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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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Biomechanical Modeling of Cesarean Section Scars and Scar Defects
Scott, A. K., Louwagie, E., Myers, K. M., & Oyen, M. L. (2024) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-031-55315-8_8 · Cited by 6
Mechanism-based organization of neural networks to emulate systems biology and pharmacology models
Mann, J., Meshkin, H., Zirkle, J., Han, X., Thrasher, B., Chaturbedi, A., Arabidarrehdor, G., & Li, Z. (2024) · Scientific Reports · doi:10.1038/s41598-024-59378-9 · Cited by 6
Gardegaront, M., Sas, A., Brizard, D., Levillain, A., Bermond, F., Confavreux, C. B., Pialat, J., van Lenthe, H., Follet, H., & Mitton, D. (2024) · Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials · doi:10.1016/j.jmbbm.2024.106676 · Cited by 9
van Asten, J. G. M., Fung, M.-T., Oomens, C. W. J., Bader, D. L., & Worsley, P. R. (2021) · Journal of Tissue Viability · doi:10.1016/j.jtv.2021.04.007 · Cited by 6
BoneDat, a database of standardized bone morphology for in silico analyses
Henyš, P., & Kuchař, M. (2025) · Scientific Data · doi:10.1038/s41597-025-05161-y · Cited by 6
Ursino, M., Villacampa, G. J., Rekowski, J., Dimairo, M., Solovyeva, O., Ashby, D., Berlin, J. D., Boix, O., Calvert, M., Chan, A., et al. (2025) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102988 · Cited by 6
Helmholz, H., Resuli, R., Tacke, M., Nourisa, J., Tomforde, S., Aydin, R. C., Willumeit‐Römer, R., & Zeller‐Plumhoff, B. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.06.023 · Cited by 5
“Specifically, if predictive mathematical models of drug/stimuli effects on different cells such as HUVEC can be developed, testing of new treat-ment strategies can be moved from in vitro experiments to in silico experiments [10].”
Computational Modeling and Digital Twin Technologies in Medical Device Development
Tudu, C., Sharma, S., & Kumar, D. (2025) · Biomedical Materials & Devices · doi:10.1007/s44174-025-00469-0 · Cited by 5
Evolution of Simulation and Digital Twin in Health Care: From Discovery to Design and Integration
Dong, Y., Lal, A., Niven, A. S., & Zhong, X. (2024) · Simulation foundations, methods and applications · doi:10.1007/978-3-031-69107-2_11 · Cited by 5
Noga, M., & Jurowski, K. (2025) · Archives of Toxicology · doi:10.1007/s00204-025-04000-8 · Cited by 5
“To effectively replace animal testing, these models must be highly accurate, reliable, and compliant with international standards (Viceconti et al. 2021).”
Gasparotti, E., Vignali, E., Scolaro, M., Haxhiademi, D., & Celi, S. (2025) · Biomechanics and Modeling in Mechanobiology · doi:10.1007/s10237-025-01952-9 · Cited by 5
“From this perspective, experimental data play a pivotal role in increasing the credibility of the computational model (V&V40 2018; Viceconti et al. 2021).”
Linking Vascular Structure and Function: Image-Based Virtual Populations of the Retina
Hernandez, R. J., Madhusudhan, S., Zheng, Y., & El‐Bouri, W. K. (2024) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.65.4.40 · Cited by 5
Cortés‐Ríos, J., Ren, T., Hanan, N., Sher, A., Nader, A., Magee, M. H., Jusko, W. J., & Desikan, R. (2025) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.3718 · Cited by 5
“…including small interfering RNAs (siRNA), entry inhibitors, capsid assembly inhibitors, antisense oligonucleotides (ASO), HBsAg secretion inhibitors, Toll-like receptor (TLR) 7/8 agonists, therapeutic vaccines, neutralizing monoclonal antibodies, and checkpoint inhibitors, are under development.”
InfluentialOn the Modeling of Transcatheter Therapies for the Aortic and Mitral Valves: A Review
Catalano, C., & Pasta, S. (2022) · Prosthesis · doi:10.3390/prosthesis4010011 · Cited by 6
“For a detailed understanding of ASME V&V 40, we recommend the article published by Viceconti and collaborators [60].”
