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In silico clinical trials for relapsing-remitting multiple sclerosis with MS TreatSim

BACKGROUND: The last few decades have seen the approval of many new treatment options for Relapsing-Remitting Multiple Sclerosis (RRMS), as well as advances in diagnostic methodology and criteria. These developments have greatly improved the available treatment options for today’s Relapsing-Remittin...

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Autores principales: Sips, Fianne L. P., Pappalardo, Francesco, Russo, Giulia, Bursi, Roberta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9665027/
https://www.ncbi.nlm.nih.gov/pubmed/36380294
http://dx.doi.org/10.1186/s12911-022-02034-x
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author Sips, Fianne L. P.
Pappalardo, Francesco
Russo, Giulia
Bursi, Roberta
author_facet Sips, Fianne L. P.
Pappalardo, Francesco
Russo, Giulia
Bursi, Roberta
author_sort Sips, Fianne L. P.
collection PubMed
description BACKGROUND: The last few decades have seen the approval of many new treatment options for Relapsing-Remitting Multiple Sclerosis (RRMS), as well as advances in diagnostic methodology and criteria. These developments have greatly improved the available treatment options for today’s Relapsing-Remitting Multiple Sclerosis patients. This increased availability of disease modifying treatments, however, has implications for clinical trial design in this therapeutic area. The availability of better diagnostics and more treatment options have not only contributed to progressively decreasing relapse rates in clinical trial populations but have also resulted in the evolution of control arms, as it is often no longer sufficient to show improvement from placebo. As a result, not only have clinical trials become longer and more expensive but comparing the results to those of “historical” trials has also become more difficult. METHODS: In order to aid design of clinical trials in RRMS, we have developed a simulator called MS TreatSim which can simulate the response of customizable, heterogeneous groups of patients to four common Relapsing-Remitting Multiple Sclerosis treatment options. MS TreatSim combines a mechanistic, agent-based model of the immune-based etiology of RRMS with a simulation framework for the generation and virtual trial simulation of populations of digital patients. RESULTS: In this study, the product was first applied to generate diverse populations of digital patients. Then we applied it to reproduce a phase III trial of natalizumab as published 15 years ago as a use case. Within the limitations of synthetic data availability, the results showed the potential of applying MS TreatSim to recreate the relapse rates of this historical trial of natalizumab. CONCLUSIONS: MS TreatSim’s synergistic combination of a mechanistic model with a clinical trial simulation framework is a tool that may advance model-based clinical trial design.
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spelling pubmed-96650272022-11-16 In silico clinical trials for relapsing-remitting multiple sclerosis with MS TreatSim Sips, Fianne L. P. Pappalardo, Francesco Russo, Giulia Bursi, Roberta BMC Med Inform Decis Mak Review BACKGROUND: The last few decades have seen the approval of many new treatment options for Relapsing-Remitting Multiple Sclerosis (RRMS), as well as advances in diagnostic methodology and criteria. These developments have greatly improved the available treatment options for today’s Relapsing-Remitting Multiple Sclerosis patients. This increased availability of disease modifying treatments, however, has implications for clinical trial design in this therapeutic area. The availability of better diagnostics and more treatment options have not only contributed to progressively decreasing relapse rates in clinical trial populations but have also resulted in the evolution of control arms, as it is often no longer sufficient to show improvement from placebo. As a result, not only have clinical trials become longer and more expensive but comparing the results to those of “historical” trials has also become more difficult. METHODS: In order to aid design of clinical trials in RRMS, we have developed a simulator called MS TreatSim which can simulate the response of customizable, heterogeneous groups of patients to four common Relapsing-Remitting Multiple Sclerosis treatment options. MS TreatSim combines a mechanistic, agent-based model of the immune-based etiology of RRMS with a simulation framework for the generation and virtual trial simulation of populations of digital patients. RESULTS: In this study, the product was first applied to generate diverse populations of digital patients. Then we applied it to reproduce a phase III trial of natalizumab as published 15 years ago as a use case. Within the limitations of synthetic data availability, the results showed the potential of applying MS TreatSim to recreate the relapse rates of this historical trial of natalizumab. CONCLUSIONS: MS TreatSim’s synergistic combination of a mechanistic model with a clinical trial simulation framework is a tool that may advance model-based clinical trial design. BioMed Central 2022-11-15 /pmc/articles/PMC9665027/ /pubmed/36380294 http://dx.doi.org/10.1186/s12911-022-02034-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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, visit http://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 Review
Sips, Fianne L. P.
Pappalardo, Francesco
Russo, Giulia
Bursi, Roberta
In silico clinical trials for relapsing-remitting multiple sclerosis with MS TreatSim
title In silico clinical trials for relapsing-remitting multiple sclerosis with MS TreatSim
title_full In silico clinical trials for relapsing-remitting multiple sclerosis with MS TreatSim
title_fullStr In silico clinical trials for relapsing-remitting multiple sclerosis with MS TreatSim
title_full_unstemmed In silico clinical trials for relapsing-remitting multiple sclerosis with MS TreatSim
title_short In silico clinical trials for relapsing-remitting multiple sclerosis with MS TreatSim
title_sort in silico clinical trials for relapsing-remitting multiple sclerosis with ms treatsim
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9665027/
https://www.ncbi.nlm.nih.gov/pubmed/36380294
http://dx.doi.org/10.1186/s12911-022-02034-x
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