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A modeling informed quantitative approach to salvage clinical trials interrupted due to COVID‐19
Many ongoing Alzheimer's disease central nervous system clinical trials are being disrupted and halted due to the COVID‐19 pandemic. They are often of a long duration’ are very complex; and involve many stakeholders, not only the scientists and regulators but also the patients and their family...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7606183/ https://www.ncbi.nlm.nih.gov/pubmed/33163611 http://dx.doi.org/10.1002/trc2.12053 |
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author | Geerts, Hugo van der Graaf, Piet |
author_facet | Geerts, Hugo van der Graaf, Piet |
author_sort | Geerts, Hugo |
collection | PubMed |
description | Many ongoing Alzheimer's disease central nervous system clinical trials are being disrupted and halted due to the COVID‐19 pandemic. They are often of a long duration’ are very complex; and involve many stakeholders, not only the scientists and regulators but also the patients and their family members. It is mandatory for us as a community to explore all possibilities to avoid losing all the knowledge we have gained from these ongoing trials. Some of these trials will need to completely restart, but a substantial number can restart after a hiatus with the proper protocol amendments. To salvage the information gathered so far, we need out‐of‐the‐box thinking for addressing these missingness problems and to combine information from the completers with those subjects undergoing complex protocols deviations and amendments after restart in a rational, scientific way. Physiology‐based pharmacokinetic (PBPK) modeling has been a cornerstone of model‐informed drug development with regard to drug exposure at the site of action, taking into account individual patient characteristics. Quantitative systems pharmacology (QSP), based on biology‐informed and mechanistic modeling of the interaction between a drug and neuronal circuits, is an emerging technology to simulate the pharmacodynamic effects of a drug in combination with patient‐specific comedications, genotypes, and disease states on functional clinical scales. We propose to combine these two approaches into the concept of computer modeling‐based virtual twin patients as a possible solution to harmonize the readouts from these complex clinical datasets in a biologically and therapeutically relevant way. |
format | Online Article Text |
id | pubmed-7606183 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-76061832020-11-05 A modeling informed quantitative approach to salvage clinical trials interrupted due to COVID‐19 Geerts, Hugo van der Graaf, Piet Alzheimers Dement (N Y) Perspectives Many ongoing Alzheimer's disease central nervous system clinical trials are being disrupted and halted due to the COVID‐19 pandemic. They are often of a long duration’ are very complex; and involve many stakeholders, not only the scientists and regulators but also the patients and their family members. It is mandatory for us as a community to explore all possibilities to avoid losing all the knowledge we have gained from these ongoing trials. Some of these trials will need to completely restart, but a substantial number can restart after a hiatus with the proper protocol amendments. To salvage the information gathered so far, we need out‐of‐the‐box thinking for addressing these missingness problems and to combine information from the completers with those subjects undergoing complex protocols deviations and amendments after restart in a rational, scientific way. Physiology‐based pharmacokinetic (PBPK) modeling has been a cornerstone of model‐informed drug development with regard to drug exposure at the site of action, taking into account individual patient characteristics. Quantitative systems pharmacology (QSP), based on biology‐informed and mechanistic modeling of the interaction between a drug and neuronal circuits, is an emerging technology to simulate the pharmacodynamic effects of a drug in combination with patient‐specific comedications, genotypes, and disease states on functional clinical scales. We propose to combine these two approaches into the concept of computer modeling‐based virtual twin patients as a possible solution to harmonize the readouts from these complex clinical datasets in a biologically and therapeutically relevant way. John Wiley and Sons Inc. 2020-11-02 /pmc/articles/PMC7606183/ /pubmed/33163611 http://dx.doi.org/10.1002/trc2.12053 Text en © 2020 The Authors. Alzheimer's & Dementia: Translational Research & Clinical Interventions published by Wiley Periodicals, Inc. on behalf of Alzheimer's Association. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Perspectives Geerts, Hugo van der Graaf, Piet A modeling informed quantitative approach to salvage clinical trials interrupted due to COVID‐19 |
title | A modeling informed quantitative approach to salvage clinical trials interrupted due to COVID‐19 |
title_full | A modeling informed quantitative approach to salvage clinical trials interrupted due to COVID‐19 |
title_fullStr | A modeling informed quantitative approach to salvage clinical trials interrupted due to COVID‐19 |
title_full_unstemmed | A modeling informed quantitative approach to salvage clinical trials interrupted due to COVID‐19 |
title_short | A modeling informed quantitative approach to salvage clinical trials interrupted due to COVID‐19 |
title_sort | modeling informed quantitative approach to salvage clinical trials interrupted due to covid‐19 |
topic | Perspectives |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7606183/ https://www.ncbi.nlm.nih.gov/pubmed/33163611 http://dx.doi.org/10.1002/trc2.12053 |
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