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Studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics
Non-clinical models to study metabolism including animal models and cell assays are often limited in terms of species translatability and predictability of human biology. This field urgently requires a push towards more physiologically accurate recapitulations of drug interactions and disease progre...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889168/ https://www.ncbi.nlm.nih.gov/pubmed/35232251 http://dx.doi.org/10.1098/rsob.210333 |
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author | Shroff, Tanvi Aina, Kehinde Maass, Christian Cipriano, Madalena Lambrecht, Joeri Tacke, Frank Mosig, Alexander Loskill, Peter |
author_facet | Shroff, Tanvi Aina, Kehinde Maass, Christian Cipriano, Madalena Lambrecht, Joeri Tacke, Frank Mosig, Alexander Loskill, Peter |
author_sort | Shroff, Tanvi |
collection | PubMed |
description | Non-clinical models to study metabolism including animal models and cell assays are often limited in terms of species translatability and predictability of human biology. This field urgently requires a push towards more physiologically accurate recapitulations of drug interactions and disease progression in the body. Organ-on-chip systems, specifically multi-organ chips (MOCs), are an emerging technology that is well suited to providing a species-specific platform to study the various types of metabolism (glucose, lipid, protein and drug) by recreating organ-level function. This review provides a resource for scientists aiming to study human metabolism by providing an overview of MOCs recapitulating aspects of metabolism, by addressing the technical aspects of MOC development and by providing guidelines for correlation with in silico models. The current state and challenges are presented for two application areas: (i) disease modelling and (ii) pharmacokinetics/pharmacodynamics. Additionally, the guidelines to integrate the MOC data into in silico models could strengthen the predictive power of the technology. Finally, the translational aspects of metabolizing MOCs are addressed, including adoption for personalized medicine and prospects for the clinic. Predictive MOCs could enable a significantly reduced dependence on animal models and open doors towards economical non-clinical testing and understanding of disease mechanisms. |
format | Online Article Text |
id | pubmed-8889168 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-88891682022-03-09 Studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics Shroff, Tanvi Aina, Kehinde Maass, Christian Cipriano, Madalena Lambrecht, Joeri Tacke, Frank Mosig, Alexander Loskill, Peter Open Biol Review Non-clinical models to study metabolism including animal models and cell assays are often limited in terms of species translatability and predictability of human biology. This field urgently requires a push towards more physiologically accurate recapitulations of drug interactions and disease progression in the body. Organ-on-chip systems, specifically multi-organ chips (MOCs), are an emerging technology that is well suited to providing a species-specific platform to study the various types of metabolism (glucose, lipid, protein and drug) by recreating organ-level function. This review provides a resource for scientists aiming to study human metabolism by providing an overview of MOCs recapitulating aspects of metabolism, by addressing the technical aspects of MOC development and by providing guidelines for correlation with in silico models. The current state and challenges are presented for two application areas: (i) disease modelling and (ii) pharmacokinetics/pharmacodynamics. Additionally, the guidelines to integrate the MOC data into in silico models could strengthen the predictive power of the technology. Finally, the translational aspects of metabolizing MOCs are addressed, including adoption for personalized medicine and prospects for the clinic. Predictive MOCs could enable a significantly reduced dependence on animal models and open doors towards economical non-clinical testing and understanding of disease mechanisms. The Royal Society 2022-03-02 /pmc/articles/PMC8889168/ /pubmed/35232251 http://dx.doi.org/10.1098/rsob.210333 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Review Shroff, Tanvi Aina, Kehinde Maass, Christian Cipriano, Madalena Lambrecht, Joeri Tacke, Frank Mosig, Alexander Loskill, Peter Studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics |
title | Studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics |
title_full | Studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics |
title_fullStr | Studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics |
title_full_unstemmed | Studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics |
title_short | Studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics |
title_sort | studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889168/ https://www.ncbi.nlm.nih.gov/pubmed/35232251 http://dx.doi.org/10.1098/rsob.210333 |
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