BackgroundMethodGenerating Virtual Populations of 3D Cardiac Anatomies with Snowflake-Net
Peng, J., Beetz, M., Banerjee, A., Chen, M., & Grau, V. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-52448-6_16 · Cited by 5
Future Directions for Quantitative Systems Pharmacology
Schoeberl, B., Musante, C. J., & Ramanujan, S. (2024) · Handbook of experimental pharmacology · doi:10.1007/164_2024_737 · Cited by 5
Viceconti, M., Lanubile, F., Carbonaro, A., Mellone, S., Curreli, C., Aldieri, A., Ranciati, S., & Montanari, A. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3552320 · Cited by 5
Self-citationBertrand, A., Tomek, J., & Rodríguez, B. (2026) · The Journal of Physiology · doi:10.1113/jp287140 · Cited by 4
Ramella, A., Campo, G. D., Barati, S., Luraghi, G., Domanin, M., Fulgheri, I., Piffaretti, G., Heijmen, R. H., Migliavacca, F., & Trimarchi, S. (2026) · European Journal of Vascular and Endovascular Surgery · doi:10.1016/j.ejvs.2026.01.008 · Cited by 4
Saxton, H., Taylor, D. J., Faulkner, G., Halliday, I., Newman, T., Schenkel, T., Morris, P., Clayton, R. H., & Xu, X. (2025) · PLoS ONE · doi:10.1371/journal.pone.0326112 · Cited by 4
“Validation must be suitable for the context of use [91], and therefore also plays a key factor in experimental design, defining the clinical measurements taken and the choice of model output.”
Albors, C., Lluch, È., Gómez, J. F., Cedilnik, N., Mountris, K. A., Mansi, T., Khamzin, S., Dokuchaev, A., Solovyova, O. E., Pueyo, E., et al. (2022) · Applied Sciences · doi:10.3390/app12136438 · Cited by 4
Method development for 14C-labeling of IgG antibodies in preparation for clinical trials
Kim, S., Kwak, J., Jung, J., & Hyun, S. (2024) · Journal of Analytical Science & Technology · doi:10.1186/s40543-024-00420-w · Cited by 4
“However, the obtained results occasionally diverge from clinical PK results, posing challenges in sufficiently demonstrating safety and efficacy (Viceconti et al. 2021).”
ResultScuoppo, R., Cannata, S., Gandolfo, C. M., Bellavia, D., & Pasta, S. (2024) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2024.112357 · Cited by 4
Cobetto, N., Fecteau, M., Caouette, C., Gay, M., Larson, A. N., Hoernschemeyer, D. G., Boeyer, M. E., El‐Hawary, R., Alanay, A., & Aubin, C. (2025) · Spine Deformity · doi:10.1007/s43390-025-01123-x · Cited by 4
Foot digital twin and in silico clinical applications
Orcajo, E. M., & Stenti, A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-823913-1.00004-x · Cited by 4
McVeigh, C., Harewood, F., King, P., Driscoll, M., Kulkarni, S., Zhao, T., Goodin, M. S., & Iles, T. L. (2023) · Springer eBooks · doi:10.1007/978-3-031-25541-0_16 · Cited by 4
Parvinian, B., Bighamian, R., Scully, C. G., Hahn, J., & Pathmanathan, P. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.705222 · Cited by 4
“A precursor to clinical applications of subject-specific mathematical models is their use in pre-clinical safety evidence generation where they can be used to build a cohort of in silico subjects to complement and/or replace traditional animal studies (Kovatchev et al., 2009; Jean-Quartier et al., 2018; Mirinejad et a…”
BackgroundBerg, L. A., Oliveira, R. S., Camps, J., Wang, Z. J., Doste, R., Bueno‐Orovio, A., dos Santos, R. W., & Rodríguez, B. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.09.647733 · Cited by 4
“These two advances have driven the field forward towards the realisation of the the ‘Digital Twin’ vision in healthcare 7–10 and in silico trials for therapy evaluation 11 .”
Dorosan, M., Chen, Y.-L., Zhuang, Q., & Lam, S. W. (2024) · JMIR Research Protocols · doi:10.2196/63875 · Cited by 3
Niżnik, Ł., Fijałkowska, O., & Jurowski, K. (2025) · Archives of Toxicology · doi:10.1007/s00204-025-04214-w · Cited by 3
“The entire process will establish the reliability of the model, as we will compare both the results of the computer simulation with the analog with the experimentally determined LD 50 dose and estimate the uncertainty in this comparison (Viceconti et al. 2021).”
Holmes, M., Martinez‐Navarro, H., Mohr, M., Chambard, J., Ballet, V., Vermersch, E., Garry, A., Schmidt, F., & Rodríguez, B. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1671199 · Cited by 3
“Simulations using these tools enable prediction and mechanistic exploration of physiological and pharmacological outcomes (Viceconti et al., 2021).”
Kashkooli, F. M., Zhan, W., Bhandari, A., Yusufaly, T., Kolios, M. C., Rahmim, A., & Soltani, M. (2026) · Frontiers in Radiology · doi:10.3389/fradi.2026.1737577 · Cited by 3
Digital twins in urology: a vision for the future of urological practice
Nedbal, C., Madaan, S., Ghulam, N., & Somani, B. K. (2026) · Therapeutic Advances in Urology · doi:10.1177/17562872261419592 · Cited by 3
“17,18 Despite the intriguing possibilities and applications of digital twins, its integration in urology faces significant challenges that may hinder its growth.”
Demiral, M., Mamedov, A., & Köklü, U. (2026) · Eng—Advances in Engineering · doi:10.3390/eng7050235 · Cited by 3
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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