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Model verification tools: a computational framework for verification assessment of mechanistic agent-based models

BACKGROUND: Nowadays, the inception of computer modeling and simulation in life science is a matter of fact. This is one of the reasons why regulatory authorities are open in considering in silico trials evidence for the assessment of safeness and efficacy of medicinal products. In this context, mec...

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Autores principales: Russo, Giulia, Parasiliti Palumbo, Giuseppe Alessandro, Pennisi, Marzio, Pappalardo, Francesco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9117838/
https://www.ncbi.nlm.nih.gov/pubmed/35590242
http://dx.doi.org/10.1186/s12859-022-04684-0
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author Russo, Giulia
Parasiliti Palumbo, Giuseppe Alessandro
Pennisi, Marzio
Pappalardo, Francesco
author_facet Russo, Giulia
Parasiliti Palumbo, Giuseppe Alessandro
Pennisi, Marzio
Pappalardo, Francesco
author_sort Russo, Giulia
collection PubMed
description BACKGROUND: Nowadays, the inception of computer modeling and simulation in life science is a matter of fact. This is one of the reasons why regulatory authorities are open in considering in silico trials evidence for the assessment of safeness and efficacy of medicinal products. In this context, mechanistic Agent-Based Models are increasingly used. Unfortunately, there is still a lack of consensus in the verification assessment of Agent-Based Models for regulatory approval needs. VV&UQ is an ASME standard specifically suited for the verification, validation, and uncertainty quantification of medical devices. However, it can also be adapted for the verification assessment of in silico trials for medicinal products. RESULTS: Here, we propose a set of automatic tools for the mechanistic Agent-Based Model verification assessment. As a working example, we applied the verification framework to an Agent-Based Model in silico trial used in the COVID-19 context. CONCLUSIONS: Using the described verification computational workflow allows researchers and practitioners to easily perform verification steps to prove Agent-Based Models robustness and correctness that provide strong evidence for further regulatory requirements.
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spelling pubmed-91178382022-05-19 Model verification tools: a computational framework for verification assessment of mechanistic agent-based models Russo, Giulia Parasiliti Palumbo, Giuseppe Alessandro Pennisi, Marzio Pappalardo, Francesco BMC Bioinformatics Software BACKGROUND: Nowadays, the inception of computer modeling and simulation in life science is a matter of fact. This is one of the reasons why regulatory authorities are open in considering in silico trials evidence for the assessment of safeness and efficacy of medicinal products. In this context, mechanistic Agent-Based Models are increasingly used. Unfortunately, there is still a lack of consensus in the verification assessment of Agent-Based Models for regulatory approval needs. VV&UQ is an ASME standard specifically suited for the verification, validation, and uncertainty quantification of medical devices. However, it can also be adapted for the verification assessment of in silico trials for medicinal products. RESULTS: Here, we propose a set of automatic tools for the mechanistic Agent-Based Model verification assessment. As a working example, we applied the verification framework to an Agent-Based Model in silico trial used in the COVID-19 context. CONCLUSIONS: Using the described verification computational workflow allows researchers and practitioners to easily perform verification steps to prove Agent-Based Models robustness and correctness that provide strong evidence for further regulatory requirements. BioMed Central 2022-05-19 /pmc/articles/PMC9117838/ /pubmed/35590242 http://dx.doi.org/10.1186/s12859-022-04684-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Russo, Giulia
Parasiliti Palumbo, Giuseppe Alessandro
Pennisi, Marzio
Pappalardo, Francesco
Model verification tools: a computational framework for verification assessment of mechanistic agent-based models
title Model verification tools: a computational framework for verification assessment of mechanistic agent-based models
title_full Model verification tools: a computational framework for verification assessment of mechanistic agent-based models
title_fullStr Model verification tools: a computational framework for verification assessment of mechanistic agent-based models
title_full_unstemmed Model verification tools: a computational framework for verification assessment of mechanistic agent-based models
title_short Model verification tools: a computational framework for verification assessment of mechanistic agent-based models
title_sort model verification tools: a computational framework for verification assessment of mechanistic agent-based models
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9117838/
https://www.ncbi.nlm.nih.gov/pubmed/35590242
http://dx.doi.org/10.1186/s12859-022-04684-0
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