R. E., Demırkol, D., & Daim, T. U. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102472 · Cited by 18
Abatan, A., Obaigbena, A., Ugwuanyi, E. D., Jacks, B. S., Umoga, U. J., Daraojimba, O. H., & Lottu, O. A. (2024) · Engineering Science & Technology Journal · doi:10.51594/estj.v5i2.831 · Cited by 20
Bas, T. G., & Duarte, V. (2024) · Pharmaceuticals · doi:10.3390/ph17070925 · Cited by 19
“In some cases, this may involve developing a mathematical model of the system that can be validated by comparing clinical trial data of the reference product with clinical trial simulations [147].”
MethodPoncelas, M. L., Barbera, L. L., Rawlinson, J., Crandall, D. G., & Aubin, C. (2021) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2021.1968380 · Cited by 17
“…of use (COU) to prevent PJF. Validation confirms that the underlying numerical model correctly represents the reality of spine instrumentation biomechanics, while verification confirms that the mathematical representation is correctly implemented in the computational model (Viceconti et al. 2021).”
BackgroundRiebel, L. L., Wang, Z. J., Martinez‐Navarro, H., Trovato, C., Camps, J., Berg, L. A., Zhou, X., Doste, R., Oliveira, R. S., dos Santos, R. W., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-67951-5 · Cited by 17
Preclinical modeling of mechanical thrombectomy
Anagnostakou, V., Epshtein, M., Kühn, A. L., King, R. M., Puri, A. S., & Gounis, M. J. (2021) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2021.110894 · Cited by 17
Challenges and opportunities in uncertainty quantification for healthcare and biological systems
Kimpton, L. M., Paun, L., Colebank, M., & Volodina, V. (2025) · Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · doi:10.1098/rsta.2024.0232 · Cited by 22
The 3D skull 0–4 years: A validated, generative, statistical shape model
Sullivan, E. O., van de Lande, L. S., Oosting, A.-J. C., Papaioannou, A., Jeelani, O., Koudstaal, M. J., Khonsari, R. H., Dunaway, D. J., Zafeiriou, S. P., & Schievano, S. (2021) · Bone Reports · doi:10.1016/j.bonr.2021.101154 · Cited by 16
Vuong, T. N. A. M., Bartolf‐Kopp, M., Andelovic, K., Jüngst, T., Farbehi, N., Wise, S. G., Hayward, C., Stevens, M. C., & Rnjak‐Kovacina, J. (2024) · Advanced Science · doi:10.1002/advs.202307627 · Cited by 17
Arduino, A., Baruffaldi, F., Bottauscio, O., Chiampi, M., Martinez, J., Zanovello, U., & Zilberti, L. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aca5e6 · Cited by 15
“…verification of the safety and efficacy ofmedical technologies, to the point that regulatory agencies recently started receiving and accepting evidence obtained in silico, i.e. throughmodelling and simulation, to the request formarketing authorization of newmedical products (Viceconti et al 2021).”
BackgroundMultiscale structure and function of the aortic valve apparatus
El-Nashar, H., Sabry, M. I., Tseng, Y., Francis, N., Latif, N., Parker, K. H., Moore, J. E., & Yacoub, M. H. (2023) · Physiological Reviews · doi:10.1152/physrev.00038.2022 · Cited by 19
Scuoppo, R., Castelbuono, S., Cannata, S., Gentile, G., Agnese, V., Bellavia, D., Gandolfo, C. M., & Pasta, S. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03215-8 · Cited by 14
“As in other engineering fields and industries, the design and efficacy of biomedical devices can be greatly improved by using computer modeling and simulations, which can play a pivotal role in accelerating the design phase and thus help companies develop more effective and reliable solutions [1].”
Cortesi, M., Liu, D., Yee, C., Marsh, D. J., & Ford, C. E. (2023) · Scientific Reports · doi:10.1038/s41598-023-42486-3 · Cited by 13
Bone mineral density modeling via random field: Normality, stationarity, sex and age dependence
Henyš, P., Vořechovský, M., Kuchař, M., Heinemann, A., Kopal, J., Ondruschka, B., & Hammer, N. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106353 · Cited by 14
Patient-specific computational modelling of endovascular treatment for intracranial aneurysms
Bisighini, B., Aguirre, M., Pierrat, B., & Avril, S. (2023) · Brain Multiphysics · doi:10.1016/j.brain.2023.100079 · Cited by 13
Grancieri, M., Viana, M. L., de Oliveira, D. F., Tostes, M. D. G. V., Ignacchiti, M. D. C., Costa, A. G. V., & Costa, N. M. B. (2023) · Nutrients · doi:10.3390/nu15143281 · Cited by 13
“However, we emphasize that these connections between SCFAs and enzymes, demonstrated in the in silico analysis, are plausible to occur, but require caution in their interpretation, since in a physiological context, other factors can interfere in these connections and in the consequent actions of the enzymes [54].”
BackgroundChallenges of Machine Learning and AI (What Is Next?), Responsible and Ethical AI
Gkontra, P., Quaglio, G., Garmendia, A. T., & Lekadir, K. (2023) · Springer eBooks · doi:10.1007/978-3-031-36678-9_17 · Cited by 17
Arminio, M., Carbonaro, D., Morbiducci, U., Gallo, D., & Chiastra, C. (2024) · Frontiers in Medical Technology · doi:10.3389/fmedt.2024.1399729 · Cited by 13
Parametric Modeling and Deep Learning for Enhancing Pain Assessment in Postanesthesia
Ghita, M., Birs, I., Copoţ, D., Muresan, C. I., Neckebroek, M. M., & Ionescu, C. M. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3274541 · Cited by 13
Fanni, B. M., Antonuccio, M. N., Pizzuto, A., Berti, S. L., Santoro, G., & Celi, S. (2023) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd10030109 · Cited by 12
“Furthermore, the Food and Drug Administration is strongly supporting the usage of computational simulations in the context of clinical in silico trials for the development of novel devices and treatment strategies, according to the V&V40 statement [9].”
MethodPhysics Guided Machine Learning for Variational Multiscale Reduced Order Modeling
Ahmed, S. E., San, O., Rasheed, A., Iliescu, T., & Veneziani, A. (2023) · SIAM Journal on Scientific Computing · doi:10.1137/22m1496360 · Cited by 12
Vignali, E., Gasparotti, E., Landini, L., & Celi, S. (2021) · Electronics · doi:10.3390/electronics10040386 · Cited by 12
van Riel, N. A. W., Tiemann, C. A., Hilbers, P. A. J., & Groen, A. K. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.536957 · Cited by 12
Mill, J., Montoliu, H., Moustafa, A.-H., Olivares, A. L., Albors, C., Aguado, A. M., Medina, E., Ceresa, M., Freixa, X., Arzamendi, D., et al. (2022) · International Journal of Bioprinting · doi:10.18063/ijb.v9i1.640 · Cited by 12
“However, to integrate in silico fluid simulations in device-related decision-making, enough credibility still needs to be built following verification, validation, and uncertainty quantification standards such as the V&V40 guidelines[41], including sensitivity analysis to identify the boundary conditions to provide mo…”
Background
Metadata
- 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
- Venue
- Methods
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 185
- Pages
- 120-127
- ISSN
- 1046-2023 (ISSN-L)
- PMC
- PMC7883933
- OpenAlex
- W3001465925
- Cited by
- 361
- References
- 48
- Citations by year
- 2026: 85 · 2025: 71 · 2024: 72 · 2023: 48 · 2022: 33 · 2021: 44
- FWCI
- 31.72
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- 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
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › 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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